NLM DIR Seminar Schedule
UPCOMING SEMINARS
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Jan. 20, 2026 Anastasia Gulyaeva
TBD -
Jan. 22, 2026 Mario Flores
AI Pipeline for Characterization of the Tumor Microenvironment -
Jan. 27, 2026 Zhaohui Liang
TBD -
Jan. 29, 2026 Mehdi Bagheri Hamaneh
FastSpel: A simple peptide spectrum predictor that achieves deep learning-level performance at a fraction of the computational cost -
Feb. 3, 2026 Matthew Diller
TBD
RECENT SEMINARS
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Jan. 8, 2026 Won Gyu Kim
LitSense 2.0: AI-powered biomedical information retrieval with sentence and passage level knowledge discovery -
Dec. 16, 2025 Sarvesh Soni
ArchEHR-QA: A Dataset and Shared Task for Grounded Question Answering from Electronic Health Records -
Dec. 2, 2025 Qingqing Zhu
CT-Bench & CARE-CT: Building Reliable Multimodal AI for Lesion Analysis in Computed Tomography -
Nov. 25, 2025 Jing Wang
MIMIC-EXT-TE: Millions Clinical Temporal Event Time-Series Dataset -
Oct. 21, 2025 Yifan Yang
TBD
Scheduled Seminars on Sept. 9, 2025
In-person: Building 38A/B2N14 NCBI Library or Meeting Link
Contact NLMDIRSeminarScheduling@mail.nih.gov with questions about this seminar.
Abstract:
Supplementary materials accompanying scientific articles are critical components of biomedical research, offering detailed datasets, experimental protocols, and extended analyses that complement the main text. These materials play an important role in enhancing transparency, reproducibility, and scientific impact by providing in depth analyses and the details necessary for reproducing experiments. However, the lack of consistent and standard formats has limited the access to supplementary materials in scientific investigations. In response, we propose a novel system aimed to enhance FAIR access to Supplementary MAterials for Research Transparency (FAIR-SMART). Specifically, we first aggregate supplementary files in a single location, standardize them into structured and machine-readable format, and make them accessible via web APIs. Next, we employ advanced large language models to automatically categorize the tabular data, which represents over 90% of the textual content in supplementary materials, enabling precise and efficient data retrieval. By bridging the gap between diverse file types and automated workflows, this work not only advances biomedical research but also highlights the transformative potential of accessible supplementary materials in shaping the behaviors and decision-making processes of the scientific community. FAIR-SMART is freely available for supplementary materials data retrieval via its APIs: https://www.ncbi.nlm.nih.gov/research/bionlp/APIs/FAIR-SMART/.