Socio-Cultural characteristics of usability of bioinformatics databases and tools
Bibliographic record
Abstract
With the increasing importance of the usability of bioinformatics systems and databases, this paper examines the socio-cultural characteristics that may affect the usability of such tools. We understand socio-cultural characteristics to be the norms, values, and beliefs that mediate the interactions between the structures and institutions of science (i.e. disciplines, universities, funding organizations), and its practitioners. These factors are not necessarily distinct from the technical features of a database, but do nevertheless affect the context in which one chooses to use a particular set of tools. We have developed three socio-cultural characteristics of bioinformatics database usability: accessibility, utility, and portability. By ‘accessibility’, we mean the social and cultural attributes that make resources open and available for use, such as intellectual property arrangements or institutional reputation and prestige. ‘Utility’ in this context means the perceived usefulness of a database, which can be determined by non-technical matters such as trust and taste. ‘Portability’ refers to the social aspects of criteria such as maintenance funding, and input and storing standards that allow a database to move through space and time. In this article, we call for a social science research programme on these — and other — socio-cultural characteristics to usability. We invite researchers in human–computer interaction, bioinformatics, usability engineering and other areas to extend their work to examine the social contexts in which these systems are used, and the sociocultural factors that mediate their use. Such a research programme would increase the multidisciplinary nature of these emergent fields, and help address the complexities of work in the post-genomic era.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".