PID Network Germany – Vision of a Networked and Open Scientific Landscape
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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 it