What do you really know about that antibody? Ask dkNET Research resources-defined here as the tools researchers use in their scientific studies-are a foundation of the biomedical enterprise. It is critical for researchers to be able to select the proper tools for their research, but also be aware of any issues that may arise in their application. Software tools and datasets may have bugs, cell lines get contaminated, knock outs may be incomplete and antibodies may have specificity problems. Such problematic resources can continue to be used in scientific studies, even after problems are detected. Many factors, including the inability to easily retrieve alerts about problematic resources, results in their continued use, wasting both time and money. To make it easy to find information about research resources and how they perform, dkNET (NIDDK Information Network, https://dknet.org), an on-line portal supported by the US National Institute of Diabetes, Digestive and Kidney diseases (NIDDK), has developed a resource information network that utilize Research Resource Identifiers (RRIDs) and natural language processes to aggregate information about individual antibodies, cell lines, organisms, digital tools, plasmids and biosamples. This information is presented in a Resource Report that provides information such as which papers have been published using these resources, who is using them and whether issues have been reported. Using this information, dkNET also provides tools to create authentication reports in support of the NIH rigor and reproducibility guidelines. The dkNET portal includes additional information to enable researchers to easily use and navigate large amounts of data and information about research resources in support of reproducible science. By the end of this webinar, participants will be familiar with the services and tools provided at dkNET and will be able to create a detailed research resource report and produce an authentication report in support of NIH mandates and policies. Presenter: Maryann Martone, PhD, FAIR Data Informatics Lab (FDI Lab), University of California, San Diego