Emily S. Boja

Data fragmentation, lack of standardization, and incomplete clinical context limit the utility and reuse of datasets in biomedical research. Core facilities (shared resources) generate a large portion of research data and are well positioned to implement FAIR (Findable, Accessible, Interoperable, and Reusable) data practices to align with the NIH Data Management and Sharing Policy. This article highlights strategies for incorporating FAIR data practices into core facility workflows. Core facilities generate diverse data types across multiple platforms. However, variability in data formats and metadata practices can pose challenges for interoperability and cross-study integration. A critical first step in addressing this challenge is to connect core-generated datasets derived from patient biospecimens with structured clinical metadata. This involves using standardized identifiers, integration with biobanks and electronic health records. Structured metadata capture during experimental workflows to ensure data provenance will enable meaningful downstream analysis. Equally important is the adoption of standardized data formats and metadata frameworks across technologies. Adoption of community standards for various data types, along with standard metadata schemas, improves data discoverability and reuse. Robust institutional infrastructure (e.g. laboratory information management systems, integrated data platforms) and coordinated governance frameworks can help to implement these efforts. Partnerships with institutional stakeholders and targeted workforce training further support the consistent implementation of effective data stewardship practices. These approaches position core facilities as central operational hubs for advancing FAIR data principles, enhancing reproducibility, and maximizing the long-term value of biomedical research data.

Despite vast investments in data collection, generation, and sharing from childhood cancer studies, for example, Gabriella Miller Kids First Research Program, Childhood Cancer Data Initiative, and other efforts, secondary use of data remains a challenge especially for rare diseases. The National Cancer Institute (NCI) Office of Data Sharing (ODS) aimed to promote FAIR (Findable, Accessible, Interoperable, Reusable) data sharing practices to enhance data utility, accelerate discovery, and foster interdisciplinary collaboration. NCI ODS launched its inaugural Data Jamboree focusing on childhood cancer alongside its third Annual Symposium in September 2025. Over 120 participants with diverse backgrounds coalesced into 23 multidisciplinary teams to solve real-world problems in a collaborative setting. The Jamboree built a diverse pool of data users and catalyzed pediatric cancer research, with several project findings being prepared for publication. Moreover, key feedback and recommendations for areas of improvement were provided to NCI in real time. The event highlighted the power of team science, data sharing, use, and reuse to accelerate childhood cancer research and improve diagnostic and therapeutic outcomes.

The National Cancer Institute’s (NCI’s) Office of Data Sharing (ODS), established in 2018, advances a comprehensive vision and strategy to ensure that the scientific outputs of NCI-supported research maximally benefit the cancer community and are in alignment with federal data management, sharing, and public access expectations. NCI’s large portfolio of scientific research spans the full continuum of cancer science from basic and population studies to translational and clinical research and generates a diverse landscape of research outputs, including data, publications, clinical trials, tools, and resources. The NCI ODS serves as a critical coordinator across this ecosystem to steward NCI’s data-sharing strategy during all phases of the research data lifecycle, engaging investigators, institutions, program staff, and technology partners via 3 pillars: Policy, Program, and Process. The office leads the NCI implementation of data sharing and public access policies, supports programs that identify and promote high-value research outputs with broad potential for reuse, and designs and manages user-centered processes for cancer data access and submission to National Institutes of Health (NIH) controlled-access repositories. ODS also draws from deep expertise to consult and provide expert data-sharing guidance across scientific domains and engage with the community on data-sharing issues. To develop a mechanism for sustained community engagement, the ODS annual Data Sharing Symposium was launched in 2023 as the ODS flagship event which serves as a venue to exchange ideas and confront emerging issues. Through these initiatives, ODS is helping to build a coordinated and FAIR data ecosystem to accelerate cancer research and improve patient care and outcomes.

