NCI's Office of Data Sharing Annual Symposium (Abstract Submission)

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# Created User IP address First Name Middle Initial Last Name Degree(s) Position/Title/Career Status Organization Organization Address Email Abstract Category Abstract Keywords Abstract Title Abstract Summary Upload Abstract Operations
593 Mon, 07/14/2025 - 13:53 Anonymous 10.208.24.51 Madison Behm MPH Fellow NCI DCCPS EGRP GEB Arlington madison.behm@nih.gov Poster Abstract The Paul Fearn Award for Excellence in Data Sharing – Recognizing Data Sharing in Cancer Control and Population Sciences Secondary use of shared data can spur new discoveries, maximize data utilization, aid in tool development and testing, and foster a culture of reproducibility and innovation. This value was recently underscored during the COVID-19 pandemic, when new diagnostic tests, preventive and treatment solutions were rapidly developed through open exchange of data and knowledge. As stewards of public funds, the National Institutes of Health (NIH) has a responsibility to maximize the value of publicly funded research through data sharing. Reflecting this, the 2023 NIH Data Management and Sharing Policy requires all NIH-funded research to follow comprehensive data sharing standards and broadens NIH commitment to sharing publicly funded scientific data. While data sharing can be costly and time consuming, incentives, such as awards and prizes, coupled with policies, can reinforce and validate the significant investment researchers make in data sharing practices. In 2025, the National Cancer Institute's Division of Cancer Control and Population Sciences (DCCPS) established the Paul Fearn Award for Excellence in Data Sharing to foster a culture that recognizes, celebrates, and promotes data sharing. This annual award recognizes DCCPS-funded Principal Investigators and teams that have demonstrated a commitment to data sharing and have created data resources that accelerate cancer research. Named posthumously for Dr. Paul Fearn, a leader who championed data sharing as Branch Chief in the Surveillance Research Program, the award aims to honor past achievements and inspire continued efforts in open data sharing within the cancer research community. In this inaugural year of the award, we are acknowledging the efforts of many years of extramural work funded by DCCPS and have selected multiple recipients from four projects for their exemplary efforts.
592 Sun, 07/13/2025 - 00:48 Anonymous 10.208.28.250 sasas sasas cds xzx xsqx xzxz cotherikf@gmail.com xzxz xzx xzxzxzxz cxzxz
7 Thu, 06/26/2025 - 13:22 Anonymous 10.208.28.5 David Jacobs B.S., MBA Senior Managing Consultant IBM Washington, D.C. dfjacobs@us.ibm.com Poster Abstract USCDI+ Cancer, HL7's FHIR, RWD, AI Data Interoperability through USCDI+ Cancer and FHIR Implementation Guides for Real-World Data The USCDI+ defines real-world data (RWD) elements to further cancer prevention, diagnosis, treatment, research, and care through enhanced data exchange. HL7's FHIR, a leading health data exchange standard, supports adaptability and interoperability across diverse healthcare environments and systems, offering significant benefits in both clinical and research contexts. By enabling secure, rapid, and uniform data flow, USCDI+, combined with FHIR, enhances health outcomes and fosters innovation through data sharing and standardization. While FHIR offers robustness through its fundamental hierarchal architecture, it adds significant complexity to the development and testing of the implementation. This presentation explores potential enhancements to USCDI+ Cancer that could bolster both centralized and decentralized clinical trials. We will also discuss our experiences developing FHIR implementation guides for RWD, offering insights that could inform and support the FHIR adoption within USCDI+ Cancer, including the potential for using AI to assist in the process.
6 Thu, 06/26/2025 - 12:59 Anonymous 10.208.24.68 Qingying (Ally) Lu MS Associate Partner IBM Fairfax qingying.lu@us.ibm.com Demo Table Request Agentic AI, Automation, Cloud, Platform Empowering Cancer Data Collaboration through Agentic AI IBM’s AI & Data Automation platform, ATOM, provides secure, cloud-based services for data sharing, discovery, and automation. It supports cancer dataset integration through two key capabilities:
AI Assistant – Enhances user interaction by responding to questions and facilitating tasks such as understanding data policies, discovering datasets and collaborators, and drafting agreements.
Agentic AI – Automates complex workflows, including metadata curation, standards compliance, data quality checks, information extraction from unstructured sources, and data anonymization.
5 Wed, 06/11/2025 - 12:31 Anonymous 10.208.28.229 Julia Komissarchik MS CEO & co-founder Glendor, Inc Draper julia@glendor.com Demo Table Request multimodal data, PHI De-identification, patient's privacy, privacy protection, BAA-free data sharing Automatic at source scalable removal of PHI from multimodal medical data to empower data sharing, while protecting patient's privacy. The rapid expansion of data in cancer research includes a wealth of multimodal datasets such as medical images, pathology slides, surgical videos, photos, and audio recordings. As researchers begin to collect, aggregate, and analyze this diverse range of data, protecting patient privacy and ensuring highly scalable de-identification of Protected Health Information (PHI) for all kinds of medical data becomes critical. This poster describes Glendor PHI Sanitizer - software for automatic at source scalable removal of PHI from multimodal medical data to empower data sharing, while protecting patient's privacy.
4 Wed, 06/04/2025 - 17:34 Anonymous 10.208.24.182 Jeff Liu MSc. Director, Data Management and Strategy Dana-Farber Cancer Institute Boston J_Liu@dfci.harvard.edu Demo Table Request data, sharing, catalog, metadata, FAIR An Institutional Cancer Research Data Catalog for Enhanced Data Sharing, Findability, and Accessibility The exponential growth of scientific data in cancer research and precision medicine presents both significant opportunities and challenges for researchers. Data fragmentation and silos across data repositories and institions, along with non-standardized formats and metadata, hampers access and utilization of diverse data resources. The Data Management Team in the Division of Population Science has collaborated with the National Cancer Institute’s Childhood Cancer Data Initiative Project team, customized its open-source codebase for the Childhood Cancer Data Catalog and launched the Dana-Farber Cancer Research Data Catalog (DFDC). Besides new metadata identified, we have also enhanced DFDC with advanced phrase-based search feature and metadata extraction utility. Our platform integrates rich metadata for institutional and community data resources, streamlining data discovery for researchers. By providing easy access to a wide range of cancer data resources, researchers and trainees at Dana-Farber are empowered to accelerate secondary data analysis, promoting data sharing and fostering collaboration and data reuse within the DFCI research community.
3 Wed, 06/04/2025 - 16:58 Anonymous 10.208.28.130 Jeff Liu MSc. Director, Data Management and Strategy Dana-Farber Cancer Institute Boston J_Liu@dfci.harvard.edu Poster Abstract cancer, data, sharing, catalog, metadata Empowering Cancer Research Discovery: An Institutional Data Catalog for Enhanced Data Sharing, Findability, and Accessibility The exponential growth of scientific data in cancer research and precision medicine presents substantial challenges for researchers striving to access and utilize diverse data resources. These challenges stem from data fragmentation across multiple databases and repositories, compounded by a lack of standardization in formats and metadata. Moreover, institutional data silos further impede collaboration and comprehensive analyses. To address these obstacles, the National Cancer Institute’s Childhood Cancer Data Initiative (CCDI) has developed the Childhood Cancer Data Catalog (CCDC), a groundbreaking searchable database housing pediatric data resources shared by the pediatric cancer research community.

