Swiss National Data and Service Center for the Humanities (DaSCH)
Bibliographic record
Abstract
This presentation introduces the infrastructure and services of the Swiss National Data and Service Center for the Humanitites (DaSCH) for researchers and research ITs. DaSCH develops and operates a FAIR long- term repository and a generic virtual research environment for complex and simple open research data in the Humanities in Switzerland, including law and theology. The primary goal of our platform is to guarantee direct access to the research data: it brings your data to life and keeps it alive in the long run. At the same time, it lets you edit, delete and enrich your data, even after it has been archived. Each object within a dataset has its own persistent identifier to allow reliable citability. We set value on interoperability with tools used by the Humanities and Cultural Sciences communities and foster the use of standards. The data is also accessible via an API, which allows computer scientists to collect data in an automated way. Our services for researchers and the community include hands-on training in the use of the DaSCH infrastructure, workshops thematizing frequently asked questions by researchers when writing a data management plan, participation in lectures, or workshops about best practices in the management and (re-)use of qualitative data in humanities research. As the coordinating institution and representative of Switzerland in DARIAH members of DaSCH actively engage in community building within Switzerland and abroad.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.205 | 0.107 |
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".