{"data":{"id":"10.4121/7016630b-8819-45bc-91ec-40a2d99b97b0","type":"dois","attributes":{"doi":"10.4121/7016630b-8819-45bc-91ec-40a2d99b97b0","prefix":"10.4121","suffix":"7016630b-8819-45bc-91ec-40a2d99b97b0","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Backer, Suzanne","nameType":"Personal","givenName":"Suzanne","familyName":"Backer","nameIdentifiers":[{"nameIdentifier":"0009-0006-2342-4246","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]}],"titles":[{"title":"Lab notebooks and extracted metadata, supporting the Msc thesis: Metadata Extraction from Scientific Lab notebooks"}],"publisher":"4TU.ResearchData","container":{},"publicationYear":2026,"subjects":[{"subject":"Plant Biology","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0607"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Information Systems","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0806"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Information and Computing Sciences","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"08"},{"subject":"Biological Sciences","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"06"},{"subject":"metadata"},{"subject":"master thesis"},{"subject":"Multi-agent systems (MAS)"},{"subject":"Metadata standards"}],"contributors":[{"name":"TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science","nameType":"Organizational","affiliation":[],"contributorType":"Other","nameIdentifiers":[]},{"name":"CropXR","nameType":"Organizational","affiliation":[],"contributorType":"Other","nameIdentifiers":[]}],"dates":[{"date":"2026-05-18","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"References","relatedIdentifier":"https://repository.tudelft.nl/record/uuid:8ea57d00-1df7-423f-ab2b-0eff4a89ff7f","relatedIdentifierType":"URL"},{"relationType":"IsVersionOf","relatedIdentifier":"10.4121/7016630b-8819-45bc-91ec-40a2d99b97b0.v1","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[".pdf, .xlsx, .json, .txt"],"version":"1","rightsList":[{"rights":"Restrictive Licence","rightsUri":"https://data.4tu.nl/info/fileadmin/user_upload/Documenten/4TU.ResearchData_Restricted_Data_2022.pdf"}],"descriptions":[{"description":"Abstract: High-quality, interpretable metadata is essential for enabling data reuse and reproducibility in the life sciences. Frameworks such as the Investigation–Study–Assay (ISA) model provide a structured way to describe experimental workflows, yet in practice metadata remains incomplete, inconsistent, and costly to produce. Lab notebooks, which record experimental procedures, offer a promising source for automating metadata creation, but their unstructured and heterogeneous nature makes extraction challenging. \u003cbr\u003eThis thesis investigates the feasibility of extracting ISA-compliant metadata from lab notebooks using a multi-agent system. It also addresses the lack of suitable evaluation methodologies for such tasks, given the inherent flexibility of the ISA framework. To this end, a prototyping methodology is adopted to develop two artefacts: (1) a prototype multi-agent system that decomposes the extraction task into specialized components, and (2) a rubric-based evaluation framework capable of assessing extraction performance beyond binary correctness. \u003cbr\u003eA feasibility study is conducted on a set of real-world lab notebooks to evaluate the proposed system. The results show that while fully automated extraction remains challenging, partially correct and meaningful metadata can be extracted, providing a practical starting point for ISA metadata creation. The study further yields actionable design recommendations for future system iterations. Moreover, the findings identify key design considerations for agent-based ISA extraction systems. Together, the prototype system, evaluation framework, and design recommendations lay the groundwork for future research toward scalable ISA metadata extraction systems.\u003cbr\u003eThis dataset includes: The EULA for this dataset The lab notebooks that were used during the feasibility study The metadata that was extracted from these lab notebooks For each lab notebook, the filled in evaluation form of the extracted metadata The signed informed consent forms, indicating that the owners of the lab notebooks consent to them being used for the purposes of this research","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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