Biomedical Data Repositories and Knowledgebases

In 2018 the Pennington/Louisiana Nutrition Obesity Research Center (NORC; P30 DK072476-16) established a repository of human subjects’ data and biospecimens of nutrition and obesity research. Currently, the repository includes data and biospecimens from 213 studies and 13,787 unique participants (68% women and 38% with obesity) funded by the National Institutes of Health, Department of Defense, United States Department of Agriculture, American Heart Association, American Diabetes Association and other government and non-profit organizations. In September 2020, an online portal (https://my.pbrc.edu/NORC/NORCRepository/Landing (link is external)) was opened to allow people to independently search the cadre of available data. As we transition to increase usage, it is imperative that we align with the FAIR and TRUST principles and to ensure we can appropriately track usage, utility, and impact. In response to NOT-OD-21-089, we have developed a comprehensive but conservative one-year project to achieve these goals. The overarching objective of this administrative supplement is to improve upon the “FAIR”-ness and “TRUST”-worthiness of the Pennington/Louisiana NORC Biorepository and its online portal. In aim 1, we will improve “FAIR”-ness by adding existing data and increasing metadata and establishing metrics for tracking and usage. In aim 2, we will improve “TRUST”-worthiness by promoting and demonstrating the methods used for data collection. Finally, aim 3, will explore the possibility for certification. This unique repository provides unique data on nutrition and obesity which seeks to benefit researchers across the country for years to come.

The Mouse Phenome Database (MPD; https://phenome.jax.org) is a widely accessed NIH-supported Biomedical Data Repository focused on primary mouse phenotype data from genetic studies of complex traits in strains and populations. For over 20 years, MPD has been a community resource developed at The Jackson Laboratory, an independent non-profit research institute, that has disseminated mouse genetic data and resources to the biomedical community since its founding. MPD, listed in the Trans-NIH Biomedical Informatics Coordinating Committee registry, accepts submission of relevant data from the community to store, organize, validate, archive, preserve, and distribute the core data from phenotyping experiments to end users in an increasingly FAIR compliant manner. We have discovered challenges and opportunities in meeting evolving requirements in enhancing MPD’s FAIR capabilities and also meeting TRUST-worthy standards. Our overarching goal is to make MPD more FAIR-compliant and TRUST-worthy while providing better metrics to evaluate usage, utility, and impact of data in MPD. Our specific tasks for this Supplement are to: 1) Implement database changes to support emerging metadata standards for the description of primary experimental data, 2) Refine our API so that it exposes data to external systems using emerging metadata standards, 3) Refine the user experience and self-curation of data so that it meets emerging metadata standards and provides intuitive data submission, and 4) Develop traceability methods for data and document user’s workflow to enhance reproducibility, tracking, and reporting of data and analytic tool usage. By simultaneously addressing our challenges, we will improve data exposure and utilization globally through integration with a modernized informatics infrastructure.

Funding provided by NIH DA028420

Aphasia is an impairment of language, affecting the production or comprehension of speech and the ability to read or write, resulting from stroke, head trauma or other neurological condition. Research to improve health related quality of life for individuals with aphasia has resulted in rich datasets and knowledge but rely on inefficient data structures that do not fully leverage efficient research and all that could be learned from existing datasets. This proposed project will align our existing data platform for aphasia research, CORE-APHASIA, with the FAIR (Findable, Accessible, Interoperable, and Reusable) and TRUST (Transparency, Responsibility, User Focus, Sustainability, and Technology) principles to improve the standardization, interoperability, and shareability resulting in a modernized CORE-APHASIA data resource and platform. Our proposed methods and approach follow guidance and processes addressed in the NIH Data Science Strategic Plan.

Understanding the physical, computational, and theoretical bases of human vocal communication, speech, is crucial to improved comprehension of voice, speech and language diseases and disorders, and improving their diagnosis, treatment, and prevention. Meeting this challenge requires knowledge of the neural and sensorimotor mechanisms of vocal motor control. Our project directly investigates the neural and sensorimotor mechanisms involved in the production of complex, natural, vocal communication signals. Our results will directly enhance brain-computer interface technology for communication and will accelerate the development of prostheses and other assistive/augmentative technologies for individuals with communications deficits due to injury or disease. We will develop a vocal prosthetic that directly translates neural signals in cortical sensorimotor and vocal-motor control regions into vocal communication signals output in real-time. Building on success using non-human primates for brain computer interfaces for general motor control, the prosthetic will be developed in songbirds, whose acoustically rich, learned vocalizations share many features with human speech. Because the songbird vocal apparatus is functionally and anatomically similar to the human larynx, and the cortical regions that control it are closely analogous to speech motor-control areas of the human brain, songbirds offer an ideal model for the proposed studies. Beyond the application of our work to human voice and speech, development of the vocal prosthetic will enable novel speech-relevant studies in the songbird model that can reveal fundamental mechanisms of vocal learning and production. As a critical component of the project, we are collecting a large dataset of simultaneously recorded neural activity from implanted multielectrode arrays (e.g., Neuropixels) along with vocalizations and additional behavioral data. These multimodal data are collected over multi-hour sessions and behaviors are spontaneous and heterogeneous. To enable effective dissemination of these data to the research community our team will, in alignment with the principles of FAIR and TRUST, develop a comprehensive data schema that meets community standards, build software tools to enable broad data reuse, develop a queryable data repository, and will provide detailed tutorials. These efforts will contribute to existing active open-source projects utilized by the neuroscience community, including Neurodata Without Borders (NWB:N). We believe that by investing in the development of this data repository, the impact of the data produced by our studies will be significantly augmented. Additionally, the software engineering tools developed will have a broader impact on data-intensive neuroscience studies of complex behaviors including and beyond speech and vocalization.

In this project, we will work with an external contractor, BioTeam Inc., with the following objectives: BioTeam Inc. will assist NIEHS in developing a framework that will inform the desired future state of NIEHS databases by using the Chemical Effects in Biological Systems (CEBS) database / knowledgebase as a model for sustainable management of environmental health data in a distributed data ecosystem. BioTeam Inc. will provide advice about the planned future state of CEBS. CEBS contains data produced by the Division of the National Toxicology Program (DNTP) over the past 45 years, moving towards an integrated Data Warehouse and permitting cross-cutting questions to be asked. DNTP is also identifying high-value datasets to host in public CEBS Data Marts for direct user query. In addition, DNTP has collected high-dimensional expression data from some subjects and currently houses these in the SRA and GEO repositories and may apply the same model to microbiome and metabolomics data in a CEBS data ecosystem such as GEN3. BioTeam Inc. will advise NIEHS on the following: sustainability and governance considerations; FAIR data sharing and TRUSTworthy repositories; interoperability with NIH data systems; common standards for measuring data use and utility; suggestions to model storage and personnel cost, and to enhance and partially-automate curation; considerations of complexity of storage of high-dimensional data in government repositories while integrating the data for analysis. Finally, BioTeam Inc. will suggest how these solutions might differ if applied to a data system that accepts environmental health data from the public in addition to the data provided by the DNTP.