Collaborating closely with the CCDC project team, the Data Management Team in the Division of Population Science at DFCI customized the open-source CCDC codebase and built a DFCI Research Data Catalog (DFDC). The DFDC platform integrates rich metadata and high-value institutional resources, including solid tumor curation projects, the Profile cohort, Rapid Heme Panel data, cBioPortal, ImmunoProfile imaging data, the Molecular Oncology Almanac. Additionally, it incorporates numerous community datasets such as those from the NIH All of Us program, GENIE, SEER, NCI’s Cancer Research Data Commons (CRDC), and hundreds of dataset entries graciously shared by the CCDC project team.

By providing researchers with easy access to a vast array of cancer data resources, DFDC streamlines the process of data discovery, enabling researchers to identify and access resources within hours rather than days or weeks. This enhanced accessibility facilitates more efficient secondary data analysis, thereby catalyzing advancements in cancer research. Furthermore, the DFDC platform serves as a platform for data sharing among DFCI researchers, fostering collaboration and encouraging greater data reuse within the scientific community.
2 Wed, 06/04/2025 - 16:49 Anonymous 10.208.28.130 David M. Higgins Ph.D. Informatics Program Manager Children's Hospital of Philadelphia Philadelphia higginsd@chop.edu Poster Abstract pediatric, The Gabriella Miller Kids First Data Resource Center Nine-year-old brain tumor patient Gabriella Miller challenged members of Congress to ?stop talking and start doing? when providing federal funding for research into cures for pediatric cancer and congenital anomalies. Her advocacy efforts resulted in the launch of the Gabriella Miller Kids First Pediatric Research Program whose goal is to uncover new insights into the biology of childhood cancer and congenital anomalies.

The Gabriella Miller Kids First Data Resource Center (Kids First DRC) has since honored her legacy by building a comprehensive data resource for genomic research into pediatric conditions. Data from more than 35,000 participants annotated with demographic and clinical information related to their diagnoses have been released for secondary research and analysis using the center?s web-based platforms.

These platforms include the Kids First Data Resource Portal, a tool for querying the Kids First cohorts to identify participants, biospecimens, and data files of interest. The Kids First Portal directly connects to CAVATICA powered by Velsera, allowing users to transfer data files which they have been approved to access. CAVATICA is a cloud analysis platform which allows researchers to carry out large scale collaborative bioinformatic analyses on genomic files. The Kids First DRC also supports PedcBioPortal, a browser-based tool for carrying out somatic data analysis.