diff --git a/.gitignore b/.gitignore
index 4eec8110..98f93b82 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,3 +2,4 @@
 .ipynb_checkpoints/
 config.json
 __pycache__/
+.DS_Store
diff --git a/data/citation.csv b/data/citation.csv
index 7562e66d..4591fb7c 100644
--- a/data/citation.csv
+++ b/data/citation.csv
@@ -16,7 +16,6 @@ https://github.com/LMescheder/GAN_stability,"author = {Lars Mescheder and Sebast
 https://github.com/LMescheder/GAN_stability,"title = {Which Training Methods for GANs do actually Converge?},"
 https://github.com/LMescheder/GAN_stability,"booktitle = {International Conference on Machine Learning (ICML)},"
 https://github.com/LMescheder/GAN_stability,year = {2018}
-https://github.com/LMescheder/GAN_stability,}
 https://github.com/NVIDIA/vid2vid,"If you find this useful for your research, please cite the following paper."
 https://github.com/NVIDIA/vid2vid,
 https://github.com/NVIDIA/vid2vid,"@inproceedings{wang2018vid2vid,"
@@ -25,7 +24,6 @@ https://github.com/NVIDIA/vid2vid,"and Andrew Tao and Jan Kautz and Bryan Catanz
 https://github.com/NVIDIA/vid2vid,"title     = {Video-to-Video Synthesis},"
 https://github.com/NVIDIA/vid2vid,"booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},"
 https://github.com/NVIDIA/vid2vid,"year      = {2018},"
-https://github.com/NVIDIA/vid2vid,}
 https://github.com/NVIDIA/vid2vid,Video-to-Video Synthesis
 https://github.com/NVIDIA/vid2vid,"Ting-Chun Wang1, Ming-Yu Liu1, Jun-Yan Zhu2, Guilin Liu1, Andrew Tao1, Jan Kautz1, Bryan Catanzaro1"
 https://github.com/NVIDIA/vid2vid,"1NVIDIA Corporation, 2MIT CSAIL"
@@ -43,15 +41,12 @@ https://github.com/XiaLiPKU/RESCAN,"booktitle={European Conference on Computer V
 https://github.com/XiaLiPKU/RESCAN,"pages={262--277},"
 https://github.com/XiaLiPKU/RESCAN,"year={2018},"
 https://github.com/XiaLiPKU/RESCAN,organization={Springer}
-https://github.com/XiaLiPKU/RESCAN,}
 https://github.com/ZhouYanzhao/PRM,Citation
 https://github.com/ZhouYanzhao/PRM,"If you find the code useful for your research, please cite:"
 https://github.com/ZhouYanzhao/PRM,"@INPROCEEDINGS{Zhou2018PRM,"
 https://github.com/ZhouYanzhao/PRM,"author = {Zhou, Yanzhao and Zhu, Yi and Ye, Qixiang and Qiu, Qiang and Jiao, Jianbin},"
 https://github.com/ZhouYanzhao/PRM,"title = {Weakly Supervised Instance Segmentation using Class Peak Response},"
 https://github.com/ZhouYanzhao/PRM,"booktitle = {CVPR},"
-https://github.com/ZhouYanzhao/PRM,year = {2018}
-https://github.com/ZhouYanzhao/PRM,}
 https://github.com/akanazawa/hmr,"Angjoo Kanazawa, Michael J. Black, David W. Jacobs, Jitendra Malik CVPR 2018"
 https://github.com/akanazawa/hmr,"@inProceedings{kanazawaHMR18,"
 https://github.com/akanazawa/hmr,"title={End-to-end Recovery of Human Shape and Pose},"
@@ -60,16 +55,11 @@ https://github.com/akanazawa/hmr,and Michael J. Black
 https://github.com/akanazawa/hmr,and David W. Jacobs
 https://github.com/akanazawa/hmr,"and Jitendra Malik},"
 https://github.com/akanazawa/hmr,"booktitle={Computer Vision and Pattern Regognition (CVPR)},"
-https://github.com/akanazawa/hmr,year={2018}
-https://github.com/akanazawa/hmr,}
-https://github.com/albertpumarola/GANimation,Citation
 https://github.com/albertpumarola/GANimation,"If you use this code or ideas from the paper for your research, please cite our paper:"
 https://github.com/albertpumarola/GANimation,"@inproceedings{pumarola2018ganimation,"
 https://github.com/albertpumarola/GANimation,"title={GANimation: Anatomically-aware Facial Animation from a Single Image},"
 https://github.com/albertpumarola/GANimation,"author={A. Pumarola and A. Agudo and A.M. Martinez and A. Sanfeliu and F. Moreno-Noguer},"
 https://github.com/albertpumarola/GANimation,"booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},"
-https://github.com/albertpumarola/GANimation,year={2018}
-https://github.com/albertpumarola/GANimation,}
 https://github.com/cgre-aachen/gempy,"For a more detailed elaboration of the theory behind GemPy, take a look at the upcoming scientific publication ""GemPy 1.0: open-source stochastic geological modeling and inversion"" by de la Varga et al. (2018)."
 https://github.com/cgre-aachen/gempy,References
 https://github.com/cgre-aachen/gempy,"de la Varga, M., Schaaf, A., and Wellmann, F.: GemPy 1.0: open-source stochastic geological modeling and inversion, Geosci. Model Dev., 12, 1-32, https://doi.org/10.5194/gmd-12-1-2019, 2019"
@@ -77,16 +67,12 @@ https://github.com/cgre-aachen/gempy,"Calcagno, P., Chilès, J. P., Courrioux, G
 https://github.com/cgre-aachen/gempy,"Lajaunie, C., Courrioux, G., & Manuel, L. (1997). Foliation fields and 3D cartography in geology: principles of a method based on potential interpolation. Mathematical Geology, 29(4), 571-584."
 https://github.com/driftingtides/hyvr,"HyVR can be attributed by citing the following journal article: Bennett, J. P., Haslauer, C. P., Ross, M., & Cirpka, O. A. (2018). An open, object-based framework for generating anisotropy in sedimentary subsurface models. Groundwater. DOI: 10.1111/gwat.12803."
 https://github.com/driving-behavior/DBNet,"DBNet was developed by MVIG, Shanghai Jiao Tong University* and SCSC Lab, Xiamen University* (alphabetical order)."
-https://github.com/driving-behavior/DBNet,Citation
 https://github.com/driving-behavior/DBNet,"If you find our work useful in your research, please consider citing:"
 https://github.com/driving-behavior/DBNet,"@InProceedings{DBNet2018,"
 https://github.com/driving-behavior/DBNet,"author = {Yiping Chen and Jingkang Wang and Jonathan Li and Cewu Lu and Zhipeng Luo and HanXue and Cheng Wang},"
 https://github.com/driving-behavior/DBNet,"title = {LiDAR-Video Driving Dataset: Learning Driving Policies Effectively},"
 https://github.com/driving-behavior/DBNet,"booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},"
 https://github.com/driving-behavior/DBNet,"month = {June},"
-https://github.com/driving-behavior/DBNet,year = {2018}
-https://github.com/driving-behavior/DBNet,}
-https://github.com/empymod/empymod,Citation
 https://github.com/empymod/empymod,"If you publish results for which you used empymod, please give credit by citing Werthmüller (2017):"
 https://github.com/empymod/empymod,"Werthmüller, D., 2017, An open-source full 3D electromagnetic modeler for 1D VTI media in Python: empymod: Geophysics, 82(6), WB9--WB19; DOI: 10.1190/geo2016-0626.1."
 https://github.com/empymod/empymod,"All releases have a Zenodo-DOI, provided on the release-page. Also consider citing Hunziker et al. (2015) and Key (2012), without which empymod would not exist."
@@ -95,7 +81,6 @@ https://github.com/endernewton/iter-reason,"author = {Xinlei Chen and Li-Jia Li
 https://github.com/endernewton/iter-reason,"title = {Iterative Visual Reasoning Beyond Convolutions},"
 https://github.com/endernewton/iter-reason,"booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},"
 https://github.com/endernewton/iter-reason,Year = {2018}
-https://github.com/endernewton/iter-reason,}
 https://github.com/endernewton/iter-reason,"@inproceedings{chen2017spatial,"
 https://github.com/endernewton/iter-reason,"author = {Xinlei Chen and Abhinav Gupta},"
 https://github.com/endernewton/iter-reason,"title = {Spatial Memory for Context Reasoning in Object Detection},"
@@ -113,7 +98,6 @@ https://github.com/facebookresearch/Detectron/,"Piotr Doll\'{a}r and Kaiming He}
 https://github.com/facebookresearch/Detectron/,"title =        {Detectron},"
 https://github.com/facebookresearch/Detectron/,"howpublished = {\url{https://github.com/facebookresearch/detectron}},"
 https://github.com/facebookresearch/Detectron/,year =         {2018}
-https://github.com/facebookresearch/Detectron/,}
 https://github.com/foolwood/DaSiamRPN,"Zheng Zhu*, Qiang Wang*, Bo Li*, Wei Wu, Junjie Yan, and Weiming Hu"
 https://github.com/foolwood/DaSiamRPN,"European Conference on Computer Vision (ECCV), 2018"
 https://github.com/foolwood/DaSiamRPN,Citing DaSiamRPN
@@ -121,20 +105,13 @@ https://github.com/foolwood/DaSiamRPN,"If you find DaSiamRPN and SiamRPN useful
 https://github.com/foolwood/DaSiamRPN,"@inproceedings{Zhu_2018_ECCV,"
 https://github.com/foolwood/DaSiamRPN,"title={Distractor-aware Siamese Networks for Visual Object Tracking},"
 https://github.com/foolwood/DaSiamRPN,"author={Zhu, Zheng and Wang, Qiang and Bo, Li and Wu, Wei and Yan, Junjie and Hu, Weiming},"
-https://github.com/foolwood/DaSiamRPN,"booktitle={European Conference on Computer Vision},"
-https://github.com/foolwood/DaSiamRPN,year={2018}
-https://github.com/foolwood/DaSiamRPN,}
 https://github.com/foolwood/DaSiamRPN,"@InProceedings{Li_2018_CVPR,"
 https://github.com/foolwood/DaSiamRPN,"title = {High Performance Visual Tracking With Siamese Region Proposal Network},"
 https://github.com/foolwood/DaSiamRPN,"author = {Li, Bo and Yan, Junjie and Wu, Wei and Zhu, Zheng and Hu, Xiaolin},"
-https://github.com/foolwood/DaSiamRPN,"booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},"
-https://github.com/foolwood/DaSiamRPN,year = {2018}
 https://github.com/google/sg2im/,"@inproceedings{johnson2018image,"
 https://github.com/google/sg2im/,"title={Image Generation from Scene Graphs},"
 https://github.com/google/sg2im/,"author={Johnson, Justin and Gupta, Agrim and Fei-Fei, Li},"
 https://github.com/google/sg2im/,"booktitle={CVPR},"
-https://github.com/google/sg2im/,year={2018}
-https://github.com/google/sg2im/,}
 https://github.com/google/sg2im/,Image Generation from Scene Graphs
 https://github.com/google/sg2im/,"Justin Johnson, Agrim Gupta, Li Fei-Fei"
 https://github.com/google/sg2im/,Presented at CVPR 2018
@@ -146,25 +123,16 @@ https://github.com/hezhangsprinter/DCPDN,[Paper Link] (CVPR'18)
 https://github.com/hezhangsprinter/DID-MDN,"@inproceedings{derain_zhang_2018,"
 https://github.com/hezhangsprinter/DID-MDN,"title={Density-aware Single Image De-raining using a Multi-stream Dense Network},"
 https://github.com/hezhangsprinter/DID-MDN,"author={Zhang, He and Patel, Vishal M},"
-https://github.com/hezhangsprinter/DID-MDN,"booktitle={CVPR},"
-https://github.com/hezhangsprinter/DID-MDN,year={2018}
-https://github.com/hezhangsprinter/DID-MDN,}
-https://github.com/hiroharu-kato/neural_renderer,Citation
 https://github.com/hiroharu-kato/neural_renderer,@InProceedings{kato2018renderer
 https://github.com/hiroharu-kato/neural_renderer,"title={Neural 3D Mesh Renderer},"
 https://github.com/hiroharu-kato/neural_renderer,"author={Kato, Hiroharu and Ushiku, Yoshitaka and Harada, Tatsuya},"
 https://github.com/hiroharu-kato/neural_renderer,"booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},"
-https://github.com/hiroharu-kato/neural_renderer,year={2018}
-https://github.com/hiroharu-kato/neural_renderer,}
 https://github.com/iannesbitt/readgssi,"Ian M. Nesbitt, François-Xavier Simon, Thomas Paulin, 2018. readgssi - an open-source tool to read and plot GSSI ground-penetrating radar data. doi:10.5281/zenodo.1439119"
 https://github.com/jiangsutx/SRN-Deblur,"Xin Tao, Hongyun Gao, Xiaoyong Shen, Jue Wang, Jiaya Jia."
 https://github.com/jiangsutx/SRN-Deblur,"@inproceedings{tao2018srndeblur,"
 https://github.com/jiangsutx/SRN-Deblur,"title={Scale-recurrent Network for Deep Image Deblurring},"
 https://github.com/jiangsutx/SRN-Deblur,"author={Tao, Xin and Gao, Hongyun and Shen, Xiaoyong and Wang, Jue and Jia, Jiaya},"
 https://github.com/jiangsutx/SRN-Deblur,"booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},"
-https://github.com/jiangsutx/SRN-Deblur,year={2018}
-https://github.com/jiangsutx/SRN-Deblur,}
-https://github.com/joferkington/mplstereonet,References
 https://github.com/joferkington/mplstereonet,"[Kamb1956]Kamb, 1959. Ice Petrofabric Observations from Blue Glacier, Washington, in Relation to Theory and Experiment. Journal of Geophysical Research, Vol. 64, No. 11, pp. 1891--1909."
 https://github.com/joferkington/mplstereonet,"[Vollmer1995]Vollmer, 1995. C Program for Automatic Contouring of Spherical Orientation Data Using a Modified Kamb Method. Computers & Geosciences, Vol. 21, No. 1, pp. 31--49."
 https://github.com/kenshohara/3D-ResNets-PyTorch,"If you use this code or pre-trained models, please cite the following:"
@@ -173,8 +141,6 @@ https://github.com/kenshohara/3D-ResNets-PyTorch,"author={Kensho Hara and Hiroka
 https://github.com/kenshohara/3D-ResNets-PyTorch,"title={Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?},"
 https://github.com/kenshohara/3D-ResNets-PyTorch,"booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},"
 https://github.com/kenshohara/3D-ResNets-PyTorch,"pages={6546--6555},"
-https://github.com/kenshohara/3D-ResNets-PyTorch,"year={2018},"
-https://github.com/kenshohara/3D-ResNets-PyTorch,}
 https://github.com/kenshohara/3D-ResNets-PyTorch,"Kensho Hara, Hirokatsu Kataoka, and Yutaka Satoh,"
 https://github.com/kenshohara/3D-ResNets-PyTorch,"Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?"","
 https://github.com/kenshohara/3D-ResNets-PyTorch,"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6546-6555, 2018."
@@ -184,8 +150,6 @@ https://github.com/msracver/Flow-Guided-Feature-Aggregation,"@inproceedings{zhu1
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,"Author = {Xizhou Zhu, Yujie Wang, Jifeng Dai, Lu Yuan, Yichen Wei},"
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,"Title = {Flow-Guided Feature Aggregation for Video Object Detection},"
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,"Conference = {ICCV},"
-https://github.com/msracver/Flow-Guided-Feature-Aggregation,Year = {2017}
-https://github.com/msracver/Flow-Guided-Feature-Aggregation,}
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,"@inproceedings{dai16rfcn,"
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,"Author = {Jifeng Dai, Yi Li, Kaiming He, Jian Sun},"
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,"Title = {{R-FCN}: Object Detection via Region-based Fully Convolutional Networks},"
@@ -201,13 +165,11 @@ https://github.com/phoenix104104/LapSRN,"author    = {Lai, Wei-Sheng and Huang,
 https://github.com/phoenix104104/LapSRN,"title     = {Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution},"
 https://github.com/phoenix104104/LapSRN,"booktitle = {IEEE Conferene on Computer Vision and Pattern Recognition},"
 https://github.com/phoenix104104/LapSRN,year      = {2017}
-https://github.com/phoenix104104/LapSRN,}
 https://github.com/phuang17/DeepMVS,"@inproceedings{DeepMVS,"
 https://github.com/phuang17/DeepMVS,"author       = ""Huang, Po-Han and Matzen, Kevin and Kopf, Johannes and Ahuja, Narendra and Huang, Jia-Bin"","
 https://github.com/phuang17/DeepMVS,"title        = ""DeepMVS: Learning Multi-View Stereopsis"","
 https://github.com/phuang17/DeepMVS,"booktitle    = ""IEEE Conference on Computer Vision and Pattern Recognition (CVPR)"","
 https://github.com/phuang17/DeepMVS,"year         = ""2018"""
-https://github.com/phuang17/DeepMVS,}
 https://github.com/pyvista/pymeshfix,Algorithm and Citation Policy
 https://github.com/pyvista/pymeshfix,"To better understand how the algorithm works, please refer to the following paper:"
 https://github.com/pyvista/pymeshfix,"M. Attene. A lightweight approach to repairing digitized polygon meshes. The Visual Computer, 2010. (c) Springer. DOI: 10.1007/s00371-010-0416-3"
@@ -229,32 +191,24 @@ https://github.com/pyvista/pyvista,"pages = {1450},"
 https://github.com/pyvista/pyvista,"author = {C. Bane Sullivan and Alexander Kaszynski},"
 https://github.com/pyvista/pyvista,"title = {{PyVista}: 3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit ({VTK})},"
 https://github.com/pyvista/pyvista,journal = {Journal of Open Source Software}
-https://github.com/pyvista/pyvista,}
 https://github.com/rowanz/neural-motifs,Bibtex
 https://github.com/rowanz/neural-motifs,"@inproceedings{zellers2018scenegraphs,"
 https://github.com/rowanz/neural-motifs,"title={Neural Motifs: Scene Graph Parsing with Global Context},"
 https://github.com/rowanz/neural-motifs,"author={Zellers, Rowan and Yatskar, Mark and Thomson, Sam and Choi, Yejin},"
 https://github.com/rowanz/neural-motifs,"booktitle = ""Conference on Computer Vision and Pattern Recognition"","
-https://github.com/rowanz/neural-motifs,year={2018}
-https://github.com/rowanz/neural-motifs,}
 https://github.com/ryersonvisionlab/two-stream-dyntex-synth,"@inproceedings{tesfaldet2018,"
 https://github.com/ryersonvisionlab/two-stream-dyntex-synth,"author = {Matthew Tesfaldet and Marcus A. Brubaker and Konstantinos G. Derpanis},"
 https://github.com/ryersonvisionlab/two-stream-dyntex-synth,"title = {Two-Stream Convolutional Networks for Dynamic Texture Synthesis},"
 https://github.com/ryersonvisionlab/two-stream-dyntex-synth,"booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},"
-https://github.com/ryersonvisionlab/two-stream-dyntex-synth,year = {2018}
-https://github.com/ryersonvisionlab/two-stream-dyntex-synth,}
 https://github.com/salihkaragoz/pose-residual-network-pytorch,"Muhammed Kocabas, Salih Karagoz, Emre Akbas. MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network. In ECCV, 2018. arxiv"
-https://github.com/salihkaragoz/pose-residual-network-pytorch,Citation
 https://github.com/salihkaragoz/pose-residual-network-pytorch,"If you find this code useful for your research, please consider citing our paper:"
 https://github.com/salihkaragoz/pose-residual-network-pytorch,"@Inproceedings{kocabas18prn,"
 https://github.com/salihkaragoz/pose-residual-network-pytorch,"Title          = {Multi{P}ose{N}et: Fast Multi-Person Pose Estimation using Pose Residual Network},"
 https://github.com/salihkaragoz/pose-residual-network-pytorch,"Author         = {Kocabas, Muhammed and Karagoz, Salih and Akbas, Emre},"
 https://github.com/salihkaragoz/pose-residual-network-pytorch,"Booktitle      = {European Conference on Computer Vision (ECCV)},"
 https://github.com/salihkaragoz/pose-residual-network-pytorch,Year           = {2018}
-https://github.com/salihkaragoz/pose-residual-network-pytorch,}
 https://github.com/whimian/pyGeoPressure,Cite pyGeoPressure as:
 https://github.com/whimian/pyGeoPressure,"Yu, (2018). PyGeoPressure: Geopressure Prediction in Python. Journal of Open Source Software, 3(30), 992, https://doi.org/10.21105/joss.00992"
-https://github.com/whimian/pyGeoPressure,BibTex:
 https://github.com/whimian/pyGeoPressure,"@article{yu2018pygeopressure,"
 https://github.com/whimian/pyGeoPressure,"title = {{PyGeoPressure}: {Geopressure} {Prediction} in {Python}},"
 https://github.com/whimian/pyGeoPressure,"author = {Yu, Hao},"
@@ -264,23 +218,17 @@ https://github.com/whimian/pyGeoPressure,pages = {922}
 https://github.com/whimian/pyGeoPressure,"number = {30},"
 https://github.com/whimian/pyGeoPressure,"year = {2018},"
 https://github.com/whimian/pyGeoPressure,"doi = {10.21105/joss.00992},"
-https://github.com/whimian/pyGeoPressure,}
 https://github.com/wuhuikai/DeepGuidedFilter,Fast End-to-End Trainable Guided Filter
 https://github.com/wuhuikai/DeepGuidedFilter,"Huikai Wu, Shuai Zheng, Junge Zhang, Kaiqi Huang"
 https://github.com/wuhuikai/DeepGuidedFilter,CVPR 2018
 https://github.com/wuhuikai/DeepGuidedFilter,"@inproceedings{wu2017fast,"
 https://github.com/wuhuikai/DeepGuidedFilter,"title     = {Fast End-to-End Trainable Guided Filter},"
 https://github.com/wuhuikai/DeepGuidedFilter,"author    = {Wu, Huikai and Zheng, Shuai and Zhang, Junge and Huang, Kaiqi},"
-https://github.com/wuhuikai/DeepGuidedFilter,"booktitle = {CVPR},"
-https://github.com/wuhuikai/DeepGuidedFilter,year = {2018}
-https://github.com/wuhuikai/DeepGuidedFilter,}
 https://github.com/yuhuayc/da-faster-rcnn,"If you find it helpful for your research, please consider citing:"
 https://github.com/yuhuayc/da-faster-rcnn,"@inproceedings{chen2018domain,"
 https://github.com/yuhuayc/da-faster-rcnn,"title={Domain Adaptive Faster R-CNN for Object Detection in the Wild},"
 https://github.com/yuhuayc/da-faster-rcnn,"author={Chen, Yuhua and Li, Wen and Sakaridis, Christos and Dai, Dengxin and Van Gool, Luc},"
 https://github.com/yuhuayc/da-faster-rcnn,"booktitle = {Computer Vision and Pattern Recognition (CVPR)},"
-https://github.com/yuhuayc/da-faster-rcnn,year={2018}
-https://github.com/yuhuayc/da-faster-rcnn,}
 https://github.com/yulunzhang/RDN,"Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu, ""Residual Dense Network for Image Super-Resolution"", CVPR 2018 (spotlight), [arXiv]"
 https://github.com/yulunzhang/RDN,"Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu, ""Residual Dense Network for Image Restoration"", arXiv 2018, [arXiv]"
 https://github.com/yulunzhang/RDN,"@InProceedings{Lim_2017_CVPR_Workshops,"
@@ -289,12 +237,9 @@ https://github.com/yulunzhang/RDN,"title = {Enhanced Deep Residual Networks for
 https://github.com/yulunzhang/RDN,"booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},"
 https://github.com/yulunzhang/RDN,"month = {July},"
 https://github.com/yulunzhang/RDN,year = {2017}
-https://github.com/yulunzhang/RDN,}
 https://github.com/yulunzhang/RDN,"@inproceedings{zhang2018residual,"
 https://github.com/yulunzhang/RDN,"title={Residual Dense Network for Image Super-Resolution},"
 https://github.com/yulunzhang/RDN,"author={Zhang, Yulun and Tian, Yapeng and Kong, Yu and Zhong, Bineng and Fu, Yun},"
-https://github.com/yulunzhang/RDN,"booktitle={CVPR},"
-https://github.com/yulunzhang/RDN,year={2018}
 https://github.com/yulunzhang/RDN,"@article{zhang2018rdnir,"
 https://github.com/yulunzhang/RDN,"title={Residual Dense Network for Image Restoration},"
 https://github.com/yulunzhang/RDN,"booktitle={arXiv},"
@@ -302,9 +247,52 @@ https://github.com/zhiqiangdon/CU-Net,"@inproceedings{tang2018quantized,"
 https://github.com/zhiqiangdon/CU-Net,"title={Quantized densely connected U-Nets for efficient landmark localization},"
 https://github.com/zhiqiangdon/CU-Net,"author={Tang, Zhiqiang and Peng, Xi and Geng, Shijie and Wu, Lingfei and Zhang, Shaoting and Metaxas, Dimitris},"
 https://github.com/zhiqiangdon/CU-Net,"booktitle={ECCV},"
-https://github.com/zhiqiangdon/CU-Net,year={2018}
-https://github.com/zhiqiangdon/CU-Net,}
 https://github.com/zhiqiangdon/CU-Net,"@inproceedings{tang2018cu,"
 https://github.com/zhiqiangdon/CU-Net,"title={CU-Net: Coupled U-Nets},"
 https://github.com/zhiqiangdon/CU-Net,"author={Tang, Zhiqiang and Peng, Xi and Geng, Shijie and Zhu, Yizhe and Metaxas, Dimitris},"
 https://github.com/zhiqiangdon/CU-Net,"booktitle={BMVC},"
+https://github.com/cltk/cltk,"Each major release of the CLTK is given a DOI, a type of unique identity for digital documents. This DOI ought to be included in your citation, as it will allow researchers to reproduce your results should the CLTK's API or codebase change. To find the CLTK's current DOI, observe the blue DOI button in the repository's home on GitHub. To the end of your bibliographic entry, append DOI plus the current identifier. You may also add version/release number, located in the pypi button at the project's GitHub repository homepage."
+https://github.com/cltk/cltk,"Thus, please cite core software as something like:"
+https://github.com/cltk/cltk,Kyle P. Johnson et al.. (2014-2019). CLTK: The Classical Language Toolkit. DOI 10.5281/zenodo.<current_release_id>
+https://github.com/cltk/cltk,A style-neutral BibTeX entry would look like this:
+https://github.com/cltk/cltk,"@Misc{johnson2014,"
+https://github.com/cltk/cltk,"author = {Kyle P. Johnson et al.},"
+https://github.com/cltk/cltk,"title = {CLTK: The Classical Language Toolkit},"
+https://github.com/cltk/cltk,"howpublished = {\url{https://github.com/cltk/cltk}},"
+https://github.com/cltk/cltk,"note = {{DOI} 10.5281/zenodo.<current_release_id>},"
+https://github.com/cltk/cltk,"year = {2014--2019},"
+https://github.com/facebookresearch/DensePose,"<a name=""CitingDensePose""></a>Citing DensePose"
+https://github.com/facebookresearch/DensePose,"If you use Densepose, please use the following BibTeX entry."
+https://github.com/facebookresearch/DensePose,"@InProceedings{Guler2018DensePose,"
+https://github.com/facebookresearch/DensePose,"  title={DensePose: Dense Human Pose Estimation In The Wild},"
+https://github.com/facebookresearch/DensePose,"  author={R\{i}za Alp G\""uler, Natalia Neverova, Iasonas Kokkinos},"
+https://github.com/facebookresearch/DensePose,"  journal={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},"
+https://github.com/facebookresearch/DensePose,  year={2018}
+https://github.com/facebookresearch/DensePose,  }
+https://github.com/facebookresearch/ResNeXt,"If you use ResNeXt in your research, please cite the paper:"
+https://github.com/facebookresearch/ResNeXt,"@article{Xie2016,"
+https://github.com/facebookresearch/ResNeXt,"  title={Aggregated Residual Transformations for Deep Neural Networks},"
+https://github.com/facebookresearch/ResNeXt,"  author={Saining Xie and Ross Girshick and Piotr Dollár and Zhuowen Tu and Kaiming He},"
+https://github.com/facebookresearch/ResNeXt,"  journal={arXiv preprint arXiv:1611.05431},"
+https://github.com/facebookresearch/ResNeXt,  year={2016}
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,"If you use this tool for your research, then kindly cite it. Click the above badge for more information regarding the complete citation for this tool and diffferent citation formats like IEEE, APA etc."
+https://github.com/microsoft/malmo,Citations
+https://github.com/microsoft/malmo,Please cite Malmo as:
+https://github.com/microsoft/malmo,"Johnson M., Hofmann K., Hutton T., Bignell D. (2016) The Malmo Platform for Artificial Intelligence Experimentation. Proc. 25th International Joint Conference on Artificial Intelligence, Ed. Kambhampati S., p. 4246. AAAI Press, Palo Alto, California USA. https://github.com/Microsoft/malmo"
+https://github.com/nextflow-io/nextflow,"If you use Nextflow in your research, please cite:"
+https://github.com/nextflow-io/nextflow,"P. Di Tommaso, et al. Nextflow enables reproducible computational workflows. Nature Biotechnology 35, 316–319 (2017) doi:10.1038/nbt.3820"
+https://github.com/pyro-ppl/pyro,"If you use Pyro, please consider citing:"
+https://github.com/pyro-ppl/pyro,"@article{bingham2018pyro,"
+https://github.com/pyro-ppl/pyro,"  author = {Bingham, Eli and Chen, Jonathan P. and Jankowiak, Martin and Obermeyer, Fritz and"
+https://github.com/pyro-ppl/pyro,"            Pradhan, Neeraj and Karaletsos, Theofanis and Singh, Rohit and Szerlip, Paul and"
+https://github.com/pyro-ppl/pyro,"            Horsfall, Paul and Goodman, Noah D.},"
+https://github.com/pyro-ppl/pyro,"  title = {{Pyro: Deep Universal Probabilistic Programming}},"
+https://github.com/pyro-ppl/pyro,"  journal = {arXiv preprint arXiv:1810.09538},"
+https://github.com/pyro-ppl/pyro,  year = {2018}
+https://github.com/scikit-image/scikit-image,"If you find this project useful, please cite:"
+https://github.com/scikit-image/scikit-image,"Stéfan van der Walt, Johannes L. Schönberger, Juan Nunez-Iglesias,"
+https://github.com/scikit-image/scikit-image,"François Boulogne, Joshua D. Warner, Neil Yager, Emmanuelle"
+https://github.com/scikit-image/scikit-image,"Gouillart, Tony Yu, and the scikit-image contributors."
+https://github.com/scikit-image/scikit-image,scikit-image: Image processing in Python. PeerJ 2:e453 (2014)
+https://github.com/scikit-image/scikit-image,https://doi.org/10.7717/peerj.453
+https://github.com/scikit-learn/scikit-learn,"If you use scikit-learn in a scientific publication, we would appreciate citations: http://scikit-learn.org/stable/about.html#citing-scikit-learn"
diff --git a/data/description.csv b/data/description.csv
index 67f97713..4f4c1411 100644
--- a/data/description.csv
+++ b/data/description.csv
@@ -57,13 +57,11 @@ https://github.com/driftingtides/hyvr,HyVR: Turning your geofantasy into reality
 https://github.com/driftingtides/hyvr,"The Hydrogeological Virtual Reality simulation package (HyVR) is a Python module that helps researchers and practitioners generate subsurface models with multiple scales of heterogeneity that are based on geological concepts. The simulation outputs can then be used to explore groundwater flow and solute transport behaviour. This is facilitated by HyVR outputs in common flow simulation packages' input formats. As each site is unique, HyVR has been designed that users can take the code and extend it to suit their particular simulation needs."
 https://github.com/driftingtides/hyvr,"The original motivation for HyVR was the lack of tools for modelling sedimentary deposits that include bedding structure model outputs (i.e., dip and azimuth). Such bedding parameters were required to approximate full hydraulic-conductivity tensors for groundwater flow modelling. HyVR is able to simulate these bedding parameters and generate spatially distributed parameter fields, including full hydraulic-conductivity tensors. More information about HyVR is available in the online technical documentation."
 https://github.com/driftingtides/hyvr,I hope you enjoy using HyVR much more than I enjoyed putting it together! I look forward to seeing what kind of funky fields you created in the course of your work.
