Challenging the status quo: A guide to open and reproducible neuroimaging for early career researchers
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
In the last decade, neuroimaging research has seen a proliferation of open tools, platforms, and standards aimed at addressing the reproducibility crisis in the field. The growing awareness on this topic is bringing about a cultural shift in the scientific community, especially among early career researchers (ECRs). As members of this demographic, we can attest to the fact that the adoption of these new tools and practices remains a challenge. This work aims to provide a practical guide for ECRs to navigate the expanding landscape of the open-science resources and make proactive decisions for their research workflows dealing with large, multiple datasets. From our own experience, we describe the common hurdles faced in typical research workflow and provide a set of solutions that could serve as a starting point for researchers looking for practical tools and protocols. Through a hypothetical scenario, we walk through the steps of curating, processing, harmonizing, and publishing a dataset while describing the tools and practices helpful for adopting FAIR (findable, accessible, interoperable, and reusable) principles. We hope this guide can help ECRs and others to simplify their daily research life as we all strive towards more open, reproducible, and translational neuroscience research.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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