diff --git a/blog/2023-year-in-review.md b/blog/2023-year-in-review.md index 4202c696..b776ca88 100644 --- a/blog/2023-year-in-review.md +++ b/blog/2023-year-in-review.md @@ -12,33 +12,34 @@ tags: - software --- + +2023 was a year of numerous strides at CCV and in the research community here at Brown. Thank you for your continued participation and support. -As we bid farewell to 2023, it's time to take a moment to reflect on the incredible journey of the past year. +Here are some quick statistics from our Brown community made in 2023. We had 1557 active Oscar users, which is a 24.66% increase in activity from the year prior. Over 3.6 million jobs were run in Oscar totalling over 57 million CPU core hours! Since 2018, the number of active oscar users has increased and well as demand for resources. Over **470** questions were asked and helped in our weekly office hours.
-Active Oscar Users in 2023 -Oscar Jobs -CPU Core Hours + Active Oscar Users in 2023 + Oscar Jobs + CPU Core Hours
+
-Active Oscar Users +
+ Active Oscar Users +
This bar chart displays an increase of active Oscar users since 2018.
+
-- In June, Oscar transitioned from General Parallel File System (GPFS) to an all-flash parallel filesystem (**VAST data**) as its primary storage pool. And furthermore, it was done without any major issues. This is arguably the biggest change Oscar has gone through in the last 10 years! -- Over **470** questions were asked and helped in our weekly office hours -- Our team has been a part of over **60+ publications** -- In June, over **175** people registered for the bootcamp -- We hosted 17 Data Science, Computing, and Visualization Workshops (DSCoVs) with the Data Science Initiative (DSI). Topics included Gene Annotation Resources in R, ChatGPT's API and Prompt Engineering, Building VR Application in Unity, and more! -- Jupyterhub supported *8* courses undergraduate and graduate courses at Brown. +In June, Oscar transitioned from General Parallel File System (GPFS) to an all-flash parallel filesystem (**VAST data**) as its primary storage pool. And furthermore, it was done without any major issues. This is arguably the biggest change Oscar has gone through in the last 10 years! +Alongside supporting Oscar for high-performance computing needs, CCVers provided academic support to the Brown community. We hosted 17 Data Science, Computing, and Visualization Workshops (DSCoVs) with the Data Science Initiative (DSI). Topics included Gene Annotation Resources in R, ChatGPT's API and Prompt Engineering, Building VR Application in Unity, and more! Jupyterhub, cloud-hosted Jupyter Notebooks for multiple users, supported *8* undergraduate and graduate courses. In June, we hosted a bootcamp, a series of online/hybrid tutorials, wherein *over 175* members of the Brown community signed up to learn about research computing resources and get hands-on practice using Oscar. -
-Projects and Collarborations -
-- We introduced our initial mobile applications: [**SOMA**](https://somatheapp.com/), an app focused on pain management, and [**MAPPS**](https://www.mappsproject.com/), an app designed to explore social interactions and their influence on disease transmission patterns. -- [**Honeycomb**](https://brown-ccv.github.io/honeycomb-docs/), a template for reproducible psychological tasks for clinic, laboratory, and home use has undergone a significant upgrade. +In 2023, we supported 60 projects and collaborations throughout Brown. The following are some highlights from those projects. + +- We launched our first mobile applications: [**SOMA**](https://somatheapp.com/), an app focused on pain management, and [**MAPPS**](https://www.mappsproject.com/), an app designed to explore social interactions and their influence on disease transmission patterns. +- [**Honeycomb**](https://brown-ccv.github.io/honeycomb-docs/), a template for reproducible psychological tasks for clinic, laboratory, and home use has undergone a significant upgrade and has released version 3.2.6. - [**Hierarchical Sequential Sampling Modeling (HSSM)**](https://lnccbrown.github.io/HSSM/) —a contemporary Python toolbox integrating cutting-edge likelihood approximation methods within the Python Bayesian ecosystem—was released on the Python Package Index (PyPI) in late June. - We released two Julia Packages - We developed and released a versatile utility script to streamline data exports to Oscar, led two XNAT Workshops for the neuroimaging community, and contributed to the MNE-BIDS open-source project. Furthermore, we enhanced the xnat2bids pipeline to accommodate EEG and Physiological data, highlighting our collective commitment to optimizing neuroimaging workflows and fostering open-source collaboration. diff --git a/images/blog/2023-year-in-review/projects-and-collaborations.png b/images/blog/2023-year-in-review/projects-and-collaborations.png deleted file mode 100644 index 1c44750e..00000000 Binary files a/images/blog/2023-year-in-review/projects-and-collaborations.png and /dev/null differ