Humanities Data Inquiry: A Community of Practice Exploring Data Issues in the Humanities and Heritage Research
Bibliographic record
Abstract
This poster provides a snapshot of the Humanities Data Inquiry (HDI), a new community of practice exploring data issues in Humanities and Cultural Heritage (HCH) research. The last decade has seen great advances in the development of infrastructure, tools, and principles for the collection, storage, discovery, and dissemination of research data, and investment in a robust and rapidly emerging Open and FAIR (Findable, Accessible, Interoperable, and Reusable) research data ecosystem. While Humanities and Cultural Heritage (HCH) researchers and projects are encouraged to engage with this evolving research data ecosystem, the fit is often poor as this ecosystem is largely built with the needs of "Big Data" Science, Technology, Engineering, and Mathematics (STEM) in mind. Where Big Data STEM typically involves large datasets produced through experiment, observation, or analysis, "Small Data" HCH research often involves deep analysis and intensive curation of relatively small data sets — particularly when it comes to datasets focused on the representation of cultural texts and objects, such as editions and exhibits. Outlining the basic problems associated with data in HCH and Open Science infrastructure frameworks, this poster describes in overview how HDI is creating a space for community engagement and input through thoughtful, bidirectional communication among grass-roots researchers and between grass-roots researchers and the organizations responsible for creating and supporting the Open and FAIR ecosystem. Finally, the poster presents areas of future development and pathways for future involvement in the community of practice as well as the program’s expected outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.325 | 0.233 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.050 | 0.119 |
| Scholarly communication | 0.067 | 0.069 |
| Open science | 0.009 | 0.097 |
| Research integrity | 0.029 | 0.051 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".