The Organization for Human Brain Mapping Time Machine: A freely accessible archive of Annual Meeting talks on YouTube
Bibliographic record
Abstract
Since 1995, the Organization for Human Brain Mapping (OHBM) Annual Meeting has provided a critical platform for sharing the latest developments in brain mapping. OHBM has recorded presentations for over a decade and made them accessible to its members through the OnDemand platform. As of 2024, this content is now openly and freely accessible to brain mapping enthusiasts worldwide—without the need to be a society member. Limited access to cutting-edge scientific content solely to paying members contradicts the longstanding ethos of OHBM, which has championed open science principles and practices for many years. In this paper, we introduce the OHBM Time Machine, a collaborative initiative undertaken by the Program, Education and Communications Committees of OHBM. The project seeks to create a free and permanent archive of all recorded Annual Meeting content, accessible to members and non-members alike. We outline the ongoing efforts to migrate all content dating back to 2015 to the OHBM YouTube channel and provide guidelines to support hosting future Annual Meeting recordings. Additionally, we discuss the benefits and challenges associated with this initiative, explaining why making this content publicly available will not diminish the value of attending the Annual Meetings. Instead, it is expected to enhance interest and awareness of OHBM as a world-leading organization of brain mapping experts. The OHBM Time Machine will provide an unparalleled educational resource, establishing a lasting record of scientific progress and the evolution of critical topics within the field of brain mapping.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.110 | 0.073 |
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".