-https://github.com/driving-behavior/DBNet,Introduction
 https://github.com/driving-behavior/DBNet,"This work is based on our research paper, which appears in CVPR 2018. We propose a large-scale dataset for driving behavior learning, namely, DBNet. You can also check our dataset webpage for a deeper introduction."
 https://github.com/driving-behavior/DBNet,"In this repository, we release demo code and partial prepared data for training with only images, as well as leveraging feature maps or point clouds. The prepared data are accessible here. (More demo models and scripts are released soon!)"
 https://github.com/driving-behavior/DBNet,"This baseline is run on dbnet-2018 challenge data and only nvidia_pn is tested. To measure difficult architectures comprehensively, several metrics are set, including accuracy under different thresholds, area under curve (AUC), max error (ME), mean error (AE) and mean of max errors (AME)."
 https://github.com/driving-behavior/DBNet,The implementations of these metrics could be found in evaluate.py.
 https://github.com/empymod/empymod,"The electromagnetic modeller empymod can model electric or magnetic responses due to a three-dimensional electric or magnetic source in a layered-earth model with vertical transverse isotropic (VTI) resistivity, VTI electric permittivity, and VTI magnetic permeability, from very low frequencies (DC) to very high frequencies (GPR). The calculation is carried out in the wavenumber-frequency domain, and various Hankel- and Fourier-transform methods are included to transform the responses into the space-frequency and space-time domains."
-https://github.com/empymod/empymod,Features
 https://github.com/empymod/empymod,"Calculates the complete (diffusion and wave phenomena) 3D electromagnetic field in a layered-earth model including vertical transverse isotropic (VTI) resistivity, VTI electric permittivity, and VTI magnetic permeability, for electric and magnetic sources as well as electric and magnetic receivers."
 https://github.com/empymod/empymod,Modelling routines:
 https://github.com/empymod/empymod,"bipole: arbitrary oriented, finite length bipoles with given source strength; space-frequency and space-time domains."
@@ -128,7 +126,6 @@ https://github.com/foolwood/DaSiamRPN,"DaSiamRPN improves the performances of Si
 https://github.com/geo-data/gdal-docker,This is an Ubuntu derived image containing the Geospatial Data Abstraction Library (GDAL) compiled with a broad range of drivers. The build process is based on that defined in the GDAL TravisCI tests.
 https://github.com/geo-data/gdal-docker,Each branch in the git repository corresponds to a supported GDAL version (e.g. 1.11.2) with the master branch following GDAL master. These branch names are reflected in the image tags on the Docker Index (e.g. branch 1.11.2 corresponds to the image geodata/gdal:1.11.2).
 https://github.com/geopandas/geopandas/,Python tools for geographic data
-https://github.com/geopandas/geopandas/,Introduction
 https://github.com/geopandas/geopandas/,GeoPandas is a project to add support for geographic data to pandas objects. It currently implements GeoSeries and GeoDataFrame types which are subclasses of pandas.Series and pandas.DataFrame respectively. GeoPandas objects can act on shapely geometry objects and perform geometric operations.
 https://github.com/geopandas/geopandas/,"GeoPandas geometry operations are cartesian. The coordinate reference system (crs) can be stored as an attribute on an object, and is automatically set when loading from a file. Objects may be transformed to new coordinate systems with the to_crs() method. There is currently no enforcement of like coordinates for operations, but that may change in the future."
 https://github.com/google/sg2im/,This is the code for the paper
@@ -199,7 +196,6 @@ https://github.com/mbloch/mapshaper,"Mapshaper is software for editing Shapefile
 https://github.com/mbloch/mapshaper,"The mapshaper command line program supports essential map making tasks like simplifying shapes, editing attribute data, clipping, erasing, dissolving, filtering and more."
 https://github.com/mbloch/mapshaper,"The web UI supports interactive simplification, attribute data editing, and running cli commands in a built-in console. Visit the public website at www.mapshaper.org or use the web UI locally via the mapshaper-gui script."
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,"This repository is implemented by Yuqing Zhu, Shuhao Fu, and Xizhou Zhu, when they are interns at MSRA."
-https://github.com/msracver/Flow-Guided-Feature-Aggregation,Introduction
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,"Flow-Guided Feature Aggregation (FGFA) is initially described in an ICCV 2017 paper. It provides an accurate and end-to-end learning framework for video object detection. The proposed FGFA method, together with our previous work of Deep Feature Flow, powered the winning entry of ImageNet VID 2017. It is worth noting that:"
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,"FGFA improves the per-frame features by aggregating nearby frame features along the motion paths. It significantly improves the object detection accuracy in videos, especially for fast moving objects."
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,"FGFA is end-to-end trainable for the task of video object detection, which is vital for improving the recognition accuracy."
@@ -262,7 +258,6 @@ https://github.com/ungarj/tilematrix,Tilematrix supports metatiling and tile buf
 https://github.com/ungarj/tilematrix,"It is very similar to mercantile but besides of supporting spherical mercator tile pyramids, it also supports geodetic (WGS84) tile pyramids."
 https://github.com/vuejs/vue,"Vue (pronounced /vjuː/, like view) is a progressive framework for building user interfaces. It is designed from the ground up to be incrementally adoptable, and can easily scale between a library and a framework depending on different use cases. It consists of an approachable core library that focuses on the view layer only, and an ecosystem of supporting libraries that helps you tackle complexity in large Single-Page Applications."
 https://github.com/whimian/pyGeoPressure,A Python package for pore pressure prediction using well log data and seismic velocity data.
-https://github.com/whimian/pyGeoPressure,Features
 https://github.com/whimian/pyGeoPressure,Overburden (or Lithostatic) Pressure Calculation
 https://github.com/whimian/pyGeoPressure,Eaton's method and Parameter Optimization
 https://github.com/whimian/pyGeoPressure,Bowers' method and Parameter Optimization
@@ -279,3 +274,64 @@ https://github.com/yulunzhang/RDN,"A very deep convolutional neural network (CNN
 https://github.com/zhiqiangdon/CU-Net,"The follwoing figure gives an illustration of naive dense U-Net, stacked U-Nets and coupled U-Nets (CU-Net). The naive dense U-Net and stacked U-Nets have shortcut connections only inside each U-Net. In contrast, the coupled U-Nets also have connections for semantic blocks across U-Nets. The CU-Net is a hybrid of naive dense U-Net and stacked U-Net, integrating the merits of both dense connectivity, intermediate supervisions and multi-stage top-down and bottom-up refinement. The resulted CU-Net could save ~70% parameters of the previous stacked U-Nets but with comparable accuracy."
 https://github.com/zhiqiangdon/CU-Net,"If we couple each U-Net pair in multiple U-Nets, the coupling connections would have quadratic growth with respect to the U-Net number. To make the model more parameter efficient, we propose the order-K coupling to trim off the long-distance coupling connections."
 https://github.com/zhiqiangdon/CU-Net,"For simplicity, each dot represents one U-Net. The red and blue lines are the shortcut connections of inside semantic blocks and outside inputs. Order-0 connectivity (Top) strings U-Nets together only by their inputs and outputs, i.e. stacked U-Nets. Order-1 connectivity (Middle) has shortcut connections for adjacent U-Nets. Similarly, order-2 connectivity (Bottom) has shortcut connections for 3 nearby U-Nets."
+https://github.com/cltk/cltk,The Classical Language Toolkit
+https://github.com/cltk/cltk,"The Classical Language Toolkit (CLTK) offers natural language processing (NLP) support for the languages of Ancient, Classical, and Medieval Eurasia. Greek, Latin, Akkadian, and the Germanic languages are currently most complete. The goals of the CLTK are to:"
+https://github.com/cltk/cltk,*   compile analysis-friendly corpora;
+https://github.com/cltk/cltk,*   collect and generate linguistic data;
+https://github.com/cltk/cltk,*   act as a free and open platform for generating scientific research.
+https://github.com/facebookresearch/DensePose,Dense Human Pose Estimation In The Wild
+https://github.com/facebookresearch/DensePose,"Rıza Alp Güler, Natalia Neverova, Iasonas Kokkinos"
+https://github.com/facebookresearch/DensePose,Dense human pose estimation aims at mapping all human pixels of an RGB image to the 3D surface of the human body. 
+https://github.com/facebookresearch/DensePose,DensePose-RCNN is implemented in the Detectron framework and is powered by Caffe2.
+https://github.com/facebookresearch/DensePose,"In this repository, we provide the code to train and evaluate DensePose-RCNN. We also provide notebooks to visualize the collected DensePose-COCO dataset and show the correspondences to the SMPL model."
+https://github.com/facebookresearch/ResNeXt,ResNeXt: Aggregated Residual Transformations for Deep Neural Networks
+https://github.com/facebookresearch/ResNeXt,ResNeXt is the foundation of their new SENet architecture (a ResNeXt-152 (64 x 4d) with the Squeeze-and-Excitation module)!
+https://github.com/facebookresearch/ResNeXt,This repository contains a Torch implementation for the ResNeXt algorithm for image classification. The code is based on fb.resnet.torch.
+https://github.com/facebookresearch/ResNeXt,"ResNeXt is a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call “cardinality” (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width."
+https://github.com/facebookresearch/pyrobot,"PyRobot is a light weight, high-level interface which provides hardware independent APIs for robotic manipulation and navigation. This repository also contains the low-level stack for LoCoBot, a low cost mobile manipulator hardware platform."
+https://github.com/gitbucket/gitbucket,GitBucket is a Git web platform powered by Scala offering:
+https://github.com/gitbucket/gitbucket,"You can try an online demo (ID: root / Pass: root) of GitBucket, and also get the latest information at GitBucket News."
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,🔥 Ultimate Facebook Scrapper
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,A bot which scrapes almost everything about a facebook user's profile including
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,"The best thing about this scraper is that the data is scraped in an organized format so that it can be used for educational/research purpose by researchers. Moreover, this scraper does not use Facebook's Graph API so there are no rate limiting issues as such. "
+https://github.com/nextflow-io/nextflow,Nextflow is a bioinformatics workflow manager that enables the development of portable and reproducible workflows.
+https://github.com/nextflow-io/nextflow,"It supports deploying workflows on a variety of execution platforms including local, HPC schedulers, AWS Batch,"
+https://github.com/nextflow-io/nextflow,"Google Genomics Pipelines, and Kubernetes. Additionally, it provides support for manage your workflow dependencies"
+https://github.com/nextflow-io/nextflow,"through built-in support for Conda, Docker, Singularity, and Modules."
+https://github.com/nextflow-io/nextflow,"Nextflow framework is based on the dataflow programming model, which greatly simplifies writing parallel and distributed pipelines without adding unnecessary complexity and letting you concentrate on the flow of data, i.e. the functional logic of the application/algorithm."
+https://github.com/nextflow-io/nextflow,"It doesn't aim to be another pipeline scripting language yet, but it is built around the idea that the Linux platform is the lingua franca of data science, since it provides many simple command line and scripting tools, which by themselves are powerful, but when chained together facilitate complex data manipulations."
+https://github.com/nextflow-io/nextflow,"In practice, this means that a Nextflow script is defined by composing many different processes. Each process can execute a given bioinformatics tool or scripting language, to which is added the ability to coordinate and synchronize the processes execution by simply specifying their inputs and outputs."
+https://github.com/nextflow-io/nextflow,"Nextflow also supports running workflows across various clouds and cloud technologies. Nextflow can create AWS EC2 or Google GCE clusters and deploy your workflow. Managed solutions from both Amazon and Google are also supported through AWS Batch and Google Genomics Pipelines. Additionally, Nextflow can run workflows on either on-prem or managed cloud Kubernetes clusters. "
+https://github.com/nextflow-io/nextflow,"Nextflow is built on two great pieces of open source software, namely <a href='http://groovy-lang.org' target='_blank'>Groovy</a>"
+https://github.com/pyro-ppl/pyro,"Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch.  Notably, it was designed with these principles in mind:"
+https://github.com/pyro-ppl/pyro,Universal: Pyro is a universal PPL - it can represent any computable probability distribution.
+https://github.com/pyro-ppl/pyro,Scalable: Pyro scales to large data sets with little overhead compared to hand-written code.
+https://github.com/pyro-ppl/pyro,"Minimal: Pyro is agile and maintainable. It is implemented with a small core of powerful, composable abstractions."
+https://github.com/pyro-ppl/pyro,"Flexible: Pyro aims for automation when you want it, control when you need it. This is accomplished through high-level abstractions to express generative and inference models, while allowing experts easy-access to customize inference."
+https://github.com/pyro-ppl/pyro,Pyro is in a beta release.  It is developed and maintained by Uber AI Labs and community contributors.
+https://github.com/reduxjs/react-redux,Official React bindings for Redux.
+https://github.com/reduxjs/react-redux,Performant and flexible.
+https://github.com/scikit-image/scikit-image,scikit-image: Image processing in Python
+https://github.com/scikit-learn/scikit-learn,scikit-learn is a Python module for machine learning built on top of
+https://github.com/scikit-learn/scikit-learn,SciPy and is distributed under the 3-Clause BSD license.
+https://github.com/scikit-learn/scikit-learn,The project was started in 2007 by David Cournapeau as a Google Summer
+https://github.com/scikit-learn/scikit-learn,"of Code project, and since then many volunteers have contributed. See"
+https://github.com/scikit-learn/scikit-learn,the About us &lt;http://scikit-learn.org/dev/about.html#authors&gt;_ page
+https://github.com/scikit-learn/scikit-learn,for a list of core contributors.
+https://github.com/scikit-learn/scikit-learn,It is currently maintained by a team of volunteers.
+https://github.com/scikit-learn/scikit-learn,Website: http://scikit-learn.org
+https://github.com/tensorflow/magenta,Magenta is a research project exploring the role of machine learning
+https://github.com/tensorflow/magenta,in the process of creating art and music.  Primarily this
+https://github.com/tensorflow/magenta,involves developing new deep learning and reinforcement learning
+https://github.com/tensorflow/magenta,"algorithms for generating songs, images, drawings, and other materials. But it's also"
+https://github.com/tensorflow/magenta,an exploration in building smart tools and interfaces that allow
+https://github.com/tensorflow/magenta,artists and musicians to extend (not replace!) their processes using
+https://github.com/tensorflow/magenta,these models.  Magenta was started by some researchers and engineers
+https://github.com/tensorflow/magenta,"from the Google Brain team,"
+https://github.com/tensorflow/magenta,but many others have contributed significantly to the project. We use
+https://github.com/tensorflow/magenta,TensorFlow and release our models and
+https://github.com/tensorflow/magenta,tools in open source on this GitHub.  If you’d like to learn more
+https://github.com/tensorflow/magenta,"about Magenta, check out our blog,"
+https://github.com/tensorflow/magenta,where we post technical details.  You can also join our discussion
+https://github.com/tensorflow/magenta,group.
+https://github.com/tensorflow/magenta,"This is the home for our Python TensorFlow library. To use our models in the browser with TensorFlow.js, head to the Magenta.js repository."
diff --git a/data/installation.csv b/data/installation.csv
index 0a25da2a..01d54273 100644
--- a/data/installation.csv
+++ b/data/installation.csv
@@ -12,14 +12,12 @@ https://github.com/JimmySuen/integral-human-pose,Python Version: 3.6
 https://github.com/JimmySuen/integral-human-pose,OS: CentOs7 (Other Linux system is also OK)
 https://github.com/JimmySuen/integral-human-pose,CUDA: 9.0 (least 8.0)
 https://github.com/JimmySuen/integral-human-pose,PyTorch:0.4.0(see issue https://github.com/JimmySuen/integral-human-pose/issues/4)
-https://github.com/JimmySuen/integral-human-pose,Installation
 https://github.com/JimmySuen/integral-human-pose,"We recommend installing python from Anaconda, installing pytorch following guide on PyTorch according to your specific CUDA & python version. In addition, you need to install dependencies below."
 https://github.com/JimmySuen/integral-human-pose,pip install scipy
 https://github.com/JimmySuen/integral-human-pose,pip install matplotlib
 https://github.com/JimmySuen/integral-human-pose,pip install opencv-python
 https://github.com/JimmySuen/integral-human-pose,pip install easydict
 https://github.com/JimmySuen/integral-human-pose,pip install pyyaml
-https://github.com/JuliaGeo/LibGEOS.jl,Installation
 https://github.com/JuliaGeo/LibGEOS.jl,"At the Julia prompt, run"
 https://github.com/JuliaGeo/LibGEOS.jl,"julia> Pkg.add(""LibGEOS"")"
 https://github.com/JuliaGeo/LibGEOS.jl,"This will install both the Julia package and GEOS shared libraries together. To just reinstall the GEOS shared libraries, run Pkg.build(""LibGEOS"")."
@@ -48,7 +46,6 @@ https://github.com/NVIDIA/vid2vid,Linux or macOS
 https://github.com/NVIDIA/vid2vid,Python 3
 https://github.com/NVIDIA/vid2vid,NVIDIA GPU + CUDA cuDNN
 https://github.com/NVIDIA/vid2vid,PyTorch 0.4
-https://github.com/NVIDIA/vid2vid,Installation
 https://github.com/NVIDIA/vid2vid,Install python libraries dominate and requests.
 https://github.com/NVIDIA/vid2vid,pip install dominate requests
 https://github.com/NVIDIA/vid2vid,"If you plan to train with face datasets, please install dlib."
@@ -60,11 +57,9 @@ https://github.com/NVIDIA/vid2vid,cd vid2vid
 https://github.com/NVIDIA/vid2vid,"Docker Image If you have difficulty building the repo, a docker image can be found in the docker folder."
 https://github.com/OpenGeoVis/PVGeo,"To begin using the PVGeo Python package, create/activate your Python virtual environment (we highly recommend using anaconda) and install PVGeo through pip:"
 https://github.com/OpenGeoVis/PVGeo,pip install PVGeo
-https://github.com/OpenGeoVis/omfvista,Installation
 https://github.com/OpenGeoVis/omfvista,Installation is simply:
 https://github.com/OpenGeoVis/omfvista,pip install omfvista
 https://github.com/OpenGeoVis/omfvista,All necessary dependencies will be installed alongside omfvista. Please note that this package heavily leverages the PyVista package.
-https://github.com/OpenGeoscience/geonotebook/,Installation
 https://github.com/OpenGeoscience/geonotebook/,System Prerequisites
 https://github.com/OpenGeoscience/geonotebook/,For default tile serving
 https://github.com/OpenGeoscience/geonotebook/,GDAL >= 2.1.0
@@ -78,7 +73,6 @@ https://github.com/OpenGeoscience/geonotebook/,mkvirtualenv -a . geonotebook
 https://github.com/OpenGeoscience/geonotebook/,# Numpy must be fully installed before rasterio
 https://github.com/OpenGeoscience/geonotebook/,pip install -r prerequirements.txt
 https://github.com/OpenGeoscience/geonotebook/,pip install -r requirements.txt
-https://github.com/OpenGeoscience/geonotebook/,pip install .
 https://github.com/OpenGeoscience/geonotebook/,# Enable both the notebook and server extensions
 https://github.com/OpenGeoscience/geonotebook/,jupyter serverextension enable --sys-prefix --py geonotebook
 https://github.com/OpenGeoscience/geonotebook/,jupyter nbextension enable --sys-prefix --py geonotebook
@@ -93,7 +87,6 @@ https://github.com/OpenGeoscience/geonotebook/,# Enable the extension
 https://github.com/OpenGeoscience/geonotebook/,# Start the javascript builder
 https://github.com/OpenGeoscience/geonotebook/,cd js
 https://github.com/OpenGeoscience/geonotebook/,npm run watch
-https://github.com/Toblerity/Fiona/,Installation
 https://github.com/Toblerity/Fiona/,"Fiona requires Python 2.7 or 3.4+ and GDAL/OGR 1.8+. To build from a source distribution you will need a C compiler and GDAL and Python development headers and libraries (libgdal1-dev for Debian/Ubuntu, gdal-dev for CentOS/Fedora)."
 https://github.com/Toblerity/Fiona/,"To build from a repository copy, you will also need Cython to build C sources from the project's .pyx files. See the project's requirements-dev.txt file for guidance."
 https://github.com/Toblerity/Fiona/,"The Kyngchaos GDAL frameworks will satisfy the GDAL/OGR dependency for OS X, as will Homebrew's GDAL Formula (brew install gdal)."
@@ -141,7 +134,6 @@ https://github.com/XiaLiPKU/RESCAN,Python>=3.6
 https://github.com/XiaLiPKU/RESCAN,Pytorch>=4.1.0
 https://github.com/XiaLiPKU/RESCAN,Opencv>=3.1.0
 https://github.com/XiaLiPKU/RESCAN,tensorboardX
-https://github.com/ZhouYanzhao/PRM,Prerequisites
 https://github.com/ZhouYanzhao/PRM,System (tested on Ubuntu 14.04LTS and Win10)
 https://github.com/ZhouYanzhao/PRM,NVIDIA GPU + CUDA CuDNN (CPU mode is also supported but significantly slower)
 https://github.com/ZhouYanzhao/PRM,Python>=3.5
@@ -174,28 +166,22 @@ https://github.com/agile-geoscience/striplog/,"Then do this to create an environ
 https://github.com/agile-geoscience/striplog/,conda create -n myenv python=3.5 numpy matplotlib
 https://github.com/agile-geoscience/striplog/,source activate myenv
 https://github.com/agile-geoscience/striplog/,Then you can do:
-https://github.com/akanazawa/hmr,Requirements
 https://github.com/akanazawa/hmr,Python 2.7
 https://github.com/akanazawa/hmr,"TensorFlow tested on version 1.3, demo alone runs with TF 1.12"
-https://github.com/akanazawa/hmr,Installation
 https://github.com/akanazawa/hmr,Setup virtualenv
 https://github.com/akanazawa/hmr,virtualenv venv_hmr
 https://github.com/akanazawa/hmr,source venv_hmr/bin/activate
 https://github.com/akanazawa/hmr,pip install -U pip
 https://github.com/akanazawa/hmr,deactivate
-https://github.com/akanazawa/hmr,pip install -r requirements.txt
 https://github.com/akanazawa/hmr,Install TensorFlow
 https://github.com/akanazawa/hmr,With GPU:
 https://github.com/akanazawa/hmr,pip install tensorflow-gpu==1.3.0
 https://github.com/akanazawa/hmr,Without GPU:
 https://github.com/akanazawa/hmr,pip install tensorflow==1.3.0
-https://github.com/akaszynski/pyansys,Installation
 https://github.com/akaszynski/pyansys,Installation through pip:
 https://github.com/akaszynski/pyansys,pip install pyansys
-https://github.com/albertpumarola/GANimation,Prerequisites
 https://github.com/albertpumarola/GANimation,"Install PyTorch (version 0.3.1), Torch Vision and dependencies from http://pytorch.org"
 https://github.com/albertpumarola/GANimation,Install requirements.txt (pip install -r requirements.txt)
-https://github.com/cgre-aachen/gempy,Installation
 https://github.com/cgre-aachen/gempy,We provide the latest release version of GemPy via the Conda and PyPi package services. We highly recommend using either PyPi as it will take care of automatically installing all dependencies.
 https://github.com/cgre-aachen/gempy,PyPi
 https://github.com/cgre-aachen/gempy,$ pip install gempy
@@ -229,7 +215,6 @@ https://github.com/d3/d3,"You can also use the standalone D3 microlibraries. For
 https://github.com/d3/d3,"<script src=""https://d3js.org/d3-selection.v1.js""></script>"
 https://github.com/driftingtides/hyvr,Installing the HyVR package
 https://github.com/driftingtides/hyvr,Installing Python
-https://github.com/driftingtides/hyvr,Windows
 https://github.com/driftingtides/hyvr,"If you are using Windows, we recommend installing the Anaconda distribution of Python 3. This distribution has the majority of dependencies that HyVR requires."
 https://github.com/driftingtides/hyvr,It is also a good idea to install the HyVR package into a virtual environment. Do this by opening a command prompt window and typing the following:
 https://github.com/driftingtides/hyvr,conda create --name hyvr_env
@@ -246,10 +231,8 @@ https://github.com/driftingtides/hyvr,"The version on PyPI should always be up t
 https://github.com/driftingtides/hyvr,git clone https://github.com/driftingtides/hyvr.git
 https://github.com/driftingtides/hyvr,To install from source you need a C compiler.
 https://github.com/driftingtides/hyvr,Installation from conda-forge will (hopefully) be coming soon.
-https://github.com/driftingtides/hyvr,Requirements
 https://github.com/driftingtides/hyvr,Python
 https://github.com/driftingtides/hyvr,"HyVR was developed for use with Python 3.4 or greater. It may be possible to use with earlier versions of Python 3, however this has not been tested."
-https://github.com/driftingtides/hyvr,Dependencies
 https://github.com/driftingtides/hyvr,numpy <= 1.13.3
 https://github.com/driftingtides/hyvr,matplotlib <= 2.1.0
 https://github.com/driftingtides/hyvr,scipy = 1.0.0
@@ -257,19 +240,15 @@ https://github.com/driftingtides/hyvr,pandas = 0.21.0
 https://github.com/driftingtides/hyvr,flopy == 3.2.9 (optional for modflow output)
 https://github.com/driftingtides/hyvr,pyevtk = 1.1.0 (optional for VTK output)
 https://github.com/driftingtides/hyvr,h5py (optional for HDF5 output)
-https://github.com/driving-behavior/DBNet,Requirements
 https://github.com/driving-behavior/DBNet,Tensorflow 1.2.0
-https://github.com/driving-behavior/DBNet,Python 2.7
 https://github.com/driving-behavior/DBNet,CUDA 8.0+ (For GPU)
 https://github.com/driving-behavior/DBNet,"Python Libraries: numpy, scipy and laspy"
 https://github.com/driving-behavior/DBNet,"The code has been tested with Python 2.7, Tensorflow 1.2.0, CUDA 8.0 and cuDNN 5.1 on Ubuntu 14.04. But it may work on more machines (directly or through mini-modification), pull-requests or test report are well welcomed."
-https://github.com/empymod/empymod,Installation
 https://github.com/empymod/empymod,You can install empymod either via conda:
 https://github.com/empymod/empymod,conda install -c prisae empymod
 https://github.com/empymod/empymod,or via pip:
 https://github.com/empymod/empymod,pip install empymod
 https://github.com/empymod/empymod,Required are Python version 3.5 or higher and the modules NumPy and SciPy. Consult the installation notes in the manual for more information regarding installation and requirements.
-https://github.com/endernewton/iter-reason,Prerequisites
 https://github.com/endernewton/iter-reason,"Tensorflow, tested with version 1.6 with Ubuntu 16.04, installed with:"
 https://github.com/endernewton/iter-reason,pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0-cp27-none-linux_x86_64.whl
 https://github.com/endernewton/iter-reason,Other packages needed can be installed with pip:
@@ -358,18 +337,15 @@ https://github.com/equinor/segyio,git clone https://github.com/equinor/segyio
 https://github.com/equinor/segyio,mkdir segyio/build
 https://github.com/equinor/segyio,cd segyio/build
 https://github.com/equinor/segyio,cmake .. -DCMAKE_BUILD_TYPE=Release -DBUILD_SHARED_LIBS=ON
-https://github.com/equinor/segyio,make
 https://github.com/equinor/segyio,make install
 https://github.com/equinor/segyio,"make install must be done as root for a system install; if you want to install in your home directory, add -DCMAKE_INSTALL_PREFIX=~/ or some other appropriate directory, or make DESTDIR=~/ install. Please ensure your environment picks up on non-standard install locations (PYTHONPATH, LD_LIBRARY_PATH and PATH)."
 https://github.com/equinor/segyio,"If you have multiple Python installations, or want to use some alternative interpreter, you can help cmake find the right one by passing -DPYTHON_EXECUTABLE=/opt/python/binary along with install prefix and build type."
 https://github.com/equinor/segyio,"To build the matlab bindings, invoke CMake with the option -DBUILD_MEX=ON. In some environments the Matlab binaries are in a non-standard location, in which case you need to help CMake find the matlab binaries by passing -DMATLAB_ROOT=/path/to/matlab."
-https://github.com/facebook/react,Installation
 https://github.com/facebook/react,"React has been designed for gradual adoption from the start, and you can use as little or as much React as you need:"
 https://github.com/facebook/react,Use Online Playgrounds to get a taste of React.
 https://github.com/facebook/react,Add React to a Website as a <script> tag in one minute.
 https://github.com/facebook/react,Create a New React App if you're looking for a powerful JavaScript toolchain.
 https://github.com/facebook/react,"You can use React as a <script> tag from a CDN, or as a react package on npm."
-https://github.com/geopandas/geopandas/,Install
 https://github.com/geopandas/geopandas/,See the installation docs for all details. GeoPandas depends on the following packages:
 https://github.com/geopandas/geopandas/,pandas
 https://github.com/geopandas/geopandas/,shapely
@@ -386,7 +362,6 @@ https://github.com/google/sg2im/,pip install -r requirements.txt   # Install dep
 https://github.com/google/sg2im/,echo $PWD > env/lib/python3.5/site-packages/sg2im.pth  # Add current directory to python path
 https://github.com/google/sg2im/,# Work for a while ...
 https://github.com/google/sg2im/,deactivate  # Exit virtual environment
-https://github.com/gprMax/gprMax,Installation
 https://github.com/gprMax/gprMax,The following steps provide guidance on how to install gprMax:
 https://github.com/gprMax/gprMax,"Install Python, required Python packages, and get the gprMax source code from GitHub"
 https://github.com/gprMax/gprMax,Install a C compiler which supports OpenMP
@@ -404,7 +379,6 @@ https://github.com/gprMax/gprMax,$ conda env create -f conda_env.yml
 https://github.com/gprMax/gprMax,"This will make sure conda is up-to-date, install Git, get the latest gprMax source code from GitHub, and create an environment for gprMax with all the necessary Python packages."
 https://github.com/gprMax/gprMax,"If you prefer to install Python and the required Python packages manually, i.e. without using Anaconda/Miniconda, look in the conda_env.yml file for a list of the requirements."
 https://github.com/gprMax/gprMax,2. Install a C compiler which supports OpenMP
-https://github.com/gprMax/gprMax,Linux
 https://github.com/gprMax/gprMax,"gcc should be already installed, so no action is required."
 https://github.com/gprMax/gprMax,macOS
 https://github.com/gprMax/gprMax,"Xcode (the IDE for macOS) comes with the LLVM (clang) compiler, but it does not currently support OpenMP, so you must install gcc. That said, it is still useful to have Xcode (with command line tools) installed. It can be downloaded from the App Store. Once Xcode is installed, download and install the Homebrew package manager and then to install gcc, run:"
@@ -419,10 +393,8 @@ https://github.com/gprMax/gprMax,(gprMax)$ python setup.py build
 https://github.com/gprMax/gprMax,(gprMax)$ python setup.py install
 https://github.com/gprMax/gprMax,You are now ready to proceed to running gprMax.
 https://github.com/gprMax/gprMax,"If you have problems with building gprMax on Microsoft Windows, you may need to add C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin to your path environment variable."
