Why choose this one? Factors in scientists' selection of bioinformatics tools
Bibliographic record
Abstract
Purpose*.The objective was to identify and understand the factors involved in scientists' selection of preferred bioinformatics tools, such as databases of gene or protein sequence information (e.g., GenBank) or programs that manipulate and analyse biological data (e.g., BLAST).*Methods*.Eight scientists maintained research diaries for a two-week period, and were then interviewed following a semi-structured interview schedule.*Analysis*.The diaries and interview transcripts were analysed using a content analysis approach to reveal the factors that affected the selection of the bioinformatics tools the scientists used.*Results*.Some of the factors (e.g., ease of use, familiarity), were similar to those identified with respect to text-based, bibliographic resources, while others (e.g., interface, scalability) were specific to the bioinformatics domain.Particularly interesting was the variation in how a single factor was defined.Often what was preferred by one group of users was not preferred by another.*Conclusions*.The identification of the broad, and sometimes contradictory, range of factors preferred by scientists has several implications.These include the need to design and develop tools to accomodate all users, (e.g., with multiple interface options), and to devise means of recommending or selecting tools on the basis of preferred factors.
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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 | 0.067 | 0.253 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".