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Join the conversation on data & knowledge management in biomedicine and discover new ideas and practical tools for analyzing, sharing, and publishing your research.
📑 Table of Contents
Spring 2023
Christopher D Harvey, Ph.D., Professor of Neurobiology & Cindy Yuan, graduate student | Harvard Medical School
Analysis and management pipelines for large-scale neuroscience imaging & electrophysiology data, Christopher D Harvey, Ph.D. and Cindy Yuan
0:00 Introduction | 1:10 Main Presentation | 43:26 Q&A with audience
Caterina Strambio De Castillia, Ph.D. | CZI Imaging Scientist, Assistant Professor of Molecular Medicine, UMass Chan Medical School
Managing the image-data life cycle for the real world: Connecting the dots from sample preparation to image acquisition, analysis and publication, Caterina Strambio De Castillia, Ph.D.
0:00 Introduction | 2:00 Main Presentation | 47:12 Q&A with audience
Rigorous and quantitative cell science crucially depends on the generation of high-quality datasets in which all relevant information (i.e., metadata) about a microscopy experiment is reported using FAIR (Findable Accessible Interoperable Reusable) principles. Significant advances in spatiotemporal resolution have led to ever-expanding microscopy datasets which, without agreed-upon community guidelines, are challenging to quantitatively analyze (including AI-assisted strategies), reproduce, and re-use. To overcome this hurdle, it is essential to integrate community-specified image documentation and quality-control guidelines within easy-to-use Research Data Management (RDM) software tools and pipelines to support the streamlined execution, tracking, and documentation of the full life-cycle of image data from sample preparation, image acquisition and analysis to publication and sharing (i.e., data provenance).
Adam Taylor, Ph.D. | Senior Research Scientist, Sage Bionetworks
Collaborative tools and approaches for FAIR data sharing and team science, Adam Taylor, Ph.D.
0:00 Introduction | 2:30 Main presentation | 37:45 Q&A with audience
As biological research has grown increasingly data-intensive, collaboration among researchers with diverse expertise and resources has become essential. At Sage Bionetworks, we work with funders and researchers to coordinate data distribution under FAIR principles and to help “teams of teams” balance incentives and achieve research goals. Our interdisciplinary team of data curators, scientists, engineers, designers, and governance experts builds tools and systems to enable this, including our NIH-recognized data repository Synapse. Our flexible approach ensures secure and adaptive stewardship, curation, and sharing of data and metadata, meeting the unique needs of each research community. We aim to make biomedical data widely available and usable, directly engaging research communities and leveraging team science-based strategies to support collaborative science. In this seminar, we will share our approach to accelerating collaborative research; our work with large consortia such as the Human Tumor Atlas Network; how you can use Synapse today; and ways of working with us to implement and enhance your data management and sharing plans.
Paula Montero Llopis, Ph.D. | Director of MicRoN Core, Harvard Medical School
Promoting Rigor & Reproducibility in fluorescence microscopy through accessible reporting tools & resources, Paula Montero Llopis, Ph.D.
0:00 Introduction | 3:02 Main presentation | 47:40 Q&A with audience
Over the past decade, biomedical research has become more quantitative and interdisciplinary. The development and advancement of new tools in light microscopy and data analysis, especially open-source methods, have played a significant role in this shift, enabling breakthroughs in biomedicine. This means, researchers can tackle more challenging questions and obtain a deeper understanding of complex biological systems than ever before. However, the rapid development presents new challenges for researchers, as an in-depth knowledge of each technology is needed to appreciate its impacts on bias and reproducibility. In this seminar, we discuss what impacts microscopy data and conclusions and provide tools and resources for designing rigorous and reproducible microscopy experiments and how to appropriately report microscopy methods.
Benjamin M. Gyori, Ph.D. | Assistant Professor, jointly appointed in Khoury College of Computer Sciences & Bioengineering
Accelerating discovery in biomedicine with machine-assisted data annotation and knowledge assembly - Benjamin M. Gyori, Ph.D.
0:00 Introduction | 6:38 Main presentation | 50:41 Q&A with audience
Making novel scientific discoveries requires integrating biomedical data and knowledge from diverse sources. However, merging disparate sets of information is time consuming and error-prone due to challenges like inconsistent naming conventions and the use of incompatible identifier resources. To address this, we introduce the Biopragmatics project, a new set of community standards and software tools to annotate data sets and make them easier to integrate. Then, we discuss the INDRA software system, which automatically assembles data and knowledge from large-scale automated processing of literature and pathway databases. We demonstrate how the knowledge assembled by INDRA can be used to generate mechanistic models and networks of biological systems, and inform novel hypotheses that can advance the field of biomedicine.
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