-https://github.com/haoliangyu/node-qa-masker,Installation
 https://github.com/haoliangyu/node-qa-masker,npm install qa-masker
 https://github.com/hezhangsprinter/DCPDN,Prerequisites:
-https://github.com/hezhangsprinter/DCPDN,Linux
 https://github.com/hezhangsprinter/DCPDN,Python 2 or 3
 https://github.com/hezhangsprinter/DCPDN,CPU or NVIDIA GPU + CUDA CuDNN (CUDA 8.0)
 https://github.com/hezhangsprinter/DCPDN,Installation:
@@ -432,22 +404,13 @@ https://github.com/hezhangsprinter/DCPDN,git clone https://github.com/pytorch/vi
 https://github.com/hezhangsprinter/DCPDN,cd vision
 https://github.com/hezhangsprinter/DCPDN,python setup.py install
 https://github.com/hezhangsprinter/DCPDN,"Install python package: numpy, scipy, PIL, pdb"
-https://github.com/hezhangsprinter/DID-MDN,Prerequisites:
-https://github.com/hezhangsprinter/DID-MDN,Linux
-https://github.com/hezhangsprinter/DID-MDN,Python 2 or 3
-https://github.com/hezhangsprinter/DID-MDN,CPU or NVIDIA GPU + CUDA CuDNN (CUDA 8.0)
-https://github.com/hezhangsprinter/DID-MDN,Installation:
 https://github.com/hezhangsprinter/DID-MDN,Install PyTorch and dependencies from http://pytorch.org (Ubuntu+Python2.7) (conda install pytorch torchvision -c pytorch)
 https://github.com/hezhangsprinter/DID-MDN,Install Torch vision from the source. (git clone https://github.com/pytorch/vision cd vision python setup.py install)
-https://github.com/hezhangsprinter/DID-MDN,"Install python package: numpy, scipy, PIL, pdb"
-https://github.com/hiroharu-kato/neural_renderer,Installation
 https://github.com/hiroharu-kato/neural_renderer,sudo python setup.py install
 https://github.com/iannesbitt/readgssi,requirements
 https://github.com/iannesbitt/readgssi,Strongly recommended to install via anaconda:
 https://github.com/iannesbitt/readgssi,obspy
-https://github.com/iannesbitt/readgssi,matplotlib
 https://github.com/iannesbitt/readgssi,numpy
-https://github.com/iannesbitt/readgssi,pandas
 https://github.com/iannesbitt/readgssi,h5py
 https://github.com/iannesbitt/readgssi,Install via pip:
 https://github.com/iannesbitt/readgssi,pynmea2
@@ -466,24 +429,18 @@ https://github.com/iannesbitt/readgssi,pip install ~/readgssi
 https://github.com/imfunniee/gitfolio,Let's Install
 https://github.com/imfunniee/gitfolio,Install gitfolio
 https://github.com/imfunniee/gitfolio,npm i gitfolio -g
-https://github.com/jiangsutx/SRN-Deblur,Prerequisites
 https://github.com/jiangsutx/SRN-Deblur,Python2.7
 https://github.com/jiangsutx/SRN-Deblur,Scipy
 https://github.com/jiangsutx/SRN-Deblur,Scikit-image
-https://github.com/jiangsutx/SRN-Deblur,numpy
 https://github.com/jiangsutx/SRN-Deblur,Tensorflow 1.4 with NVIDIA GPU or CPU (cpu testing is very slow)
-https://github.com/jiangsutx/SRN-Deblur,Installation
 https://github.com/jiangsutx/SRN-Deblur,Clone this project to your machine.
 https://github.com/jiangsutx/SRN-Deblur,git clone https://github.com/jiangsutx/SRN-Deblur.git
 https://github.com/jiangsutx/SRN-Deblur,cd SRN-Deblur
-https://github.com/joferkington/mplstereonet,Install
 https://github.com/joferkington/mplstereonet,mplstereonet can be installed from PyPi using pip by:
 https://github.com/joferkington/mplstereonet,pip install mplstereonet
 https://github.com/joferkington/mplstereonet,"Alternatively, you can download the source and install locally using (from the main directory of the repository):"
-https://github.com/joferkington/mplstereonet,python setup.py install
 https://github.com/joferkington/mplstereonet,"If you're planning on developing mplstereonet or would like to experiment with making local changes, consider setting up a development installation so that your changes are reflected when you import the package:"
 https://github.com/joferkington/mplstereonet,python setup.py develop
-https://github.com/jupyter-widgets/ipyleaflet,Installation
 https://github.com/jupyter-widgets/ipyleaflet,Using conda:
 https://github.com/jupyter-widgets/ipyleaflet,$ conda install -c conda-forge ipyleaflet
 https://github.com/jupyter-widgets/ipyleaflet,Using pip:
@@ -517,7 +474,6 @@ https://github.com/kenshohara/3D-ResNets-PyTorch,"FFmpeg, FFprobe"
 https://github.com/kenshohara/3D-ResNets-PyTorch,wget http://johnvansickle.com/ffmpeg/releases/ffmpeg-release-64bit-static.tar.xz
 https://github.com/kenshohara/3D-ResNets-PyTorch,tar xvf ffmpeg-release-64bit-static.tar.xz
 https://github.com/kenshohara/3D-ResNets-PyTorch,cd ./ffmpeg-3.3.3-64bit-static/; sudo cp ffmpeg ffprobe /usr/local/bin;
-https://github.com/kenshohara/3D-ResNets-PyTorch,Python 3
 https://github.com/kinverarity1/lasio/,Install the usual way:
 https://github.com/kinverarity1/lasio/,$ pip install lasio
 https://github.com/kosmtik/kosmtik,Install or Update
@@ -528,27 +484,22 @@ https://github.com/kosmtik/kosmtik,npm install kosmtik
 https://github.com/kosmtik/kosmtik,This will create a node_modules/kosmtik folder. You then have to replace all occurences of kosmtik below with node node_modules/kosmtik/index.js.
 https://github.com/kosmtik/kosmtik,To reinstall all plugins:
 https://github.com/kosmtik/kosmtik,kosmtik plugins --reinstall
-https://github.com/mapbox/geojson-vt,Install
 https://github.com/mapbox/geojson-vt,Install using NPM (npm install geojson-vt) or Yarn (yarn add geojson-vt)
-https://github.com/mapbox/rasterio,Installation
 https://github.com/mapbox/rasterio,Please install Rasterio in a virtual environment so that its requirements don't tamper with your system's Python.
 https://github.com/mapbox/rasterio,SSL certs
 https://github.com/mapbox/rasterio,"The Linux wheels on PyPI are built on CentOS and libcurl expects certs to be in /etc/pki/tls/certs/ca-bundle.crt. Ubuntu's certs, for example, are in a different location. You may need to use the CURL_CA_BUNDLE environment variable to specify the location of SSL certs on your computer. On an Ubuntu system set the variable as shown below."
 https://github.com/mapbox/rasterio,$ export CURL_CA_BUNDLE=/etc/ssl/certs/ca-certificates.crt
-https://github.com/mapbox/rasterio,Dependencies
 https://github.com/mapbox/rasterio,Rasterio has a C library dependency: GDAL >=1.11. GDAL itself depends on some other libraries provided by most major operating systems and also depends on the non standard GEOS and PROJ4 libraries. How to meet these requirement will be explained below.
 https://github.com/mapbox/rasterio,Rasterio's Python dependencies are listed in its requirements.txt file.
 https://github.com/mapbox/rasterio,Development also requires (see requirements-dev.txt) Cython and other packages.
 https://github.com/mapbox/rasterio,Binary Distributions
 https://github.com/mapbox/rasterio,Use a binary distributions that directly or indirectly provide GDAL if possible.
-https://github.com/mapbox/rasterio,Linux
 https://github.com/mapbox/rasterio,Rasterio distributions are available from UbuntuGIS and Anaconda's conda-forge channel.
 https://github.com/mapbox/rasterio,Manylinux1 wheels are available on PyPI.
 https://github.com/mapbox/rasterio,OS X
 https://github.com/mapbox/rasterio,"Binary distributions with GDAL, GEOS, and PROJ4 libraries included are available for OS X versions 10.7+ starting with Rasterio version 0.17. To install, run pip install rasterio. These binary wheels are preferred by newer versions of pip."
 https://github.com/mapbox/rasterio,"If you don't want these wheels and want to install from a source distribution, run pip install rasterio --no-binary rasterio instead."
 https://github.com/mapbox/rasterio,"The included GDAL library is fairly minimal, providing only the format drivers that ship with GDAL and are enabled by default. To get access to more formats, you must build from a source distribution (see below)."
-https://github.com/mapbox/rasterio,Windows
 https://github.com/mapbox/rasterio,Binary wheels for rasterio and GDAL are created by Christoph Gohlke and are available from his website.
 https://github.com/mapbox/rasterio,"To install rasterio, simply download both binaries for your system (rasterio and GDAL) and run something like this from the downloads folder:"
 https://github.com/mapbox/rasterio,$ pip install -U pip
@@ -569,7 +520,6 @@ https://github.com/mapbox/rasterio,$ brew update
 https://github.com/mapbox/rasterio,$ brew install gdal
 https://github.com/mapbox/rasterio,$ pip install --no-use-wheel rasterio
 https://github.com/mapbox/rasterio,"Alternatively, you can install GDAL binaries from kyngchaos. You will then need to add the installed location /Library/Frameworks/GDAL.framework/Programs to your system path."
-https://github.com/mapbox/rasterio,You can download a binary distribution of GDAL from here. You will also need to download the compiled libraries and headers (include files).
 https://github.com/mapbox/rasterio,"When building from source on Windows, it is important to know that setup.py cannot rely on gdal-config, which is only present on UNIX systems, to discover the locations of header files and libraries that rasterio needs to compile its C extensions. On Windows, these paths need to be provided by the user. You will need to find the include files and the library files for gdal and use setup.py as follows."
 https://github.com/mapbox/rasterio,$ python setup.py build_ext -I<path to gdal include files> -lgdal_i -L<path to gdal library>
 https://github.com/mapbox/rasterio,$ python setup.py install
@@ -577,7 +527,6 @@ https://github.com/mapbox/rasterio,We have had success compiling code using the
 https://github.com/mapbox/rasterio,Note: The GDAL dll (gdal111.dll) and gdal-data directory need to be in your Windows PATH otherwise rasterio will fail to work.
 https://github.com/mapbox/tilelive-mapnik,npm install tilelive-mapnik
 https://github.com/mapbox/tilelive-mapnik,Though tilelive is not a dependency of tilelive-mapnik you will want to install it to actually make use of tilelive-mapnik through a reasonable API.
-https://github.com/mapbox/tippecanoe,Installation
 https://github.com/mapbox/tippecanoe,The easiest way to install tippecanoe on OSX is with Homebrew:
 https://github.com/mapbox/tippecanoe,$ brew install tippecanoe
 https://github.com/mapbox/tippecanoe,On Ubuntu it will usually be easiest to build from the source repository:
@@ -597,7 +546,6 @@ https://github.com/msracver/Flow-Guided-Feature-Aggregation,pip install easydict
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,"For Windows users, Visual Studio 2015 is needed to compile cython module."
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,Requirements: Hardware
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,Any NVIDIA GPUs with at least 8GB memory should be OK.
-https://github.com/msracver/Flow-Guided-Feature-Aggregation,Installation
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,"Clone the Flow-Guided Feature Aggregation repository, and we call the directory that you cloned as ${FGFA_ROOT}."
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,git clone https://github.com/msracver/Flow-Guided-Feature-Aggregation.git
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,"For Windows users, run cmd .\init.bat. For Linux user, run sh ./init.sh. The scripts will build cython module automatically and create some folders."
@@ -615,7 +563,6 @@ https://github.com/msracver/Flow-Guided-Feature-Aggregation,make -j4
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,3.4 Install the MXNet Python binding by
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,"Note: If you will actively switch between different versions of MXNet, please follow 3.5 instead of 3.4"
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,cd python
-https://github.com/msracver/Flow-Guided-Feature-Aggregation,sudo python setup.py install
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,"3.5 For advanced users, you may put your Python packge into ./external/mxnet/$(YOUR_MXNET_PACKAGE), and modify MXNET_VERSION in ./experiments/fgfa_rfcn/cfgs/*.yaml to $(YOUR_MXNET_PACKAGE). Thus you can switch among different versions of MXNet quickly."
 https://github.com/nypl-spacetime/map-vectorizer,If you use PIP (recommended) you will get the necessary Python packages with: pip install -r requirements.txt
 https://github.com/nypl-spacetime/map-vectorizer,install.packages('rgdal')
@@ -628,12 +575,10 @@ https://github.com/odoe/generator-arcgis-js-app,npm install -g bower
 https://github.com/odoe/generator-arcgis-js-app,Bower is a required dependency of using the packages in the generated app.
 https://github.com/odoe/generator-arcgis-js-app,"To install generator-arcgis-js-app from npm, run:"
 https://github.com/odoe/generator-arcgis-js-app,npm install -g generator-arcgis-js-app
-https://github.com/ondrolexa/apsg,Installation
 https://github.com/ondrolexa/apsg,PyPI
 https://github.com/ondrolexa/apsg,"To install APSG, just execute"
 https://github.com/ondrolexa/apsg,pip install apsg
 https://github.com/ondrolexa/apsg,"Alternatively, you download the package manually from the Python Package Index https://pypi.org/project/apsg, unzip it, navigate into the package, and use the command:"
-https://github.com/ondrolexa/apsg,python setup.py install
 https://github.com/ondrolexa/apsg,Upgrading via pip
 https://github.com/ondrolexa/apsg,"To upgrade an existing version of APSG from PyPI, execute"
 https://github.com/ondrolexa/apsg,pip install apsg --upgrade --no-deps
@@ -646,7 +591,6 @@ https://github.com/ondrolexa/apsg,Conda
 https://github.com/ondrolexa/apsg,The APSG package is also available through conda-forge.
 https://github.com/ondrolexa/apsg,Installing apsg
 https://github.com/ondrolexa/apsg,Installing apsg from the conda-forge channel can be achieved by adding conda-forge to your channels with:
-https://github.com/ondrolexa/apsg,conda config --add channels conda-forge
 https://github.com/ondrolexa/apsg,"Once the conda-forge channel has been enabled, apsg can be installed with:"
 https://github.com/ondrolexa/apsg,conda install apsg
 https://github.com/ondrolexa/apsg,It is possible to list all of the versions of apsg available on your platform with:
@@ -658,7 +602,6 @@ https://github.com/ondrolexa/apsg,"Or, you can fork the GitHub repository from h
 https://github.com/phoenix104104/LapSRN,Requirements and Dependencies
 https://github.com/phoenix104104/LapSRN,MATLAB (we test with MATLAB R2017a on Ubuntu 16.04 and Windows 7)
 https://github.com/phoenix104104/LapSRN,Cuda & Cudnn (we test with Cuda 8.0 and Cudnn 5.1)
-https://github.com/phoenix104104/LapSRN,Installation
 https://github.com/phoenix104104/LapSRN,Download repository:
 https://github.com/phoenix104104/LapSRN,$ git clone https://github.com/phoenix104104/LapSRN.git
 https://github.com/phoenix104104/LapSRN,Run install.m in MATLAB to compile MatConvNet:
@@ -666,7 +609,6 @@ https://github.com/phoenix104104/LapSRN,# Start MATLAB
 https://github.com/phoenix104104/LapSRN,$ matlab
 https://github.com/phoenix104104/LapSRN,>> install
 https://github.com/phoenix104104/LapSRN,"If you install MatConvNet in your own path, you need to change the corresponding path in install.m, train_LapSRN.m and test_LapSRN.m."
-https://github.com/phuang17/DeepMVS,Requirements
 https://github.com/phuang17/DeepMVS,python 2.7
 https://github.com/phuang17/DeepMVS,numpy 1.13.1
 https://github.com/phuang17/DeepMVS,pytorch 0.3.0 and torchvision: Follow the instructions from their website.
@@ -683,7 +625,6 @@ https://github.com/phuang17/DeepMVS,pyquaternion 0.9.0: Run pip install pyquater
 https://github.com/phuang17/DeepMVS,pydensecrf: Run pip install pydensecrf.
 https://github.com/phuang17/DeepMVS,cuda 8.0.61 and 6GB GPU RAM (required for gpu support): The testing codes use up to 4GB of the GPU RAM with the default configuration.
 https://github.com/phuang17/DeepMVS,COLMAP 3.2: Follow the instructions from their website.
-https://github.com/pysal/pysal/,Installation
 https://github.com/pysal/pysal/,PySAL is available through Anaconda (in the defaults or conda-forge channel) and Enthought Canopy. We recommend installing PySAL from conda-forge:
 https://github.com/pysal/pysal/,conda install pysal
 https://github.com/pysal/pysal/,PySAL can be installed using pip:
@@ -691,55 +632,42 @@ https://github.com/pysal/pysal/,pip install pysal
 https://github.com/pysal/pysal/,As of version 2.0.0 PySAL has shifted to Python 3 only.
 https://github.com/pysal/pysal/,Users who need an older stable version of PySAL that is Python 2 compatible can install version 1.14.3 through pip or conda:
 https://github.com/pysal/pysal/,conda install pysal==1.14.3
-https://github.com/pyvista/pymeshfix,Installation
 https://github.com/pyvista/pymeshfix,From PyPI
 https://github.com/pyvista/pymeshfix,pip install pymeshfix
 https://github.com/pyvista/pymeshfix,From source at GitHub
 https://github.com/pyvista/pymeshfix,git clone https://github.com/pyvista/pymeshfix
 https://github.com/pyvista/pymeshfix,cd pymeshfix
-https://github.com/pyvista/pymeshfix,pip install .
-https://github.com/pyvista/pyvista,Installation
 https://github.com/pyvista/pyvista,PyVista can be installed from PyPI using pip on Python >= 3.5:
 https://github.com/pyvista/pyvista,pip install pyvista
 https://github.com/pyvista/pyvista,"You can also visit PyPi, Anaconda, or GitHub to download the source."
-https://github.com/pyvista/tetgen,Installation
-https://github.com/pyvista/tetgen,From PyPI
 https://github.com/pyvista/tetgen,pip install tetgen
-https://github.com/pyvista/tetgen,From source at GitHub
 https://github.com/pyvista/tetgen,git clone https://github.com/pyvista/tetgen
 https://github.com/pyvista/tetgen,cd tetgen
-https://github.com/pyvista/tetgen,pip install .
 https://github.com/rowanz/neural-motifs,"Install python3.6 and pytorch 3. I recommend the Anaconda distribution. To install PyTorch if you haven't already, use conda install pytorch=0.3.0 torchvision=0.2.0 cuda90 -c pytorch."
 https://github.com/rowanz/neural-motifs,Update the config file with the dataset paths. Specifically:
 https://github.com/rowanz/neural-motifs,"Visual Genome (the VG_100K folder, image_data.json, VG-SGG.h5, and VG-SGG-dicts.json). See data/stanford_filtered/README.md for the steps I used to download these."
 https://github.com/rowanz/neural-motifs,You'll also need to fix your PYTHONPATH: export PYTHONPATH=/home/rowan/code/scene-graph
 https://github.com/rowanz/neural-motifs,Compile everything. run make in the main directory: this compiles the Bilinear Interpolation operation for the RoIs as well as the Highway LSTM.
-https://github.com/ryersonvisionlab/two-stream-dyntex-synth,Requirements
 https://github.com/ryersonvisionlab/two-stream-dyntex-synth,"Tensorflow 1.3 (or latest, although not tested)"
 https://github.com/ryersonvisionlab/two-stream-dyntex-synth,Preferably a Titan X for synthesizing 12 frames
 https://github.com/ryersonvisionlab/two-stream-dyntex-synth,Appearance-stream tfmodel
 https://github.com/ryersonvisionlab/two-stream-dyntex-synth,Dynamics-stream tfmodel
 https://github.com/ryersonvisionlab/two-stream-dyntex-synth,Dynamic textures
 https://github.com/ryersonvisionlab/two-stream-dyntex-synth,Static textures (for dynamics style transfer)
-https://github.com/ryersonvisionlab/two-stream-dyntex-synth,Setup
 https://github.com/ryersonvisionlab/two-stream-dyntex-synth,Store the appearance-stream tfmodel in ./models.
 https://github.com/ryersonvisionlab/two-stream-dyntex-synth,Store the dynamics-stream tfmodel in ./models. The filepath to this model is your --dynamics_model path.
-https://github.com/salihkaragoz/pose-residual-network-pytorch,Prerequisites
 https://github.com/salihkaragoz/pose-residual-network-pytorch,python
 https://github.com/salihkaragoz/pose-residual-network-pytorch,pytorch
-https://github.com/salihkaragoz/pose-residual-network-pytorch,numpy
 https://github.com/salihkaragoz/pose-residual-network-pytorch,tqdm
 https://github.com/salihkaragoz/pose-residual-network-pytorch,pycocotools
 https://github.com/salihkaragoz/pose-residual-network-pytorch,progress
 https://github.com/salihkaragoz/pose-residual-network-pytorch,scikit-image
-https://github.com/salihkaragoz/pose-residual-network-pytorch,Installing
 https://github.com/salihkaragoz/pose-residual-network-pytorch,Clone this repository git clone https://github.com/salihkaragoz/pose-residual-network-pytorch.git
 https://github.com/salihkaragoz/pose-residual-network-pytorch,Install Pytorch
 https://github.com/salihkaragoz/pose-residual-network-pytorch,pip install -r src/requirements.txt
 https://github.com/salihkaragoz/pose-residual-network-pytorch,To download COCO dataset train2017 and val2017 annotations run: bash data/coco.sh. (data size: ~240Mb)
 https://github.com/sentinelsat/sentinelsat,Install sentinelsat through pip:
 https://github.com/sentinelsat/sentinelsat,pip install sentinelsat
-https://github.com/tensorflow/tensorflow,Installation
 https://github.com/tensorflow/tensorflow,To install the current release for CPU-only:
 https://github.com/tensorflow/tensorflow,pip install tensorflow
 https://github.com/tensorflow/tensorflow,Use the GPU package for CUDA-enabled GPU cards:
@@ -753,13 +681,9 @@ https://github.com/twbs/bootstrap,Install with npm: npm install bootstrap
 https://github.com/twbs/bootstrap,Install with yarn: yarn add bootstrap@4.3.1
 https://github.com/twbs/bootstrap,Install with Composer: composer require twbs/bootstrap:4.3.1
 https://github.com/twbs/bootstrap,Install with NuGet: CSS: Install-Package bootstrap Sass: Install-Package bootstrap.sass
-https://github.com/ungarj/tilematrix,Installation
 https://github.com/ungarj/tilematrix,Use pip to install the latest stable version:
 https://github.com/ungarj/tilematrix,pip install tilematrix
 https://github.com/ungarj/tilematrix,Manually install the latest development version
-https://github.com/ungarj/tilematrix,pip install -r requirements.txt
-https://github.com/ungarj/tilematrix,python setup.py install
-https://github.com/vuejs/vue-devtools/,Installation
 https://github.com/vuejs/vue-devtools/,Get the Chrome Extension / (beta channel)
 https://github.com/vuejs/vue-devtools/,Get the Firefox Addon / (beta channel)
 https://github.com/vuejs/vue-devtools/,Get standalone Electron app (works with any environment!)
@@ -772,7 +696,6 @@ https://github.com/vuejs/vue-devtools/,npm run build
 https://github.com/vuejs/vue-devtools/,Open Chrome extension page
 https://github.com/vuejs/vue-devtools/,"Check ""developer mode"""
 https://github.com/vuejs/vue-devtools/,"Click ""load unpacked extension"", and choose shells/chrome."
-https://github.com/whimian/pyGeoPressure,Installation
 https://github.com/whimian/pyGeoPressure,pyGeoPressure is on PyPI:
 https://github.com/whimian/pyGeoPressure,pip install pygeopressure
 https://github.com/wuhuikai/DeepGuidedFilter,Prepare Environment
@@ -782,7 +705,6 @@ https://github.com/wuhuikai/DeepGuidedFilter,cd DeepGuidedFilter && git checkout
 https://github.com/wuhuikai/DeepGuidedFilter,Install dependencies (PyTorch version).
 https://github.com/wuhuikai/DeepGuidedFilter,conda install opencv
 https://github.com/wuhuikai/DeepGuidedFilter,conda install pytorch=0.2.0 cuda80 -c soumith
-https://github.com/wuhuikai/DeepGuidedFilter,pip install -r requirements.txt
 https://github.com/wuhuikai/DeepGuidedFilter,(Optional) Install dependencies for MonoDepth (Tensorflow version).
 https://github.com/wuhuikai/DeepGuidedFilter,cd ComputerVision/MonoDepth
 https://github.com/wuhuikai/DeepGuidedFilter,Install Released Version
@@ -793,9 +715,216 @@ https://github.com/wuhuikai/DeepGuidedFilter,pip install guided-filter-tf
 https://github.com/yuhuayc/da-faster-rcnn,Build Caffe and pycaffe (see: Caffe installation instructions)
 https://github.com/yuhuayc/da-faster-rcnn,Build the Cython modules
 https://github.com/yuhuayc/da-faster-rcnn,cd $FRCN_ROOT/lib
-https://github.com/yuhuayc/da-faster-rcnn,make
-https://github.com/zhiqiangdon/CU-Net,Prerequisites
 https://github.com/zhiqiangdon/CU-Net,This package has the following requirements:
-https://github.com/zhiqiangdon/CU-Net,Python 2.7
 https://github.com/zhiqiangdon/CU-Net,Pytorch v0.4.0 or Pytorch v0.1.12
 https://github.com/zhiqiangdon/CU-Net,Note that the script name with string prev-version requires Pytorch v0.1.12.
+https://github.com/cltk/cltk,"CLTK supports Python versions 3.6 and 3.7. The software only runs on POSIX–compliant operating systems (Linux, Mac OS X, FreeBSD, etc.)."
+https://github.com/cltk/cltk,bash
+https://github.com/cltk/cltk,$ pip install cltk
+https://github.com/cltk/cltk,See docs for complete installation instructions.
+https://github.com/cltk/cltk,"The CLTK organization curates corpora which can be downloaded directly or, better, imported by the toolkit."
+https://github.com/facebookresearch/DensePose,"Please find installation instructions for Caffe2 and DensePose in INSTALL.md, a document based on the Detectron installation instructions."
+https://github.com/facebookresearch/ResNeXt,See the fb.resnet.torch installation instructions for a step-by-step guide.
+https://github.com/facebookresearch/ResNeXt,- Install Torch on a machine with CUDA GPU
+https://github.com/facebookresearch/ResNeXt,- Install cuDNN v4 or v5 and the Torch cuDNN bindings
+https://github.com/facebookresearch/ResNeXt,- Download the ImageNet dataset and move validation images to labeled subfolders
+https://github.com/facebookresearch/pyrobot,Installing both PyRobot and LoCoBot dependencies
+https://github.com/facebookresearch/pyrobot,Install Ubuntu 16.04
+https://github.com/facebookresearch/pyrobot,Download the installation script
+https://github.com/facebookresearch/pyrobot,sudo apt update
+https://github.com/facebookresearch/pyrobot,sudo apt-get install curl
+https://github.com/facebookresearch/pyrobot,curl 'https://raw.githubusercontent.com/facebookresearch/pyrobot/master/robots/LoCoBot/install/locobot_install_all.sh' &gt; locobot_install_all.sh
+https://github.com/facebookresearch/pyrobot,"Run the script to install everything (ROS, realsense driver, etc.). Please connect the nuc machine to a realsense camera before running the following commands."
+https://github.com/facebookresearch/pyrobot,chmod +x locobot_install_all.sh 
+https://github.com/facebookresearch/pyrobot,./locobot_install_all.sh
+https://github.com/facebookresearch/pyrobot,Installing just PyRobot
+https://github.com/facebookresearch/pyrobot,Install Ubuntu 16.04 
+https://github.com/facebookresearch/pyrobot,Install ROS kinetic
+https://github.com/facebookresearch/pyrobot,Install KDL
+https://github.com/facebookresearch/pyrobot,sudo apt-get -y install ros-kinetic-orocos-kdl ros-kinetic-kdl-parser-py ros-kinetic-python-orocos-kdl ros-kinetic-trac-ik
+https://github.com/facebookresearch/pyrobot,Install Python virtual environment
+https://github.com/facebookresearch/pyrobot,sudo apt-get -y install python-virtualenv
+https://github.com/facebookresearch/pyrobot,"virtualenv_name=""pyenv_pyrobot"""
+https://github.com/facebookresearch/pyrobot,VIRTUALENV_FOLDER=~/${virtualenv_name}
+https://github.com/facebookresearch/pyrobot,virtualenv --system-site-packages -p python2.7 $VIRTUALENV_FOLDER
+https://github.com/facebookresearch/pyrobot,Install PyRobot 
+https://github.com/facebookresearch/pyrobot,cd ~
+https://github.com/facebookresearch/pyrobot,mkdir -p low_cost_ws/src
+https://github.com/facebookresearch/pyrobot,cd ~/low_cost_ws/src
+https://github.com/facebookresearch/pyrobot,source ~/${virtualenv_name}/bin/activate
+https://github.com/facebookresearch/pyrobot,git clone --recurse-submodules https://github.com/facebookresearch/pyrobot.git
+https://github.com/facebookresearch/pyrobot,cd pyrobot/
+https://github.com/gitbucket/gitbucket,"GitBucket requires Java8. You have to install it, if it is not already installed."
+https://github.com/gitbucket/gitbucket,Download the latest gitbucket.war from the releases page and run it by java -jar gitbucket.war.
+https://github.com/gitbucket/gitbucket,Go to http://[hostname]:8080/ and log in with ID: root / Pass: root.
+https://github.com/gitbucket/gitbucket,You can specify following options:
+https://github.com/gitbucket/gitbucket,--port=[NUMBER]
+https://github.com/gitbucket/gitbucket,--prefix=[CONTEXTPATH]
+https://github.com/gitbucket/gitbucket,--host=[HOSTNAME]
+https://github.com/gitbucket/gitbucket,--gitbucket.home=[DATA_DIR]
+https://github.com/gitbucket/gitbucket,--temp_dir=[TEMP_DIR]
+https://github.com/gitbucket/gitbucket,--max_file_size=[MAX_FILE_SIZE]
+https://github.com/gitbucket/gitbucket,"TEMP_DIR is used as the temporary directory for the jetty application context. This is the directory into which the gitbucket.war file is unpacked, the source files are compiled, etc. If given this parameter must match the path of an existing directory or the application will quit reporting an error; if not given the path used will be a tmp directory inside the gitbucket home."
+https://github.com/gitbucket/gitbucket,MAX_FILE_SIZE is the max file size for upload files.
+https://github.com/gitbucket/gitbucket,"You can also deploy gitbucket.war to a servlet container which supports Servlet 3.0 (like Jetty, Tomcat, JBoss, etc)"
+https://github.com/gitbucket/gitbucket,"For more information about installation on Mac or Windows Server (with IIS), or configuration of Apache or Nginx and also integration with other tools or services such as Jenkins or Slack, see Wiki."
+https://github.com/gitbucket/gitbucket,"To upgrade GitBucket, replace gitbucket.war with the new version, after stopping GitBucket. All GitBucket data is stored in HOME/.gitbucket by default. So if you want to back up GitBucket's data, copy this directory to the backup location."
+https://github.com/gitbucket/gitbucket,Plugins
+https://github.com/gitbucket/gitbucket,GitBucket has a plug-in system that allows extra functionality. Officially the following plug-ins are provided:
+https://github.com/gitbucket/gitbucket,gitbucket-gist-plugin
+https://github.com/gitbucket/gitbucket,gitbucket-emoji-plugin
+https://github.com/gitbucket/gitbucket,gitbucket-pages-plugin
+https://github.com/gitbucket/gitbucket,gitbucket-notifications-plugin
+https://github.com/gitbucket/gitbucket,You can find more plugins made by the community at GitBucket community plugins.
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,"You will need to install latest version of Google Chrome. Moreover, you need to install selenium module as well using"
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,pip install selenium
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,"Run the code using Python 3. Also, the code is multi-platform and is tested on both Windows and Linux."
