PID Network Germany – Vision of a Networked and Open Scientific Landscape
Bibliographic record
Abstract
In an increasingly digital scientific landscape, the permanent and reliable identification of resources linked to research processes, its actors and their research products by means of Persistent Identifiers (PIDs) has become indispensable. However, the growing importance of PIDs in everyday research and increasingly in cultural contexts also increases the demands on their efficient usability. At the same time, users are confronted with a great variety of very different offers of PID systems and their possible fields of application. The project "PID Network Germany", funded by the German Research Foundation (DFG) and scheduled to run for 36 months, therefore aims to establish a network of existing and currently forming actors around the persistent identification of persons, organizations, publications, resources, and infrastructures in the field of digital communication in science and culture. This will not only optimize the dissemination and networking of PID systems in Germany, but also their embedding in international infrastructures such as knowledge graphs. The findings from the project will result in recommendations in a national PID roadmap for Germany, thus sharpening the vision of an interconnected and open scientific landscape. Under this guiding principle, the poster provides an overview of the different use cases of PIDs that we will focus on in the project's context. This is intended to illustrate the heterogeneous PID landscape with a focus on Germany and identify potential needs for action.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".