2021 EMDataResource Ligand Model Challenge Dataset
Bibliographic record
Abstract
This is the full dataset of the 2021 Cryo-EM Map-based Model Ligand Challenge sponsored by EMDataResource (www.emdataresource.org, challenges.emdataresource.org, model-compare.emdataresource.org). The Ligand Model Challenge aimed to assess the reliability and reproducibility of modeling ligands bound to protein and protein/nucleic-acid complexes in cryogenic electron microscopy (cryo-EM) maps determined at near-atomic (1.9-2.5 Å) resolution. Three published maps were selected as targets: (1) E. coli beta-galactosidase with inhibitor, (2) SARS-CoV-2 RNA-dependent RNA polymerase with covalently bound nucleotide analog, and (3) SARS-CoV-2 ion channel ORF3a with bound lipid. Sixty-one models were submitted from 17 independent research groups, each with supporting workflow details. File Descriptions: 2021-EMDataResource-Ligand-Challenge-web.pdf: Archive of challenges website content describing the challenge including News, Overview, Goals, Model Evaluation, Timeline, Targets, How to Participate, Q&A, Advisory Committee, Ligand Images T010X.zip: Submitted model files for each target (mmCIF and PDB formats) used for analyses S1_Ligand_Challenge_Statistics_Submission_Form.pdf: Overall model statistics, model submission form guide S2_submission_metadata.xlsx: Complete set of metadata collected for each submitted model S3_ligandchallengescores.xlsx: Compiled set of scores for this challenge from the model-compare site. WrapUp_MeetingAgendaLigandChallenge.pdf: Agenda of the July 26-28 2021 wrap up meeting zoom_discussion_mc_results.mp4: July 14 Model-Compare site analysis live demo recording
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 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.155 | 0.166 |
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".