Data primer : making digital humanities research data public
Bibliographic record
Abstract
"Data management and curation are important processes for digital humanists: without proper planning and management, the value of the data as well as the labour involved in researching, collecting, and analyzing the data, could be lost! Data Primer: Making Digital Humanities Research Data Public helps integrate best practices when writing a Data Management Plan for research funding applications; it will also improve data curation strategies for collecting, managing, and publishing digital files and formats alongside traditional textual scholarship. This Data Primer was collaboratively authored by over 30 Digital Humanities researchers and research assistants, and was peer-reviewed by data professionals. It serves as an overview of the different aspects of data curation and management best practices for digital humanities researchers. The Data Flow and Discovery Tool and related best practices can be applied across the broad spectrum of digital humanities methodologies. A SSHRC-funded SpokenWeb partnership is used as an example throughout the Data Primer to illustrate how best practices were followed and incorporated into a data curation project. This data primer is endorsed by the National Training Expert Group of the Digital Research Alliance of Canada."-- Open Library (eCampusOntario)
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 | 0.047 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.127 | 0.086 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".