Survey of bioinformatics courses and concentrations in ALA-accredited master’s programs
Bibliographic record
Abstract
Introduction: The interdisciplinary field of bioinformatics is considered an information-based discipline by many. Yet, it is unclear how Master of Library and Information Science (MLIS) degrees prepare librarians to apply their expertise in this unique, often non-textual information environment. The goal of this study is to identify the availability of MLIS-based bioinformatics educational opportunities to provide an update to the current bioinformatics landscape in North American MLIS programs or iSchools. Methods: We conducted a survey of available bioinformatics courses and program concentrations within 69 ALA-accredited master's programs. Using course catalogues and program descriptions on department websites, we identified the existence of courses and concentrations specific or related to the field of bioinformatics. We also surveyed the availability of associated certificate programs or degree alternatives. Results: Only two library and information science (LIS)-based bioinformatics courses are currently offered to MLIS students in ALA-accredited programs. There are no bioinformatics concentrations offered in the programs surveyed, however two graduate certificates could be applied towards an ALA-accredited master's degree. Students interested in related fields can pursue degree alternatives, including eight dual degree options. Discussion: The scarcity of LIS-based bioinformatics courses and program concentrations may suggest that LIS has not adopted bioinformatics into their field nor curricula. As a result, students interested in pursuing careers in bioinformatics and related disciplines must actively seek out opportunities for education and professional development. Bioinformatics degree options within MLIS or iSchools points towards an increased dialogue and acceptance of the connection between bioinformatics and information science, but the lack of ALA -accreditation limits possibilities for emerging librarians.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".