wrapping up citation for Intro

01-Intro.Rmd

@@ -12,7 +12,7 @@ At the core, community cyberinfrastructure starts first with agreed upon communi
 Independent of infrastructure, community conventions also benefit the community scientifically. From the standpoint of data analysis, synthesizing data that are not standardized and interoperable is time-consuming, error-prone, and not scalable. At the same time, from the standpoint of data production, adopting community standards after data have already been generated is also challenging, especially for long-running projects producing high volumes of data. As a relatively new research area, ecological forecasting has the opportunity to adopt community conventions now, while the community is relatively small and time series are relatively short. This would not only facilitate independent validation of individual forecasts, but also larger efforts at cross-forecast synthesis (Figure 1) and the testing of grand challenge questions about the patterns of predictability across ecological systems [@dietze_prediction_2017]. It would also allow the community to generate multi-model forecasts and to run forecast model intercomparisons, such as the National Ecological Observatory Network (NEON) Ecological Forecasting Challenge organized by the Ecological Forecasting Initiative’s Research Coordination Network (EFI-RCN) [@thomas_ecological_2021]. Specifically, within model intercomparison projects having community conventions makes it easier to communicate protocols to participants, to verify that submissions from participating teams are correct and complete, and to facilitate analyses by ensuring contributions are interoperable. These benefits can also extend across communities, if one research community uses outputs from one model intercomparison project as inputs into another. For example, many long-term ecological forecasts are driven by the ensemble climate outputs from the Coupled Model Intercomparison Project (CMIP), which itself relies on emissions scenarios derived from socioeconomic models [@oneill_2016; @arora_carbonconcentration_2020]. Overall, community conventions play a key role in making ecological forecasts FAIR (Findable, Accessible, Interoperable, and Reusable), in particular tackling the interoperability and reusability that are widely considered to be the more challenging half of FAIR [@wilkinson_fair_2016].
 
 The need for ecological forecasting conventions and standards is recognized by the community [@dietze_iterative_2018], and conventions emerged as a top priority at the inaugural conference of the Ecological Forecasting Initiative (EFI) in 2019, which had an attendance of ~100 people. EFI (ecoforecast.org) is a grassroots, international, and interdisciplinary consortium that aims to build a community of practice around ecological forecasting, with a particular emphasis on near-term iterative forecasts [@dietze_forecasting_2019]. Discussions about standards and conventions initially occurred across four different EFI working groups (Cyberinfrastructure, Methods, Social Science, and Theory), with the last particularly interested in making sure any community standard would enable cross-forecast synthesis and comparative analysis. A series of cross-working group calls led to the launch of a stand-alone EFI Standards working group in early 2020, and an initial draft convention was released in time for the EFI-RCN 2020 conference in May 2020, a virtual meeting of ~200 people. The proposed convention was adopted by the EFI-RCN as part of the NEON Ecological Forecasting Challenge, and as part of the competition design phase (June-Dec 2020) and the Standards working group continued to refine the convention based on feedback from the five design teams and >90 teams participating in the first and second rounds (Jan 2021-Dec 2022) of the challenge. EFI membership is open to anyone, as is participation in EFI working groups and the NEON Ecological Forecasting Challenge, and by the end of 2022 EFI had engaged >3000 academic, agency, NGO, and industry scientists and partners through a broad mix of conferences, workshops, working groups, international chapters, webinars, journal articles, white papers, social media, videos, and policy briefs. The EFI network operates following the Integrated, Coordinated, Open, Networked (ICON) principles [@dwivedi_biogeosciences_2022], and this convention was thus developed in an open and inclusive manner and has been vetted by hundreds of researchers within the ecological forecasting community.
-Overall, while not a formal specification or schema itself, this document lays out the design principles, concepts, and requirements needed to implement the EFI community conventions for forecast file formats, forecast metadata, and forecast archiving. This allows these conventions to be implemented formally, as well as for the serialization of specific forecast output and metadata formats that adhere to this convention. The adoption of community conventions in turn facilitates the development of community tools around those formats, such as the R packages and Docker containers developed around the EFI NEON challenge that support forecast submission, validation, scoring, interactive visualization, and redistribution [@thomas_ecological_2021](Thomas et al. 2021, 2023*********). In other cases, community conventions have facilitated the development of sophisticated community tools for model calibration, validation, sensitivity analysis, and iterative data assimilation [@fer_beyond_2021].
+Overall, while not a formal specification or schema itself, this document lays out the design principles, concepts, and requirements needed to implement the EFI community conventions for forecast file formats, forecast metadata, and forecast archiving. This allows these conventions to be implemented formally, as well as for the serialization of specific forecast output and metadata formats that adhere to this convention. The adoption of community conventions in turn facilitates the development of community tools around those formats, such as the R packages and Docker containers developed around the EFI NEON challenge that support forecast submission, validation, scoring, interactive visualization, and redistribution [@thomas_ecological_2021; @thomas_neon_2023]. In other cases, community conventions have facilitated the development of sophisticated community tools for model calibration, validation, sensitivity analysis, and iterative data assimilation [@fer_beyond_2021].
 
 ```{r conceptual_figure, fig.align = 'center', out.width="100%",fig.cap = "Figure 1: EFI standards from the stage of the individual forecast to the synthesis of multiple forecasts.",echo=FALSE}
 knitr::include_graphics("img/conceptual.png")

EFIStandards.bib

@@ -578,7 +578,7 @@ @article{oneill_2016
   volume = {9},
   pages  = {3461–3482},
   journal = {Geoscientific Model Development},
-  authore = {O’Neill, B. C. and C. Tebaldi and D. P. van Vuuren and V. Eyring and P. Friedlingstein and G. Hurtt and R. Knutti and E. Kriegler and J.-F. Lamarque and J. Lowe and G. A. Meehl and R. Moss and K. Riahi and B. M. Sanderson},
+  author = {O’Neill, B. C. and Tebaldi, C. and van Vuuren, D. P. and Eyring, V.  and  Friedlingstein, P. and Hurtt, G. and Knutti, R. and Kriegler, E. and Lamarque, J.-F. and Lowe, J. and Meehl,  G. A. and Moss,  R. and Riahi, K. and Sanderson, B. M.},
   year = {2016}
 }