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,The tool uses latest version of Chrome Web Driver. I have placed the webdriver along with the code but if that version doesn't work then replace the chrome web driver with the latest one.
+https://github.com/nextflow-io/nextflow,Download the package
+https://github.com/nextflow-io/nextflow,"Nextflow does not require any installation procedure, just download the distribution package by copying and pasting"
+https://github.com/nextflow-io/nextflow,this command in your terminal:
+https://github.com/nextflow-io/nextflow,curl -fsSL https://get.nextflow.io | bash
+https://github.com/nextflow-io/nextflow,It creates the nextflow executable file in the current directory. You may want to move it to a folder accessible from your $PATH.
+https://github.com/nextflow-io/nextflow,Download from Conda
+https://github.com/nextflow-io/nextflow,Nextflow can also be installed from Bioconda
+https://github.com/nextflow-io/nextflow,conda install -c bioconda nextflow
+https://github.com/nextflow-io/nextflow,make compile
+https://github.com/nextflow-io/nextflow,"The very first time you run it, it will automatically download all the libraries required by the build process."
+https://github.com/nextflow-io/nextflow,It may take some minutes to complete.
+https://github.com/nextflow-io/nextflow,"When complete, execute the program by using the launch.sh script in the project directory."
+https://github.com/nextflow-io/nextflow,The self-contained runnable Nextflow packages can be created by using the following command:
+https://github.com/nextflow-io/nextflow,make pack
+https://github.com/nextflow-io/nextflow,In order to install the compiled packages use the following command:
+https://github.com/nextflow-io/nextflow,Then you will be able to run nextflow using the nextflow launcher script in the project root folder.
+https://github.com/nextflow-io/nextflow,Known compilation problems
+https://github.com/nextflow-io/nextflow,Nextflow required JDK 8 to be compiled. The Java compiler used by the build process can be choose by setting the
+https://github.com/nextflow-io/nextflow,JAVA_HOME environment variable accordingly.
+https://github.com/nextflow-io/nextflow,"If the compilation stops reporting the error: java.lang.VerifyError: Bad &lt;init&gt; method call from inside of a branch,"
+https://github.com/nextflow-io/nextflow,this is due to a bug affecting the following Java JDK:
+https://github.com/nextflow-io/nextflow,1.8.0 update 11
+https://github.com/nextflow-io/nextflow,1.8.0 update 20
+https://github.com/nextflow-io/nextflow,Upgrade to a newer JDK to avoid to this issue. Alternatively a possible workaround is to define the following variable
+https://github.com/nextflow-io/nextflow,in your environment:
+https://github.com/nextflow-io/nextflow,_JAVA_OPTIONS='-Xverify:none'
+https://github.com/nextflow-io/nextflow,Read more at these links:
+https://github.com/nextflow-io/nextflow,https://bugs.openjdk.java.net/browse/JDK-8051012
+https://github.com/nextflow-io/nextflow,https://jira.codehaus.org/browse/GROOVY-6951
+https://github.com/nextflow-io/nextflow,IntelliJ IDEA
+https://github.com/nextflow-io/nextflow,Nextflow development with IntelliJ IDEA requires the latest version of the IDE (2019.1.2 or later).
+https://github.com/nextflow-io/nextflow,"If you have it installed in your computer, follow the steps below in order to use it with Nextflow:"
+https://github.com/pyro-ppl/pyro,Installing a stable Pyro release
+https://github.com/pyro-ppl/pyro,Install using pip:
+https://github.com/pyro-ppl/pyro,Pyro supports Python 3.4+.
+https://github.com/pyro-ppl/pyro,sh
+https://github.com/pyro-ppl/pyro,pip install pyro-ppl
+https://github.com/pyro-ppl/pyro,Install from source:
+https://github.com/pyro-ppl/pyro,git clone git@github.com:pyro-ppl/pyro.git
+https://github.com/pyro-ppl/pyro,cd pyro
+https://github.com/pyro-ppl/pyro,git checkout master  # master is pinned to the latest release
+https://github.com/pyro-ppl/pyro,Install with extra packages:
+https://github.com/pyro-ppl/pyro,"To install the dependencies required to run the probabilistic models included in the examples/tutorials directories, please use the following command:"
+https://github.com/pyro-ppl/pyro,pip install pyro-ppl[extras]
+https://github.com/pyro-ppl/pyro,Make sure that the models come from the same release version of the Pyro source code as you have installed.
+https://github.com/pyro-ppl/pyro,Installing Pyro dev branch
+https://github.com/pyro-ppl/pyro,For recent features you can install Pyro from source.
+https://github.com/pyro-ppl/pyro,pip install git+https://github.com/pyro-ppl/pyro.git
+https://github.com/pyro-ppl/pyro,"or, with the extras dependency to run the probabilistic models included in the examples/tutorials directories:"
+https://github.com/pyro-ppl/pyro,pip install git+https://github.com/pyro-ppl/pyro.git#egg=project[extras]
+https://github.com/pyro-ppl/pyro,git clone https://github.com/pyro-ppl/pyro
+https://github.com/pyro-ppl/pyro,pip install .  # pip install .[extras] for running models in examples/tutorials
+https://github.com/reduxjs/react-redux,React Redux requires React 16.8.3 or later.
+https://github.com/reduxjs/react-redux,npm install --save react-redux
+https://github.com/reduxjs/react-redux,This assumes that you’re using npm package manager 
+https://github.com/reduxjs/react-redux,with a module bundler like Webpack or 
+https://github.com/reduxjs/react-redux,Browserify to consume CommonJS 
+https://github.com/reduxjs/react-redux,modules.
+https://github.com/reduxjs/react-redux,"If you don’t yet use npm or a modern module bundler, and would rather prefer a single-file UMD build that makes ReactRedux available as a global object, you can grab a pre-built version from cdnjs. We don’t recommend this approach for any serious application, as most of the libraries complementary to Redux are only available on npm."
+https://github.com/reduxjs/react-redux,React Native
+https://github.com/reduxjs/react-redux,"As of React Native 0.18, React Redux 5.x should work with React Native. If you have any issues with React Redux 5.x on React Native, run npm ls react and make sure you don’t have a duplicate React installation in your node_modules. We recommend that you use npm@3.x which is better at avoiding these kinds of issues."
+https://github.com/scikit-image/scikit-image,Installation from binaries
+https://github.com/scikit-image/scikit-image,Debian/Ubuntu: sudo apt-get install python-skimage
+https://github.com/scikit-image/scikit-image,OSX: pip install scikit-image
+https://github.com/scikit-image/scikit-image,Anaconda: conda install -c conda-forge scikit-image
+https://github.com/scikit-image/scikit-image,Windows: Download Windows binaries
+https://github.com/scikit-image/scikit-image,Also see installing scikit-image.
+https://github.com/scikit-image/scikit-image,Installation from source
+https://github.com/scikit-image/scikit-image,Install dependencies using:
+https://github.com/scikit-image/scikit-image,"Then, install scikit-image using:"
+https://github.com/scikit-image/scikit-image,$ pip install .
+https://github.com/scikit-image/scikit-image,"If you plan to develop the package, you may run it directly from source:"
+https://github.com/scikit-image/scikit-image,$ pip install -e .  # Do this once to add package to Python path
+https://github.com/scikit-image/scikit-image,"Every time you modify Cython files, also run:"
+https://github.com/scikit-image/scikit-image,$ python setup.py build_ext -i  # Build binary extensions
+https://github.com/scikit-learn/scikit-learn,~~~~~~~~~~~~
+https://github.com/scikit-learn/scikit-learn,scikit-learn requires:
+https://github.com/scikit-learn/scikit-learn,Python (>= 3.5)
+https://github.com/scikit-learn/scikit-learn,NumPy (>= 1.11.0)
+https://github.com/scikit-learn/scikit-learn,SciPy (>= 0.17.0)
+https://github.com/scikit-learn/scikit-learn,joblib (>= 0.11)
+https://github.com/scikit-learn/scikit-learn,Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4.
+https://github.com/scikit-learn/scikit-learn,scikit-learn 0.21 and later require Python 3.5 or newer.
+https://github.com/scikit-learn/scikit-learn,"Scikit-learn plotting capabilities (i.e., functions start with ""plot_"") require"
+https://github.com/scikit-learn/scikit-learn,Matplotlib (>= 1.5.1). For running the examples Matplotlib >= 1.5.1 is
+https://github.com/scikit-learn/scikit-learn,"required. A few examples require scikit-image >= 0.12.3, a few examples require"
+https://github.com/scikit-learn/scikit-learn,pandas >= 0.18.0.
+https://github.com/scikit-learn/scikit-learn,User installation
+https://github.com/scikit-learn/scikit-learn,~~~~~~~~~~~~~~~~~
+https://github.com/scikit-learn/scikit-learn,"If you already have a working installation of numpy and scipy,"
+https://github.com/scikit-learn/scikit-learn,the easiest way to install scikit-learn is using pip   ::
+https://github.com/scikit-learn/scikit-learn,pip install -U scikit-learn
+https://github.com/scikit-learn/scikit-learn,or conda::
+https://github.com/scikit-learn/scikit-learn,conda install scikit-learn
+https://github.com/scikit-learn/scikit-learn,The documentation includes more detailed installation instructions &lt;http://scikit-learn.org/stable/install.html&gt;_.
+https://github.com/scikit-learn/scikit-learn,Changelog
+https://github.com/scikit-learn/scikit-learn,See the changelog &lt;http://scikit-learn.org/dev/whats_new.html&gt;__
+https://github.com/scikit-learn/scikit-learn,for a history of notable changes to scikit-learn.
+https://github.com/sequelize/sequelize,```bash
+https://github.com/sequelize/sequelize,$ npm install --save sequelize # This will install v5
+https://github.com/sequelize/sequelize,And one of the following:
+https://github.com/sequelize/sequelize,$ npm install --save pg pg-hstore # Postgres
+https://github.com/sequelize/sequelize,$ npm install --save mysql2
+https://github.com/sequelize/sequelize,$ npm install --save mariadb
+https://github.com/sequelize/sequelize,$ npm install --save sqlite3
+https://github.com/sequelize/sequelize,$ npm install --save tedious # Microsoft SQL Server
+https://github.com/tensorflow/magenta,Magenta maintains a pip package for easy
+https://github.com/tensorflow/magenta,"installation. We recommend using Anaconda to install it, but it can work in any"
+https://github.com/tensorflow/magenta,standard Python environment. We support both Python 2 (>= 2.7) and Python 3 (>= 3.5).
+https://github.com/tensorflow/magenta,These instructions will assume you are using Anaconda.
+https://github.com/tensorflow/magenta,"Note that if you want to enable GPU support, you should follow the GPU Installation instructions below."
+https://github.com/tensorflow/magenta,Automated Install (w/ Anaconda)
+https://github.com/tensorflow/magenta,"If you are running Mac OS X or Ubuntu, you can try using our automated"
+https://github.com/tensorflow/magenta,installation script. Just paste the following command into your terminal.
+https://github.com/tensorflow/magenta,curl https://raw.githubusercontent.com/tensorflow/magenta/master/magenta/tools/magenta-install.sh &gt; /tmp/magenta-install.sh
+https://github.com/tensorflow/magenta,bash /tmp/magenta-install.sh
+https://github.com/tensorflow/magenta,"After the script completes, open a new terminal window so the environment"
+https://github.com/tensorflow/magenta,variable changes take effect.
+https://github.com/tensorflow/magenta,The Magenta libraries are now available for use within Python programs and
+https://github.com/tensorflow/magenta,"Jupyter notebooks, and the Magenta scripts are installed in your path!"
+https://github.com/tensorflow/magenta,Note that you will need to run source activate magenta to use Magenta every
+https://github.com/tensorflow/magenta,time you open a new terminal window.
+https://github.com/tensorflow/magenta,Manual Install (w/o Anaconda)
+https://github.com/tensorflow/magenta,"If the automated script fails for any reason, or you'd prefer to install by"
+https://github.com/tensorflow/magenta,"hand, do the following steps."
+https://github.com/tensorflow/magenta,Install the Magenta pip package:
+https://github.com/tensorflow/magenta,pip install magenta
+https://github.com/tensorflow/magenta,"NOTE: In order to install the rtmidi package that we depend on, you may need to install headers for some sound libraries. On Linux, this command should install the necessary packages:"
+https://github.com/tensorflow/magenta,sudo apt-get install build-essential libasound2-dev libjack-dev
+https://github.com/tensorflow/magenta,GPU Installation
+https://github.com/tensorflow/magenta,"If you have a GPU installed and you want Magenta to use it, you will need to"
+https://github.com/tensorflow/magenta,"follow the Manual Install instructions, but with a few"
+https://github.com/tensorflow/magenta,modifications.
+https://github.com/tensorflow/magenta,"First, make sure your system meets the requirements to run tensorflow with GPU support."
+https://github.com/tensorflow/magenta,"Next, follow the Manual Install instructions, but install the"
+https://github.com/tensorflow/magenta,magenta-gpu package instead of the magenta package:
+https://github.com/tensorflow/magenta,pip install magenta-gpu
+https://github.com/tensorflow/magenta,The only difference between the two packages is that magenta-gpu depends on
+https://github.com/tensorflow/magenta,tensorflow-gpu instead of tensorflow.
+https://github.com/tensorflow/magenta,Magenta should now have access to your GPU.
+https://github.com/tensorflow/magenta,Using Magenta
+https://github.com/tensorflow/magenta,"You can now train our various models and use them to generate music, audio, and images. You can"
+https://github.com/tensorflow/magenta,find instructions for each of the models by exploring the models directory.
+https://github.com/tensorflow/magenta,"To get started, create your own melodies with TensorFlow using one of the various configurations of our Melody RNN model; a recurrent neural network for predicting melodies."
+https://github.com/tensorflow/magenta,Playing a MIDI Instrument
+https://github.com/tensorflow/magenta,"After you've trained one of the models above, you can use our MIDI interface to play with it interactively."
+https://github.com/tensorflow/magenta,"We also have created several demos that provide a UI for this interface, making it easier to use (e.g., the browser-based AI Jam)."
+https://github.com/tensorflow/magenta,Development Environment
diff --git a/data/invocation.csv b/data/invocation.csv
index fad35ca4..4f0f6400 100644
--- a/data/invocation.csv
+++ b/data/invocation.csv
@@ -36,7 +36,6 @@ https://github.com/LMescheder/GAN_stability,python interpolate_class.py PATH_TO_
 https://github.com/NSGeophysics/GPRPy,Running the software
 https://github.com/NSGeophysics/GPRPy,"After installation, you can run the script from the Anaconda Prompt (or your Python-enabled prompt) by running either"
 https://github.com/NSGeophysics/GPRPy,gprpy
-https://github.com/NSGeophysics/GPRPy,or
 https://github.com/NSGeophysics/GPRPy,python -m gprpy
 https://github.com/NSGeophysics/GPRPy,The first time you run GPRPy it could take a while to initialize. GPRPy will ask you if you want to run the profile [p] or WARR / CMP [c] user interface. Type
 https://github.com/NSGeophysics/GPRPy,p
@@ -81,7 +80,6 @@ https://github.com/OpenGeoVis/omfvista,p.screenshot('wolfpass.png')
 https://github.com/OpenGeoscience/geonotebook/,Run the notebook:
 https://github.com/OpenGeoscience/geonotebook/,cd notebooks/
 https://github.com/OpenGeoscience/geonotebook/,jupyter notebook
-https://github.com/Toblerity/Fiona/,Usage
 https://github.com/Toblerity/Fiona/,Collections
 https://github.com/Toblerity/Fiona/,"Records are read from and written to file-like Collection objects returned from the fiona.open() function. Records are mappings modeled on the GeoJSON format. They don't have any spatial methods of their own, so if you want to do anything fancy with them you will probably need Shapely or something like it. Here is an example of using Fiona to read some records from one data file, change their geometry attributes, and write them to a new data file."
 https://github.com/Toblerity/Fiona/,import fiona
@@ -172,7 +170,6 @@ https://github.com/Toblerity/Fiona/,"{count"": 67,
       ""STATE_FIPS"": ""str:80"",
       ""WILDRNP020"": ""int:10"",
       ""STATE"": ""str:80}}}"
-https://github.com/Toblerity/Shapely,Usage
 https://github.com/Toblerity/Shapely,Here is the canonical example of building an approximately circular patch by buffering a point.
 https://github.com/Toblerity/Shapely,>>> from shapely.geometry import Point
 https://github.com/Toblerity/Shapely,">>> patch = Point(0.0, 0.0).buffer(10.0)"
@@ -252,7 +249,6 @@ https://github.com/akaszynski/pyansys,grid = archive.parse_vtk()
 https://github.com/akaszynski/pyansys,grid.plot()
 https://github.com/akaszynski/pyansys,# write this as a vtk xml file
 https://github.com/akaszynski/pyansys,grid.Write('hex.vtu')
-https://github.com/akaszynski/pyansys,import pyvista as pv
 https://github.com/akaszynski/pyansys,grid = pv.UnstructuredGrid('hex.vtu')
 https://github.com/akaszynski/pyansys,Loading the Result File
 https://github.com/akaszynski/pyansys,This example reads in binary results from a modal analysis of a beam from ANSYS.
@@ -331,7 +327,6 @@ https://github.com/d3/d3,In Node:
 https://github.com/d3/d3,"var d3 = require(""d3"");"
 https://github.com/d3/d3,You can also require individual modules and combine them into a d3 object using Object.assign:
 https://github.com/d3/d3,"var d3 = Object.assign({}, require(""d3-format""), require(""d3-geo""), require(""d3-geo-projection""));"
-https://github.com/driftingtides/hyvr,Usage
 https://github.com/driftingtides/hyvr,To use HyVR you have to create a configuration file with your settings. You can then run HyVR the following way:
 https://github.com/driftingtides/hyvr,(hyvr_env) $ python -m hyvr my_configfile.ini
 https://github.com/driftingtides/hyvr,"HyVR will then run and store all results in a subdirectory. If no configfile is given, it will run a test case instead:"
@@ -557,7 +552,6 @@ https://github.com/hezhangsprinter/DID-MDN,python test.py --dataroot ./facades/g
 https://github.com/hezhangsprinter/DID-MDN,python derain_train_2018.py  --dataroot ./facades/DID-MDN-training/Rain_Medium/train2018new  --valDataroot ./facades/github --exp ./check --netG ./pre_trained/netG_epoch_9.pth.
 https://github.com/hezhangsprinter/DID-MDN,Make sure you download the training sample and put in the right folder
 https://github.com/hezhangsprinter/DID-MDN,python train_rain_class.py  --dataroot ./facades/DID-MDN-training/Rain_Medium/train2018new  --exp ./check_class
-https://github.com/hezhangsprinter/DID-MDN,python demo.py --dataroot ./your_dataroot --valDataroot ./your_dataroot --netG ./pre_trained/netG_epoch_9.pth   
 https://github.com/hiroharu-kato/neural_renderer,Running examples
 https://github.com/hiroharu-kato/neural_renderer,python ./examples/example1.py
 https://github.com/hiroharu-kato/neural_renderer,python ./examples/example2.py
@@ -641,9 +635,7 @@ https://github.com/jiangsutx/SRN-Deblur,"To test the model, pre-defined height a
 https://github.com/jiangsutx/SRN-Deblur,"Otherwise, the images will be downsampled by the largest scale factor to be fed into the placeholder. And results will be upsampled to the original size."
 https://github.com/jiangsutx/SRN-Deblur,"According to our experience, --height=720 and --width=1280 work well on a Gefore GTX 1050 TI with 4GB memory. For example,"
 https://github.com/jiangsutx/SRN-Deblur,python run_model.py --height=720 --width=1280
-https://github.com/jiangsutx/SRN-Deblur,Evaluation
 https://github.com/jiangsutx/SRN-Deblur,The quantitative results of PSNR and SSIM in the paper is calculated using MATLAB built-in function psnr() and ssim() based on the generated color results.
-https://github.com/jiangsutx/SRN-Deblur,Training
 https://github.com/jiangsutx/SRN-Deblur,We trained our model using the dataset from DeepDeblur_release. Please put the dataset into training_set/. And the provided datalist_gopro.txt can be used to train the model.
 https://github.com/jiangsutx/SRN-Deblur,"Hyper parameters such as batch size, learning rate, epoch number can be tuned through command line:"
 https://github.com/jiangsutx/SRN-Deblur,python run_model.py --phase=train --batch=16 --lr=1e-4 --epoch=4000
@@ -665,7 +657,6 @@ https://github.com/joferkington/mplstereonet,"ax.pole(strike, dip, 'g^', markers
 https://github.com/joferkington/mplstereonet,"ax.rake(strike, dip, -25)"
 https://github.com/joferkington/mplstereonet,ax.grid()
 https://github.com/joferkington/mplstereonet,plt.show()
-https://github.com/joferkington/mplstereonet,import numpy as np
 https://github.com/joferkington/mplstereonet,"fig, ax = mplstereonet.subplots()"
 https://github.com/joferkington/mplstereonet,"strike, dip = 90, 80"
 https://github.com/joferkington/mplstereonet,num = 10
@@ -796,7 +787,6 @@ https://github.com/kinverarity1/lasio/,"...,"
 https://github.com/kinverarity1/lasio/,"[ 1670.   ,   123.45 ,  2550.   , ...,   123.45 ,   110.2  ,   105.6  ],"
 https://github.com/kinverarity1/lasio/,"[ 1669.875,   123.45 ,  2550.   , ...,   123.45 ,   110.2  ,   105.6  ]])"
 https://github.com/kinverarity1/lasio/,"You can also retrieve and load data as a pandas DataFrame, build LAS files from scratch, write them back to disc, and export to Excel, amongst other things."
-https://github.com/kosmtik/kosmtik,Usage
 https://github.com/kosmtik/kosmtik,"To get command line help, run:"
 https://github.com/kosmtik/kosmtik,kosmtik -h
 https://github.com/kosmtik/kosmtik,"To run a Carto project (or .yml, .yaml):"
@@ -808,14 +798,12 @@ https://github.com/kosmtik/kosmtik,And then pick one and install it like this:
 https://github.com/kosmtik/kosmtik,kosmtik plugins --install pluginname
 https://github.com/kosmtik/kosmtik,For example:
 https://github.com/kosmtik/kosmtik,kosmtik plugins --install kosmtik-map-compare [--install kosmtik-overlay…]
-https://github.com/mapbox/geojson-vt,Usage
 https://github.com/mapbox/geojson-vt,// build an initial index of tiles
 https://github.com/mapbox/geojson-vt,var tileIndex = geojsonvt(geoJSON);
 https://github.com/mapbox/geojson-vt,// request a particular tile
 https://github.com/mapbox/geojson-vt,"var features = tileIndex.getTile(z, x, y).features;"
 https://github.com/mapbox/geojson-vt,// show an array of tile coordinates created so far
 https://github.com/mapbox/geojson-vt,"console.log(tileIndex.tileCoords); // [{z: 0, x: 0, y: 0}, ...]"
-https://github.com/mapbox/rasterio,import numpy as np
 https://github.com/mapbox/rasterio,import rasterio
 https://github.com/mapbox/rasterio,# Read raster bands directly to Numpy arrays.
 https://github.com/mapbox/rasterio,#
@@ -838,7 +826,6 @@ https://github.com/mapbox/rasterio,profile = src.profile
 https://github.com/mapbox/rasterio,"profile.update(dtype=rasterio.uint8, count=1, compress='lzw')"
 https://github.com/mapbox/rasterio,"with rasterio.open('example-total.tif', 'w', **profile) as dst:"
 https://github.com/mapbox/rasterio,"dst.write(total.astype(rasterio.uint8), 1)"
-https://github.com/mapbox/tilelive-mapnik,Usage
 https://github.com/mapbox/tilelive-mapnik,var tilelive = require('tilelive');
 https://github.com/mapbox/tilelive-mapnik,require('tilelive-mapnik').registerProtocols(tilelive);
 https://github.com/mapbox/tilelive-mapnik,"tilelive.load('mapnik:///path/to/file.xml', function(err, source) {"
@@ -859,7 +846,6 @@ https://github.com/msracver/Flow-Guided-Feature-Aggregation,Demo
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,"To run the demo with our trained model (on ImageNet DET + VID train), please download the model manually from OneDrive, and put it under folder model/."
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,Make sure it looks like this:
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,./model/rfcn_fgfa_flownet_vid-0000.params
-https://github.com/msracver/Flow-Guided-Feature-Aggregation,Run
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,python ./fgfa_rfcn/demo.py
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,Preparation for Training & Testing
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,"Please download ILSVRC2015 DET and ILSVRC2015 VID dataset, and make sure it looks like this:"
@@ -872,7 +858,6 @@ https://github.com/msracver/Flow-Guided-Feature-Aggregation,./data/ILSVRC2015/Im
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,"Please download ImageNet pre-trained ResNet-v1-101 model and Flying-Chairs pre-trained FlowNet model manually from OneDrive, and put it under folder ./model. Make sure it looks like this:"
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,./model/pretrained_model/resnet_v1_101-0000.params
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,./model/pretrained_model/flownet-0000.params
-https://github.com/msracver/Flow-Guided-Feature-Aggregation,Usage
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,"All of our experiment settings (GPU #, dataset, etc.) are kept in yaml config files at folder ./experiments/fgfa_rfcn/cfgs."
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,"Two config files have been provided so far, namely, frame baseline (R-FCN) and the proposed FGFA for ImageNet VID. We use 4 GPUs to train models on ImageNet VID."
 https://github.com/msracver/Flow-Guided-Feature-Aggregation,"To perform experiments, run the python script with the corresponding config file as input. For example, to train and test FGFA with R-FCN, use the following command"
@@ -883,7 +868,6 @@ https://github.com/nypl-spacetime/map-vectorizer,Run the script on the provided
 https://github.com/nypl-spacetime/map-vectorizer,usage: vectorize_map.py [-h] --gimp-path GIMP_PATH [--chunksize CHUNKSIZE]
 https://github.com/nypl-spacetime/map-vectorizer,[--image-processing-configuration-file VECTORIZE_CONFIG]
 https://github.com/nypl-spacetime/map-vectorizer,<input file or dir>
-https://github.com/odoe/generator-arcgis-js-app,Usage
 https://github.com/odoe/generator-arcgis-js-app,"grunt - default task, will output code to a dist folder with sourcemaps."
 https://github.com/odoe/generator-arcgis-js-app,grunt dev - will start a local server on at http://localhost:8282/ and watch for changes. Uses livereload to refresh browser with each update.
 https://github.com/odoe/generator-arcgis-js-app,http://localhost:8282/dist/ - application
@@ -933,7 +917,6 @@ https://github.com/phuang17/DeepMVS,"By default, the script resizes the images t
 https://github.com/phuang17/DeepMVS,"To evaluate the predicted results, run"
 https://github.com/phuang17/DeepMVS,python python/eval.py --load_bin --image_path path/to/images --sparse_path path/to/sparse --output_path path/to/output/directory --gt_path path/to/gt/directory --image_width 810 --image_height 540 --size_mismatch crop_pad
 https://github.com/phuang17/DeepMVS,"In gt_path, the ground truth disparity maps should be stored in npy format with filenames being <image_name>.depth.npy. If the ground truths are depth maps instead of disparity maps, please add --gt_type depth flag."
-https://github.com/pyvista/pymeshfix,Examples
 https://github.com/pyvista/pymeshfix,Test installation with the following from Python:
 https://github.com/pyvista/pymeshfix,from pymeshfix import examples
 https://github.com/pyvista/pymeshfix,# Test of pymeshfix without VTK module
@@ -982,14 +965,11 @@ https://github.com/pyvista/pymeshfix,# Output mesh
 https://github.com/pyvista/pymeshfix,tin.save_file(outfile)
 https://github.com/pyvista/pymeshfix,# or return numpy arrays
 https://github.com/pyvista/pymeshfix,"vclean, fclean = tin.return_arrays()"
-https://github.com/pyvista/tetgen,import pyvista as pv
 https://github.com/pyvista/tetgen,import tetgen
-https://github.com/pyvista/tetgen,import numpy as np
 https://github.com/pyvista/tetgen,sphere = pv.Sphere()
 https://github.com/pyvista/tetgen,tet = tetgen.TetGen(sphere)
 https://github.com/pyvista/tetgen,"tet.tetrahedralize(order=1, mindihedral=20, minratio=1.5)"
 https://github.com/pyvista/tetgen,grid = tet.grid
-https://github.com/pyvista/tetgen,grid.plot()
 https://github.com/pyvista/tetgen,# get cell centroids
 https://github.com/pyvista/tetgen,"cells = grid.cells.reshape(-1, 5)[:, 1:]"
 https://github.com/pyvista/tetgen,cell_center = grid.points[cells].mean(1)
@@ -1029,10 +1009,7 @@ https://github.com/ryersonvisionlab/two-stream-dyntex-synth,python synthesize.py
 https://github.com/ryersonvisionlab/two-stream-dyntex-synth,Store your chosen static texture in /data/textures. The filepath to this texture is your --appearance_target path. This texture should be the last frame of a previously generated sequence.
 https://github.com/ryersonvisionlab/two-stream-dyntex-synth,Static texture synthesis
 https://github.com/ryersonvisionlab/two-stream-dyntex-synth,python synthesize.py --type=sta --gpu=<NUMBER> --runid=<NAME> --appearance_target=data/textures/<IMAGE>
-https://github.com/salihkaragoz/pose-residual-network-pytorch,Training
-https://github.com/salihkaragoz/pose-residual-network-pytorch,python train.py
 https://github.com/salihkaragoz/pose-residual-network-pytorch,For more options look at opt.py
-https://github.com/salihkaragoz/pose-residual-network-pytorch,Testing
 https://github.com/salihkaragoz/pose-residual-network-pytorch,Download pre-train model
 https://github.com/salihkaragoz/pose-residual-network-pytorch,python test.py --test_cp=PathToPreTrainModel/PRN.pth.tar
 https://github.com/sentinelsat/sentinelsat,sentinelsat -u <user> -p <password> -g <search_polygon.geojson> --sentinel 2 --cloud 30
@@ -1106,7 +1083,6 @@ https://github.com/wuhuikai/DeepGuidedFilter,Download and Unzip Pretrained Model
 https://github.com/wuhuikai/DeepGuidedFilter,Run on an Image !
 https://github.com/wuhuikai/DeepGuidedFilter,python monodepth_simple.py --image_path ../../images/depth.jpg --checkpoint_path [MODEL_PATH] --guided_filter
 https://github.com/wuhuikai/DeepGuidedFilter,See Here or python monodepth_simple.py -h for more details.
-https://github.com/wuhuikai/DeepGuidedFilter,Usage
 https://github.com/wuhuikai/DeepGuidedFilter,PyTorch Version
 https://github.com/wuhuikai/DeepGuidedFilter,from guided_filter_pytorch.guided_filter import FastGuidedFilter
 https://github.com/wuhuikai/DeepGuidedFilter,"hr_y = FastGuidedFilter(r, eps)(lr_x, lr_y, hr_x)"
@@ -1122,7 +1098,6 @@ https://github.com/yuhuayc/da-faster-rcnn,"Prepare the dataset, source domain da
 https://github.com/yuhuayc/da-faster-rcnn,To train the Domain Adaptive Faster R-CNN:
 https://github.com/yuhuayc/da-faster-rcnn,cd $FRCN_ROOT
 https://github.com/yuhuayc/da-faster-rcnn,./tools/train_net.py --gpu {GPU_ID} --solver models/da_faster_rcnn/solver.prototxt --weights data/imagenet_models/VGG16.v2.caffemodel --imdb voc_2007_trainval --iters  {NUM_ITER}  --cfg  {CONFIGURATION_FILE}
-https://github.com/yuhuayc/da-faster-rcnn,Example
 https://github.com/yuhuayc/da-faster-rcnn,An example of adapting from Cityscapes dataset to Foggy Cityscapes dataset is provided:
 https://github.com/yuhuayc/da-faster-rcnn,"Download the datasets from here. Specifically, we will use gtFine_trainvaltest.zip, leftImg8bit_trainvaltest.zip and leftImg8bit_trainvaltest_foggy.zip."
 https://github.com/yuhuayc/da-faster-rcnn,Prepare the data using the scripts in 'prepare_data/prepare_data.m'.
