Exploiting analysis history to support collaborative data analysis
Bibliographic record
Abstract
Coordination is critical in distributed collaborative analysis of multidimensional data. Collaborating analysts need to understand what each person has done and what avenues of analysis remain uninvestigated in order to effectively coordinate their efforts. Although visualization history has the potential to communicate such information, common history representations typically show sequential lists of past work, making it difficult to understand the analytic coverage of the data dimension space (i.e. which data dimensions have been investigated and in what combinations). This makes it difficult for collaborating analysts to plan their next steps, particularly when the number of dimensions is large and team members are distributed. We introduce the notion of representing past analysis history from a dimension coverage perspective to enable analysts to see which data dimensions have been explored in which combinations. Through two user studies, we investigated whether 1) a dimension oriented view improves understanding of past coverage information, and 2) the addition of dimension coverage information aids coordination. Our findings demonstrate that a representation of dimension coverage reduces the time required to identify and investigate unexplored regions and increases the accuracy of this understanding. In addition, it results in a larger overall coverage of the dimension space, one element of effective team coordination.
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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 | 0.010 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".