An Online Community of Data Enthusiasts Collaborates to Seek, Share, and Make Sense of Data
Bibliographic record
Abstract
A Review of: Stvilia, B., & Gibradze, L. (2022). Seeking and sharing datasets in an online community of data enthusiasts. Library & Information Science Research 44(3). https://doi.org/10.1016/j.lisr.2022.101160 Objective – To understand the major activities, tools, sources, and challenges of online communities focused on datasets. Design – Content analysis informed by activity theory. Setting – The r/Datasets subreddit, a web forum for sharing, seeking, and discussing datasets. Subjects – 1232 “hot” or “top” discussion threads (1232 original posts and 6813 responding comments) first posted between 2010 and 2020. Methods – The researchers used Reddit’s API to collect their sample of threads. Using a random subset of the sample, the researchers developed a coding scheme for content analysis, which identified major themes in the data. Through this process, they controlled for quality: each researcher coded half the subset independently, then together evaluated their intercoder reliability and discussed and resolved disagreements. The researchers also employed labelled latent Dirchlet allocation to construct topic models corresponding to the theme’s manual content analysis, which produced profiles of the top 100 terms most likely to appear in that topic. Finally, the researchers extracted URLs from threads in the sample to ascertain types of information and data sources used by the community. Presenting their findings, the researchers discussed notable themes and proposed a metadata model for describing datasets, the Data Q&A metadata (DQAM) model. Main Results – The r/Datasets community engages in three distinct activities: asking and answering questions, disseminating information, and community building. The closely related Q&A and dissemination activities shared themes of obtaining and aggregating data, sensemaking, collaborating and crowdsourcing, and data evaluation. Community members frequently discussed tools, competencies, and sources for data work. Major challenges for members of the community related to the general themes of data quality, accessibility, ethics, and legality. A proposed 16-element metadata schema should meet the needs of data enthusiasts. Conclusion – The content analysis reveals a dedicated community engaged in an array of data-seeking and data-sharing activities. Data producers should be mindful of how their data can be accessed and used outside of their original professional or scholarly contexts.
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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 | 0.050 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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; 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".