Personal Visualization: Exploring Data in Everyday Life
Bibliographic record
Abstract
Individuals are beginning to seek how they can explore and understand the data that affect their personal lives. This includes biometric personal data such as health-related data, self-monitoring, sports and performance improvement; and other personal data such as online social networks, energy consumption, web activity, and photo collections. Personal life brings a new context to both visualization and visual analytics, and with that new context comes new research and design challenges. Personal visualization explores challenges related to collecting, visualizing, and making sense of data of personal interest. Understanding individuals’ needs in the context of their personal data and designing appropriate tools to support visualization and analysis of this data is a crucial and emergent challenge. This should consider factors such as data collection challenges, casual visualization, attractive and enjoyable interfaces, sensemaking and storytelling strategies, statistical reasoning for non statistical experts, and engagement. In particular, such designs should carefully consider pleasure, gamification, and engagement with the data instead of efficiency to perform low-level functional tasks quickly and accurately. We expect that advances in personal visualization will empower everyday people to make improvements to their lives and their communities. In this workshop, we will bring together people from academia and industry to discuss future perspectives in the emerging area of personal visualization. Our goals are to gather the community working on the topic of personal visualization and converge on a research agenda for the community.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.008 |
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".