@@ -1130,7 +1105,6 @@ https://github.com/yuhuayc/da-faster-rcnn,Train the Domain Adaptive Faster R-CNN
 https://github.com/yuhuayc/da-faster-rcnn,./tools/train_net.py --gpu {GPU_ID} --solver models/da_faster_rcnn/solver.prototxt --weights data/imagenet_models/VGG16.v2.caffemodel --imdb voc_2007_trainval --iters  70000  --cfg  models/da_faster_rcnn/faster_rcnn_end2end.yml
 https://github.com/yuhuayc/da-faster-rcnn,Test the trained model:
 https://github.com/yuhuayc/da-faster-rcnn,./tools/test_net.py --gpu {GPU_ID} --def models/da_faster_rcnn/test.prototxt --net output/faster_rcnn_end2end/voc_2007_trainval/vgg16_da_faster_rcnn_iter_70000.caffemodel --imdb voc_2007_test --cfg models/da_faster_rcnn/faster_rcnn_end2end.yml
-https://github.com/zhiqiangdon/CU-Net,Training
 https://github.com/zhiqiangdon/CU-Net,python cu-net.py --gpu_id 0 --exp_id cu-net-2 --layer_num 2 --order 1 --loss_num 2 --is_train true --bs 24
 https://github.com/zhiqiangdon/CU-Net,Validation
 https://github.com/zhiqiangdon/CU-Net,python cu-net.py --gpu_id 0 --exp_id cu-net-2 --layer_num 2 --order 1 --loss_num 2 --resume_prefix your_pretrained_model.pth.tar --is_train false --bs 24
@@ -1138,3 +1112,45 @@ https://github.com/zhiqiangdon/CU-Net,Model Options
 https://github.com/zhiqiangdon/CU-Net,layer_num     # number of coupled U-Nets
 https://github.com/zhiqiangdon/CU-Net,order         # the order of coupling
 https://github.com/zhiqiangdon/CU-Net,loss_num      # number of losses. Losses are uniformly distributed along the CU-Net. Each U-Net at most has one loss. (loss_num <= layer_num)
+https://github.com/facebookresearch/ResNeXt,"Please follow fb.resnet.torch for the general usage of the code, including how to use pretrained ResNeXt models for your own task."
+https://github.com/facebookresearch/ResNeXt,There are two new hyperparameters need to be specified to determine the bottleneck template:
+https://github.com/facebookresearch/ResNeXt,-baseWidth and -cardinality
+https://github.com/facebookresearch/ResNeXt,To train ResNeXt-50 (32x4d) on 8 GPUs for ImageNet:
+https://github.com/facebookresearch/ResNeXt,bash
+https://github.com/facebookresearch/ResNeXt,th main.lua -dataset imagenet -bottleneckType resnext_C -depth 50 -baseWidth 4 -cardinality 32 -batchSize 256 -nGPU 8 -nThreads 8 -shareGradInput true -data [imagenet-folder]
+https://github.com/facebookresearch/ResNeXt,To reproduce CIFAR results (e.g. ResNeXt 16x64d for cifar10) on 8 GPUs:
+https://github.com/facebookresearch/ResNeXt,th main.lua -dataset cifar10 -bottleneckType resnext_C -depth 29 -baseWidth 64 -cardinality 16 -weightDecay 5e-4 -batchSize 128 -nGPU 8 -nThreads 8 -shareGradInput true
+https://github.com/facebookresearch/ResNeXt,"To get comparable results using 2/4 GPUs, you should change the batch size and the corresponding learning rate:"
+https://github.com/facebookresearch/ResNeXt,th main.lua -dataset cifar10 -bottleneckType resnext_C -depth 29 -baseWidth 64 -cardinality 16 -weightDecay 5e-4 -batchSize 64 -nGPU 4 -LR 0.05 -nThreads 8 -shareGradInput true
+https://github.com/facebookresearch/ResNeXt,th main.lua -dataset cifar10 -bottleneckType resnext_C -depth 29 -baseWidth 64 -cardinality 16 -weightDecay 5e-4 -batchSize 32 -nGPU 2 -LR 0.025 -nThreads 8 -shareGradInput true
+https://github.com/facebookresearch/ResNeXt,Note: CIFAR datasets will be automatically downloaded and processed for the first time. Note that in the arXiv paper CIFAR results are based on pre-activated bottleneck blocks and a batch size of 256. We found that better CIFAR test acurracy can be achieved using original bottleneck blocks and a batch size of 128.
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,How to Run
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,"There's a file named ""input.txt"". You can add as many profiles as you want in the following format with each link on a new line:"
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,https://www.facebook.com/andrew.ng.96
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,https://www.facebook.com/zuck
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,Make sure the link only contains the username or id number at the end and not any other stuff. Make sure its in the format mentioned above.
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,Note: There are two modes to download Friends Profile Pics and the user's Photos: Large Size and Small Size. You can change the following variables. By default they are set to Small Sized Pics because its really quick while Large Size Mode takes time depending on the number of pictures to download
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,whether to download the full image or its thumbnail (small size)
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,if small size is True then it will be very quick else if its False then it will open each photo to download it
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,and it will take much more time
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,friends_small_size = True
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,photos_small_size = True
+https://github.com/microsoft/malmo,cd CSharp_Examples
+https://github.com/microsoft/malmo,CSharpExamples_RunMission.exe
+https://github.com/microsoft/malmo,"To build the sample yourself, open CSharp_Examples/RunMission.csproj in Visual Studio."
+https://github.com/microsoft/malmo,Or from the command-line:
+https://github.com/microsoft/malmo,"Then, on Windows:"
+https://github.com/microsoft/malmo,msbuild RunMission.csproj /p:Platform=x64
+https://github.com/microsoft/malmo,bin\x64\Debug\CSharpExamples_RunMission.exe
+https://github.com/microsoft/malmo,Running a Java agent:
+https://github.com/microsoft/malmo,cd Java_Examples
+https://github.com/microsoft/malmo,java -cp MalmoJavaJar.jar:JavaExamples_run_mission.jar -Djava.library.path=. JavaExamples_run_mission (on Linux or MacOSX)
+https://github.com/microsoft/malmo,java -cp MalmoJavaJar.jar;JavaExamples_run_mission.jar -Djava.library.path=. JavaExamples_run_mission (on Windows)
+https://github.com/microsoft/malmo,Running an Atari agent: (Linux only)
+https://github.com/microsoft/malmo,cd Python_Examples
+https://github.com/microsoft/malmo,python3 ALE_HAC.py
+https://github.com/pyro-ppl/pyro,Running Pyro from a Docker Container
+https://github.com/pyro-ppl/pyro,Refer to the instructions here.
+https://github.com/scikit-learn/scikit-learn,"After installation, you can launch the test suite from outside the"
+https://github.com/scikit-learn/scikit-learn,source directory (you will need to have pytest >= 3.3.0 installed)::
+https://github.com/scikit-learn/scikit-learn,pytest sklearn
diff --git a/data/none.csv b/data/none.csv
new file mode 100644
index 00000000..3f00e770
--- /dev/null
+++ b/data/none.csv
@@ -0,0 +1,371 @@
+URL,excerpt
+https://github.com/cltk/cltk,Documentation
+https://github.com/cltk/cltk,The docs are at docs.cltk.org.
+https://github.com/cltk/cltk,Tutorials
+https://github.com/cltk/cltk,"For interactive tutorials, in the form of Jupyter Notebooks, see https://github.com/cltk/tutorials."
+https://github.com/cltk/cltk,Contributing
+https://github.com/cltk/cltk,"See the Quickstart for contributors for an overview of the process. If you're looking to start with a small contribution, see the Issue tracker for ""easy"" jobs needing to be done. Bigger projects may be found at Project ideas page. Of course, new ideas are always welcome."
+https://github.com/cltk/cltk,"Many contributors have made substantial contributions to the CLTK. For scholarship about particular code, it might be proper to cite these individuals as authors of the work under discussion."
+https://github.com/cltk/cltk,Gratitude
+https://github.com/cltk/cltk,We are thankful for the following organizations that have offered support:
+https://github.com/cltk/cltk,"Google Summer of Code (sponsoring two students, 2016, 2017; three students 2018)"
+https://github.com/cltk/cltk,JetBrains (licenses for PyCharm)
+https://github.com/cltk/cltk,Google Cloud Platform (with credits for the Classical Language Archive and API)
+https://github.com/cltk/cltk,License
+https://github.com/cltk/cltk,"The CLTK is Copyright (c) 2014-2019 Kyle P. Johnson, under the MIT License. See LICENSE for details."
+https://github.com/facebookresearch/DensePose,DensePose:
+https://github.com/facebookresearch/DensePose,[densepose.org] [arXiv] [BibTeX]
+https://github.com/facebookresearch/DensePose,Installation
+https://github.com/facebookresearch/DensePose,Inference-Training-Testing
+https://github.com/facebookresearch/DensePose,"After installation, please see GETTING_STARTED.md  for examples of inference and training and testing."
+https://github.com/facebookresearch/DensePose,Notebooks
+https://github.com/facebookresearch/DensePose,Visualization of DensePose-COCO annotations:
+https://github.com/facebookresearch/DensePose,See notebooks/DensePose-COCO-Visualize.ipynb to visualize the DensePose-COCO annotations on the images:
+https://github.com/facebookresearch/DensePose,DensePose-COCO in 3D:
+https://github.com/facebookresearch/DensePose,See notebooks/DensePose-COCO-on-SMPL.ipynb to localize the DensePose-COCO annotations on the 3D template (SMPL) model:
+https://github.com/facebookresearch/DensePose,Visualize DensePose-RCNN Results:
+https://github.com/facebookresearch/DensePose,See notebooks/DensePose-RCNN-Visualize-Results.ipynb to visualize the inferred DensePose-RCNN Results.
+https://github.com/facebookresearch/DensePose,DensePose-RCNN Texture Transfer:
+https://github.com/facebookresearch/DensePose,See notebooks/DensePose-RCNN-Texture-Transfer.ipynb to localize the DensePose-COCO annotations on the 3D template (SMPL) model:
+https://github.com/facebookresearch/DensePose,This source code is licensed under the license found in the LICENSE file in the root directory of this source tree.
+https://github.com/facebookresearch/ResNeXt,"By Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, Kaiming He"
+https://github.com/facebookresearch/ResNeXt,"UC San Diego, Facebook AI Research"
+https://github.com/facebookresearch/ResNeXt,Table of Contents
+https://github.com/facebookresearch/ResNeXt,Introduction
+https://github.com/facebookresearch/ResNeXt,Citation
+https://github.com/facebookresearch/ResNeXt,Requirements and Dependencies
+https://github.com/facebookresearch/ResNeXt,Training
+https://github.com/facebookresearch/ResNeXt,ImageNet Pretrained Models
+https://github.com/facebookresearch/ResNeXt,Third-party re-implementations
+https://github.com/facebookresearch/ResNeXt,News
+https://github.com/facebookresearch/ResNeXt,Congrats to the ILSVRC 2017 classification challenge winner WMW.
+https://github.com/facebookresearch/ResNeXt,Check out Figure 6 in the new Memory-Efficient Implementation of DenseNets paper for a comparision between ResNeXts and DenseNets. <sub>（DenseNet cosine is DenseNet trained with cosine learning rate schedule.）</sub>
+https://github.com/facebookresearch/ResNeXt,"Figure: Training curves on ImageNet-1K. (Left): ResNet/ResNeXt-50 with the same complexity (~4.1 billion FLOPs, ~25 million parameters); (Right): ResNet/ResNeXt-101 with the same complexity (~7.8 billion FLOPs, ~44 million parameters)."
+https://github.com/facebookresearch/ResNeXt,1x Complexity Configurations Reference Table
+https://github.com/facebookresearch/ResNeXt,| baseWidth | cardinality |
+https://github.com/facebookresearch/ResNeXt,|---------- | ----------- |
+https://github.com/facebookresearch/ResNeXt,| 64        | 1           |
+https://github.com/facebookresearch/ResNeXt,| 40        | 2           |
+https://github.com/facebookresearch/ResNeXt,| 24        | 4           |
+https://github.com/facebookresearch/ResNeXt,| 14        | 8           |
+https://github.com/facebookresearch/ResNeXt,| 4         | 32          |
+https://github.com/facebookresearch/ResNeXt,ImageNet pretrained models are licensed under CC BY-NC 4.0.
+https://github.com/facebookresearch/ResNeXt,Single-crop (224x224) validation error rate
+https://github.com/facebookresearch/ResNeXt,| Network             | GFLOPS | Top-1 Error |  Download   |
+https://github.com/facebookresearch/ResNeXt,| ------------------- | ------ | ----------- | ------------|
+https://github.com/facebookresearch/ResNeXt,| ResNet-50 (1x64d)   |  ~4.1  |  23.9        | Original ResNet-50       |
+https://github.com/facebookresearch/ResNeXt,| ResNeXt-50 (32x4d)  |  ~4.1  |  22.2        | Download (191MB)       |
+https://github.com/facebookresearch/ResNeXt,| ResNet-101 (1x64d)  |  ~7.8  |  22.0        | Original ResNet-101      |
+https://github.com/facebookresearch/ResNeXt,| ResNeXt-101 (32x4d) |  ~7.8  |  21.2        | Download (338MB)      |
+https://github.com/facebookresearch/ResNeXt,| ResNeXt-101 (64x4d) |  ~15.6 |  20.4        | Download (638MB)       |
+https://github.com/facebookresearch/ResNeXt,"Besides our torch implementation, we recommend to see also the following third-party re-implementations and extensions:"
+https://github.com/facebookresearch/ResNeXt,Training code in PyTorch code
+https://github.com/facebookresearch/ResNeXt,Converting ImageNet pretrained model to PyTorch model and source. code
+https://github.com/facebookresearch/ResNeXt,Training code in MXNet and pretrained ImageNet models code
+https://github.com/facebookresearch/ResNeXt,"Caffe prototxt, pretrained ImageNet models (with ResNeXt-152), curves codecode"
+https://github.com/facebookresearch/pyrobot,"<a href=""https://www.pyrobot.org/""><img class=""doc_vid"" src=""docs/website/website/static/img/pyrobot.svg""></a>"
+https://github.com/facebookresearch/pyrobot,What can you do with PyRobot?
+https://github.com/facebookresearch/pyrobot,Getting Started
+https://github.com/facebookresearch/pyrobot,The Team
+https://github.com/facebookresearch/pyrobot,Future features
+https://github.com/facebookresearch/pyrobot,"<p align=""center"">"
+https://github.com/facebookresearch/pyrobot,"    <img src=""https://thumbs.gfycat.com/FickleSpeedyChimneyswift-size_restricted.gif"", height=""180"">"
+https://github.com/facebookresearch/pyrobot,"    <img src=""https://thumbs.gfycat.com/FinishedWeirdCockerspaniel-size_restricted.gif"", height=""180"">"
+https://github.com/facebookresearch/pyrobot,"    <img src=""https://thumbs.gfycat.com/WeightyLeadingGrub-size_restricted.gif"", height=""180"">"
+https://github.com/facebookresearch/pyrobot,</p>
+https://github.com/facebookresearch/pyrobot,"Warning: As realsense keeps updating, compatibility issues might occur if you accidentally update "
+https://github.com/facebookresearch/pyrobot,"realsense-related packages from Software Updater in ubuntu. Therefore, we recommend you not to update"
+https://github.com/facebookresearch/pyrobot,any libraries related to realsense. Check the list of updates carefully when ubuntu prompts software udpates.
+https://github.com/facebookresearch/pyrobot,Please refer to pyrobot.org and locobot.org
+https://github.com/facebookresearch/pyrobot,"Adithya Murali, Tao Chen, Dhiraj Gandhi, Kalyan Vasudev, Lerrel Pinto, Saurabh Gupta and Abhinav Gupta. We would also like to thank everyone who has helped PyRobot in any way."
+https://github.com/facebookresearch/pyrobot,"We are planning several features, namely:"
+https://github.com/facebookresearch/pyrobot,* Interfacing with other simulators like AI Habitat
+https://github.com/facebookresearch/pyrobot,* Gravity compensation
+https://github.com/facebookresearch/pyrobot,* PyRobot interface for UR5
+https://github.com/facebookresearch/pyrobot,"@article{pyrobot2019,"
+https://github.com/facebookresearch/pyrobot,"  title={PyRobot: An Open-source Robotics Framework for Research and Benchmarking},"
+https://github.com/facebookresearch/pyrobot,"  author={Adithyavairavan Murali and Tao Chen and Kalyan Vasudev Alwala and Dhiraj Gandhi and Lerrel Pinto and Saurabh Gupta and Abhinav Gupta},"
+https://github.com/facebookresearch/pyrobot,"  journal={arXiv preprint arXiv:1906.08236},"
+https://github.com/facebookresearch/pyrobot,  year={2019}
+https://github.com/facebookresearch/pyrobot,}
+https://github.com/facebookresearch/pyrobot,"PyRobot is under MIT license, as found in the LICENSE file."
+https://github.com/gitbucket/gitbucket,GitBucket    
+https://github.com/gitbucket/gitbucket,Easy installation
+https://github.com/gitbucket/gitbucket,Intuitive UI
+https://github.com/gitbucket/gitbucket,High extensibility by plugins
+https://github.com/gitbucket/gitbucket,API compatibility with GitHub
+https://github.com/gitbucket/gitbucket,Features
+https://github.com/gitbucket/gitbucket,The current version of GitBucket provides many features such as:
+https://github.com/gitbucket/gitbucket,Public / Private Git repositories (with http/https and ssh access)
+https://github.com/gitbucket/gitbucket,GitLFS support
+https://github.com/gitbucket/gitbucket,Repository viewer including an online file editor
+https://github.com/gitbucket/gitbucket,"Issues, Pull Requests and Wiki for repositories"
+https://github.com/gitbucket/gitbucket,Activity timeline and email notifications
+https://github.com/gitbucket/gitbucket,Account and group management with LDAP integration
+https://github.com/gitbucket/gitbucket,a Plug-in system
+https://github.com/gitbucket/gitbucket,"If you want to try the development version of GitBucket, see the Developer's Guide."
+https://github.com/gitbucket/gitbucket,Support
+https://github.com/gitbucket/gitbucket,"If you have any questions about GitBucket, see Wiki and check issues whether there is a same question or request in the past."
+https://github.com/gitbucket/gitbucket,"If you can't find same question and report, send it to gitter room before raising an issue."
+https://github.com/gitbucket/gitbucket,"The highest priority of GitBucket is the ease of installation and API compatibility with GitHub, so your feature request might be rejected if they go against those principles."
+https://github.com/gitbucket/gitbucket,What's New in 4.31.x
+https://github.com/gitbucket/gitbucket,4.31.2 - 7 Apr 2019
+https://github.com/gitbucket/gitbucket,Bug and security fix
+https://github.com/gitbucket/gitbucket,4.31.1 - 17 Mar 2019
+https://github.com/gitbucket/gitbucket,Bug fix
+https://github.com/gitbucket/gitbucket,4.31.0 - 17 Mar 2019
+https://github.com/gitbucket/gitbucket,Docker support in CI plugin
+https://github.com/gitbucket/gitbucket,Verify GPG key signed commit
+https://github.com/gitbucket/gitbucket,OAuth2 Token (sent as a parameter) authentication support and new APIs in Web API
+https://github.com/gitbucket/gitbucket,OGP (Open Graph protocol) support
+https://github.com/gitbucket/gitbucket,Username completion with avatars
+https://github.com/gitbucket/gitbucket,See the change log for all of the updates.
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,uploaded photos
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,tagged photos
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,videos
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,"friends list and their profile photos (including Followers, Following, Work Friends, College Friends etc)"
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,and all public posts/statuses available on the user's timeline.
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,This tool is being used by thousands of developers weekly and we are pretty amazed at this response! Thankyou guys!🎉
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,"For details regarding citing/referencing this tool for your research, check the 'Citation' section below."
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,Note
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,"At its core, this tool uses xpaths of 'divs' to extract data from them. Since Facebook keeps on updating its site frequently and the 'divs' get changed. Consequently, we have to update the divs accordingly to correctly scrape the data. "
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,"The developers of this tool have devoted a lot of time and effort in developing and most importantly maintaining this tool for quite a lot time now. In order to keep this amazing tool alive, we need support from you geeks."
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,"The code is pretty intuitive and easy to understand, so you can update the relevant xpaths in the code when you feel that you have tried many profiles and the data isn't being scraped for any of them (that's a hint that Facebook has updated their site) and generate a pull request. That's quite an easy thing to do. Thanks!"
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,Sample
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,Usage
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,Important Message
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,"This tool is for research purposes only. Hence, the developers of this tool won't be responsible for any misuse of data collected using this tool. "
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,Authors
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,You can get in touch with us on our LinkedIn Profiles:
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,Haris Muneer
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,You can also follow my GitHub Profile to stay updated about my latest projects: 
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,Hassaan Elahi
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,You can also follow my GitHub Profile to stay updated about my latest projects:
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,If you liked the repo then kindly support it by giving it a star ⭐!
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,Contributions Welcome
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,If you find any bug in the code or have any improvements in mind then feel free to generate a pull request.
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,Issues
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,"If you face any issue, you can create a new issue in the Issues Tab and I will be glad to help you out."
+https://github.com/harismuneer/Ultimate-Facebook-Scraper,"Copyright (c) 2018-present, harismuneer, Hassaan-Elahi"
+https://github.com/microsoft/malmo,Code of Conduct
+https://github.com/microsoft/malmo,This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
+https://github.com/nextflow-io/nextflow,"""Dataflow variables are spectacularly expressive in concurrent programming"""
+https://github.com/nextflow-io/nextflow,"<br>Henri E. Bal , Jennifer G. Steiner , Andrew S. Tanenbaum"
+https://github.com/nextflow-io/nextflow,Quick overview
+https://github.com/nextflow-io/nextflow,Contents
+https://github.com/nextflow-io/nextflow,Rationale
+https://github.com/nextflow-io/nextflow,Quick start
+https://github.com/nextflow-io/nextflow,Tool Management
+https://github.com/nextflow-io/nextflow,Conda environments
+https://github.com/nextflow-io/nextflow,Docker and Singularity
+https://github.com/nextflow-io/nextflow,Environment Modules
+https://github.com/nextflow-io/nextflow,HPC Schedulers
+https://github.com/nextflow-io/nextflow,SGE
+https://github.com/nextflow-io/nextflow,Univa Grid Engine
+https://github.com/nextflow-io/nextflow,LSF
+https://github.com/nextflow-io/nextflow,SLURM
+https://github.com/nextflow-io/nextflow,PBS/Torque
+https://github.com/nextflow-io/nextflow,HTCondor (experimental)
+https://github.com/nextflow-io/nextflow,Cloud Support
+https://github.com/nextflow-io/nextflow,AWS Batch
+https://github.com/nextflow-io/nextflow,AWS EC2
+https://github.com/nextflow-io/nextflow,Google Cloud
+https://github.com/nextflow-io/nextflow,Google Genomics Pipelines
+https://github.com/nextflow-io/nextflow,Kubernetes
+https://github.com/nextflow-io/nextflow,Community
+https://github.com/nextflow-io/nextflow,Build from source
+https://github.com/nextflow-io/nextflow,Citations
+https://github.com/nextflow-io/nextflow,Credits
+https://github.com/nextflow-io/nextflow,"With the rise of big data, techniques to analyse and run experiments on large datasets are increasingly necessary."
+https://github.com/nextflow-io/nextflow,"Parallelization and distributed computing are the best ways to tackle this problem, but the tools commonly available to the bioinformatics community often lack good support for these techniques, or provide a model that fits badly with the specific requirements in the bioinformatics domain and, most of the time, require the knowledge of complex tools or low-level APIs."
+https://github.com/nextflow-io/nextflow,Nextflow documentation is available at this link http://docs.nextflow.io
+https://github.com/nextflow-io/nextflow,"Nextflow supports common HPC schedulers, abstracting the submission of jobs from the user. "
+https://github.com/nextflow-io/nextflow,Currently the following clusters are supported:
+https://github.com/nextflow-io/nextflow,"For example to submit the execution to a SGE cluster create a file named nextflow.config, in the directory"
+https://github.com/nextflow-io/nextflow,"where the pipeline is going to be launched, with the following content:"
+https://github.com/nextflow-io/nextflow,nextflow
+https://github.com/nextflow-io/nextflow,process {
+https://github.com/nextflow-io/nextflow,  executor='sge'
+https://github.com/nextflow-io/nextflow,  queue='&lt;your execution queue&gt;'
+https://github.com/nextflow-io/nextflow,"In doing that, processes will be executed by Nextflow as SGE jobs using the qsub command. Your "
+https://github.com/nextflow-io/nextflow,"pipeline will behave like any other SGE job script, with the benefit that Nextflow will "
+https://github.com/nextflow-io/nextflow,"automatically and transparently manage the processes synchronisation, file(s) staging/un-staging, etc.  "
+https://github.com/nextflow-io/nextflow,Cloud support
+https://github.com/nextflow-io/nextflow,Currently supported cloud platforms:
+https://github.com/nextflow-io/nextflow,  + AWS Batch
+https://github.com/nextflow-io/nextflow,  + AWS EC2
+https://github.com/nextflow-io/nextflow,  + Google GCE
+https://github.com/nextflow-io/nextflow,  + Google Genomics Pipelines
+https://github.com/nextflow-io/nextflow,  + Kubernetes
+https://github.com/nextflow-io/nextflow,Tool management
+https://github.com/nextflow-io/nextflow,Containers
+https://github.com/nextflow-io/nextflow,"Nextflow has first class support for containerization. It supports both Docker and Singularity container engines. Additionally, Nextflow can easily switch between container engines enabling workflow portability. "
+https://github.com/nextflow-io/nextflow,```nextflow
+https://github.com/nextflow-io/nextflow,process samtools {
+https://github.com/nextflow-io/nextflow,  container 'biocontainers/samtools:1.3.1'
+https://github.com/nextflow-io/nextflow,""""""""
+https://github.com/nextflow-io/nextflow,  samtools --version 
+https://github.com/nextflow-io/nextflow,"  """""""
+https://github.com/nextflow-io/nextflow,```
+https://github.com/nextflow-io/nextflow,Conda environments provide another option for managing software packages in your workflow. 
+https://github.com/nextflow-io/nextflow,Environment modules commonly found in HPC environments can also be used to manage the tools used in a Nextflow workflow. 
+https://github.com/nextflow-io/nextflow,"You can post questions, or report problems by using the Nextflow discussion forum"
+https://github.com/nextflow-io/nextflow,or the Nextflow channel on Gitter.
+https://github.com/nextflow-io/nextflow,Nextflow also hosts a yearly workshop showcasing researcher's workflows and advancements in the langauge. Talks from the past workshops are available on the Nextflow YouTube Channel
+https://github.com/nextflow-io/nextflow,The nf-core project is a community effort aggregating high quality Nextflow workflows which can be used by the community. 
+https://github.com/nextflow-io/nextflow,Required dependencies
+https://github.com/nextflow-io/nextflow,Compiler Java 8
+https://github.com/nextflow-io/nextflow,Runtime Java 8 or later
+https://github.com/nextflow-io/nextflow,"Nextflow is written in Groovy (a scripting language for the JVM). A pre-compiled,"
+https://github.com/nextflow-io/nextflow,"ready-to-run, package is available at the Github releases page,"
+https://github.com/nextflow-io/nextflow,thus it is not necessary to compile it in order to use it.
+https://github.com/nextflow-io/nextflow,"If you are interested in modifying the source code, or contributing to the project, it worth knowing that"
+https://github.com/nextflow-io/nextflow,the build process is based on the Gradle build automation system.
+https://github.com/nextflow-io/nextflow,You can compile Nextflow by typing the following command in the project home directory on your computer:
+https://github.com/nextflow-io/nextflow,Clone the Nextflow repository to a directory in your computer.
+https://github.com/nextflow-io/nextflow,"Open IntelliJ IDEA and choose ""Import project"" in the ""File"" menu bar."
+https://github.com/nextflow-io/nextflow,"Select the Nextflow project root directory in your computer and click ""OK""."
+https://github.com/nextflow-io/nextflow,"Then, choose the ""Gradle"" item in the ""external module"" list and click on ""Next"" button."
+https://github.com/nextflow-io/nextflow,"Confirm the default import options and click on ""Finish"" to finalize the project configuration."
+https://github.com/nextflow-io/nextflow,"When the import process complete, select the ""Project structure"" command in the ""File"" menu bar."
+https://github.com/nextflow-io/nextflow,"In the showed dialog click on the ""Project"" item in the list of the left, and make sure that"
+https://github.com/nextflow-io/nextflow,"   the ""Project SDK"" choice on the right contains Java 8."
+https://github.com/nextflow-io/nextflow,Set the code formatting options with setting provided here.
+https://github.com/nextflow-io/nextflow,Project contribution are more than welcome. See the CONTRIBUTING file for details.
+https://github.com/nextflow-io/nextflow,Build servers
+https://github.com/nextflow-io/nextflow,Travis-CI
+https://github.com/nextflow-io/nextflow,Groovy Joint build
+https://github.com/nextflow-io/nextflow,The Nextflow framework is released under the Apache 2.0 license.
+https://github.com/nextflow-io/nextflow,and <a href='http://www.gpars.org/' target='_blank'>Gpars</a>.
+https://github.com/nextflow-io/nextflow,YourKit is kindly supporting this open source project with its full-featured Java Profiler.
+https://github.com/nextflow-io/nextflow,Read more http://www.yourkit.com
+https://github.com/pyro-ppl/pyro,Getting Started |
+https://github.com/pyro-ppl/pyro,Documentation |
+https://github.com/pyro-ppl/pyro,Community |
+https://github.com/pyro-ppl/pyro,"For more information, check out our blog post."
+https://github.com/reduxjs/react-redux,React Redux
+https://github.com/reduxjs/react-redux,The React Redux docs are now published at https://react-redux.js.org .
+https://github.com/reduxjs/react-redux,We're currently expanding and rewriting our docs content - check back soon for more updates!
+https://github.com/reduxjs/react-redux,How Does It Work?
+https://github.com/reduxjs/react-redux,We do a deep dive on how React Redux works in this readthesource episode.  
+https://github.com/reduxjs/react-redux,"Also, the post The History and Implementation of React-Redux "
+https://github.com/reduxjs/react-redux,"explains what it does, how it works, and how the API and implementation have evolved over time."
+https://github.com/reduxjs/react-redux,Enjoy!
+https://github.com/reduxjs/react-redux,MIT
+https://github.com/scikit-image/scikit-image,Website (including documentation): https://scikit-image.org/
+https://github.com/scikit-image/scikit-image,Mailing list: https://mail.python.org/mailman/listinfo/scikit-image
+https://github.com/scikit-image/scikit-image,Source: https://github.com/scikit-image/scikit-image
+https://github.com/scikit-image/scikit-image,Benchmarks: https://pandas.pydata.org/speed/scikit-image/
+https://github.com/scikit-image/scikit-image,License (Modified BSD)
+https://github.com/scikit-image/scikit-image,"Copyright (C) 2011, the scikit-image team"
+https://github.com/scikit-image/scikit-image,All rights reserved.
+https://github.com/scikit-image/scikit-image,"Redistribution and use in source and binary forms, with or without"
+https://github.com/scikit-image/scikit-image,"modification, are permitted provided that the following conditions are"
+https://github.com/scikit-image/scikit-image,met:
+https://github.com/scikit-image/scikit-image,Redistributions of source code must retain the above copyright
+https://github.com/scikit-image/scikit-image,"    notice, this list of conditions and the following disclaimer."
