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Record W6912124845 · doi:10.5281/zenodo.1456790

Preservation And Accessibility Of Primary Research Data, Presentation Of The "Data And Service Center For Humanities" (Dasch)

2018· article· en· W6912124845 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2018
Typearticle
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsInteroperabilityPresentation (obstetrics)Service (business)ReuseData curationPublishingMetadataDigital preservationOrder (exchange)

Abstract

fetched live from OpenAlex

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 (<em>F</em>indable, <em>A</em>ccessible, <em>I</em>nteroperable, and <em>R</em>e-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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Open science
Consensus categoriesScholarly communication, Open science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.564
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0030.019
Open science0.0090.030
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.315
GPT teacher head0.378
Teacher spread0.063 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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