David Molik

Methods: <br/> The proposed is to design a program where ARS would contribute to ongoing efforts by Cooperator to build communities of practice around bioinformatics. Bi-weekly meetings will be used to generate designs for workshops, build potential partnerships, and define the problem space. Hands-on workshops will cover basic concepts and tools, including program design, version control, data management, task automation, and fundamental data skills. Participants will be encouraged to help one another and to apply what they have learned to their own research problems and workflows. Additionally, Cooperator and ARS will test design of workshops with a co-hosted Carpentries workshop summer 2024 covering basic bioinformatics skills. <br/> <br/>Objectives: <br/> This project is to design an on-going partnership with Cooperator for the ARS to contribute to and be a part of bioinformatics training being organized by Cooperator. The objective of the proposed work is to build workforce and research capacity with computational and data skills, which would help researchers and ARS collaborating students improve the quality and efficiency of their work by teaching them how to use open-source software tools and computing resources. The design will provide a proposed series of workshops which will engage participants through a combination of theory and hands-on experience, while providing valuable knowledge on access to further training, opportunities, and resources. <br/>

This multi-year interagency USDA Agricultural Research Service project (3020-10400-001-009-I) develops a metagenomic surveillance approach that uses house flies as environmental sentinels for existing, emerging, and cryptic microbial threats to U.S. military personnel. Initial work at Fort Riley, Kansas, combines long- and short-read sequencing, bioinformatics analysis, and geospatial data to identify pathogenic microorganisms, antimicrobial resistance and virulence genes, and other microbial indicators in the environment. These data are used to characterize the prevalence and distribution of microbial threats and to develop risk maps linking detected organisms and genes to potential environmental sources. Later phases expand the approach toward predictive and forecasting models and application at additional military installations. The user served as a Co-Principal Investigator during the first year of the project.

The AgBioData Research Coordination Network (RCN) will accelerate research in agricultural science by increasing the accessibility and reuse of large- scale biological data. In the face of increasing human populations, global climate change and other challenges, there is a critical need to discover new ways to increase agricultural yields while reducing environmental impacts. Such advances in agricultural science are increasingly led by data-driven approaches, which rely on data that are properly stored and made available to the research community through online resources. With NSF support, this RCN will leverage grass roots efforts of the AgBioData Consortium (www.agbiodata.org) to expand the network of database experts and biocurators to include research scientists, funders, and data publishers to establish and disseminate standards, best practices, and recommendations for agricultural data management. The training component will increase participation of a broadly representative constituency, including those traditionally underrepresented in the sciences and junior researchers, and will prepare them to be leaders in agricultural data sciences. The AgBioData RCN will focus on increasing the value of Big Scientific Data through the FAIR (findable, accessible, interoperable, and reusable) model. Annual meetings will bring together multi-disciplinary scientists from all the major U.S. agricultural genomic, genetic, and breeding (GGB) databases and allied resources, accelerating synergistic efforts to make the huge amount of data curated by AgBioData databases FAIR. This will facilitate shortened data processing/curation times, simplified data management, and more standardized data handling between databases, making it easier for researchers to find and use data. The larger network of all data stakeholders will work together to identify and prioritize the most pressing data and metadata standardization needs and to develop and implement processes to solve them. Working groups will be created to focus on key community data issues, including unified nomenclature, metadata standards, data federation, and recommendations for emerging data types. The network will directly support data-generating scientists by developing clearly defined FAIR data management guides with a framework on how to maximize data visibility and data reuse for common types of biological data and a foundational FAIR data management educational curriculum appropriate for academic courses or short training modules. Lastly, a sustainability review and report will provide a roadmap for the future of GGB databases. The AgBioData Consortium is a model for how database managers, researchers, educators, and publishers can work together to be more resource-efficient and how, as central resources for the communities they serve, they can use a collective voice to benefit all scientists, both domestically and abroad. This project is jointly funded by the Plant Genome Research Program of the Division of Integrative Organismal Biology and the Capacity: Cyberinfrastructure program of the Division of Biological Infrastructure in NSF's Biological Sciences Directorate. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.