Carl Kesselman

The paper describes the development of a metadata federation system for NIH Common Fund data to improve discoverability without data movement.

The paper develops a workflow to identify transcription factor binding sites across 27 human tissues using DNase-seq data, resulting in a comprehensive footprint atlas with high-quality predictions.

The paper presents tools for ensuring FAIRness in big biomedical data analysis through a case study on creating a transcription factor binding site atlas from ENCODE data.

BDQC identifies data outliers and anomalies in large-scale genomic pipelines without domain knowledge, improving data quality and enabling new insights.

The paper introduces MultiCellDS, a standard and community-driven initiative for sharing multicellular data to enhance research in cell biology.

The paper introduces MultiCellDS, a standard for multicellular data curation, and presents MultiCellDB as a database to store associated data, enhancing data sharing and utility in biological and clinical research.