Open Science and Accelerating Discovery in Rare and Neglected Diseases
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
New medicines for many diseases, in particular neurodegenerative disorders, are not forthcoming, despite patient demands and billions of dollars spent on biomedical research globally. Traditional publishing methods in biomedical sciences are generally slow and disseminate manuscripts, sometimes without the inclusion of primary data, to a privileged audience affiliated to institutions which can afford publication subscription costs. To overcome this barrier to progressive scientific endeavors, many researchers are championing the use of preprints, transparent subject-relevant data repositories, open access journals and open lab notebooks in an effort to more effectively and efficiently communicate their research to a wider audience. In this talk I shall discuss these options and the decisions I have made as an early career researcher, to share my research output on Huntington's disease in real-time through an open lab notebook. Included will be a discussion of the motivations, methods and assessment of open online publishing, including an evaluation of my own open notebook endeavors.This paper is the text of the keynote delivered at the ELPUB2017 conference.
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.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 it