MDverse: Shedding Light on the Dark Matter of Molecular Dynamics Simulations.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37205542", "kind": "science-record", "revision": 2, "content_hash": "sha256:0acac1b6dc23f377b7c069b35d14dc94b48c07ee5188ba309ad1c4076420fa14", "created_at": "2026-11-01T19:01:54Z", "updated_at": "2026-11-01T21:30:48Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "MDverse: Shedding Light on the Dark Matter of Molecular Dynamics Simulations.", "doi": "10.1101/2023.05.02.538537", "pmid": "37205542", "authors": ["Johanna K S Tiemann", "Magdalena Szczuka", "Lisa Bouarroudj", "Mohamed Oussaren", "Steven Garcia", "Rebecca J Howard", "Lucie Delemotte", "Erik Lindahl", "Marc Baaden", "Kresten Lindorff-Larsen", "Matthieu Chavent", "Pierre Poulain"], "journal": "bioRxiv : the preprint server for biology", "year": 2024, "abstract": "The rise of open science and the absence of a global dedicated data repository for molecular dynamics (MD) simulations has led to the accumulation of MD files in generalist data repositories, constituting the dark matter of MD - data that is technically accessible, but neither indexed, curated, or easily searchable. Leveraging an original search strategy, we found and indexed about 250,000 files and 2,000 datasets from Zenodo, Figshare and Open Science Framework. With a focus on files produced by the Gromacs MD software, we illustrate the potential offered by the mining of publicly available MD data. We identified systems with specific molecular composition and were able to characterize essential parameters of MD simulation such as temperature and simulation length, and could identify model resolution, such as all-atom and coarse-grain. Based on this analysis, we inferred metadata to propose a search engine prototype to explore the MD data. To continue in this direction, we call on the community to pursue the effort of sharing MD data, and to report and standardize metadata to reuse this valuable matter.", "claims": [{"id": "claim-method", "statement": "To circumvent this issue, we developed an original and specific search strategy that we called Explore and Expand (Ex2) (see Fig. 1-A and Materials and Methods section) and that relies on a combination of file types and keywords queries.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37205542", "locator": "sections[2] \"Results\" paragraph 4", "quote": "To circumvent this issue, we developed an original and specific search strategy that we called Explore and Expand (Ex2) (see Fig. 1-A and Materials and Methods section) and that relies on a combination of file types and keywords queries."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data files produced from the data collection and processing are shared in Parquet format in the Zenodo repository: https://zenodo.org/record/7856806\nPython scripts to search and index MD files, and to download and parse .mdp and .gro files are open-source, freely available on GitHub (https://github.com/MDverse/mdws) and archived in Software Heritage (swh:1:dir:4d30b00345a732dcf9f79d3c8bfae38b35b8f2c4). A detailed documentation is provided along the scripts to easily reproduce the data collection and processing.\nJupyter notebooks used to analyze results and create the figures of this paper are open-source, freely available on GitHub (https://github.com/MDverse/mdda) and archived in Software Heritage (swh:1:dir:1f8497f72134cef0a9724c955bb03c751f52cccd).\nThe code of the MDverse data explorer web application is open-source, freely available on GitHub (https://github.com/MDverse/mdde) and archived in Software Heritage (swh:1:dir:1fc8b8eaabf4a9087e6d5b0ec5ed97031482bcbf)", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37205542", "locator": "sections[33] \"Data and code availability\" paragraphs 1-5", "quote": "Data files produced from the data collection and processing are shared in Parquet format in the Zenodo repository: https://zenodo.org/record/7856806\nPython scripts to search and index MD files, and to download and parse .mdp and .gro files are open-source, freely available on GitHub (https://github.com/MDverse/mdws) and archived in Software Heritage (swh:1:dir:4d30b00345a732dcf9f79d3c8bfae38b35b8f2c4). A detailed documentation is provided along the scripts to easily reproduce the data collection and processing.\nJupyter notebooks used to analyze results and create the figures of this paper are open-source, freely available on GitHub (https://github.com/MDverse/mdda) and archived in Software Heritage (swh:1:dir:1f8497f72134cef0a9724c955bb03c751f52cccd).\nThe code of the MDverse data explorer web application is open-source, freely available on GitHub (https://github.com/MDverse/mdde) and archived in Software Heritage (swh:1:dir:1fc8b8eaabf4a9087e6d5b0ec5ed97031482bcbf)"}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using the Explore and Expand search strategy, the study indexed about 250,000 files from about 2,000 datasets representing 14 TB of molecular dynamics data deposited in generalist repositories between August 2012 and March 2023.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37205542", "locator": "sections[2] \"Results\" paragraph 5", "quote": "Globally, we indexed about 250,000 files and 2,000 datasets that represented 14 TB of data deposited between August 2012 and March 2023 (see Table 1)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Using the Explore and Expand search strategy, the study indexed about 250,000 files from about 2,000 datasets representing 14 TB of molecular dynamics data deposited in generalist repositories between August 2012 and March 2023.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:37205542"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37205542", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1101/2023.05.02.538537", "sha256": "sha256:8d9af8a629c2af28a7806bca79b861acf09a2f7f829c379d7aa5fa2c95c8a520", "bytes": 52580, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1101/2023.05.02.538537", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10187166", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": null, "specific": null, "label": "Explore and Expand (Ex2) search strategy", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "To circumvent this issue, we developed an original and specific search strategy that we called Explore and Expand (Ex2) (see Fig. 1-A and Materials and Methods section) and that relies on a combination of file types and keywords queries."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R4", "expressible": false, "required_operations": [], "missing_operations": [], "basis": "The reader quoted the method the paper names (Explore and Expand (Ex2) search strategy), but the corpus's method extractor could not map it onto a DSL operation, so the method is published but not expressible."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R4", "reachable": false, "accessions": [], "resolved_accessions": [], "statements": ["Data files produced from the data collection and processing are shared in Parquet format in the Zenodo repository: https://zenodo.org/record/7856806\nPython scripts to search and index MD files, and to download and parse .mdp and .gro files are open-source, freely available on GitHub (https://github.com/MDverse/mdws) and archived in Software Heritage (swh:1:dir:4d30b00345a732dcf9f79d3c8bfae38b35b8f2c4). 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