The STRESS-EU database: A European resource of human acute stress studies for the worldwide research community
Bibliographic record
Abstract
Our current understanding of the human stress response and its role in health, resilience, and (psycho)pathology stems largely from acute stress studies in controlled laboratory settings. Comparability of findings across these individual studies is comprised, as sample size are often small, between-individual variation in the stress response is large and variation in stress-induction procedures and measurement timing is substantial. To overcome this, 16 research groups across Europe have established the STRESS-EU database. A unique resource with individual participant data (n = 6576) of acute stress studies to promote data reuse and facilitate both meta-analytical and proof-of-principle analyses with high statistical power, that can be hypothesis- or data-driven. This short communication highlights the structure, content, access and contribution procedures and future plans of the STRESS-EU database and invited researchers worldwide to contribute to this data resource.
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 | 0.019 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.019 | 0.034 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.061 | 0.020 |
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".