{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33741990", "kind": "science-record", "revision": 6, "content_hash": "sha256:36f30ea9da28024f4ee923335ed4b74519cac31dc701365f8ac60193dd80c128", "created_at": "2026-11-01T15:41:29Z", "updated_at": "2026-11-01T21:24:55Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The Amsterdam Open MRI Collection, a set of multimodal MRI datasets for individual difference analyses.", "doi": "10.1038/s41597-021-00870-6", "pmid": "33741990", "authors": ["Lukas Snoek", "Maite M van der Miesen", "Tinka Beemsterboer", "Andries van der Leij", "Annemarie Eigenhuis", "H Steven Scholte"], "journal": "Scientific data", "year": 2021, "abstract": "We present the Amsterdam Open MRI Collection (AOMIC): three datasets with multimodal (3 T) MRI data including structural (T1-weighted), diffusion-weighted, and (resting-state and task-based) functional BOLD MRI data, as well as detailed demographics and psychometric variables from a large set of healthy participants (N = 928, N = 226, and N = 216). Notably, task-based fMRI was collected during various robust paradigms (targeting naturalistic vision, emotion perception, working memory, face perception, cognitive conflict and control, and response inhibition) for which extensively annotated event-files are available. For each dataset and data modality, we provide the data in both raw and preprocessed form (both compliant with the Brain Imaging Data Structure), which were subjected to extensive (automated and manual) quality control. All data is publicly available from the OpenNeuro data sharing platform.", "claims": [{"id": "claim-method", "statement": "To do so, using the nistats Python package, we ran mass-univariate first-level GLS models (using an AR1 noise model) based on a design matrix including task-regressors based on the events convolved with a canonical HRF function as well as a discrete cosine basis set functioning as a high-pass filter of 128 seconds and six motion regressors and computed first-level contrast maps for each subject which were subsequently analyzed in a random effects group-level (intercept) model, resulting in whole-brain z-value maps.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33741990", "locator": "sections[52] \"Functional (BOLD) scans\" paragraph 8", "quote": "To do so, using the nistats Python package, we ran mass-univariate first-level GLS models (using an AR1 noise model) based on a design matrix including task-regressors based on the events convolved with a canonical HRF function as well as a discrete cosine basis set functioning as a high-pass filter of 128 seconds and six motion regressors and computed first-level contrast maps for each subject which were subsequently analyzed in a random effects group-level (intercept) model, resulting in whole-brain z-value maps."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All code used for curating, annotating, and (pre)processing AOMIC are version-controlled using git and can be found in project-specific Github repositories within the NILAB-UvA Github organization: https://github.com/orgs/NILAB-UvA. Many pre and postprocessing steps were identical across datasets, so the code for these procedures is stored in a single repository: https://github.com/NILAB-UvA/AOMIC-common-scripts. Possible parameters are all hard-coded within the scripts, except for a single positional parameter pointing to the directory to be processed. For custom Python-based scripts, we used Python version 3.7. All code was developed on a Linux system with 56 CPUs (Intel Xeon E5-2680 v4, 2.40 GHz) and 126GB RAM running Ubuntu 16.04. All curation, preprocessing, and analyses were run on said Linux system, apart from the Fmriprep, MRIQC, and Freesurfer analyses, which were run in a Docker container provided by those software packages. Custom code was parallelized to run on multiple CPUs concurrently using the Python package joblib (https://joblib.readthedocs.io).\nFor curation, preprocessing, and analysis of the datasets, we used a combination of existing packages and custom scripts (written in Python or bash). To convert the data to the Brain Imaging Data Structure (BIDS), we used the in-house developed, publicly available software package bidsify (v0.3; https://github.com/NILAB-UvA/bidsify), which in turn uses the dcm2niix (v1.0.20181125)94 to convert the Philips PAR/REC files to compressed nifti files. In contrast to the data from PIOP1 and PIOP2 (which were converted to nifti using dcm2niix), r2aGUI (v2.7.0; http://r2agui.sourceforge.net/) was used to convert the data from ID1000. Because r2aGUI does not correct the gradient table of DWI scans for slice angulation, we used the angulation_correction_Achieva Matlab script (version December 29, 2007) from Jonathan Farrell to do so (available for posterity at https://github.com/NILAB-UvA/ID1000/blob/master/code/bidsify/DTI_gradient_table_ID1000.m). To remove facial characteristics from anatomical scans, we used the pydeface package (v.1.1.0)86. Finally, to convert the raw physiology files (i.e., Philips “SCANPHYSLOG” files) to BIDS, we used the in-house developed, publicly available Python package scanphyslog2bids (v0.1; https://github.com/lukassnoek/scanphyslog2bids). The outputs from the BIDS-conversion pipeline were checked using the bids-validator software package (v1.4.3).