+https://github.com/scikit-image/scikit-image,Redistributions in binary form must reproduce the above copyright
+https://github.com/scikit-image/scikit-image,"    notice, this list of conditions and the following disclaimer in"
+https://github.com/scikit-image/scikit-image,    the documentation and/or other materials provided with the
+https://github.com/scikit-image/scikit-image,    distribution.
+https://github.com/scikit-image/scikit-image,Neither the name of skimage nor the names of its contributors may be
+https://github.com/scikit-image/scikit-image,    used to endorse or promote products derived from this software without
+https://github.com/scikit-image/scikit-image,    specific prior written permission.
+https://github.com/scikit-image/scikit-image,THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+https://github.com/scikit-image/scikit-image,"IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED"
+https://github.com/scikit-image/scikit-image,WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+https://github.com/scikit-image/scikit-image,"DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,"
+https://github.com/scikit-image/scikit-image,"INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES"
+https://github.com/scikit-image/scikit-image,"(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR"
+https://github.com/scikit-image/scikit-image,"SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)"
+https://github.com/scikit-image/scikit-image,"HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,"
+https://github.com/scikit-image/scikit-image,"STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING"
+https://github.com/scikit-image/scikit-image,"IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE"
+https://github.com/scikit-image/scikit-image,POSSIBILITY OF SUCH DAMAGE.
+https://github.com/scikit-learn/scikit-learn,scikit-learn
+https://github.com/scikit-learn/scikit-learn,Development
+https://github.com/scikit-learn/scikit-learn,We welcome new contributors of all experience levels. The scikit-learn
+https://github.com/scikit-learn/scikit-learn,"community goals are to be helpful, welcoming, and effective. The"
+https://github.com/scikit-learn/scikit-learn,Development Guide &lt;http://scikit-learn.org/stable/developers/index.html&gt;_
+https://github.com/scikit-learn/scikit-learn,"has detailed information about contributing code, documentation, tests, and"
+https://github.com/scikit-learn/scikit-learn,more. We've included some basic information in this README.
+https://github.com/scikit-learn/scikit-learn,Important links
+https://github.com/scikit-learn/scikit-learn,Official source code repo: https://github.com/scikit-learn/scikit-learn
+https://github.com/scikit-learn/scikit-learn,Download releases: https://pypi.org/project/scikit-learn/
+https://github.com/scikit-learn/scikit-learn,Issue tracker: https://github.com/scikit-learn/scikit-learn/issues
+https://github.com/scikit-learn/scikit-learn,Source code
+https://github.com/scikit-learn/scikit-learn,You can check the latest sources with the command::
+https://github.com/scikit-learn/scikit-learn,git clone https://github.com/scikit-learn/scikit-learn.git
+https://github.com/scikit-learn/scikit-learn,"To learn more about making a contribution to scikit-learn, please see our"
+https://github.com/scikit-learn/scikit-learn,Contributing guide
+https://github.com/scikit-learn/scikit-learn,&lt;https://scikit-learn.org/dev/developers/contributing.html&gt;_.
+https://github.com/scikit-learn/scikit-learn,See the web page http://scikit-learn.org/dev/developers/advanced_installation.html#testing
+https://github.com/scikit-learn/scikit-learn,for more information.
+https://github.com/scikit-learn/scikit-learn,Random number generation can be controlled during testing by setting
+https://github.com/scikit-learn/scikit-learn,the ``SKLEARN_SEED`` environment variable.
+https://github.com/scikit-learn/scikit-learn,Submitting a Pull Request
+https://github.com/scikit-learn/scikit-learn,"Before opening a Pull Request, have a look at the"
+https://github.com/scikit-learn/scikit-learn,full Contributing page to make sure your code complies
+https://github.com/scikit-learn/scikit-learn,with our guidelines: http://scikit-learn.org/stable/developers/index.html
+https://github.com/scikit-learn/scikit-learn,Project History
+https://github.com/scikit-learn/scikit-learn,The project was started in 2007 by David Cournapeau as a Google Summer
+https://github.com/scikit-learn/scikit-learn,"of Code project, and since then many volunteers have contributed. See"
+https://github.com/scikit-learn/scikit-learn,the  About us &lt;http://scikit-learn.org/dev/about.html#authors&gt;_ page
+https://github.com/scikit-learn/scikit-learn,for a list of core contributors.
+https://github.com/scikit-learn/scikit-learn,The project is currently maintained by a team of volunteers.
+https://github.com/scikit-learn/scikit-learn,Note: scikit-learn was previously referred to as scikits.learn.
+https://github.com/scikit-learn/scikit-learn,Help and Support
+https://github.com/scikit-learn/scikit-learn,HTML documentation (stable release): http://scikit-learn.org
+https://github.com/scikit-learn/scikit-learn,HTML documentation (development version): http://scikit-learn.org/dev/
+https://github.com/scikit-learn/scikit-learn,FAQ: http://scikit-learn.org/stable/faq.html
+https://github.com/scikit-learn/scikit-learn,Communication
+https://github.com/scikit-learn/scikit-learn,Mailing list: https://mail.python.org/mailman/listinfo/scikit-learn
+https://github.com/scikit-learn/scikit-learn,IRC channel: #scikit-learn at webchat.freenode.net
+https://github.com/scikit-learn/scikit-learn,Stack Overflow: https://stackoverflow.com/questions/tagged/scikit-learn
+https://github.com/scikit-learn/scikit-learn,Website: http://scikit-learn.org
+https://github.com/sequelize/sequelize,Sequelize
+https://github.com/sequelize/sequelize,New to Sequelize? Take a look at the Tutorials and Guides. You might also be interested in the API Reference.
+https://github.com/sequelize/sequelize,v5 Release
+https://github.com/sequelize/sequelize,You can find the upgrade guide and changelog here.
+https://github.com/sequelize/sequelize,Responsible disclosure
+https://github.com/sequelize/sequelize,Resources
+https://github.com/sequelize/sequelize,v5 Documentation
+https://github.com/sequelize/sequelize,v4 Documentation
+https://github.com/sequelize/sequelize,v3 Documentation
+https://github.com/sequelize/sequelize,"If you have any security issue to report, contact project maintainers privately. You can find contact information in CONTACT.md."
+https://github.com/sequelize/sequelize,Changelog
+https://github.com/sequelize/sequelize,Slack
+https://github.com/sequelize/sequelize,Stack Overflow
+https://github.com/sequelize/sequelize,Tools
+https://github.com/sequelize/sequelize,Sequelize & TypeScript
+https://github.com/sequelize/sequelize,Enhanced TypeScript with decorators
+https://github.com/sequelize/sequelize,Sequelize & GraphQL
+https://github.com/sequelize/sequelize,Add-ons & Plugins
+https://github.com/sequelize/sequelize,Sequelize CLI
+https://github.com/sequelize/sequelize,Learning
+https://github.com/sequelize/sequelize,Express Example
+https://github.com/sequelize/sequelize,Translations
+https://github.com/sequelize/sequelize,English v5 (OFFICIAL)
+https://github.com/tensorflow/magenta,Using Magenta
+https://github.com/tensorflow/magenta,Playing a MIDI Instrument
+https://github.com/tensorflow/magenta,Development Environment (Advanced)
+https://github.com/tensorflow/magenta,"If you want to develop on Magenta, you'll need to set up the full Development Environment."
+https://github.com/tensorflow/magenta,"First, clone this repository:"
+https://github.com/tensorflow/magenta,bash
+https://github.com/tensorflow/magenta,git clone https://github.com/tensorflow/magenta.git
+https://github.com/tensorflow/magenta,"Next, install the dependencies by changing to the base directory and executing the setup command:"
+https://github.com/tensorflow/magenta,pip install -e .
+https://github.com/tensorflow/magenta,"You can now edit the files and run scripts by calling Python as usual. For example, this is how you would run the melody_rnn_generate script from the base directory:"
+https://github.com/tensorflow/magenta,python magenta/models/melody_rnn/melody_rnn_generate --config=...
+https://github.com/tensorflow/magenta,You can also install the (potentially modified) package with:
+https://github.com/tensorflow/magenta,pip install .
+https://github.com/tensorflow/magenta,"Before creating a pull request, please also test your changes with:"
+https://github.com/tensorflow/magenta,pip install pytest-pylint
+https://github.com/tensorflow/magenta,pytest
+https://github.com/tensorflow/magenta,PIP Release
+https://github.com/tensorflow/magenta,"To build a new version for pip, bump the version and then run:"
+https://github.com/tensorflow/magenta,python setup.py test
+https://github.com/tensorflow/magenta,python setup.py bdist_wheel --universal
+https://github.com/tensorflow/magenta,python setup.py bdist_wheel --universal --gpu
+https://github.com/tensorflow/magenta,twine upload dist/magenta-N.N.N-py2.py3-none-any.whl
+https://github.com/tensorflow/magenta,twine upload dist/magenta_gpu-N.N.N-py2.py3-none-any.whl
diff --git a/data/repos/cltk-cltk-README.md b/data/repos/cltk-cltk-README.md
new file mode 100644
index 00000000..a613fadc
--- /dev/null
+++ b/data/repos/cltk-cltk-README.md
@@ -0,0 +1,81 @@
+# The Classical Language Toolkit
+
+[![PyPi downloads](http://img.shields.io/pypi/v/cltk.svg?style=flat)](https://pypi.python.org/pypi/cltk/) [![Documentation Status](https://readthedocs.org/projects/cltk/badge/?version=latest)](http://docs.cltk.org/en/latest/?badge=latest) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.593336.svg)](https://doi.org/10.5281/zenodo.593336)
+
+[![Build Status](https://travis-ci.org/cltk/cltk.svg?branch=master)](https://travis-ci.org/cltk/cltk) [![codecov.io](http://codecov.io/github/cltk/cltk/coverage.svg?branch=master)](http://codecov.io/github/cltk/cltk?branch=master)
+
+[![Join the chat at https://gitter.im/cltk/cltk](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/cltk/cltk?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+
+
+## About
+
+The Classical Language Toolkit (CLTK) offers natural language processing (NLP) support for the languages of Ancient, Classical, and Medieval Eurasia. Greek, Latin, Akkadian, and the Germanic languages are currently most complete. The goals of the CLTK are to:
+*   compile analysis-friendly corpora;
+*   collect and generate linguistic data;
+*   act as a free and open platform for generating scientific research.
+
+
+## Documentation
+
+The docs are at [docs.cltk.org](http://docs.cltk.org).
+
+
+### Installation
+
+CLTK supports Python versions 3.6 and 3.7. The software only runs on POSIX–compliant operating systems (Linux, Mac OS X, FreeBSD, etc.).
+
+``` bash
+$ pip install cltk
+```
+
+See docs for [complete installation instructions](http://docs.cltk.org/en/latest/installation.html).
+
+The [CLTK organization curates corpora](https://github.com/cltk) which can be downloaded directly or, better, [imported by the toolkit](http://docs.cltk.org/en/latest/importing_corpora.html).
+
+
+### Tutorials
+
+For interactive tutorials, in the form of Jupyter Notebooks, see <https://github.com/cltk/tutorials>.
+
+
+## Contributing
+
+See the [Quickstart for contributors](https://github.com/cltk/cltk/wiki/Quickstart-for-contributors) for an overview of the process. If you're looking to start with a small contribution, see the [Issue tracker for "easy" jobs](https://github.com/cltk/cltk/issues?q=is%3Aopen+is%3Aissue+label%3Aeasy) needing to be done. Bigger projects may be found at [Project ideas](https://github.com/cltk/cltk/wiki/Project-ideas) page. Of course, new ideas are always welcome.
+
+
+## Citation
+
+Each major release of the CLTK is given a [DOI](http://en.wikipedia.org/wiki/Digital_object_identifier), a type of unique identity for digital documents. This DOI ought to be included in your citation, as it will allow researchers to reproduce your results should the CLTK's API or codebase change. To find the CLTK's current DOI, observe the blue `DOI` button in the repository's home on GitHub. To the end of your bibliographic entry, append `DOI ` plus the current identifier. You may also add version/release number, located in the `pypi` button at the project's GitHub repository homepage.
+
+Thus, please cite core software as something like:
+```
+Kyle P. Johnson et al.. (2014-2019). CLTK: The Classical Language Toolkit. DOI 10.5281/zenodo.<current_release_id>
+```
+
+A style-neutral BibTeX entry would look like this:
+```
+@Misc{johnson2014,
+author = {Kyle P. Johnson et al.},
+title = {CLTK: The Classical Language Toolkit},
+howpublished = {\url{https://github.com/cltk/cltk}},
+note = {{DOI} 10.5281/zenodo.<current_release_id>},
+year = {2014--2019},
+}
+```
+
+
+[Many contributors](https://github.com/cltk/cltk/blob/master/contributors.md) have made substantial contributions to the CLTK. For scholarship about particular code, it might be proper to cite these individuals as authors of the work under discussion.
+
+
+## Gratitude
+
+We are thankful for the following organizations that have offered support:
+
+* Google Summer of Code (sponsoring two students, 2016, 2017; three students 2018)
+* JetBrains (licenses for PyCharm)
+* Google Cloud Platform (with credits for the Classical Language Archive and API)
+
+
+## License
+
+The CLTK is Copyright (c) 2014-2019 Kyle P. Johnson, under the MIT License. See [LICENSE](https://github.com/cltk/cltk/blob/master/LICENSE) for details.
diff --git a/data/repos/facebookresearch-DensePose-README.md b/data/repos/facebookresearch-DensePose-README.md
new file mode 100644
index 00000000..33a1b1fa
--- /dev/null
+++ b/data/repos/facebookresearch-DensePose-README.md
@@ -0,0 +1,81 @@
+# DensePose: 
+**Dense Human Pose Estimation In The Wild**
+
+_Rıza Alp Güler, Natalia Neverova, Iasonas Kokkinos_
+
+[[`densepose.org`](https://densepose.org)] [[`arXiv`](https://arxiv.org/abs/1802.00434)] [[`BibTeX`](#CitingDensePose)]
+
+Dense human pose estimation aims at mapping all human pixels of an RGB image to the 3D surface of the human body. 
+DensePose-RCNN is implemented in the [Detectron](https://github.com/facebookresearch/Detectron) framework and is powered by [Caffe2](https://github.com/caffe2/caffe2).
+
+<div align="center">
+  <img src="https://drive.google.com/uc?export=view&id=1qfSOkpueo1kVZbXOuQJJhyagKjMgepsz" width="700px" />
+</div>
+
+
+In this repository, we provide the code to train and evaluate DensePose-RCNN. We also provide notebooks to visualize the collected DensePose-COCO dataset and show the correspondences to the SMPL model.
+
+## Installation
+
+Please find installation instructions for Caffe2 and DensePose in [`INSTALL.md`](INSTALL.md), a document based on the [Detectron](https://github.com/facebookresearch/Detectron) installation instructions.
+
+## Inference-Training-Testing
+
+After installation, please see [`GETTING_STARTED.md`](GETTING_STARTED.md)  for examples of inference and training and testing.
+
+## Notebooks
+
+### Visualization of DensePose-COCO annotations:
+
+See [`notebooks/DensePose-COCO-Visualize.ipynb`](notebooks/DensePose-COCO-Visualize.ipynb) to visualize the DensePose-COCO annotations on the images:
+
+<div align="center">
+  <img src="https://drive.google.com/uc?export=view&id=1uYRJkIA24KkJU2i4sMwrKa61P0xtZzHk" width="800px" />
+</div>
+
+---
+
+### DensePose-COCO in 3D:
+
+See [`notebooks/DensePose-COCO-on-SMPL.ipynb`](notebooks/DensePose-COCO-on-SMPL.ipynb) to localize the DensePose-COCO annotations on the 3D template ([`SMPL`](http://smpl.is.tue.mpg.de)) model:
+
+<div align="center">
+  <img src="https://drive.google.com/uc?export=view&id=1m32oyMuE7AZd3EOf9k8zHpr75C8bHlYj" width="500px" />
+</div>
+
+---
+### Visualize DensePose-RCNN Results:
+
+See [`notebooks/DensePose-RCNN-Visualize-Results.ipynb`](notebooks/DensePose-RCNN-Visualize-Results.ipynb) to visualize the inferred DensePose-RCNN Results.
+
+<div align="center">
+  <img src="https://drive.google.com/uc?export=view&id=1k4HtoXpbDV9MhuyhaVcxDrXnyP_NX896" width="900px" />
+</div>
+
+---
+### DensePose-RCNN Texture Transfer:
+
+See [`notebooks/DensePose-RCNN-Texture-Transfer.ipynb`](notebooks/DensePose-RCNN-Texture-Transfer.ipynb) to localize the DensePose-COCO annotations on the 3D template ([`SMPL`](http://smpl.is.tue.mpg.de)) model:
+
+<div align="center">
+  <img src="https://drive.google.com/uc?export=view&id=1r-w1oDkDHYnc1vYMbpXcYBVD1-V3B4Le" width="900px" />
+</div>
+
+## License
+
+This source code is licensed under the license found in the [`LICENSE`](LICENSE) file in the root directory of this source tree.
+
+## <a name="CitingDensePose"></a>Citing DensePose
+
+If you use Densepose, please use the following BibTeX entry.
+
+```
+  @InProceedings{Guler2018DensePose,
+  title={DensePose: Dense Human Pose Estimation In The Wild},
+  author={R\{i}za Alp G\"uler, Natalia Neverova, Iasonas Kokkinos},
+  journal={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year={2018}
+  }
+```
+
+
diff --git a/data/repos/facebookresearch-ResNeXt-README.md b/data/repos/facebookresearch-ResNeXt-README.md
new file mode 100644
index 00000000..97885f46
--- /dev/null
+++ b/data/repos/facebookresearch-ResNeXt-README.md
@@ -0,0 +1,108 @@
+# ResNeXt: Aggregated Residual Transformations for Deep Neural Networks
+
+By [Saining Xie](http://vcl.ucsd.edu/~sxie), [Ross Girshick](http://www.rossgirshick.info/), [Piotr Dollár](https://pdollar.github.io/), [Zhuowen Tu](http://pages.ucsd.edu/~ztu/), [Kaiming He](http://kaiminghe.com)
+
+UC San Diego, Facebook AI Research
+
+### Table of Contents
+0. [Introduction](#introduction)
+0. [Citation](#citation)
+0. [Requirements and Dependencies](#requirements-and-dependencies)
+0. [Training](#training)
+0. [ImageNet Pretrained Models](#imagenet-pretrained-models)
+0. [Third-party re-implementations](#third-party-re-implementations)
+
+#### News
+* Congrats to the ILSVRC 2017 classification challenge winner [WMW](http://image-net.org/challenges/LSVRC/2017/results).
+ResNeXt is the foundation of their new SENet architecture (a **ResNeXt-152 (64 x 4d)** with the Squeeze-and-Excitation module)!
+* Check out Figure 6 in the new [Memory-Efficient Implementation of DenseNets](https://arxiv.org/pdf/1707.06990.pdf) paper for a comparision between ResNeXts and DenseNets. <sub>（*DenseNet cosine is DenseNet trained with cosine learning rate schedule.*）</sub>
+<p align="center">
+<img src="http://vcl.ucsd.edu/resnext/resnextvsdensenet.png" width="480">
+</p>
+
+
+### Introduction
+
+This repository contains a [Torch](http://torch.ch) implementation for the [ResNeXt](https://arxiv.org/abs/1611.05431) algorithm for image classification. The code is based on [fb.resnet.torch](https://github.com/facebook/fb.resnet.torch).
+
+[ResNeXt](https://arxiv.org/abs/1611.05431) is a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call “cardinality” (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width.
+
+
+![teaser](http://vcl.ucsd.edu/resnext/teaser.png)
+##### Figure: Training curves on ImageNet-1K. (Left): ResNet/ResNeXt-50 with the same complexity (~4.1 billion FLOPs, ~25 million parameters); (Right): ResNet/ResNeXt-101 with the same complexity (~7.8 billion FLOPs, ~44 million parameters).
+-----
+
+### Citation
+If you use ResNeXt in your research, please cite the paper:
+```
+@article{Xie2016,
+  title={Aggregated Residual Transformations for Deep Neural Networks},
+  author={Saining Xie and Ross Girshick and Piotr Dollár and Zhuowen Tu and Kaiming He},
+  journal={arXiv preprint arXiv:1611.05431},
+  year={2016}
+}
+```
+
+### Requirements and Dependencies
+See the fb.resnet.torch [installation instructions](https://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md) for a step-by-step guide.
+- Install [Torch](http://torch.ch/docs/getting-started.html) on a machine with CUDA GPU
+- Install [cuDNN v4 or v5](https://developer.nvidia.com/cudnn) and the Torch [cuDNN bindings](https://github.com/soumith/cudnn.torch/tree/R4)
+- Download the [ImageNet](http://image-net.org/download-images) dataset and [move validation images](https://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md#download-the-imagenet-dataset) to labeled subfolders
+
+### Training
+
+Please follow [fb.resnet.torch](https://github.com/facebook/fb.resnet.torch) for the general usage of the code, including [how](https://github.com/facebook/fb.resnet.torch/tree/master/pretrained) to use pretrained ResNeXt models for your own task.
+
+There are two new hyperparameters need to be specified to determine the bottleneck template:
+
+**-baseWidth** and **-cardinality**
+
+### 1x Complexity Configurations Reference Table
+
+| baseWidth | cardinality |
+|---------- | ----------- |
+| 64        | 1           |
+| 40        | 2           |
+| 24        | 4           |
+| 14        | 8           |
+| 4         | 32          |
+
+
+To train ResNeXt-50 (32x4d) on 8 GPUs for ImageNet:
+```bash
+th main.lua -dataset imagenet -bottleneckType resnext_C -depth 50 -baseWidth 4 -cardinality 32 -batchSize 256 -nGPU 8 -nThreads 8 -shareGradInput true -data [imagenet-folder]
+```
+
+To reproduce CIFAR results (e.g. ResNeXt 16x64d for cifar10) on 8 GPUs:
+```bash
+th main.lua -dataset cifar10 -bottleneckType resnext_C -depth 29 -baseWidth 64 -cardinality 16 -weightDecay 5e-4 -batchSize 128 -nGPU 8 -nThreads 8 -shareGradInput true
+```
+To get comparable results using 2/4 GPUs, you should change the batch size and the corresponding learning rate:
+```bash
+th main.lua -dataset cifar10 -bottleneckType resnext_C -depth 29 -baseWidth 64 -cardinality 16 -weightDecay 5e-4 -batchSize 64 -nGPU 4 -LR 0.05 -nThreads 8 -shareGradInput true
+th main.lua -dataset cifar10 -bottleneckType resnext_C -depth 29 -baseWidth 64 -cardinality 16 -weightDecay 5e-4 -batchSize 32 -nGPU 2 -LR 0.025 -nThreads 8 -shareGradInput true
+```
+Note: CIFAR datasets will be automatically downloaded and processed for the first time. Note that in the arXiv paper CIFAR results are based on pre-activated bottleneck blocks and a batch size of 256. We found that better CIFAR test acurracy can be achieved using original bottleneck blocks and a batch size of 128.
+
+### ImageNet Pretrained Models
+ImageNet pretrained models are licensed under CC BY-NC 4.0.
+
+[![CC BY-NC 4.0](https://i.creativecommons.org/l/by-nc/4.0/88x31.png)](https://creativecommons.org/licenses/by-nc/4.0/)
+
+#### Single-crop (224x224) validation error rate
+| Network             | GFLOPS | Top-1 Error |  Download   |
+| ------------------- | ------ | ----------- | ------------|
+| ResNet-50 (1x64d)   |  ~4.1  |  23.9        | [Original ResNet-50](https://github.com/facebook/fb.resnet.torch/tree/master/pretrained)       |
+| ResNeXt-50 (32x4d)  |  ~4.1  |  22.2        | [Download (191MB)](https://dl.fbaipublicfiles.com/resnext/imagenet_models/resnext_50_32x4d.t7)       |
+| ResNet-101 (1x64d)  |  ~7.8  |  22.0        | [Original ResNet-101](https://github.com/facebook/fb.resnet.torch/tree/master/pretrained)      |
+| ResNeXt-101 (32x4d) |  ~7.8  |  21.2        | [Download (338MB)](https://dl.fbaipublicfiles.com/resnext/imagenet_models/resnext_101_32x4d.t7)      |
+| ResNeXt-101 (64x4d) |  ~15.6 |  20.4        | [Download (638MB)](https://dl.fbaipublicfiles.com/resnext/imagenet_models/resnext_101_64x4d.t7)       |
+
+### Third-party re-implementations
+
+Besides our torch implementation, we recommend to see also the following third-party re-implementations and extensions:
+
+1. Training code in PyTorch [code](https://github.com/prlz77/ResNeXt.pytorch)
+1. Converting ImageNet pretrained model to PyTorch model and source. [code](https://github.com/clcarwin/convert_torch_to_pytorch)
+1. Training code in MXNet and pretrained ImageNet models [code](https://github.com/dmlc/mxnet/tree/master/example/image-classification#imagenet-1k)
+1. Caffe prototxt, pretrained ImageNet models (with ResNeXt-152), curves [code](https://github.com/cypw/ResNeXt-1)[code](https://github.com/terrychenism/ResNeXt)
diff --git a/data/repos/facebookresearch-pyrobot-README.md b/data/repos/facebookresearch-pyrobot-README.md
new file mode 100644
index 00000000..65bfaa5d
--- /dev/null
+++ b/data/repos/facebookresearch-pyrobot-README.md
@@ -0,0 +1,101 @@
+<a href="https://www.pyrobot.org/"><img class="doc_vid" src="docs/website/website/static/img/pyrobot.svg"></a>
+
+[PyRobot](https://www.pyrobot.org/) is a light weight, high-level interface which provides hardware independent APIs for robotic manipulation and navigation. This repository also contains the low-level stack for [LoCoBot](http://locobot.org), a low cost mobile manipulator hardware platform.
+
+- [What can you do with PyRobot?](#what-can-you-do-with-pyrobot)
+- [Installation](#installation)
+- [Getting Started](#getting-started)
+- [The Team](#the-team)
+- [Citation](#citation)
+- [License](#license)
+- [Future features](#Future-features)
+
+## What can you do with PyRobot?
+
+<p align="center">
+    <img src="https://thumbs.gfycat.com/FickleSpeedyChimneyswift-size_restricted.gif", height="180">
+    <img src="https://thumbs.gfycat.com/FinishedWeirdCockerspaniel-size_restricted.gif", height="180">
+    <img src="https://thumbs.gfycat.com/WeightyLeadingGrub-size_restricted.gif", height="180">
+</p>
+
+## Installation
+
+### Installing both PyRobot and LoCoBot dependencies
+
+* Install **Ubuntu 16.04**
+
+* Download the installation script
+```bash
+sudo apt update
+sudo apt-get install curl
+curl 'https://raw.githubusercontent.com/facebookresearch/pyrobot/master/robots/LoCoBot/install/locobot_install_all.sh' > locobot_install_all.sh
+```
+
+* Run the script to install everything (ROS, realsense driver, etc.). **Please connect the nuc machine to a realsense camera before running the following commands**.
+```bash
+chmod +x locobot_install_all.sh 
+./locobot_install_all.sh
+```
+
+### Installing just PyRobot
+
+* Install **Ubuntu 16.04** 
+
+* Install [ROS kinetic](http://wiki.ros.org/kinetic/Installation/Ubuntu)
+
+* Install KDL
+
+```bash
+sudo apt-get -y install ros-kinetic-orocos-kdl ros-kinetic-kdl-parser-py ros-kinetic-python-orocos-kdl ros-kinetic-trac-ik
+```
+
+* Install Python virtual environment
+
+```bash
+sudo apt-get -y install python-virtualenv
+virtualenv_name="pyenv_pyrobot"
+VIRTUALENV_FOLDER=~/${virtualenv_name}
+virtualenv --system-site-packages -p python2.7 $VIRTUALENV_FOLDER
+```
+
+* Install PyRobot 
+
+```bash
+cd ~
+mkdir -p low_cost_ws/src
+cd ~/low_cost_ws/src
+source ~/${virtualenv_name}/bin/activate
+git clone --recurse-submodules https://github.com/facebookresearch/pyrobot.git
+cd pyrobot/
+pip install .
+```
+
+**Warning**: As realsense keeps updating, compatibility issues might occur if you accidentally update 
+realsense-related packages from `Software Updater` in ubuntu. Therefore, we recommend you not to update
+any libraries related to realsense. Check the list of updates carefully when ubuntu prompts software udpates.
+
+## Getting Started
+Please refer to [pyrobot.org](https://pyrobot.org/) and [locobot.org](http://locobot.org)
+
+## The Team
+
+[Adithya Murali](http://adithyamurali.com/), [Tao Chen](https://taochenshh.github.io), [Dhiraj Gandhi](http://www.cs.cmu.edu/~dgandhi/), Kalyan Vasudev, [Lerrel Pinto](http://www.cs.cmu.edu/~lerrelp/), [Saurabh Gupta](http://saurabhg.web.illinois.edu) and [Abhinav Gupta](http://www.cs.cmu.edu/~abhinavg/). We would also like to thank everyone who has helped PyRobot in any way.
+
+## Future features
+
+We are planning several features, namely:
+* Interfacing with other simulators like [AI Habitat](https://aihabitat.org)
+* Gravity compensation
+* PyRobot interface for [UR5](https://www.universal-robots.com)
+
+## Citation
+```
+@article{pyrobot2019,
+  title={PyRobot: An Open-source Robotics Framework for Research and Benchmarking},
+  author={Adithyavairavan Murali and Tao Chen and Kalyan Vasudev Alwala and Dhiraj Gandhi and Lerrel Pinto and Saurabh Gupta and Abhinav Gupta},
+  journal={arXiv preprint arXiv:1906.08236},
+  year={2019}
+}
+```
+## License
+PyRobot is under MIT license, as found in the LICENSE file.
diff --git a/data/repos/facebookresearch-wav2letter-README.md b/data/repos/facebookresearch-wav2letter-README.md
new file mode 100644
index 00000000..0634a084
--- /dev/null
+++ b/data/repos/facebookresearch-wav2letter-README.md
@@ -0,0 +1,48 @@
+# wav2letter++
+
+[![CircleCI](https://circleci.com/gh/facebookresearch/wav2letter.svg?style=svg)](https://circleci.com/gh/facebookresearch/wav2letter)
+
+wav2letter++ is a fast open source speech processing toolkit from the Speech Team at Facebook AI Research.
+It is written entirely in C++ and uses the [ArrayFire](https://github.com/arrayfire/arrayfire) tensor library and the [flashlight](https://github.com/facebookresearch/flashlight) machine learning library for maximum efficiency.
+Our approach is detailed in this [arXiv paper](https://arxiv.org/abs/1812.07625).
+
+The goal of this software is to facilitate research in end-to-end models for speech recognition.
+
+The previous version of wav2letter (written in Lua) can be found in the "wav2letter-lua" branch under the repository.
+
+## Building wav2letter++
+See [Building Instructions](docs/installation.md) for details.
+
+## Full documentation
+- [Data Preparation](docs/data_prep.md)
+- [Training](docs/train.md)
+- [Testing / Decoding](docs/decoder.md)
+
+To get started with wav2letter++, checkout the [tutorials](tutorials) section.
+
+We also provide complete recipes for WSJ, Timit and Librispeech and they can be found in [recipes](recipes) folder.
+
+## Citation
+
+If you use the code in your paper, then please cite it as:
+
+```
+@article{pratap2018w2l,
+  author          = {Vineel Pratap, Awni Hannun, Qiantong Xu, Jeff Cai, Jacob Kahn, Gabriel Synnaeve, Vitaliy Liptchinsky, Ronan Collobert},
+  title           = {wav2letter++: The Fastest Open-source Speech Recognition System},
+  journal         = {CoRR},
+  volume          = {abs/1812.07625},
+  year            = {2018},
+  url             = {https://arxiv.org/abs/1812.07625},
+}
+```
+
+## Join the wav2letter community
+* Facebook page: https://www.facebook.com/groups/717232008481207/
+* Google group: https://groups.google.com/forum/#!forum/wav2letter-users
+* Contact: vineelkpratap@fb.com, awni@fb.com, qiantong@fb.com, jcai@fb.com, jacobkahn@fb.com, gab@fb.com, vitaliy888@fb.com, locronan@fb.com
+
+See the [CONTRIBUTING](CONTRIBUTING.md) file for how to help out.