The US federal government is committed to maximizing its return on biomedical research investment. This tenet is exemplified by policies that promote broad, responsible sharing of research products generated with public funds, including the recent National Institutes of Health (NIH) Final Data Management and Sharing Policy and the NIH Updated Public Access Policy. Scientific data management and sharing must occur in a FAIR (findable, accessible, interoperable, and reusable) manner for it to be broadly usable and thereby most impactful. The National Cancer Institute (NCI) Office of Data Sharing conducted a series of workshops to identify clinical features and profiles derived from patients and study participants that inform these respective basic, translational, clinical, and populational science research analyses. The workshop outputs lay the groundwork for developing best practice recommendations on high-value, impactful clinical data features to collect and share with the wider research community. The workshop also highlighted additional data types and methodologies represented across the NCI-funded research portfolio that need structured outputs to be better defined to similarly allow broad sharing in a FAIR manner. In this article, we summarize the workshop discussions on data use challenges, present potential solutions, and outline attendee consensus on the minimum patient-derived clinical information needed to complete a wide spectrum of cancer research. We further propose preliminary guidance for policymakers and researchers to implement regarding collection and management of human derived clinical data in consistent and impactful ways that can improve the sharing process and outcomes for data end users.

The Molecular Characterization Initiative (MCI), a key effort of the National Cancer Institute’s Childhood Cancer Data Initiative (CCDI), was launched in 2022 in collaboration with the Children’s Oncology Group (COG) to bring comprehensive genomic and molecular profiling to children, adolescents, and young adults diagnosed with cancer. The MCI provides paired tumor and germline molecular testing, with results returned to clinicians to inform care. Deidentified data are made available to the research community through the CCDI Data Ecosystem to facilitate the discovery of new treatment strategies. This commentary outlines the MCI’s development, key accomplishments to date, and its role in laying the foundation for standardized clinical diagnostics in pediatric oncology.

Summary: The precisionFDA Truth Challenge V2 aimed to assess the state-of-the-art of variant calling in difficult-to-map regions and the Major Histocompatibility Complex (MHC). Starting with FASTQ files, 20 challenge participants applied their variant calling pipelines and submitted 64 variant callsets for one or more sequencing technologies (~35X Illumina, ~35X PacBio HiFi, and ~50X Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with the new GIAB benchmark sets and genome stratifications. Challenge submissions included a number of innovative methods for all three technologies, with graph-based and machine-learning methods scoring best for short-read and long-read datasets, respectively. New methods out-performed the 2016 Truth Challenge winners, and new machine-learning approaches combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants.

Clinical samples contained in biorepositories represent an important resource for investigating the many factors that drive human biology. The biological and chemical markers contained in clinical samples provide important measures of health and disease that when combined with such medical evaluation data can aid in decision making by physicians. Nearly all disciplines in medicine and every “omic” depend upon the readouts obtained from such samples, whether the measured analyte is a gene, a protein, a lipid, or a metabolite. There are many steps in sample processing, storage, and management that need to understood by the researchers who utilize biorepositories in their own work. These include not only the preservation of the desired analytes in the sample but also good understanding of the moral and legal framework required for subject protection irrespective of where the samples have been collected. Today there is a great deal of effort in the community to align and standardize both the methodology of sample collection and storage performed in different locations and the necessary frameworks of subject protection including informed consent and institutional review of the studies being performed. There is a growing trend in developing biorepositories around the focus of large population-based studies that address both active and silent nonsymptomatic disease. Logistically these studies generate large numbers of clinical samples and practically place increasing demand upon health care systems to provide uniform sample handling, processing, storage, and documentation of both the sample and the subject as well to ensure that safeguards exist to protect the rights of the study subjects for deciding upon the fates of their samples. Currently the authority to regulate the entire scope of biorepository usage exists as national practice in law in only a few countries. Such legal protection is a necessary component within the framework of biorepositories, both now and in the future. In this brief overview, we provide practical information to the potential users of biorepositories about some of the current developments in both the methodology of sample acquisition and in the regulatory environment governing their use.