\nAnatomical and functional MRI preprocessing were done using Fmriprep (v1.4.1; see the Derivatives section for extensive information about Fmriprep’s preprocessing pipeline)42. For our DWI preprocessing pipeline, we used tools from the MRtrix3 package (www.mrtrix.org; v3.0_RC3)60 and FSL (v6.0.1)95. For the VBM and dual regression pipelines, we used FSL (v6.0.1)71–73. To create the files with Freesurfer-based metrics across all participants, we used Freesurfer version 6.0.096. Physiological nuisance regressors (RETROICOR and HRV/RVT regressors) were estimated using the TAPAS PhysIO Matlab package (v3.2.0)75.\nFirst-level functional MRI analyses for technical validation were implemented using the Python package nistats (v0.0.1b2)59 and nilearn (v0.6.2)59,97. For the inter-subject correlation analysis the Brain Imaging Analysis Kit was used (BrainIAK, http://brainiak.org, v0.10; RRID:SCR_014824)92. Plotting brain images was done using FSLeyes (v0.32)98 and plotting statistical plots was done using the Python packages seaborn99 and Matplotlib100.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33741990", "locator": "sections[64] \"Code availability\" paragraphs 1-5", "quote": "All code used for curating, annotating, and (pre)processing AOMIC are version-controlled using git and can be found in project-specific Github repositories within the NILAB-UvA Github organization: https://github.com/orgs/NILAB-UvA. Many pre and postprocessing steps were identical across datasets, so the code for these procedures is stored in a single repository: https://github.com/NILAB-UvA/AOMIC-common-scripts. Possible parameters are all hard-coded within the scripts, except for a single positional parameter pointing to the directory to be processed. For custom Python-based scripts, we used Python version 3.7. All code was developed on a Linux system with 56 CPUs (Intel Xeon E5-2680 v4, 2.40 GHz) and 126GB RAM running Ubuntu 16.04. All curation, preprocessing, and analyses were run on said Linux system, apart from the Fmriprep, MRIQC, and Freesurfer analyses, which were run in a Docker container provided by those software packages. Custom code was parallelized to run on multiple CPUs concurrently using the Python package joblib (https://joblib.readthedocs.io).\nFor curation, preprocessing, and analysis of the datasets, we used a combination of existing packages and custom scripts (written in Python or bash). To convert the data to the Brain Imaging Data Structure (BIDS), we used the in-house developed, publicly available software package bidsify (v0.3; https://github.com/NILAB-UvA/bidsify), which in turn uses the dcm2niix (v1.0.20181125)94 to convert the Philips PAR/REC files to compressed nifti files. In contrast to the data from PIOP1 and PIOP2 (which were converted to nifti using dcm2niix), r2aGUI (v2.7.0; http://r2agui.sourceforge.net/) was used to convert the data from ID1000. Because r2aGUI does not correct the gradient table of DWI scans for slice angulation, we used the angulation_correction_Achieva Matlab script (version December 29, 2007) from Jonathan Farrell to do so (available for posterity at https://github.com/NILAB-UvA/ID1000/blob/master/code/bidsify/DTI_gradient_table_ID1000.m). To remove facial characteristics from anatomical scans, we used the pydeface package (v.1.1.0)86. Finally, to convert the raw physiology files (i.e., Philips “SCANPHYSLOG” files) to BIDS, we used the in-house developed, publicly available Python package scanphyslog2bids (v0.1; https://github.com/lukassnoek/scanphyslog2bids). The outputs from the BIDS-conversion pipeline were checked using the bids-validator software package (v1.4.3).\nAnatomical and functional MRI preprocessing were done using Fmriprep (v1.4.1; see the Derivatives section for extensive information about Fmriprep’s preprocessing pipeline)42. For our DWI preprocessing pipeline, we used tools from the MRtrix3 package (www.mrtrix.org; v3.0_RC3)60 and FSL (v6.0.1)95. For the VBM and dual regression pipelines, we used FSL (v6.0.1)71–73. To create the files with Freesurfer-based metrics across all participants, we used Freesurfer version 6.0.096. 