title: ai4lam Metadata/Discovery WG Monthly Meeting # June 08, 2021 9 AM California \| 12 PM Washington DC \| 5 PM UK \| 6 PM Oslo & Paris **Attending** - *Name (institution)* - Tim Thompson (Yale) - Jeremy Nelson (Stanford) - Nicole Coleman (Stanford) - Amanda Whitmire (Standord) - Erik Radio (Colorado) - Massimo Petrozzi (Computer History Museum) - **Regrets** - *Name* **Notetaker (alpha by first name): **Tim []{#anchor}Helpful Links - [*Metadata WG Zotero Group Library*](https://www.zotero.org/groups/2709151/ai4lam_metadata_wg/library) []{#anchor-1}Project Documents and Data - [*WG charter*](https://drive.google.com/file/d/1ypcx2F30siqr-KYOKFZtVv8h9PIS9a77/view?usp=sharing) ```{=html} ``` - [*WG Google Drive folder*](https://drive.google.com/drive/folders/1cpZtbjKadgD30794fD97XY-EChUSy2r9?usp=sharing) []{#anchor-2}Agenda Topics 1. Updates, announcements, intros 2. Stanford Taxa Project - Nicole Coleman, Amanda Whitmire, Jeremy Nelson 3. Sinopia Knowledge Graph using kglab (time permitting) Jeremy Nelson Stanford Taxa Project - Marine station library. 100+ year collection: theses, dissertations, research artifacts, including student work (1946-present) - Using measurements from the past in context of climate change - But data is hidden in texts - Digitizing student papers, etc., to extract species observations\--i.e., species occurrence - Going into GBIF database (6 billion entries) - OCR on images, parsed TEI XML through GROBID → Segmentation of text into divs - Using NER to identify species - Having human review tagged text to confirm species occurrence (can\'t be 100% unsupervised) → Making confirmation as fast as possible - Collaborating with colleagues in British Columbia, etc. (also > with historical papers) - Broad range of potential stakeholders for tool - Oversight: Stanford AI Steering Group - Process and project design template - Lit review - Reflections on other projects - E.g., COPIOUS project - Using Biodiversity Heritage Library - Focused on Philippines - Text/data processing → Segmentation can influence results → species references not necessarily sequential - Expression of observations can be complex → narrative, not > consistently structured (context varies) - First step: ingest paper into GNRD tool → frequencies provide > clues as to subject of paper - Catalog of Life as knowledge source → link to WoRMS → which > links to Wikipedia/Wikidata → Description of habitat - GBIF also a data source - Q: At what point does human intervention occur in the process? - Created Taxa app to allow librarian to verify in a UI - Rethinking pipeline to try to make prefiltering smarter in order > to limit what\'s included for verification via app - Interested in process more generally to make it applicable as a > model → More emphasis on data sources, preparing the > groundwork - What\'s the implication of using contemporary ML models on > historical sources? - Q: How was model trained? Was expert knowledge leveraged? - Custom spaCy lookup defined using values from WoRMS - Also custom geographic data from West Coast + list of habitats - Body of TEI organized into divs → each div run through NER > pipeline - Verifier represents divs - Pattern-based initial approach had some limitations - Looking into entity rules, other built-in parts of spaCy to improve the model - Currently focusing on locations entities for improvement → minimizing false positives - All initial setup was done in one week! - Data problems could be spotted because subject expert + metadata > expert were part of the team. → Building more complete > knowledge graph. - More coordination costs, but serves the goals of the project - Building a tool for experts to be able to verify - Example of what the library can provide to address a research problem using AI - Also unique doing this in the context of science, biodiversity. - Created \"methods planner\" to communicate about steps in pipeline. Processes can be treated as routine, but involve many decision points along the way. - Metadata about ML pipelines and processes → How can we > standardize it, document decision points in a structured way. - Looked at data nutrition labels, datasheets for datasets → > robust readme file - But definitions of \"data\" can be ambiguous → at what point do > we call it \"data\" → How much processing has to be done > before it\'s \"data\" - Line between what\'s data & what\'s metadata → e.g. are > identifiers data or metadata → Yes, but the border is > contextual - But to the extent that outputs are hosted in library, we have to > put them somewhere, relate them to other objects → Risk of not > fully appreciating the transformation of data along the way. - Treating text/corpora into primary texts/objects of concern - Question of goals: why are we working with data? For access? For > research? How to preserve project for future in order to > understand how \"data\" was defined. With AI, we can mobilize > both data + metadata → Yet risk of data deluge - Still important to keep the distinction for future understanding, reuse/reproducibility of work - Possibilities for how we can rethink relation between data & > metadata → Theses/dissertations FAST project example → Better > approach is unsupervised topic modeling (Andromeda Yelton\'s > work), rather than supervised assignment of subject terms → > But means we need different kinds of discovery tools. Recording of today's call is available at https://stanford.zoom.us/rec/share/woYmC8o5Rqa63TycKWzUuKEN9_s95WMfiD7y0rnDRIINJ_BuFMYEab8kQZILyhza.LwrpUEBKa5Jg7T6O