PlayMyData: a curated dataset of multi-platform videogames
Bibliographic record
Abstract
About This repository contains the source code implementation used to replicate the experimental results obtained in the submitted to the 21st International Conference on Mining Software Repositories (MSR204). "PlayMyData: a curated dataset of multi-platform videogames" authored by: Andrea D'Angelo(1), Claudio Di Sipio, Cristiano Politowsky (2) and Riccardo Rubei (1) Università degli Studi dell'Aquila, Italy (2) University of Montreal, Canada Introduction PlayMyData is a multi-purpose, comprehensive videogame dataset of videogames released from 1993 up to November 2023. It contains metadata like titles, platforms, a summary of the story, and release data. It also integrates data from HowLongToBeat on completion times. Data description The dataset is structured as follows: all_games_PlayStation.csv: It contains IGDB metadata collected for the PlayStation platforms. screenshots.zip: It contains the collected screenshots from IGDB, grouped by genre all_games_Xbox.csv: It contains IGDB metadata collected for the Xbox platforms. all_games_PC.csv: It contains IGDB metadata collected for the PC. all_games_Nintendo.csv: It contains IGDB metadata collected for the Nintendo platforms. platforms.csv: It contains all gaming platforms available on IGDB and the corresponding ID. genres.csv: It contains the list of genres available on IGDB and the corresponding ID. all_videos.csv: It contains the gameplay URLs and the corresponding name as it appears on IGDB, e.g. "Gameplay Video" video_ids.csv: It contains the mapping with games and the list of gameplay videos. Some entries are missing. How to collect PlayMyData To collect PlayMyData, please refer to the supporting GitHub repo available at: https://github.com/riccardoRubei/MSR2024-Data-Showcase
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.054 |
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".