+
+## License
+wav2letter++ is BSD-licensed, as found in the [LICENSE](LICENSE) file.
diff --git a/data/repos/gitbucket-gitbucket-README.md b/data/repos/gitbucket-gitbucket-README.md
new file mode 100644
index 00000000..2ab4ef7f
--- /dev/null
+++ b/data/repos/gitbucket-gitbucket-README.md
@@ -0,0 +1,86 @@
+GitBucket [![Gitter chat](https://badges.gitter.im/gitbucket/gitbucket.svg)](https://gitter.im/gitbucket/gitbucket) [![Build Status](https://travis-ci.org/gitbucket/gitbucket.svg?branch=master)](https://travis-ci.org/gitbucket/gitbucket) [![Maven Central](https://maven-badges.herokuapp.com/maven-central/io.github.gitbucket/gitbucket_2.12/badge.svg)](https://maven-badges.herokuapp.com/maven-central/io.github.gitbucket/gitbucket_2.12) [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/gitbucket/gitbucket/blob/master/LICENSE)
+=========
+
+GitBucket is a Git web platform powered by Scala offering:
+
+- Easy installation
+- Intuitive UI
+- High extensibility by plugins
+- API compatibility with GitHub
+
+You can try an [online demo](https://gitbucket.herokuapp.com/) *(ID: root / Pass: root)* of GitBucket, and also get the latest information at [GitBucket News](https://gitbucket.github.io/gitbucket-news/).
+
+Features
+--------
+The current version of GitBucket provides many features such as:
+
+- Public / Private Git repositories (with http/https and ssh access)
+- GitLFS support
+- Repository viewer including an online file editor
+- Issues, Pull Requests and Wiki for repositories
+- Activity timeline and email notifications
+- Account and group management with LDAP integration
+- a Plug-in system
+
+If you want to try the development version of GitBucket, see the [Developer's Guide](https://github.com/gitbucket/gitbucket/blob/master/doc/readme.md).
+
+Installation
+--------
+GitBucket requires **Java8**. You have to install it, if it is not already installed.
+
+1. Download the latest **gitbucket.war** from [the releases page](https://github.com/gitbucket/gitbucket/releases) and run it by `java -jar gitbucket.war`.
+2. Go to `http://[hostname]:8080/` and log in with ID: **root** / Pass: **root**.
+
+You can specify following options:
+
+- `--port=[NUMBER]`
+- `--prefix=[CONTEXTPATH]`
+- `--host=[HOSTNAME]`
+- `--gitbucket.home=[DATA_DIR]`
+- `--temp_dir=[TEMP_DIR]`
+- `--max_file_size=[MAX_FILE_SIZE]`
+
+`TEMP_DIR` is used as the [temporary directory for the jetty application context](https://www.eclipse.org/jetty/documentation/9.3.x/ref-temporary-directories.html). This is the directory into which the `gitbucket.war` file is unpacked, the source files are compiled, etc. If given this parameter **must** match the path of an existing directory or the application will quit reporting an error; if not given the path used will be a `tmp` directory inside the gitbucket home.
+
+`MAX_FILE_SIZE` is the max file size for upload files.
+
+You can also deploy `gitbucket.war` to a servlet container which supports Servlet 3.0 (like Jetty, Tomcat, JBoss, etc)
+
+For more information about installation on Mac or Windows Server (with IIS), or configuration of Apache or Nginx and also integration with other tools or services such as Jenkins or Slack, see [Wiki](https://github.com/gitbucket/gitbucket/wiki).
+
+To upgrade GitBucket, replace `gitbucket.war` with the new version, after stopping GitBucket. All GitBucket data is stored in `HOME/.gitbucket` by default. So if you want to back up GitBucket's data, copy this directory to the backup location.
+
+Plugins
+--------
+GitBucket has a plug-in system that allows extra functionality. Officially the following plug-ins are provided:
+
+- [gitbucket-gist-plugin](https://github.com/gitbucket/gitbucket-gist-plugin)
+- [gitbucket-emoji-plugin](https://github.com/gitbucket/gitbucket-emoji-plugin)
+- [gitbucket-pages-plugin](https://github.com/gitbucket/gitbucket-pages-plugin)
+- [gitbucket-notifications-plugin](https://github.com/gitbucket/gitbucket-notifications-plugin)
+
+You can find more plugins made by the community at [GitBucket community plugins](https://gitbucket-plugins.github.io/).
+
+Support
+--------
+
+- If you have any questions about GitBucket, see [Wiki](https://github.com/gitbucket/gitbucket/wiki) and check issues whether there is a same question or request in the past.
+- If you can't find same question and report, send it to [gitter room](https://gitter.im/gitbucket/gitbucket) before raising an issue.
+- The highest priority of GitBucket is the ease of installation and API compatibility with GitHub, so your feature request might be rejected if they go against those principles.
+
+What's New in 4.31.x
+-------------
+### 4.31.2 - 7 Apr 2019
+- Bug and security fix
+
+### 4.31.1 - 17 Mar 2019
+- Bug fix
+
+### 4.31.0 - 17 Mar 2019
+- Docker support in CI plugin
+- Verify GPG key signed commit
+- OAuth2 Token (sent as a parameter) authentication support and new APIs in Web API
+- OGP (Open Graph protocol) support
+- Username completion with avatars
+
+See the [change log](CHANGELOG.md) for all of the updates.
diff --git a/data/repos/harismuneer-Ultimate-Facebook-Scraper-README.md b/data/repos/harismuneer-Ultimate-Facebook-Scraper-README.md
new file mode 100644
index 00000000..d8d17013
--- /dev/null
+++ b/data/repos/harismuneer-Ultimate-Facebook-Scraper-README.md
@@ -0,0 +1,125 @@
+# 🔥 Ultimate Facebook Scrapper 
+
+[![DOI](https://zenodo.org/badge/145763277.svg)](https://zenodo.org/badge/latestdoi/145763277)
+
+[![Build Status](https://img.shields.io/badge/Build-Passing-brightgreen.svg?style=for-the-badge&logo=appveyor)](#)
+[![Open Source Love svg1](https://badges.frapsoft.com/os/v1/open-source.svg?v=103)](#)
+[![GitHub Forks](https://img.shields.io/github/forks/harismuneer/Ultimate-Facebook-Scraper.svg?style=social&label=Fork&maxAge=2592000)](https://www.github.com/harismuneer/Ultimate-Facebook-Scraper/fork)
+[![GitHub Issues](https://img.shields.io/github/issues/harismuneer/Ultimate-Facebook-Scraper.svg?style=flat&label=Issues&maxAge=2592000)](https://www.github.com/harismuneer/Ultimate-Facebook-Scraper/issues)
+[![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat&label=Contributions&colorA=red&colorB=black	)](#)
+
+
+A bot which scrapes almost everything about a facebook user's profile including
+
+* uploaded photos
+* tagged photos
+* videos
+* friends list and their profile photos (including Followers, Following, Work Friends, College Friends etc)
+* and all public posts/statuses available on the user's timeline.
+
+The best thing about this scraper is that the data is scraped in an organized format so that it can be used for educational/research purpose by researchers. Moreover, this scraper does not use Facebook's Graph API so there are no rate limiting issues as such. 
+
+**This tool is being used by thousands of developers weekly and we are pretty amazed at this response! Thankyou guys!🎉**
+
+For details regarding **citing/referencing** this tool for your research, check the 'Citation' section below.
+
+## Note
+At its core, this tool uses xpaths of **'divs'** to extract data from them. Since Facebook keeps on updating its site frequently and the 'divs' get changed. Consequently, we have to update the divs accordingly to correctly scrape the data. 
+
+The developers of this tool have devoted a lot of time and effort in developing and most importantly maintaining this tool for quite a lot time now. **In order to keep this amazing tool alive, we need support from you geeks.**
+
+The code is pretty intuitive and easy to understand, so you can update the relevant xpaths in the code when you feel that you have tried many profiles and the data isn't being scraped for any of them (that's a hint that Facebook has updated their site) and generate a pull request. That's quite an easy thing to do. Thanks!
+
+## Sample
+<p align="middle">
+  <img src="../master/images/main.png" width="700"/>
+ </p>
+
+
+## Screenshot
+<p align="middle">
+  <img src="../master/images/screenshot.png" width="700"/>
+ </p>
+
+
+----------------------------------------------------------------------------------------------------------------------------------------
+## Usage
+
+### Installation
+You will need to install latest version of [Google Chrome](https://www.google.com/chrome/). Moreover, you need to install selenium module as well using
+
+```
+pip install selenium
+```
+
+Run the code using Python 3. Also, the code is multi-platform and is tested on both Windows and Linux.
+The tool uses latest version of [Chrome Web Driver](http://chromedriver.chromium.org/downloads). I have placed the webdriver along with the code but if that version doesn't work then replace the chrome web driver with the latest one.
+
+### How to Run
+There's a file named "input.txt". You can add as many profiles as you want in the following format with each link on a new line:
+
+```
+https://www.facebook.com/andrew.ng.96
+https://www.facebook.com/zuck
+```
+
+Make sure the link only contains the username or id number at the end and not any other stuff. Make sure its in the format mentioned above.
+
+Note: There are two modes to download Friends Profile Pics and the user's Photos: Large Size and Small Size. You can change the following variables. By default they are set to Small Sized Pics because its really quick while Large Size Mode takes time depending on the number of pictures to download
+
+```
+# whether to download the full image or its thumbnail (small size)
+# if small size is True then it will be very quick else if its False then it will open each photo to download it
+# and it will take much more time
+friends_small_size = True
+photos_small_size = True
+```
+----------------------------------------------------------------------------------------------------------------------------------------
+
+## Citation
+
+[![DOI](https://zenodo.org/badge/145763277.svg)](https://zenodo.org/badge/latestdoi/145763277)
+
+If you use this tool for your research, then kindly cite it. Click the above badge for more information regarding the complete citation for this tool and diffferent citation formats like IEEE, APA etc.
+
+
+
+
+----------------------------------------------------------------------------------------------------------------------------------------
+
+## Important Message
+This tool is for research purposes only. Hence, the developers of this tool won't be responsible for any misuse of data collected using this tool. 
+
+----------------------------------------------------------------------------------------------------------------------------------------
+
+## Authors
+You can get in touch with us on our LinkedIn Profiles:
+
+#### Haris Muneer
+[![LinkedIn Link](https://img.shields.io/badge/Connect-harismuneer-blue.svg?logo=linkedin&longCache=true&style=social&label=Connect
+)](https://www.linkedin.com/in/harismuneer)
+
+You can also follow my GitHub Profile to stay updated about my latest projects: [![GitHub Follow](https://img.shields.io/badge/Connect-harismuneer-blue.svg?logo=Github&longCache=true&style=social&label=Follow)](https://github.com/harismuneer)
+
+#### Hassaan Elahi
+[![LinkedIn Link](https://img.shields.io/badge/Connect-Hassaan--Elahi-blue.svg?logo=linkedin&longCache=true&style=social&label=Connect)](https://www.linkedin.com/in/hassaan-elahi/)
+
+You can also follow my GitHub Profile to stay updated about my latest projects:[![GitHub Follow](https://img.shields.io/badge/Connect-Hassaan--Elahi-blue.svg?logo=Github&longCache=true&style=social&label=Follow)](https://github.com/Hassaan-Elahi)
+
+
+If you liked the repo then kindly support it by giving it a star ⭐!
+
+## Contributions Welcome
+[![forthebadge](https://forthebadge.com/images/badges/built-with-love.svg)](#)
+
+If you find any bug in the code or have any improvements in mind then feel free to generate a pull request.
+
+## Issues
+[![GitHub Issues](https://img.shields.io/github/issues/harismuneer/Ultimate-Facebook-Scraper.svg?style=flat&label=Issues&maxAge=2592000)](https://www.github.com/harismuneer/Ultimate-Facebook-Scraper/issues)
+
+If you face any issue, you can create a new issue in the Issues Tab and I will be glad to help you out.
+
+## License
+[![MIT](https://img.shields.io/cocoapods/l/AFNetworking.svg?style=style&label=License&maxAge=2592000)](../master/LICENSE)
+
+Copyright (c) 2018-present, harismuneer, Hassaan-Elahi                                                        
diff --git a/data/repos/microsoft-malmo-README.md b/data/repos/microsoft-malmo-README.md
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+# Malmö #
+
+Project Malmö is a platform for Artificial Intelligence experimentation and research built on top of Minecraft. We aim to inspire a new generation of research into challenging new problems presented by this unique environment.
+
+[![Join the chat at https://gitter.im/Microsoft/malmo](https://badges.gitter.im/Microsoft/malmo.svg)](https://gitter.im/Microsoft/malmo?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) [![Build Status](https://travis-ci.org/Microsoft/malmo.svg?branch=master)](https://travis-ci.org/Microsoft/malmo) [![license](https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000)](https://github.com/Microsoft/malmo/blob/master/LICENSE.txt)
+----
+    
+## Getting Started ##
+
+### MalmoEnv ###
+
+MalmoEnv implements an Open AI "gym"-like environment in Python without any native code (communicating directly with Java Minecraft). If you only need this functionallity then please see [MalmoEnv](https://github.com/Microsoft/malmo/tree/master/MalmoEnv). This will most likely be the preferred way to develop with Malmo Minecraft going forward.
+
+If you wish to use the "native" Malmo implementation, either install the "Malmo native Python wheel" (if available for your platform) or a pre-built binary release (more on these options below). Building Malmo yourself from source is always an option!
+
+Advantages:
+    
+1. No native code - you don't have to build or install platform dependent code.
+2. A single network connection is used to run missions. No dynamic ports means it's more virtualization friendly.
+3. A simpler multi-agent coordination protocol. 
+One Minecraft client instance, one single port is used to start missions.
+4. Less impedance miss-match with the gym api.
+
+Disadvantages:
+
+1. The existing Malmo examples are not supported (as API used is different). 
+Marlo envs should work with this [port](https://github.com/AndKram/marLo/tree/malmoenv).
+2. The API is more limited (e.g. selecting video options) - can edit mission xml directly.
+
+### Malmo as a native Python wheel ###
+
+On common Windows, MacOSX and Linux variants it is possible to use ```pip3 install malmo``` to install Malmo as a python with native code package: [Pip install for Malmo](https://github.com/Microsoft/malmo/blob/master/scripts/python-wheel/README.md). Once installed, the malmo Python module can be used to download source and examples and start up Minecraft with the Malmo game mod. 
+
+Alternatively, a pre-built version of Malmo can be installed as follows:
+
+1. [Download the latest *pre-built* version, for Windows, Linux or MacOSX.](https://github.com/Microsoft/malmo/releases)   
+      NOTE: This is _not_ the same as downloading a zip of the source from Github. _Doing this **will not work** unless you are planning to build the source code yourself (which is a lengthier process). If you get errors along the lines of "`ImportError: No module named MalmoPython`" it will probably be because you have made this mistake._
+
+2. Install the dependencies for your OS: [Windows](doc/install_windows.md), [Linux](doc/install_linux.md), [MacOSX](doc/install_macosx.md).
+
+3. Launch Minecraft with our Mod installed. Instructions below.
+
+4. Launch one of our sample agents, as Python, C#, C++ or Java. Instructions below.
+
+5. Follow the [Tutorial](https://github.com/Microsoft/malmo/blob/master/Malmo/samples/Python_examples/Tutorial.pdf) 
+
+6. Explore the [Documentation](http://microsoft.github.io/malmo/). This is also available in the readme.html in the release zip.
+
+7. Read the [Blog](http://microsoft.github.io/malmo/blog) for more information.
+
+If you want to build from source then see the build instructions for your OS: [Windows](doc/build_windows.md), [Linux](doc/build_linux.md), [MacOSX](doc/build_macosx.md).
+
+----
+
+## Problems: ##
+
+We're building up a [Troubleshooting](https://github.com/Microsoft/malmo/wiki/Troubleshooting) page of the wiki for frequently encountered situations. If that doesn't work then please ask a question on our [chat page](https://gitter.im/Microsoft/malmo) or open a [new issue](https://github.com/Microsoft/malmo/issues/new).
+
+----
+
+## Launching Minecraft with our Mod: ##
+
+Minecraft needs to create windows and render to them with OpenGL, so the machine you do this from must have a desktop environment.
+
+Go to the folder where you unzipped the release, then:
+
+`cd Minecraft`  
+`launchClient` (On Windows)  
+`./launchClient.sh` (On Linux or MacOSX)
+
+or, e.g. `launchClient -port 10001` to launch Minecraft on a specific port.
+
+on Linux or MacOSX: `./launchClient.sh -port 10001`
+
+*NB: If you run this from a terminal, the bottom line will say something like "Building 95%" - ignore this - don't wait for 100%! As long as a Minecraft game window has opened and is displaying the main menu, you are good to go.*
+
+By default the Mod chooses port 10000 if available, and will search upwards for a free port if not, up to 11000.
+The port chosen is shown in the Mod config page.
+
+To change the port while the Mod is running, use the `portOverride` setting in the Mod config page.
+
+The Mod and the agents use other ports internally, and will find free ones in the range 10000-11000 so if administering
+a machine for network use these TCP ports should be open.
+
+----
+
+## Launch an agent: ##
+
+#### Running a Python agent: ####
+
+```
+cd Python_Examples
+python3 run_mission.py
+``` 
+
+#### Running a C++ agent: ####
+
+`cd Cpp_Examples`
+
+To run the pre-built sample:
+
+`run_mission` (on Windows)  
+`./run_mission` (on Linux or MacOSX)
+
+To build the sample yourself:
+
+`cmake .`  
+`cmake --build .`  
+`./run_mission` (on Linux or MacOSX)  
+`Debug\run_mission.exe` (on Windows)
+
+#### Running a C# agent: ####
+
+To run the pre-built sample (on Windows):
+
+`cd CSharp_Examples`  
+`CSharpExamples_RunMission.exe`
+
+To build the sample yourself, open CSharp_Examples/RunMission.csproj in Visual Studio.
+
+Or from the command-line:
+
+`cd CSharp_Examples`
+
+Then, on Windows:  
+```
+msbuild RunMission.csproj /p:Platform=x64
+bin\x64\Debug\CSharpExamples_RunMission.exe
+```
+
+#### Running a Java agent: ####
+
+`cd Java_Examples`  
+`java -cp MalmoJavaJar.jar:JavaExamples_run_mission.jar -Djava.library.path=. JavaExamples_run_mission` (on Linux or MacOSX)  
+`java -cp MalmoJavaJar.jar;JavaExamples_run_mission.jar -Djava.library.path=. JavaExamples_run_mission` (on Windows)
+
+#### Running an Atari agent: (Linux only) ####
+
+```
+cd Python_Examples
+python3 ALE_HAC.py
+```
+
+----
+
+# Citations #
+
+Please cite Malmo as:
+
+Johnson M., Hofmann K., Hutton T., Bignell D. (2016) [_The Malmo Platform for Artificial Intelligence Experimentation._](http://www.ijcai.org/Proceedings/16/Papers/643.pdf) [Proc. 25th International Joint Conference on Artificial Intelligence](http://www.ijcai.org/Proceedings/2016), Ed. Kambhampati S., p. 4246. AAAI Press, Palo Alto, California USA. https://github.com/Microsoft/malmo
+
+----
+
+# Code of Conduct #
+
+This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
diff --git a/data/repos/nextflow-io-nextflow-README.md b/data/repos/nextflow-io-nextflow-README.md
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+![Nextflow logo](https://github.com/nextflow-io/trademark/blob/master/nextflow2014_no-bg.png)
+
+*"Dataflow variables are spectacularly expressive in concurrent programming"*
+<br>[Henri E. Bal , Jennifer G. Steiner , Andrew S. Tanenbaum](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.145.7873)
+
+
+[![Chat on Gitter](https://img.shields.io/gitter/room/nextflow-io/nextflow.svg?colorB=26af64&style=popout)](https://gitter.im/nextflow-io/nextflow)
+[![Nextflow version](https://img.shields.io/github/release/nextflow-io/nextflow.svg?colorB=26af64&style=popout)](https://github.com/nextflow-io/nextflow/releases/latest)
+[![Nextflow Twitter](https://img.shields.io/twitter/url/https/nextflowio.svg?colorB=26af64&&label=%40nextflow&style=popout)](https://twitter.com/nextflowio)
+[![Nextflow Publication](https://img.shields.io/badge/Published-Nature%20Biotechnology-26af64.svg?colorB=26af64&style=popout)](https://www.nature.com/articles/nbt.3820)
+[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?colorB=26af64&style=popout)](http://bioconda.github.io/recipes/nextflow/README.html)
+[![Nextflow license](https://img.shields.io/github/license/nextflow-io/nextflow.svg?colorB=26af64&style=popout)](https://github.com/nextflow-io/nextflow/blob/master/COPYING)
+
+Quick overview
+==============
+Nextflow is a bioinformatics workflow manager that enables the development of portable and reproducible workflows.
+It supports deploying workflows on a variety of execution platforms including local, HPC schedulers, AWS Batch,
+Google Genomics Pipelines, and Kubernetes. Additionally, it provides support for manage your workflow dependencies
+through built-in support for Conda, Docker, Singularity, and Modules.
+
+## Contents
+- [Rationale](#rationale)
+- [Quick start](#quick-start)
+- [Documentation](#documentation)
+- [Tool Management](#tool-management)
+  - [Conda environments](#conda-environments)
+  - [Docker and Singularity](#containers)
+  - [Environment Modules](#environment-modules)
+- [HPC Schedulers](#hpc-schedulers)
+  - [SGE](#hpc-schedulers)
+  - [Univa Grid Engine](#hpc-schedulers)
+  - [LSF](#hpc-schedulers)
+  - [SLURM](#hpc-schedulers)
+  - [PBS/Torque](#hpc-schedulers)
+  - [HTCondor (experimental)](#hpc-schedulers)
+- [Cloud Support](#cloud-support)
+  - [AWS Batch](#cloud-support)
+  - [AWS EC2](#cloud-support)
+  - [Google Cloud](#cloud-support)
+  - [Google Genomics Pipelines](#cloud-support)
+  - [Kubernetes](#cloud-support)
+- [Community](#community)
+- [Build from source](#build-from-source)
+- [Contributing](#contributing)
+- [License](#license)
+- [Citations](#citations)
+- [Credits](#credits)
+
+
+Rationale
+=========
+
+With the rise of big data, techniques to analyse and run experiments on large datasets are increasingly necessary.
+
+Parallelization and distributed computing are the best ways to tackle this problem, but the tools commonly available to the bioinformatics community often lack good support for these techniques, or provide a model that fits badly with the specific requirements in the bioinformatics domain and, most of the time, require the knowledge of complex tools or low-level APIs.
+
+Nextflow framework is based on the dataflow programming model, which greatly simplifies writing parallel and distributed pipelines without adding unnecessary complexity and letting you concentrate on the flow of data, i.e. the functional logic of the application/algorithm.
+
+It doesn't aim to be another pipeline scripting language yet, but it is built around the idea that the Linux platform is the *lingua franca* of data science, since it provides many simple command line and scripting tools, which by themselves are powerful, but when chained together facilitate complex data manipulations.
+
+In practice, this means that a Nextflow script is defined by composing many different processes. Each process can execute a given bioinformatics tool or scripting language, to which is added the ability to coordinate and synchronize the processes execution by simply specifying their inputs and outputs.
+
+
+
+Quick start
+============
+
+Download the package
+--------------------
+
+Nextflow does not require any installation procedure, just download the distribution package by copying and pasting
+this command in your terminal:
+
+```
+curl -fsSL https://get.nextflow.io | bash
+```
+
+It creates the ``nextflow`` executable file in the current directory. You may want to move it to a folder accessible from your ``$PATH``.
+
+Download from Conda
+-------------------
+
+Nextflow can also be installed from Bioconda
+
+```
+conda install -c bioconda nextflow 
+```
+
+Documentation
+=============
+
+Nextflow documentation is available at this link http://docs.nextflow.io
+
+
+HPC Schedulers
+==============
+
+*Nextflow* supports common HPC schedulers, abstracting the submission of jobs from the user. 
+
+Currently the following clusters are supported:
+
+  + [SGE](https://www.nextflow.io/docs/latest/executor.html#sge)
+  + [Univa Grid Engine](https://www.nextflow.io/docs/latest/executor.html#sge)
+  + [LSF](https://www.nextflow.io/docs/latest/executor.html#lsf)
+  + [SLURM](https://www.nextflow.io/docs/latest/executor.html#slurm)
+  + [PBS/Torque](https://www.nextflow.io/docs/latest/executor.html#pbs-torque)
+  + [HTCondor (experimental)](https://www.nextflow.io/docs/latest/executor.html#htcondor)
+
+For example to submit the execution to a SGE cluster create a file named `nextflow.config`, in the directory
+where the pipeline is going to be launched, with the following content:
+
+```nextflow
+process {
+  executor='sge'
+  queue='<your execution queue>'
+}
+```
+
+In doing that, processes will be executed by Nextflow as SGE jobs using the `qsub` command. Your 
+pipeline will behave like any other SGE job script, with the benefit that *Nextflow* will 
+automatically and transparently manage the processes synchronisation, file(s) staging/un-staging, etc.  
+
+
+Cloud support
+=============
+*Nextflow* also supports running workflows across various clouds and cloud technologies. *Nextflow* can create AWS EC2 or Google GCE clusters and deploy your workflow. Managed solutions from both Amazon and Google are also supported through AWS Batch and Google Genomics Pipelines. Additionally, *Nextflow* can run workflows on either on-prem or managed cloud Kubernetes clusters. 
+
+Currently supported cloud platforms:
+  + [AWS Batch](https://www.nextflow.io/docs/latest/awscloud.html#aws-batch)
+  + [AWS EC2](https://www.nextflow.io/docs/latest/awscloud.html)
+  + [Google GCE](https://www.nextflow.io/docs/latest/google.html)
+  + [Google Genomics Pipelines](https://www.nextflow.io/docs/latest/google.html#google-pipelines)
+  + [Kubernetes](https://www.nextflow.io/docs/latest/kubernetes.html)
+
+
+
+Tool management
+================
+
+Containers
+----------------
+
+*Nextflow* has first class support for containerization. It supports both [Docker](https://www.nextflow.io/docs/latest/docker.html) and [Singularity](https://www.nextflow.io/docs/latest/singularity.html) container engines. Additionally, *Nextflow* can easily switch between container engines enabling workflow portability. 
+
+```nextflow
+process samtools {
+  container 'biocontainers/samtools:1.3.1'
+
+  """
+  samtools --version 
+  """
+
+}
+```
+
+Conda environments
+------------------
+
+[Conda environments](https://www.nextflow.io/docs/latest/conda.html) provide another option for managing software packages in your workflow. 
+
+
+Environment Modules
+-------
+
+[Environment modules](https://www.nextflow.io/docs/latest/process.html#module) commonly found in HPC environments can also be used to manage the tools used in a *Nextflow* workflow. 
+
+
+Community
+=========
+
+You can post questions, or report problems by using the Nextflow [discussion forum](https://groups.google.com/forum/#!forum/nextflow)
+or the [Nextflow channel on Gitter](https://gitter.im/nextflow-io/nextflow).
+
+*Nextflow* also hosts a yearly workshop showcasing researcher's workflows and advancements in the langauge. Talks from the past workshops are available on the [Nextflow YouTube Channel](https://www.youtube.com/channel/UCB-5LCKLdTKVn2F4V4KlPbQ)
+
+The [nf-core](https://nf-co.re/) project is a community effort aggregating high quality *Nextflow* workflows which can be used by the community. 
+
+
+Build from source
+=================
+
+Required dependencies
+---------------------
+
+* Compiler Java 8
+* Runtime Java 8 or later
+
+Build from source
+-----------------
+
+*Nextflow* is written in [Groovy](http://groovy-lang.org) (a scripting language for the JVM). A pre-compiled,
+ready-to-run, package is available at the [Github releases page](https://github.com/nextflow-io/nextflow/releases),
+thus it is not necessary to compile it in order to use it.
+
+If you are interested in modifying the source code, or contributing to the project, it worth knowing that
+the build process is based on the [Gradle](http://www.gradle.org/) build automation system.
+
+You can compile *Nextflow* by typing the following command in the project home directory on your computer:
+
+```bash
+make compile
+```
+
+The very first time you run it, it will automatically download all the libraries required by the build process.
+It may take some minutes to complete.
+
+When complete, execute the program by using the `launch.sh` script in the project directory.
+
+The self-contained runnable Nextflow packages can be created by using the following command:
+
+```bash
+make pack
+```
+
+In order to install the compiled packages use the following command:
+
+```bash
+make install
+```
+
+Then you will be able to run nextflow using the `nextflow` launcher script in the project root folder.
+
+Known compilation problems
+---------------------------
+
+Nextflow required JDK 8 to be compiled. The Java compiler used by the build process can be choose by setting the
+`JAVA_HOME` environment variable accordingly.
+
+
+If the compilation stops reporting the error: `java.lang.VerifyError: Bad <init> method call from inside of a branch`,
+this is due to a bug affecting the following Java JDK:
+
+- 1.8.0 update 11
+- 1.8.0 update 20
+
+Upgrade to a newer JDK to avoid to this issue. Alternatively a possible workaround is to define the following variable
+in your environment:
+
+```bash
+_JAVA_OPTIONS='-Xverify:none'
+```
+
+Read more at these links:
+
+- https://bugs.openjdk.java.net/browse/JDK-8051012
+- https://jira.codehaus.org/browse/GROOVY-6951
+
+
+IntelliJ IDEA
+---------------
+
+Nextflow development with [IntelliJ IDEA](https://www.jetbrains.com/idea/) requires the latest version of the IDE (2019.1.2 or later).
+
+If you have it installed in your computer, follow the steps below in order to use it with Nextflow:
+
+1. Clone the Nextflow repository to a directory in your computer.
+2. Open IntelliJ IDEA and choose "Import project" in the "File" menu bar.
+3. Select the Nextflow project root directory in your computer and click "OK".
+4. Then, choose the "Gradle" item in the "external module" list and click on "Next" button.
+5. Confirm the default import options and click on "Finish" to finalize the project configuration.
+6. When the import process complete, select the "Project structure" command in the "File" menu bar.
+7. In the showed dialog click on the "Project" item in the list of the left, and make sure that
+   the "Project SDK" choice on the right contains Java 8.
+8. Set the code formatting options with setting provided [here](https://github.com/nextflow-io/nextflow/blob/master/CONTRIBUTING.md#ide-settings).
+
+
+
+Contributing
+============
+
+Project contribution are more than welcome. See the [CONTRIBUTING](CONTRIBUTING.md) file for details.
+
+
+Build servers
+=============
+
+  * [Travis-CI](https://travis-ci.org/nextflow-io/nextflow)
+  * [Groovy Joint build](http://ci.groovy-lang.org/project.html?projectId=JointBuilds_Nextflow&guest=1)
+
+License
+=======
+
+The *Nextflow* framework is released under the Apache 2.0 license.
+
+Citations
+=========
+
+If you use Nextflow in your research, please cite:
+
+P. Di Tommaso, et al. Nextflow enables reproducible computational workflows. Nature Biotechnology 35, 316–319 (2017) doi:[10.1038/nbt.3820](http://www.nature.com/nbt/journal/v35/n4/full/nbt.3820.html)
+
+Credits
+=======
+
+Nextflow is built on two great pieces of open source software, namely <a href='http://groovy-lang.org' target='_blank'>Groovy</a>
+and <a href='http://www.gpars.org/' target='_blank'>Gpars</a>.
+
+YourKit is kindly supporting this open source project with its full-featured Java Profiler.