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same name (excluding extension)", "All modalities"], ["DWI derivatives", "", ""], ["_dwi (.nii.gz)", "Preprocessed diffusion-weighted scan", "DWI"], ["_dwi (.bvec)", "Preprocessed gradient table", "DWI"], ["_dwi (.bval)", "Preprocessed b-value sequence", "DWI"], ["_mask (.nii.gz)", "Binary brain mask (0: not brain, 1: brain)", "DWI"], ["_diffmodel", "Estimated parameters from the diffusion tensor model", "DWI"], ["_EVECS", "Eigenvectors from the diffusion tensor model", "DWI"], ["_FA", "Fractional anisotropy map", "DWI"], ["VBM derivatives", "", ""], ["GMvolume", "Estimated voxelwise gray matter volume map", "Anatomical MRI"], ["Freesurfer morphological statistics derivatives", "", ""], ["_stats", "Morphological statistics per anatomical region from a particular atlas", "Anatomical MRI"], ["Physiology derivatives", "", ""], ["_regressors", "Physiology-derived RETROICOR, HRV, and RVT regressors for BOLD-MRI data", "Physiology"]], "row_count": 44, "column_count": 3, "numeric_cells": 6, 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"https://openneuro.org/datasets/ds002790"], ["Neurovault URL", "https://neurovault.org/collections/7105/", "https://neurovault.org/collections/7103/", "https://neurovault.org/collections/7104/"]], "row_count": 3, "column_count": 4, "numeric_cells": 7, "header_rows": 1, "locator": "section[48]/table-wrap[1]#Tab5"}}, {"id": "asset:table:pmid:33741990:9", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:35c36f221701bce6ced43c3dd5572eb6f7e09cc488a105b7345c5e5fd94145c7", "bytes": 675, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[56]/table-wrap[1]#Tab6", "version": null, "note": "Correlations between total score, and subscales of the IST and relevant external variables (background SES and educational level).", "structure": {"pmcid": "PMC7979787", "section_index": 56, "section_title": "Intelligence Structure Test (IST)", "row_count": 7, "column_count": 7}, "table": {"label": "Table 6", "caption": "Correlations between total score, and subscales of the IST and relevant external variables (background SES and educational level).", "rows": [["IST (N = 926)", "IST Int", "IST Crystal", "IST Memory", "IST Fluid", "Background SES", "Education"], ["IST Int", "1", "0.82**", "0.79**", "0.96**", "0.35**", "0.46**"], ["IST Crystallized", "", "1", "0.58**", "0.68**", "0.37**", "0.44**"], ["IST Memory", "", "", "1", "0.65**", "0.25**", "0.39**"], ["IST Fluid", "", "", "", "1", "0.31**", "0.41**"], ["Background SES", "", "", "", "", "1", "0.38**"], ["Education", "", "", "", "", "", "1"]], "row_count": 7, "column_count": 7, "numeric_cells": 22, "header_rows": 1, "locator": "section[56]/table-wrap[1]#Tab6"}}, {"id": "asset:table:pmid:33741990:10", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:170195eb2411743c12eb544c260054af196b32a02de1d795da1e48c7db9628be", "bytes": 1231, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[57]/table-wrap[1]#Tab7", "version": null, "note": "Cross-correlations for the subscales of the NEO-FFI for the ID1000, PIOP1 and PIOP2 samples.", "structure": {"pmcid": "PMC7979787", "section_index": 57, "section_title": "Personality: NEO-FFI", "row_count": 18, "column_count": 6}, "table": {"label": "Table 7", "caption": "Cross-correlations for the subscales of the NEO-FFI for the ID1000, PIOP1 and PIOP2 samples.", "rows": [["ID1000 (n = 927)", "Neuroticism", "Extraversion", "Openness", "Agreeableness", "Conscientiousness"], ["Neuroticism", "1", "−0.30**", "0.15**", "−0.08*", "−0.43**"], ["Extraversion", "", "1", "0.20**", "−0.13**", "0.12**"], ["Openness", "", "", "1", "−0.06", "−0.19**"], ["Agreeableness", "", "", "", "1", "0.16**"], ["Conscientiousness", "", "", "", "", "1"], ["PIOP1 (N = 216)", "Neuroticism", "Extraversion", "Openness", "Agreeableness", "Conscientiousness"], ["Neuroticism", "1", "−0.29**", "0.18**", "0.00", "−0.25**"], ["Extraversion", "", "1", "−0.05", "0.20**", "0.14*"], ["Openness", "", "", "1", "0.08", "−0.18**"], ["Agreeableness", "", "", "", "1", "0.20**"], ["Conscientiousness", "", "", "", "", "1"], ["PIOP2 (N = 226)", "Neuroticism", "Extraversion", "Openness", "Agreeableness", "Conscientiousness"], ["Neuroticism", "1", "−0.38**", "0.12", "−0.07", "−0.24**"], ["Extraversion", "", "1", "0.11", "0.26**", "0.25**"], ["Openness", "", "", "1", "0.02", "−0.04"], ["Agreeableness", "", "", "", "1", "0.15*"], ["Conscientiousness", "", "", "", "", "1"]], "row_count": 18, "column_count": 6, "numeric_cells": 48, "header_rows": 1, "locator": "section[57]/table-wrap[1]#Tab7"}}, {"id": "asset:table:pmid:33741990:11", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:f02ef530b8e049d22f69f7ca8a675a66828f279023f320573c49380054f88243", "bytes": 506, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[58]/table-wrap[1]#Tab8", "version": null, "note": "Cross-correlations for the subscales of the BIS/BAS for the ID1000 sample. The pattern of correlations is consistent with that reported in literature.", "structure": {"pmcid": "PMC7979787", "section_index": 58, "section_title": "BIS/BAS", "row_count": 5, "column_count": 5}, "table": {"label": "Table 8", "caption": "Cross-correlations for the subscales of the BIS/BAS for the ID1000 sample. The pattern of correlations is consistent with that reported in literature.", "rows": [["BIS/BAS (N = 928)", "BAS drive", "BAS fun", "BAS reward", "BIS"], ["BAS drive", "1", "0.45**", "0.34**", "−0.19**"], ["BAS fun", "", "1", "0.39**", "−0.13**"], ["BAS reward", "", "", "1", "0.19**"], ["BIS", "", "", "", "1"]], "row_count": 5, "column_count": 5, "numeric_cells": 11, "header_rows": 1, "locator": "section[58]/table-wrap[1]#Tab8"}}], "gaps": []}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "meta_analysis", "specific": "random_effects", "label": "Meta-analysis / evidence synthesis", "dsl_operations": ["random_effects_pool"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["study_level_effect_estimates"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "To do so, using the nistats Python package, we ran mass-univariate first-level GLS models (using an AR1 noise model) based on a design matrix including task-regressors based on the events convolved with a canonical HRF function as well as a discrete cosine basis set functioning as a high-pass filter of 128 seconds and six motion regressors and computed first-level contrast maps for each subject which were subsequently analyzed in a random effects group-level (intercept) model, resulting in whole-brain z-value maps."}}], "targets": [], "gaps": []}, "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": "R1", "expressible": true, "required_operations": ["random_effects_pool"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: random_effects."}, "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": ["All code used for curating, annotating, and (pre)processing AOMIC are version-controlled using git and can be found in project-specific Github repositories within the NILAB-UvA Github organization: https://github.com/orgs/NILAB-UvA. Many pre and postprocessing steps were identical across datasets, so the code for these procedures is stored in a single repository: https://github.com/NILAB-UvA/AOMIC-common-scripts. Possible parameters are all hard-coded within the scripts, except for a single positional parameter pointing to the directory to be processed. For custom Python-based scripts, we used Python version 3.7. All code was developed on a Linux system with 56 CPUs (Intel Xeon E5-2680 v4, 2.40 GHz) and 126GB RAM running Ubuntu 16.04. All curation, preprocessing, and analyses were run on said Linux system, apart from the Fmriprep, MRIQC, and Freesurfer analyses, which were run in a Docker container provided by those software packages. Custom code was parallelized to run on multiple CPUs concurrently using the Python package joblib (https://joblib.readthedocs.io).\nFor curation, preprocessing, and analysis of the datasets, we used a combination of existing packages and custom scripts (written in Python or bash). To convert the data to the Brain Imaging Data Structure (BIDS), we used the in-house developed, publicly available software package bidsify (v0.3; https://github.com/NILAB-UvA/bidsify), which in turn uses the dcm2niix (v1.0.20181125)94 to convert the Philips PAR/REC files to compressed nifti files. In contrast to the data from PIOP1 and PIOP2 (which were converted to nifti using dcm2niix), r2aGUI (v2.7.0; http://r2agui.sourceforge.net/) was used to convert the data from ID1000. Because r2aGUI does not correct the gradient table of DWI scans for slice angulation, we used the angulation_correction_Achieva Matlab script (version December 29, 2007) from Jonathan Farrell to do so (available for posterity at https://github.com/NILAB-UvA/ID1000/blob/master/code/bidsify/DTI_gradient_table_ID1000.m). To remove facial characteristics from anatomical scans, we used the pydeface package (v.1.1.0)86. Finally, to convert the raw physiology files (i.e., Philips “SCANPHYSLOG” files) to BIDS, we used the in-house developed, publicly available Python package scanphyslog2bids (v0.1; https://github.com/lukassnoek/scanphyslog2bids). The outputs from the BIDS-conversion pipeline were checked using the bids-validator software package (v1.4.3).