25 Years of BOSC, the Bioinformatics Open Source Conference
Bibliographic record
Abstract
<ns3:p>The 25th annual Bioinformatics Open Source Conference (BOSC 2024, open-bio.org/events/bosc-2024) was part of the 2024 conference on Intelligent Systems for Molecular Biology (ISMB 2024). Launched in 2000 and held yearly since, BOSC is the premier meeting covering open-source bioinformatics and open science. ISMB 2024 was held in Montréal, Canada, with an online participation option. A total of nearly 2000 people attended; about 200 people participated in BOSC sessions. Over the course of two days, BOSC covered a wide range of topics in open science and open source bioinformatics, including Data Analysis, Open Data, Visualization, Developer Tools and Libraries, Standards and Frameworks for Open Science, and Open AI/ML. Mélanie Courtot delivered an impactful first keynote with a perspective on how “The Data Shows We Need Better Data”. The second keynote speaker, Andrew Su, discussed “Open Data, Knowledge Graphs, and Large Language Models.” BOSC ended with a panel, “Open Source AI/ML: A Game Changer for Bioinformatics?,” in which Lawrence Hunter and Thomas Hervé Mboa Nkoudou joined BOSC’s keynote speakers as panelists. Immediately following BOSC, the CollaborationFest was held at Montréal’s University of Québec campus. First launched in 2010, CoFest is a collaborative work event held yearly around BOSC. This year’s CoFest included 42 participants who worked together on 10 projects.</ns3:p>
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".