Providing DDI metadata for OAI-PMH harvesting a Dataverse repository
Bibliographic record
Abstract
The approach that leads to the adoption of DDI as a standard to document resources from a research data bank, is, in a great share, the promise of users being able to use and compare different data sources. To facilitate discovery and searching, aggregator portals gather different sources of metadata in one place. The OAI-PMH protocol (Open Archives Initiative's Protocol for Metadata Harvesting) is widely used in this context for compiling metadata from various sources, including but not limited to data repositories. The DDI documentation standard has been a toolbox of choice at the CDSP of Sciences Po from the start of its documentation activities in 2006. Since 2016, the CDSP has been carrying out experiments with the open-source repository software Dataverse of the Dataverse Project© that is being developed at Harvard Institute for Quantitative Social Science (IQSS), along with collaborators and contributors worldwide and supported by the Global Dataverse Community Consortium. First dedicated to the Human and Social Sciences, Dataverse is now widely used beyond this first domain, and is the underlying infrastructure of several national repositories as in the Netherlands, Canada, France or more recently Denmark. From 2020 onwards, the CDSP is in charge of Science Po's institutional research data repository, data.sciencespo. Among numerous assets, Dataverse features an OAI-PMH server that provides embedded metadata either Dublin Core or DDI. The CDSP relies on this facility to act as a metadata provider for several portals of various scope and characteristics (domain, languages of sources and research output types). In the course of our dialogue with the OAI-PMH service providers, we have identified several points of attention that we will share here. In addition, we will also highlight how metadata describing data repositories available on portals such as re3data or FAIRsharing.org, can contribute to support data discovery by end users.
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 | 0.033 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.046 | 0.067 |
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".