A Human Mobility Dataset Collected via LBSLab
Bibliographic record
Abstract
Location-Based Services (LBS) have been prosperous owing to technological advancements of smart devices. Analyzing location based user generated data is a helpful way to understand human mobility patterns, further fueling applications such as recommender systems and urban computing. In this data descriptor, we introduce a dataset collected by LBSLab, a smartphone-based system implemented as a mini-program in the WeChat app, designed for large scale data collection from the smartphones of the participants with their informed consent. We provide activity data of multiple types including logins, profile viewing, weather checking, and check-ins with location information (latitude and longitude), POI and mood indicated, collected from 467 users over a duration of 11 days. We present some basic data analysis and expect the reuse of the data will allow researchers to better understand user behaviors of LBSs, human mobility, and also temporal and spatial characteristics of people’s mood. For further information about the LBSLab system, you can check out our position paper here: https://user.informatik.uni-goettingen.de/~ychen/papers/LBSLab-UbiComp18.pdf And also the Youtube video here: https://www.youtube.com/watch?v=m8r-1jqvYWc
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.016 |
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".