Different Portals - Different Views ... on the Same Content
Bibliographic record
Abstract
The proliferation of digital portals for accessing environmental and geoscientific data has significantly enhanced the ability of researchers, policymakers, and the public to retrieve and utilize critical information. Furthermore, metadata content can be harvested, which brings the added value that information is collected only once and then presented in different web presences.In this way, it is possible to tailor the presentation of metadata attributes to the relevant user groups - optimally presented according to their priorities. The Earth Data Portal (https://earth-data.de/), as a collaborative effort of the Helmholtz centers of the research field Earth and Environment enables querying data from multiple repositories, particularly from the Helmholtz research centers. In addition, umwelt.info (https://umwelt.info/de), operated by the German Environment Agency, offers a user-friendly interface of environmental monitoring data tailored for seamless access by the general public. The respective metadata content, on the other hand, is certainly best presented on the website of the source repository. This poster provides a general overview of the highlighted portals and delves into the specifics of their implementations, with a focus on the use of Persistent Identifiers (PIDs). PIDs play a crucial role in ensuring the long-term accessibility and citability of data, and the discussion will cover how each portal integrates PIDs to enhance data management and retrieval processes. Ultimately, the goal is to weigh how extensively the content depth should be represented, i.e., how granular the metadata attributes should be displayed, and what advantages an overarching perspective offers.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.020 | 0.028 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.052 | 0.038 |
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".