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TheGeneralist Repository Ecosystem Initiative(GREI) hosted a 2-day virtual workshop presented by the seven participating generalist repositories on January 24 & 25, 2023.TheNIH Office of Data Science Strategysponsored workshop focused on GREI's vision of developing collaborative approaches for data management and sharing through inclusion of the generalist repositories in the NIH data ecosystem, and better enabling search and discovery of NIH-funded data in the generalist repositories. The workshop featured keynote speakers and panel discussions with leaders in open data from the research community, NIH, and data community organizations. Interactive training sessions were led by GREI repositories, presenting use-case-specific guidance on sharing and discovering data in generalist repositories. Presenters also gathered community feedback to inform future GREI work andto enhance support for NIH data sharing with common functionality, interoperability, and “coopetition” among generalist repositories.

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Good data, project, or software documentation makes your work more discoverable, transparent, and reusable. Read the Docs makes documentation easy by automating the building, versioning, and hosting of your docs, plus it’s free and open source! In this hands-on BioData Club workshop attendees will learn about documentation best practices and publish a documentation website using Read the Docs and Markdown.

This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/5551162. This review is the result of a virtual, live-streamed preprint journal club organized and hosted by PREreview and OHSU's BioData Club. The discussion was joined by 9 people, including OHSU researchers and the event organizing team. Wylezinski et al. investigated the impact of clinical and social determinants of health (SDOH) risk factors on the COVID-19 case growth in Tennessee. To that aim, they used a variety of publicly available data to train machine learning (ML) models to predict COVID-19 rates, and ranked each SDOH factor's impact on the model's performance. The study shows that COVID-19 case growth data shows disparities in socioeconomic, environmental, demographic, and health outcomes (particularly, mental health). Such approaches could benefit community, policy, and research responses to disease outbreaks, COVID-19 and health disparities more generally. Our group particularly appreciated the use of openly available datasets. We do, however, have major concerns about the SDOH risk factors used. Description of the dataset and the ML models could benefit from more details. Major and minor concerns, as well as some suggestions on how to address them, are listed below. Major concerns and feedback The SDOH risk factors brought up confusion as they were not clearly defined nor was an explanation on how they were measured provided. Additionally, several distinct SDOH risk factors were combined with no explanation, such as race/ethnicity and poverty. We believe that listing all SDOH risk factors, their definition, source, and how they were measured in a table would greatly help with understanding the results and their implication on policy. Description of the data is scarce. A descriptive table of the data that was used for training the models would be helpful. Also, the feature sets used for the ML models seem to be highly correlated, but there is no description of the methods used to account for such variables. State the results for each ML model used would be beneficial. Along the same lines, the manuscript mentions multiple methods were used for the training, but it is unclear which method is illustrated in the figures. We recommend stating that explicitly in the text and figure caption. While the methods section states cross-validation and hold-out methods were used, there are no further details on how it was implemented. We recommend stating the cross-validation method used and the number of samples that were held out. We also suggest mentioning the software package used for implementing the ML models and if existing packages were used, please cite the source. Some of the data are difficult to interpret (see minor comments below) and therefore it is hard to evaluate whether the conclusions are supported by the findings. Some conclusions seem overstated when correlating vaccination status and infection rates. We think it would be appropriate to present the conclusions with more transparency and acknowledgment regarding limitations. It would also be helpful to show that counties with low infection rates are color-coded for vaccination rates (complementary to Supplementary Figure 1C). There is not sufficient detail provided to allow the reproduction and validation of the study unless requesting more information from the authors, and it is unclear what qualifies as a "reasonable request". We would have found it helpful to have read information on the software package used, input samples and summary statistics, input features used, etc. In Figure 1, the color coding and size are difficult to interpret. It would be useful to have the captions better explain how the reader is supposed to interpret the visualization. Related to this, it is unclear what the difference (conceptually) is between blue and black dots. Figure 1 could be reconfigured, maybe with the addition of numbers and/or a smaller summary figure, to better display data and consistency. There are inconsistencies between the data presented in Figure 1 and the text. Specifically, the 'race and ethnicity' SDOH risk factor increases in the figure while the text mentions that this SDOH risk factor decreases, and 7 timepoints are shown in the figure while the text mentions 13 timepoints were taken. We recommend updating these inconsistencies for easier understanding. SDOH risk factors in Figure 1 could be correlated but there seem to be no methods to control for this. It would be helpful to understand if this possible confounding effect was accounted for and controlled for. Minor concerns and feedback It is unclear if there were consistent data and variable definitions across the datasets used. Any differences found in the samples in terms of data collection may require a control for differences in the site. It would be helpful to include in the methods section whether all features in Figure 1 were used in the ML model. It is unclear how county data was aggregated into groups. We recommend either to be more descriptive or find a simpler way to present some of the data (e.g., top 5 counties with the highest increase or decrease in infection rates). The ethical concerns surrounding this study have not been adequately discussed. Specifically, it would be important to discuss and acknowledge the study questions and its methodology in respect to recommendations and discussions about public health research involving race (e.g., https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2837428). It seems unclear whether and how using multiple datasets reduces implicit bias from the researchers or from the datasets. It would be useful if there were a more extended explanation on the matter. Is there any reason why the Supplementary figures are not the main figures in the paper? We recommend moving them to the main paper as they present key information needed to interpret the results. Supplementary Figure 2C, the legend reads as a bias with "Best" and "Worst" rather than keeping consistent language with "Highest" and "Lowest". We recommend updating this. Supplementary Figure 2 could benefit from being replaced as textual description and/or possibly adding a figure for vaccination rate for each SDOH risk factor used. We recommend stating study limitations clearly in the conclusions. For instance, the different coronavirus variants were not taken into account in the prediction, possibly because of a lack of data, yet those undoubtedly would impact infection rates. Similarly, there is no explanation for why the specific time frame right after the onset of vaccine rollout was chosen; this period of time is rather short and involves a relatively small amount of data. It would be important to reflect on how these choices might have impacted the predictive models. We thank the authors for posting this work as a preprint and hope our feedback will help improve the next version of the manuscript. Acknowledgments The organizing team is grateful to all the participants of the PREreview + BioData Club's Open Reviewers Workshop. We especially thank those who engaged in the live-streamed preprint journal club discussion held during our last module of the workshop. It was a pleasure to have such a lively group.

