Trends in the Electron Microscopy Data Bank (EMDB)
Bibliographic record
Abstract
Recent technological advances, such as the introduction of the direct electron detector, have transformed the field of cryo-EM and the landscape of molecular and cellular structural biology. This study analyses these trends from the vantage point of the Electron Microscopy Data Bank (EMDB), the public archive for three-dimensional EM reconstructions. Over 1000 entries were released in 2016, representing almost a quarter of the total number of entries (4431). Structures at better than 6 Å resolution now represent one of the fastest-growing categories, while the share of annually released tomography-related structures is approaching 20%. The use of direct electron detectors is growing very rapidly: they were used for 70% of the structures released in 2016, in contrast to none before 2011. Microscopes from FEI have an overwhelming lead in terms of usage, and the use of the RELION software package continues to grow rapidly after having attained a leading position in the field. China is rapidly emerging as a major player in the field, supplementing the US, Germany and the UK as the big four. Similarly, Tsinghua University ranks only second to the MRC Laboratory for Molecular Biology in terms of involvement in publications associated with cryo-EM structures at better than 4 Å resolution. Overall, the numbers point to a rapid democratization of the field, with more countries and institutes becoming involved.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.013 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.046 |
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".