Linking Scientific Results to Data, Analyses, and Code: A Demo
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HYBRID
12 - 1 PM (lunch will be served)
37 Hillhouse, Conf Room 102 or on Zoom
In person attendees should RSVP by 4/29/26. Lunch will be served. A Zoom link will be provided to any online attendees after registering below.
Join us for a one-hour hybrid session combining a presentation, live demo, and Q&A on TIB Knowledge Loom. Attendees will learn how to create a traceable, citable record of their scientific work using the TIB Knowledge Loom, making their research reproducible and reusable for themselves and others. For researchers who analyze data in R, Python, or similar tools, this means less time reconstructing past work and fewer methodological questions from collaborators or reviewers. For librarians and data stewards, it offers a practical model for structured, reusable scientific documentation. Faculty are especially encouraged to attend and consider how the TIB Knowledge Loom might support transparency and rigor in their research group’s work. Submissions to the TIB Knowledge Loom are welcome at any time, and free virtual office hours are also available for personalized guidance.
About the TIB Knowledge Loom: The TIB Knowledge Loom (https://knowledgeloom.tib.eu/) is an Open Science digital library that links scientific results to the data, analyses, and code used to produce them—supporting transparent, reproducible research. We curate knowledge at the level of scientific statements from sources including peer-reviewed articles, scientific reports, book chapters, and datasets. A collection of statements associated with a single source is called a Loom record. For instance, a Loom record associated with a peer-reviewed article documents the data analysis steps used to produce specific results presented in the article, key information that is often not fully or clearly described in the article itself. You can find an example of a Loom record here: https://doi.org/10.82209/n80m-qw77. When you click on a statement in this record, you will see the data, analyses, and code that support it. This makes it clear which datasets were used in each analysis, and which analyses and code produced specific results. Upon publication, each Loom record and statement receives its own persistent identifier (DOI). This makes it easy to give proper credit to datasets and code, and to trace the flow of knowledge across scientific works. The digital library is open source. As part of the TIB—Leibniz Information Centre for Science and Technology, our curation and publication services are free of charge.
Bio: Lauren Snyder is a researcher at the TIB—Leibniz Information Centre for Science and Technology and co-founder of the TIB Knowledge Loom. Her work focuses on helping research communities adopt tools that support open, reproducible, and reusable science. As an interdisciplinary researcher with a background in agroecology and food systems, she serves as a liaison between the computer scientists developing these approaches and the research communities that can benefit from them.
Lauren holds a B.A. in ecology and conservation biology from Boston University and a Ph.D. in ecology and evolutionary biology from Cornell University. Before moving to Germany in 2021, she worked as the research and education program manager at the Organic Farming Research Foundation, where she led the organization's grant program, research forum, and national surveys of U.S. organic farmers and ranchers.