This is a protocol for sharing datasets on Zenodo.We have instructions on converting Excel spreadsheets (.xls/.xlsx) into comma-separated values (.csv) files. In addition, inspired by the Open Data Institute’s open data certification badges (https://certificates.theodi.org/en/about/badgelevels), which identifies different “levels of achievement” in publishing data openly, we developed questions for researchers to categorize their own .csv datasets as Bronze or Silver. Bronze datasets are smaller, single-release, often self-assembled datasets that require minimal computer interaction. Examples of bronze datasets could include a list of materials (e.g. antibodies) used in an experiment, gene expression data from a hand-curated list, or summary patient data from a truncated cohort. Silver datasets are larger, often machine-generated datasets that will be processed and analyzed predominantly by programs or algorithms. Examples of silver datasets could include a comprehensive list of cancer cell lines and characteristics, -omics data, or an export of all patient information from a clinical trial. Both categories of dataset can be validated using Data Curator, an open-source software program with a graphic user interface, that allows researchers to validate (bronze) and/or annotate the dataset with suitable machine-readable metadata (silver) according to our instructions. This protocol is part of a manuscript,"An institutional approach to open science:operationalizing the sharing of biomedical research objects through relevant publication platforms," that isin preparation.This project was supported by funding from the Cancer Early Detection Advanced Research (CEDAR) center at Oregon Health & Science University’s Knight Cancer Institute.

This is a protocol for sharing protocols/workflows on protocols.io. We adaptedmany of recommendations used by Nature Protocols (available at: https://www.nature.com/nprot/for-authors/preparing-your-submission) to the existing fields in protocols.io. There are some important qualities that we emphasized in these guidelines. For example, along with a summary description of the protocol, we recommended adding to the Abstract section an acknowledgement section as well as funding and conflicts of interest statement. In addition, to automatically generate keywords, we recommend using MeSH on Demand, where MeSH terms (“Medical Subject Headings,” a controlled vocabulary used to index life sciences research) are produced from copying/pasting the Abstract summary description. This protocol is part of a manuscript,"An institutional approach to open science:operationalizing the sharing of biomedical research objects through relevant publication platforms," that isin preparation.This project was supported by funding from the Cancer Early Detection Advanced Research (CEDAR) center at Oregon Health & Science University’s Knight Cancer Institute.

The Metrics Toolkit is an open access resource aimed at helping researchers and evaluators understand and responsibly use research metrics, including bibliometrics and altmetrics.The Toolkit provides evidence-based information about research metrics across disciplines, including how each metric is calculated, where it can be found, and how it should (and should not) be applied. It can be used to learn about specific metrics, and also to choose appropriate metrics based on the type of impact you want to demonstrate, the research output you’re gathering data for, and the discipline of the work.

Presented at the 2018 Medical Library Association Annual Meeting in Atlanta, GA

Presentation given at the Force 2017 conference in Berlin, Germany on Radian, a community effort to increase the discovery and reuse of data resources. Use Radian to discover ideas and share resources about data science, visualization, data management, data policy, and applications of data for the public good.