Scientific discovery depends on access to data and the knowledge this data makes possible.Research data sharing is increasingly recognized as a priority for organizations to support the successful conduct of research.The National Institutes of Health states, "data sharing enables researchers to rigorously test the validity of research findings, strengthen analyses through combined datasets, reuse hard-to-generate data, and explore new frontiers of discovery" [1].Conversely, in the absence of data sharing, there are increased risks related to the robustness, rigor, and replicability of results, and the potential of valuable data is diminished.For these reasons and more, institutional data sharing capacity is a critical topic for organizations to scrutinize, discuss, and advance.Advocacy and support for data sharing are often discussed with an emphasis on understanding and supporting the practices of individual investigators or scientific communities [2,3].However, a researcher's ability to successfully engage in and benefit from sound data sharing depends on their organizational setting and, specifically, the organization's data sharing capacity.For example, sharing data is easier and more equitable when organizational processes and procedures are established and documented, and research workforce members can access centralized training and infrastructure resources.The effect of how an organization approaches and supports data sharing extends beyond the success of its investigators.Institutions that share data can participate in innovative largescale initiatives and pursue new funding opportunities.Universities that contribute to creating

OBJECTIVE: In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations. MATERIALS AND METHODS: We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements. RESULTS: Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback. DISCUSSION: We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate. CONCLUSION: By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require.

Natural hazards (NHs) associated with climate change have been increasing in frequency and intensity. These acute events impact humans both directly and through their effects on social and environmental determinants of health. Rather than relying on a fully reactive incident response disposition, it is crucial to ramp up preparedness initiatives for worsening case scenarios. In this perspective, we review the landscape of NH effects for human health and explore the potential of health informatics to address associated challenges, specifically from a preparedness angle. We outline important components in a health informatics agenda for hazard preparedness involving hazard-disease associations, social determinants of health, and hazard forecasting models, and call for novel methods to integrate them toward projecting healthcare needs in the wake of a hazard. We describe potential gaps and barriers in implementing these components and propose some high-level ideas to address them.

OBJECTIVE: Information gaps that accompany hurricanes and floods limit researchers' ability to determine the impact of disasters on population health. Defining key use cases for sharing complex disaster data with research communities and facilitators, and barriers to doing so are key to promoting population health research for disaster recovery. MATERIALS AND METHODS: We conducted a mixed-methods needs assessment with 15 population health researchers using interviews and card sorting. Interviews examined researchers' information needs by soliciting barriers and facilitators in the context of their expertise and research practices. Card sorting ranked priority use cases for disaster preparedness. RESULTS: Seven barriers and 6 facilitators emerged from interviews. Barriers to collaborative research included process limitations, collaboration dynamics, and perception of research importance. Barriers to data and technology adoption included data gaps, limitations in information quality, transparency issues, and difficulty to learn. Facilitators to collaborative research included collaborative engagement and human resource processes. Facilitators to data and technology adoption included situation awareness, data quality considerations, adopting community standards, and attractive to learn. Card sorting prioritized 15 use cases and identified 30 additional information needs for population health research in disaster preparedness. CONCLUSIONS: Population health researchers experience barriers to collaboration and adoption of data and technology that contribute to information gaps and limit disaster preparedness. The priority use cases we identified can help address information gaps by informing the design of supportive research tools and practices for disaster preparedness. Supportive tools should include information on data collection practices, quality assurance, and education resources usable during failures in electric or telecommunications systems.