Open Neuroimaging Laboratory: An Opensource Web Framework For Collaboration Around Brain Imaging Data.
Bibliographic record
Abstract
A > A0 poster which is designed for fabric print and can be cut and sewed into two T-Shirts. This project has received the OHBM best abstract merit award. The poster was presented at the 2017 Annual Meeting of the Organization for Human Brain Mapping (OHBM) in Vancouver, 25–29 June. Project: Significant investment has been made into collecting and sharing brain imaging data for thousands of individuals. One key challenge, however, limits the usability of this data: To work with it, researchers need to download the data locally; and curation, editing and analysis are then done redundantly by each research group. This painstaking process results in a large proportion of shared data not being analysed, wasting time and funding. With the Open Neuroimaging Laboratory, we solve this challenge by creating an opensource Web framework that provides direct access to this wealth of data, and allows people to perform analyses collaboratively, using only a browser. With its two first applications – BrainBox1 and MetaSearch2 – the OpenNeuroLab facilitates finding, improving, and reusing the massive amount of brain MRI data available online. By requiring nothing but a Web browser, our virtual neuroimaging laboratory lowers the barriers for researchers, students, and citizen scientists to help scientific discovery.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".