Preservation And Accessibility Of Primary Research Data, Presentation Of The "Data And Service Center For Humanities" (Dasch)
Bibliographic record
Abstract
Presentation of the "Data and Service Center for humanities" (DaSCH) at Force11 2018. The primary goals of the DaSCH are Preservation of research data in the humanities and their long-term data curation. Ensuring permanent access to research data in order to make it available for further research and thus facilitating the reuse of existing research data in future research. Providing services for researchers to assist them with the data life cycle management. Encouraging the digital networking of databases created in Switzerland or in other countries. Collaboration and networking with other institutions on digital literacy. For doing so the DaSCH developped dedicated open-source software, Knora (https://www.knora.org/), Salsah (http://www.salsah.org/) and Sipi (http://www.sipi.io/). For so diverse fields, the developped tools are generics and the DaSCH offers the consultancy and support of researchers and research projects in the Humanities regarding the creation, the use, the re-use and the long-term curation of digital data. And it operated the required technical infrastructure to effectively store the data in a fully the FAIR compliant (Findable, Accessible, Interoperable, and Re-usable) repository, making it interoperable by using OWL domain ontologies and publishing it through REST API.
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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.409 | 0.213 |
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".