Data Resource Profile: the Physical Activity Cohort Repository (PACe)
Bibliographic record
Abstract
Key Features * The Physical Activity Cohort Repository (PACe) project created a searchable online database of cohort studies that have prospectively collected data on physical activity and/or sedentary behaviour (self-report and/or device-based measurement) at three or more timepoints. Only cohorts with ≥ 1,000 participants at baseline were included. * The PACe is a freely available resource created to encourage researchers to return to existing cohorts to apply contemporary causal inference methods that appropriately deal with time varying exposures and confounders, to improve the quality of evidence pertaining to physical activity, sedentary behaviour and health. * Two hundred and nineteen unique cohorts were included in the initial iteration of the PACe, representing all World Health Organization regions. The earliest included cohort started in 1922 (the Terman Life-Cycle Study) and extends to ongoing cohorts. * The development of the PACe was an international effort; this resource was designed to facilitate global collaboration and building research capacity in low- and middle-income countries. * We will update the repository every two years, to ensure that new cohorts and cohorts adding additional waves of follow-up are captured.
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 | 0.007 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.489 | 0.195 |
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".