+Read more http://www.yourkit.com
diff --git a/data/repos/pyro-ppl-pyro-README.md b/data/repos/pyro-ppl-pyro-README.md
new file mode 100644
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--- /dev/null
+++ b/data/repos/pyro-ppl-pyro-README.md
@@ -0,0 +1,93 @@
+<div align="center">
+  <a href="http://pyro.ai"> <img width="220px" height="220px" src="docs/source/_static/img/pyro_logo_with_text.png"></a>
+</div>
+
+-----------------------------------------
+
+[![Build Status](https://travis-ci.com/pyro-ppl/pyro.svg?branch=dev)](https://travis-ci.com/uber/pyro)
+[![codecov.io](https://codecov.io/github/pyro-ppl/pyro/branch/dev/graph/badge.svg)](https://codecov.io/github/uber/pyro)
+[![Latest Version](https://badge.fury.io/py/pyro-ppl.svg)](https://pypi.python.org/pypi/pyro-ppl)
+[![Documentation Status](https://readthedocs.org/projects/pyro-ppl/badge/?version=dev)](http://pyro-ppl.readthedocs.io/en/stable/?badge=dev)
+
+[Getting Started](http://pyro.ai/examples) |
+[Documentation](http://docs.pyro.ai/) |
+[Community](http://forum.pyro.ai/) |
+[Contributing](https://github.com/pyro-ppl/pyro/blob/master/CONTRIBUTING.md)
+
+Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch.  Notably, it was designed with these principles in mind:
+
+- **Universal**: Pyro is a universal PPL - it can represent any computable probability distribution.
+- **Scalable**: Pyro scales to large data sets with little overhead compared to hand-written code.
+- **Minimal**: Pyro is agile and maintainable. It is implemented with a small core of powerful, composable abstractions.
+- **Flexible**: Pyro aims for automation when you want it, control when you need it. This is accomplished through high-level abstractions to express generative and inference models, while allowing experts easy-access to customize inference.
+
+Pyro is in a beta release.  It is developed and maintained by [Uber AI Labs](http://uber.ai) and community contributors.
+For more information, check out our [blog post](http://eng.uber.com/pyro).
+
+## Installing
+
+### Installing a stable Pyro release
+
+**Install using pip:**
+
+Pyro supports Python 3.4+.
+
+```sh
+pip install pyro-ppl
+```
+
+**Install from source:**
+```sh
+git clone git@github.com:pyro-ppl/pyro.git
+cd pyro
+git checkout master  # master is pinned to the latest release
+pip install .
+```
+
+**Install with extra packages:**
+
+To install the dependencies required to run the probabilistic models included in the `examples`/`tutorials` directories, please use the following command:
+```sh
+pip install pyro-ppl[extras] 
+```
+Make sure that the models come from the same release version of the [Pyro source code](https://github.com/pyro-ppl/pyro/releases) as you have installed.
+
+### Installing Pyro dev branch
+
+For recent features you can install Pyro from source.
+
+**Install using pip:**
+
+```sh
+pip install git+https://github.com/pyro-ppl/pyro.git
+```
+
+or, with the `extras` dependency to run the probabilistic models included in the `examples`/`tutorials` directories:
+```sh
+pip install git+https://github.com/pyro-ppl/pyro.git#egg=project[extras]
+```
+
+**Install from source:**
+
+```sh
+git clone https://github.com/pyro-ppl/pyro
+cd pyro
+pip install .  # pip install .[extras] for running models in examples/tutorials
+```
+
+## Running Pyro from a Docker Container
+
+Refer to the instructions [here](docker/README.md).
+
+## Citation
+If you use Pyro, please consider citing:
+```
+@article{bingham2018pyro,
+  author = {Bingham, Eli and Chen, Jonathan P. and Jankowiak, Martin and Obermeyer, Fritz and
+            Pradhan, Neeraj and Karaletsos, Theofanis and Singh, Rohit and Szerlip, Paul and
+            Horsfall, Paul and Goodman, Noah D.},
+  title = {{Pyro: Deep Universal Probabilistic Programming}},
+  journal = {arXiv preprint arXiv:1810.09538},
+  year = {2018}
+}
+```
diff --git a/data/repos/reduxjs-react-redux-README.md b/data/repos/reduxjs-react-redux-README.md
new file mode 100644
index 00000000..b5a1e1cb
--- /dev/null
+++ b/data/repos/reduxjs-react-redux-README.md
@@ -0,0 +1,49 @@
+React Redux
+=========================
+
+Official React bindings for [Redux](https://github.com/reduxjs/redux).  
+Performant and flexible.
+
+[![build status](https://img.shields.io/travis/reduxjs/react-redux/master.svg?style=flat-square)](https://travis-ci.org/reduxjs/react-redux) [![npm version](https://img.shields.io/npm/v/react-redux.svg?style=flat-square)](https://www.npmjs.com/package/react-redux)
+[![npm downloads](https://img.shields.io/npm/dm/react-redux.svg?style=flat-square)](https://www.npmjs.com/package/react-redux)
+[![redux channel on discord](https://img.shields.io/badge/discord-redux@reactiflux-61DAFB.svg?style=flat-square)](http://www.reactiflux.com)
+
+
+## Installation
+
+React Redux requires **React 16.8.3 or later.**
+
+```
+npm install --save react-redux
+```
+
+This assumes that you’re using [npm](http://npmjs.com/) package manager 
+with a module bundler like [Webpack](https://webpack.js.org/) or 
+[Browserify](http://browserify.org/) to consume [CommonJS 
+modules](https://webpack.js.org/api/module-methods/#commonjs).
+
+If you don’t yet use [npm](http://npmjs.com/) or a modern module bundler, and would rather prefer a single-file [UMD](https://github.com/umdjs/umd) build that makes `ReactRedux` available as a global object, you can grab a pre-built version from [cdnjs](https://cdnjs.com/libraries/react-redux). We *don’t* recommend this approach for any serious application, as most of the libraries complementary to Redux are only available on [npm](http://npmjs.com/).
+
+## React Native
+
+As of React Native 0.18, React Redux 5.x should work with React Native. If you have any issues with React Redux 5.x on React Native, run `npm ls react` and make sure you don’t have a duplicate React installation in your `node_modules`. We recommend that you use `npm@3.x` which is better at avoiding these kinds of issues.
+
+
+## Documentation
+
+The React Redux docs are now published at **https://react-redux.js.org** .
+
+We're currently expanding and rewriting our docs content - check back soon for more updates!
+
+## How Does It Work?
+
+We do a deep dive on how React Redux works in [this readthesource episode](https://www.youtube.com/watch?v=VJ38wSFbM3A).  
+
+Also, the post [The History and Implementation of React-Redux](https://blog.isquaredsoftware.com/2018/11/react-redux-history-implementation/) 
+explains what it does, how it works, and how the API and implementation have evolved over time.
+
+Enjoy!
+
+## License
+
+MIT
diff --git a/data/repos/scikit-image-scikit-image-README.md b/data/repos/scikit-image-scikit-image-README.md
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+++ b/data/repos/scikit-image-scikit-image-README.md
@@ -0,0 +1,86 @@
+# scikit-image: Image processing in Python
+[![Image.sc forum](https://img.shields.io/badge/dynamic/json.svg?label=forum&url=https%3A%2F%2Fforum.image.sc%2Ftags%2Fscikit-image.json&query=%24.topic_list.tags.0.topic_count&colorB=brightgreen&suffix=%20topics&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAA4AAAAOCAYAAAAfSC3RAAABPklEQVR42m3SyyqFURTA8Y2BER0TDyExZ+aSPIKUlPIITFzKeQWXwhBlQrmFgUzMMFLKZeguBu5y+//17dP3nc5vuPdee6299gohUYYaDGOyyACq4JmQVoFujOMR77hNfOAGM+hBOQqB9TjHD36xhAa04RCuuXeKOvwHVWIKL9jCK2bRiV284QgL8MwEjAneeo9VNOEaBhzALGtoRy02cIcWhE34jj5YxgW+E5Z4iTPkMYpPLCNY3hdOYEfNbKYdmNngZ1jyEzw7h7AIb3fRTQ95OAZ6yQpGYHMMtOTgouktYwxuXsHgWLLl+4x++Kx1FJrjLTagA77bTPvYgw1rRqY56e+w7GNYsqX6JfPwi7aR+Y5SA+BXtKIRfkfJAYgj14tpOF6+I46c4/cAM3UhM3JxyKsxiOIhH0IO6SH/A1Kb1WBeUjbkAAAAAElFTkSuQmCC)](https://forum.image.sc/tags/scikit-image)
+[![Stackoverflow](https://img.shields.io/badge/stackoverflow-Ask%20questions-blue.svg)](https://stackoverflow.com/questions/tagged/scikit-image)
+[![project chat](https://img.shields.io/badge/zulip-join_chat-brightgreen.svg)](https://skimage.zulipchat.com)
+[![codecov.io](https://codecov.io/github/scikit-image/scikit-image/coverage.svg?branch=master)](https://codecov.io/github/scikit-image/scikit-image?branch=master)
+
+- **Website (including documentation):** [https://scikit-image.org/](https://scikit-image.org)
+- **Mailing list:** [https://mail.python.org/mailman/listinfo/scikit-image](https://mail.python.org/mailman/listinfo/scikit-image)
+- **Source:** [https://github.com/scikit-image/scikit-image](https://github.com/scikit-image/scikit-image)
+- **Benchmarks:** [https://pandas.pydata.org/speed/scikit-image/](https://pandas.pydata.org/speed/scikit-image/)
+
+## Installation from binaries
+
+- **Debian/Ubuntu:** ``sudo apt-get install python-skimage``
+- **OSX:** ``pip install scikit-image``
+- **Anaconda:** ``conda install -c conda-forge scikit-image``
+- **Windows:** Download [Windows binaries](http://www.lfd.uci.edu/~gohlke/pythonlibs/#scikit-image)
+
+Also see [installing ``scikit-image``](INSTALL.rst).
+
+## Installation from source
+
+Install dependencies using:
+
+```
+pip install -r requirements.txt
+```
+
+Then, install scikit-image using:
+
+```
+$ pip install .
+```
+
+If you plan to develop the package, you may run it directly from source:
+
+```
+$ pip install -e .  # Do this once to add package to Python path
+```
+
+Every time you modify Cython files, also run:
+
+```
+$ python setup.py build_ext -i  # Build binary extensions
+```
+
+## License (Modified BSD)
+
+Copyright (C) 2011, the scikit-image team
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+ 1. Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+ 2. Redistributions in binary form must reproduce the above copyright
+    notice, this list of conditions and the following disclaimer in
+    the documentation and/or other materials provided with the
+    distribution.
+ 3. Neither the name of skimage nor the names of its contributors may be
+    used to endorse or promote products derived from this software without
+    specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
+
+## Citation
+
+If you find this project useful, please cite:
+
+> Stéfan van der Walt, Johannes L. Schönberger, Juan Nunez-Iglesias,
+> François Boulogne, Joshua D. Warner, Neil Yager, Emmanuelle
+> Gouillart, Tony Yu, and the scikit-image contributors.
+> *scikit-image: Image processing in Python*. PeerJ 2:e453 (2014)
+> https://doi.org/10.7717/peerj.453
diff --git a/data/repos/scikit-learn-scikit-learn-README.md b/data/repos/scikit-learn-scikit-learn-README.md
new file mode 100644
index 00000000..51f7a2cb
--- /dev/null
+++ b/data/repos/scikit-learn-scikit-learn-README.md
@@ -0,0 +1,170 @@
+.. -*- mode: rst -*-
+
+|Azure|_ |Travis|_ |Codecov|_ |CircleCI|_ |Python35|_ |PyPi|_ |DOI|_
+
+.. |Azure| image:: https://dev.azure.com/scikit-learn/scikit-learn/_apis/build/status/scikit-learn.scikit-learn?branchName=master
+.. _Azure: https://dev.azure.com/scikit-learn/scikit-learn/_build/latest?definitionId=1&branchName=master
+
+.. |Travis| image:: https://api.travis-ci.org/scikit-learn/scikit-learn.svg?branch=master
+.. _Travis: https://travis-ci.org/scikit-learn/scikit-learn
+
+.. |Codecov| image:: https://codecov.io/github/scikit-learn/scikit-learn/badge.svg?branch=master&service=github
+.. _Codecov: https://codecov.io/github/scikit-learn/scikit-learn?branch=master
+
+.. |CircleCI| image:: https://circleci.com/gh/scikit-learn/scikit-learn/tree/master.svg?style=shield&circle-token=:circle-token
+.. _CircleCI: https://circleci.com/gh/scikit-learn/scikit-learn
+
+.. |Python35| image:: https://img.shields.io/badge/python-3.5-blue.svg
+.. _Python35: https://badge.fury.io/py/scikit-learn
+
+.. |PyPi| image:: https://badge.fury.io/py/scikit-learn.svg
+.. _PyPi: https://badge.fury.io/py/scikit-learn
+
+.. |DOI| image:: https://zenodo.org/badge/21369/scikit-learn/scikit-learn.svg
+.. _DOI: https://zenodo.org/badge/latestdoi/21369/scikit-learn/scikit-learn
+
+scikit-learn
+============
+
+scikit-learn is a Python module for machine learning built on top of
+SciPy and is distributed under the 3-Clause BSD license.
+
+The project was started in 2007 by David Cournapeau as a Google Summer
+of Code project, and since then many volunteers have contributed. See
+the `About us <http://scikit-learn.org/dev/about.html#authors>`_ page
+for a list of core contributors.
+
+It is currently maintained by a team of volunteers.
+
+Website: http://scikit-learn.org
+
+
+Installation
+------------
+
+Dependencies
+~~~~~~~~~~~~
+
+scikit-learn requires:
+
+- Python (>= 3.5)
+- NumPy (>= 1.11.0)
+- SciPy (>= 0.17.0)
+- joblib (>= 0.11)
+
+**Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4.**
+scikit-learn 0.21 and later require Python 3.5 or newer.
+
+Scikit-learn plotting capabilities (i.e., functions start with "plot_") require
+Matplotlib (>= 1.5.1). For running the examples Matplotlib >= 1.5.1 is
+required. A few examples require scikit-image >= 0.12.3, a few examples require
+pandas >= 0.18.0.
+
+User installation
+~~~~~~~~~~~~~~~~~
+
+If you already have a working installation of numpy and scipy,
+the easiest way to install scikit-learn is using ``pip``   ::
+
+    pip install -U scikit-learn
+
+or ``conda``::
+
+    conda install scikit-learn
+
+The documentation includes more detailed `installation instructions <http://scikit-learn.org/stable/install.html>`_.
+
+
+Changelog
+---------
+
+See the `changelog <http://scikit-learn.org/dev/whats_new.html>`__
+for a history of notable changes to scikit-learn.
+
+Development
+-----------
+
+We welcome new contributors of all experience levels. The scikit-learn
+community goals are to be helpful, welcoming, and effective. The
+`Development Guide <http://scikit-learn.org/stable/developers/index.html>`_
+has detailed information about contributing code, documentation, tests, and
+more. We've included some basic information in this README.
+
+Important links
+~~~~~~~~~~~~~~~
+
+- Official source code repo: https://github.com/scikit-learn/scikit-learn
+- Download releases: https://pypi.org/project/scikit-learn/
+- Issue tracker: https://github.com/scikit-learn/scikit-learn/issues
+
+Source code
+~~~~~~~~~~~
+
+You can check the latest sources with the command::
+
+    git clone https://github.com/scikit-learn/scikit-learn.git
+
+Contributing
+~~~~~~~~~~~~
+
+To learn more about making a contribution to scikit-learn, please see our
+`Contributing guide
+<https://scikit-learn.org/dev/developers/contributing.html>`_.
+
+Testing
+~~~~~~~
+
+After installation, you can launch the test suite from outside the
+source directory (you will need to have ``pytest`` >= 3.3.0 installed)::
+
+    pytest sklearn
+
+See the web page http://scikit-learn.org/dev/developers/advanced_installation.html#testing
+for more information.
+
+    Random number generation can be controlled during testing by setting
+    the ``SKLEARN_SEED`` environment variable.
+
+Submitting a Pull Request
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Before opening a Pull Request, have a look at the
+full Contributing page to make sure your code complies
+with our guidelines: http://scikit-learn.org/stable/developers/index.html
+
+
+Project History
+---------------
+
+The project was started in 2007 by David Cournapeau as a Google Summer
+of Code project, and since then many volunteers have contributed. See
+the  `About us <http://scikit-learn.org/dev/about.html#authors>`_ page
+for a list of core contributors.
+
+The project is currently maintained by a team of volunteers.
+
+**Note**: `scikit-learn` was previously referred to as `scikits.learn`.
+
+
+Help and Support
+----------------
+
+Documentation
+~~~~~~~~~~~~~
+
+- HTML documentation (stable release): http://scikit-learn.org
+- HTML documentation (development version): http://scikit-learn.org/dev/
+- FAQ: http://scikit-learn.org/stable/faq.html
+
+Communication
+~~~~~~~~~~~~~
+
+- Mailing list: https://mail.python.org/mailman/listinfo/scikit-learn
+- IRC channel: ``#scikit-learn`` at ``webchat.freenode.net``
+- Stack Overflow: https://stackoverflow.com/questions/tagged/scikit-learn
+- Website: http://scikit-learn.org
+
+Citation
+~~~~~~~~
+
+If you use scikit-learn in a scientific publication, we would appreciate citations: http://scikit-learn.org/stable/about.html#citing-scikit-learn
diff --git a/data/repos/sequelize-sequelize-README.md b/data/repos/sequelize-sequelize-README.md
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+++ b/data/repos/sequelize-sequelize-README.md
@@ -0,0 +1,74 @@
+# Sequelize
+
+[![npm version](https://badgen.net/npm/v/sequelize)](https://www.npmjs.com/package/sequelize)
+[![Travis Build Status](https://badgen.net/travis/sequelize/sequelize?icon=travis)](https://travis-ci.org/sequelize/sequelize)
+[![Appveyor Build Status](https://ci.appveyor.com/api/projects/status/9l1ypgwsp5ij46m3/branch/master?svg=true)](https://ci.appveyor.com/project/sushantdhiman/sequelize/branch/master)
+[![npm downloads](https://badgen.net/npm/dm/sequelize)](https://www.npmjs.com/package/sequelize)
+[![codecov](https://badgen.net/codecov/c/github/sequelize/sequelize?icon=codecov)](https://codecov.io/gh/sequelize/sequelize)
+[![Last commit](https://badgen.net/github/last-commit/sequelize/sequelize)](https://github.com/sequelize/sequelize)
+[![Merged PRs](https://badgen.net/github/merged-prs/sequelize/sequelize)](https://github.com/sequelize/sequelize)
+[![GitHub stars](https://badgen.net/github/stars/sequelize/sequelize)](https://github.com/sequelize/sequelize)
+[![Bountysource](https://www.bountysource.com/badge/team?team_id=955&style=bounties_received)](https://www.bountysource.com/teams/sequelize/issues?utm_source=Sequelize&utm_medium=shield&utm_campaign=bounties_received)
+[![Slack Status](http://sequelize-slack.herokuapp.com/badge.svg)](http://sequelize-slack.herokuapp.com/)
+[![node](https://badgen.net/npm/node/sequelize)](https://www.npmjs.com/package/sequelize)
+[![License](https://badgen.net/github/license/sequelize/sequelize)](https://github.com/sequelize/sequelize/blob/master/LICENSE)
+[![semantic-release](https://img.shields.io/badge/%20%20%F0%9F%93%A6%F0%9F%9A%80-semantic--release-e10079.svg)](https://github.com/semantic-release/semantic-release)
+
+Sequelize is a promise-based Node.js ORM for Postgres, MySQL, MariaDB, SQLite and Microsoft SQL Server. It features solid transaction support, relations, eager and lazy loading, read replication and more.
+
+Sequelize follows [SEMVER](http://semver.org). Supports Node v6 and above to use ES6 features.
+
+New to Sequelize? Take a look at the [Tutorials and Guides](https://sequelize.org/master). You might also be interested in the [API Reference](https://sequelize.org/master/identifiers).
+
+## v5 Release
+
+You can find the upgrade guide and changelog [here](https://sequelize.org/master/manual/upgrade-to-v5.html).
+
+## Table of Contents
+- [Installation](#installation)
+- [Documentation](#documentation)
+- [Responsible disclosure](#responsible-disclosure)
+- [Resources](#resources)
+
+## Installation
+
+```bash
+$ npm install --save sequelize # This will install v5
+
+# And one of the following:
+$ npm install --save pg pg-hstore # Postgres
+$ npm install --save mysql2
+$ npm install --save mariadb
+$ npm install --save sqlite3
+$ npm install --save tedious # Microsoft SQL Server
+```
+
+## Documentation
+- [v5 Documentation](https://sequelize.org/master)
+- [v4 Documentation](https://sequelize.org/v4)
+- [v3 Documentation](https://sequelize.org/v3)
+- [Contributing](https://github.com/sequelize/sequelize/blob/master/CONTRIBUTING.md)
+
+## Responsible disclosure
+If you have any security issue to report, contact project maintainers privately. You can find contact information in [CONTACT.md](https://github.com/sequelize/sequelize/blob/master/CONTACT.md).
+
+## Resources
+
+- [Changelog](https://github.com/sequelize/sequelize/releases)
+- [Slack](http://sequelize-slack.herokuapp.com/)
+- [Stack Overflow](https://stackoverflow.com/questions/tagged/sequelize.js)
+
+### Tools
+- [Sequelize & TypeScript](https://sequelize.org/master/manual/typescript.html)
+- [Enhanced TypeScript with decorators](https://github.com/RobinBuschmann/sequelize-typescript)
+- [Sequelize & GraphQL](https://github.com/mickhansen/graphql-sequelize)
+- [Add-ons & Plugins](https://sequelize.org/master/manual/resources.html)
+- [Sequelize CLI](https://github.com/sequelize/cli)
+
+### Learning
+- [Getting Started](https://sequelize.org/master/manual/getting-started)
+- [Express Example](https://github.com/sequelize/express-example)
+
+### Translations
+- [English v5](https://sequelize.org/master) (OFFICIAL)
+- [中文文档 v4/v5](https://github.com/demopark/sequelize-docs-Zh-CN) (UNOFFICIAL)
diff --git a/data/repos/tensorflow-magenta-README.md b/data/repos/tensorflow-magenta-README.md
new file mode 100644
index 00000000..23f21d37
--- /dev/null
+++ b/data/repos/tensorflow-magenta-README.md
@@ -0,0 +1,157 @@
+
+<img src="magenta-logo-bg.png" height="75">
+
+[![Build Status](https://travis-ci.org/tensorflow/magenta.svg?branch=master)](https://travis-ci.org/tensorflow/magenta)
+ [![PyPI version](https://badge.fury.io/py/magenta.svg)](https://badge.fury.io/py/magenta)
+
+**Magenta** is a research project exploring the role of machine learning
+in the process of creating art and music.  Primarily this
+involves developing new deep learning and reinforcement learning
+algorithms for generating songs, images, drawings, and other materials. But it's also
+an exploration in building smart tools and interfaces that allow
+artists and musicians to extend (not replace!) their processes using
+these models.  Magenta was started by some researchers and engineers
+from the [Google Brain team](https://research.google.com/teams/brain/),
+but many others have contributed significantly to the project. We use
+[TensorFlow](https://www.tensorflow.org) and release our models and
+tools in open source on this GitHub.  If you’d like to learn more
+about Magenta, check out our [blog](https://magenta.tensorflow.org),
+where we post technical details.  You can also join our [discussion
+group](https://groups.google.com/a/tensorflow.org/forum/#!forum/magenta-discuss).
+
+This is the home for our Python TensorFlow library. To use our models in the browser with [TensorFlow.js](https://js.tensorflow.org/), head to the [Magenta.js](https://github.com/tensorflow/magenta-js) repository.
+
+## Getting Started
+
+* [Installation](#installation)
+* [Using Magenta](#using-magenta)
+* [Playing a MIDI Instrument](#playing-a-midi-instrument)
+* [Development Environment (Advanced)](#development-environment)
+
+## Installation
+
+Magenta maintains a [pip package](https://pypi.python.org/pypi/magenta) for easy
+installation. We recommend using Anaconda to install it, but it can work in any
+standard Python environment. We support both Python 2 (>= 2.7) and Python 3 (>= 3.5).
+These instructions will assume you are using Anaconda.
+
+Note that if you want to enable GPU support, you should follow the [GPU Installation](#gpu-installation) instructions below.
+
+### Automated Install (w/ Anaconda)
+
+If you are running Mac OS X or Ubuntu, you can try using our automated
+installation script. Just paste the following command into your terminal.
+
+```bash
+curl https://raw.githubusercontent.com/tensorflow/magenta/master/magenta/tools/magenta-install.sh > /tmp/magenta-install.sh
+bash /tmp/magenta-install.sh
+```
+
+After the script completes, open a new terminal window so the environment
+variable changes take effect.
+
+The Magenta libraries are now available for use within Python programs and
+Jupyter notebooks, and the Magenta scripts are installed in your path!
+
+Note that you will need to run `source activate magenta` to use Magenta every
+time you open a new terminal window.
+
+### Manual Install (w/o Anaconda)
+
+If the automated script fails for any reason, or you'd prefer to install by
+hand, do the following steps.
+
+Install the Magenta pip package:
+
+```bash
+pip install magenta
+```
+
+**NOTE**: In order to install the `rtmidi` package that we depend on, you may need to install headers for some sound libraries. On Linux, this command should install the necessary packages:
+
+```bash
+sudo apt-get install build-essential libasound2-dev libjack-dev
+```
+
+The Magenta libraries are now available for use within Python programs and
+Jupyter notebooks, and the Magenta scripts are installed in your path!
+
+### GPU Installation
+
+If you have a GPU installed and you want Magenta to use it, you will need to
+follow the [Manual Install](#manual-install) instructions, but with a few
+modifications.
+
+First, make sure your system meets the [requirements to run tensorflow with GPU support](
+https://www.tensorflow.org/install/install_linux#nvidia_requirements_to_run_tensorflow_with_gpu_support).
+
+Next, follow the [Manual Install](#manual-install) instructions, but install the
+`magenta-gpu` package instead of the `magenta` package:
+
+```bash
+pip install magenta-gpu
+```
+
+The only difference between the two packages is that `magenta-gpu` depends on
+`tensorflow-gpu` instead of `tensorflow`.
+
+Magenta should now have access to your GPU.
+
+## Using Magenta
+
+You can now train our various models and use them to generate music, audio, and images. You can
+find instructions for each of the models by exploring the [models directory](magenta/models).
+
+To get started, create your own melodies with TensorFlow using one of the various configurations of our [Melody RNN](magenta/models/melody_rnn) model; a recurrent neural network for predicting melodies.
+
+## Playing a MIDI Instrument
+
+After you've trained one of the models above, you can use our [MIDI interface](magenta/interfaces/midi) to play with it interactively.
+
+We also have created several [demos](https://github.com/tensorflow/magenta-demos) that provide a UI for this interface, making it easier to use (e.g., the browser-based [AI Jam](https://github.com/tensorflow/magenta-demos/tree/master/ai-jam-js)).
+
+## Development Environment
+If you want to develop on Magenta, you'll need to set up the full Development Environment.
+
+First, clone this repository:
+
+```bash
+git clone https://github.com/tensorflow/magenta.git
+```
+
+Next, install the dependencies by changing to the base directory and executing the setup command:
+
+```bash
+pip install -e .
+```
+
+You can now edit the files and run scripts by calling Python as usual. For example, this is how you would run the `melody_rnn_generate` script from the base directory:
+
+```bash
+python magenta/models/melody_rnn/melody_rnn_generate --config=...
+```
+
+You can also install the (potentially modified) package with:
+
+```bash
+pip install .
+```
+
+Before creating a pull request, please also test your changes with:
+
+```bash
+pip install pytest-pylint
+pytest
+```
+
+## PIP Release
+
+To build a new version for pip, bump the version and then run:
+
+```bash
+python setup.py test
+python setup.py bdist_wheel --universal
+python setup.py bdist_wheel --universal --gpu
+twine upload dist/magenta-N.N.N-py2.py3-none-any.whl
+twine upload dist/magenta_gpu-N.N.N-py2.py3-none-any.whl
+```
diff --git a/helper_scripts/splitcsvcategory.py b/helper_scripts/splitcsvcategory.py
index dc0f9bfa..d0752746 100644
--- a/helper_scripts/splitcsvcategory.py
+++ b/helper_scripts/splitcsvcategory.py
@@ -2,13 +2,44 @@
 # splitcsvcategory.py
 
 import pandas as pd
-
+import os.path 
 df = pd.read_csv('~/Documents/ISI2019/reposwithexcerpts.csv')
 df.drop_duplicates(inplace=True)
 df.sort_values(by=['URL', 'category'], inplace=True)
+if os.path.exists('../data/none.csv'):
+    df_none=pd.read_csv('../data/none.csv')
+    df_none=df_none.append((df[df['category']=='none'])[['URL', 'excerpt']])
+    df_none.drop_duplicates(subset="excerpt").to_csv(path_or_buf='../data/none.csv', index=False)
+else:
+    (df[df['category']=='none']) [['URL', 'excerpt']].to_csv(path_or_buf='../data/none.csv', index=False)
+
+
+if os.path.exists('../data/description.csv'):
+    df_description=pd.read_csv('../data/description.csv')
+    df_description=df_description.append((df[df['category']=='description']) [['URL', 'excerpt']])
+    df_description.drop_duplicates(subset="excerpt").to_csv(path_or_buf='../data/description.csv', index=False)
+else:
+    (df[df['category']=='description']) [['URL', 'excerpt']].to_csv(path_or_buf='../data/description.csv', index=False)
+
+
+if os.path.exists('../data/installation.csv'):
+    df_installation=pd.read_csv('../data/installation.csv')
+    df_installation=df_installation.append((df[df['category']=='installation'])[['URL', 'excerpt']])
+    df_installation.drop_duplicates(subset="excerpt").to_csv(path_or_buf='../data/installation.csv', index=False)
+else:
+    (df[df['category']=='installation'])[['URL', 'excerpt']].to_csv(path_or_buf='../data/installation.csv', index=False)
+
+    
+if os.path.exists('../data/invocation.csv'):
+    df_invocation=pd.read_csv('../data/invocation.csv')
+    df_invocation=df_invocation.append((df[df['category']=='invocation'])[['URL', 'excerpt']])
+    df_invocation.drop_duplicates(subset="excerpt").to_csv(path_or_buf='../data/invocation.csv', index=False)
+else:
+    (df[df['category']=='invocation']).to_csv(path_or_buf='../data/invocation.csv', index=False)
 
-print(df.to_string())
-(df[df['category']=='description'])[['URL', 'excerpt']].to_csv(path_or_buf='~/Documents/ISI2019/SM2KG/data/description.csv', index=False)
-(df[df['category']=='installation'])[['URL', 'excerpt']].to_csv(path_or_buf='~/Documents/ISI2019/SM2KG/data/installation.csv', index=False)
-(df[df['category']=='invocation'])[['URL', 'excerpt']].to_csv(path_or_buf='~/Documents/ISI2019/SM2KG/data/invocation.csv', index=False)
-(df[df['category']=='citation'])[['URL', 'excerpt']].to_csv(path_or_buf='~/Documents/ISI2019/SM2KG/data/citation.csv', index=False)
+if os.path.exists('../data/citation.csv'):
+    df_citation=pd.read_csv('~/Documents/ISI2019/SM2KG/data/citation.csv')
+    df_citation=df_citation.append((df[df['category']=='citation'])[['URL', 'excerpt']])
+    df_citation.drop_duplicates(subset="excerpt").to_csv(path_or_buf='../data/citation.csv', index=False)
+else:
+    (df[df['category']=='citation'])[['URL', 'excerpt']].to_csv(path_or_buf='../data/citation.csv', index=False)