Impact of shared facilities in advancing solid-state NMR research: 2025 edition
Bibliographic record
Abstract
Shared research facilities (SRFs) offer researchers cost-effective access to advanced analytical instrumentation that individual laboratories may find challenging to acquire or maintain. By centralizing resources, SRFs support a diverse user community including students, early-career scientists, senior principal investigators, and industrial collaborators, while providing expert technical support and ensuring efficient use of infrastructure and funding. These facilities not only drive research productivity and foster interdisciplinary collaboration, but also serve as centers for training the next generation of scientists. In this article, SRFs that offer solid-state nuclear magnetic resonance (NMR) capabilities are discussed, highlighting representative examples, their accessibility, governance models, technical operations, application areas, and data-sharing practices. Usage data reveal that solid-state NMR-based SRFs strongly align with high-priority research goals, contributing to impactful projects across chemistry, life sciences, and materials science, as reflected in publication outcomes. The article also emphasizes that the collaborative networks among SRFs enhance knowledge exchange and resource coordination. Such coordinated inter-facility partnerships are expected to address emerging challenges, ultimately supporting sustainable infrastructure that meets the evolving needs of the solid-state NMR community.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".