How can we make GPS tracking studies more open, reproducible, and collaborative? A vision for the OpenGPS platform
Bibliographic record
Abstract
• OpenGPS addresses challenges in data sharing, reproducibility, and collaboration. • A three-phase plan: metadata collection, data archiving, analysis tools. • It ensures privacy with multi-tiered access, encryption, and anonymization methods. • Standardized formats and governance framework support open and FAIR data practices. • It fosters global research, enabling large-scale meta-analyses in mobility studies. This paper introduces OpenGPS, a platform envisioned for the archiving and processing of human mobility GPS data. The OpenGPS addresses the need for a centralized, privacy-preserving system that securely stores, shares, and analyzes GPS tracking datasets. The platform is envisioned to develop in three phases. Phase I focuses on collecting metadata from existing GPS tracking studies worldwide, providing a foundation for future research. Phase II involves archiving GPS data with standardized formats and robust privacy safeguards, ensuring data is accessible while protecting individual privacy. Phase III integrates advanced analytical tools and workflows directly into the platform, enabling efficient analysis and fostering collaboration among researchers. The OpenGPS aims to overcome the limitations of current human mobility studies by offering a standardized repository that enhances reproducibility and openness in research. By facilitating the sharing of data and methodologies, the OpenGPS will promote new insights and innovations in human mobility research. This platform is poised to become a critical resource for the scientific community, bridging gaps in data availability, and enabling comprehensive meta-analyses across different geographical and temporal scales. Through OpenGPS, researchers can collaborate more effectively, share resources, and advance the understanding of human mobility patterns globally.
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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 | 0.350 | 0.364 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.038 | 0.075 |
| Open science | 0.013 | 0.053 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".