\nAnatomical and functional MRI preprocessing were done using Fmriprep (v1.4.1; see the Derivatives section for extensive information about Fmriprep’s preprocessing pipeline)42. For our DWI preprocessing pipeline, we used tools from the MRtrix3 package (www.mrtrix.org; v3.0_RC3)60 and FSL (v6.0.1)95. For the VBM and dual regression pipelines, we used FSL (v6.0.1)71–73. To create the files with Freesurfer-based metrics across all participants, we used Freesurfer version 6.0.096. Physiological nuisance regressors (RETROICOR and HRV/RVT regressors) were estimated using the TAPAS PhysIO Matlab package (v3.2.0)75.\nFirst-level functional MRI analyses for technical validation were implemented using the Python package nistats (v0.0.1b2)59 and nilearn (v0.6.2)59,97. For the inter-subject correlation analysis the Brain Imaging Analysis Kit was used (BrainIAK, http://brainiak.org, v0.10; RRID:SCR_014824)92. Plotting brain images was done using FSLeyes (v0.32)98 and plotting statistical plots was done using the Python packages seaborn99 and Matplotlib100."], "basis": "The paper names no accession to its source data, so the data has to come from the authors."}}, "can_run": {"dsl_operations": ["random_effects_pool"], "assets": ["source:pmid:33741990", "asset:table:pmid:33741990:1", "asset:table:pmid:33741990:2", "asset:table:pmid:33741990:3", "asset:table:pmid:33741990:4", "asset:table:pmid:33741990:5", "asset:table:pmid:33741990:6", "asset:table:pmid:33741990:7", "asset:table:pmid:33741990:8", "asset:table:pmid:33741990:9", "asset:table:pmid:33741990:10", "asset:table:pmid:33741990:11"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:None"]}, "gaps": ["data:accession_absent"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-10-31T18:28:16Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 3, "usage": {"prompt_tokens": 81670, "completion_tokens": 3673, "cached_tokens": 784}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:33741990", "type": "paper", "label": "pmid:33741990"}, {"id": "source:pmid:33741990", "type": "manuscript", "label": "PMID 33741990 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:33741990", "type": "paper", "label": "pmid:33741990"}, {"id": "source:pmid:33741990", "type": "manuscript", "label": "PMID 33741990 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-33741990", "type": "extraction", "used": ["source:pmid:33741990"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-None", "type": "accession_resolution", "used": ["data:geo:None"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:33741990", "parent_hash": "sha256:7b9ae10a10e9342a90c8a2d71763992febba27dd9d63f2be6d72a25423800ae8", "fork_of": null, "generation": 5}, "gaps": ["execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:obtain_source_data blocked on data:accession_absent"], "assays": [{"role": "analysis", "definition_id": "meta_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["random_effects_pool"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["To do so, using the nistats Python package, we ran mass-univariate first-level GLS models (using an AR1 noise model) based on a design matrix including task-regressors based on the events convolved with a canonical HRF function as well as a discrete cosine basis set functioning as a high-pass filter of 128 seconds and six motion regressors and computed first-level contrast maps for each subject which were subsequently analyzed in a random effects group-level (intercept) model, resulting in whole-brain z-value maps."], "order_basis": "analysis_only", "id": "assay:1"}]},"meta":{"api_version":"v1","automation_ladder_snapshot":"ba34b49490d9bfaf","automation_ladder_caveats":["Method extraction is rule-based and has no hand-judged accuracy sample. Evidence bands order textual support; method agreement intervals are null (n=0), never probabilities. Work-type validation does not validate methods.","Operation coverage describes only explicitly named methods. It does not prove a complete protocol, compatible parameters, successful compilation or re-execution. Unrecognised or ambiguous methods require review.","Data reachability is a textual accession or public-data statement. No dataset was downloaded or checked for access, completeness or licence.","Capabilities are planning states seeded from the dated equipment register. No bench ownership, completed quote or partner booking is inferred. Procurement counts are dependencies, not guaranteed replications or financial returns.","Tier and blocker counts are deterministic assessments of this corpus, not measured agreement. R3 is reserved for procurement; unresolved methods and data require human work (R4). The most restrictive axis wins, with capability, method, data as tie-breaks. Resolving one blocker can leave another at the same tier."],"engine":"corpus_engine","engine_provider":"rule_based_v1+lex99e1195c7d6f","run_id":"run-20261023T122449232913","source":"esearch","source_detail":"PubMed mirror biomed_pubmed (read-only)","run_status":"complete","imported_at":"2026-11-03T20:16:52+00:00","generated_at":"2026-11-04T00:29:21+00:00","total_papers":5529,"total_assays_tracked":29,"automatable_assay_families":6,"validated":true,"caveat_count":9,"blocking_caveats":5,"caveats":[{"code":"low_evidence_dominates","basis":["4,152 of 5,529 classifications (75.1%) matched no cue evidence and sit at the 0.05 floor.","34 of 230 data_only calls (14.8%) are in that group."],"claim":"Work type and confidence bands","gloss":"Most records were classified from their title and framing alone, with assay-cue coverage at zero.","detail":"Most classifications in this run rest on no matched evidence at all. At the evidence floor the work type comes from title framing and a fixed prior, not from anything in the paper, so the published work-type split should be read as a rough triage signal rather than a measured distribution. The impact is bounded but not removed: since 2026-10-23 the engine excludes floor-confidence records from the shortlist entirely and publishes them separately as held out, so the recommendation is unaffected even though the census is.","status":"unverified","severity":"high"},{"code":"runtime_is_modelled","basis":["Runtime is summed from published per-assay hour figures. Timing awaits the first executed replication.","4,407 of 5,529 records (79.7%) model to 0.00 hours because no assay was matched, which the ranking then reads as \"cheapest\".","0 of 10 shortlisted records carry a 0.00-hour estimate."],"claim":"Estimated replication runtime","gloss":"The hour figures are estimates built from assay mentions; timing awaits the first executed replication.","detail":"The hour figures are a cost model derived from the bench steps implied by each matched assay family. The first executed replication will supply measured timing. Records without an assay match currently receive a zero-hour model value.","status":"unverified","severity":"high"},{"code":"signal_score_without_citations","basis":["5,529 of 5,529 records (100.0%) have no citation count from the source metadata."],"claim":"Signal score","gloss":"The signal score uses publication-type weight because the mirror supplied zero citation counts.","detail":"The source supplied no citation counts for any record in this run, so the signal score is publication-type weight only. The citation term of the ranking is inert here, and the shortlist is ordered by publication type alone.","status":"unverified","severity":"high"},{"code":"abstract_absent","basis":["1,667 of 5,529 records (30.2%) carry no abstract at all."],"claim":"Evidence base for classification","gloss":"Some records contain a title and publication type only, defining the available classification evidence.","detail":"Where the mirror contains a title and publication types only, those fields define the classifier evidence. A methods section is required for fuller automated and human review.","status":"unverified","severity":"medium"},{"code":"classifier_agreement_measured","basis":["Published two-way decision agreement 77.7% (310 of 399 decidable records) from a stratified hand-check of 438 real records.","Three-way diagnostic agreement 73.9% (288 of 390 decidable records).","data_only agreement 75.2% (100 of 133 decidable).","48 record(s) could not be decided from title and abstract and were excluded from the denominator.","56 analysis-only records were published under another work type, so the data_only share is not an upper bound."],"claim":"Published automatable decision","gloss":"This figure has been checked against a human reading the same papers, so we know how often it is right.","detail":"The published automatable / not-automatable distribution comes from a rule-based classifier that was hand-checked against a fixed ground-truth definition. The agreement rates below are measured, not estimated. The three-way work type remains a diagnostic and is reported separately.","status":"measured","severity":"high"},{"code":"llm_comparison_not_measured","basis":["rule_based_v1+lex99e1195c7d6f produced 5,529 of 5,529 classifications.","Measured on 438 labelled records; these are RULE-BASED figures, not model results."],"claim":"Rule-based versus LLM agreement","gloss":"Every number here is the rule engine’s own. Model comparison begins when a working API credential is connected and measured.","detail":"The published two-way decision agreed with a human reading on 310 of 399 decidable records (77.7%). The diagnostic three-way label agreed on 288 of 390 decidable records (73.9%). Every percentage in this run belongs to the rule engine. Model comparison begins when a working API credential is connected and measured.","status":"not_measured","severity":"high"},{"code":"confidence_is_a_band","basis":["low band: 135 of 210 hand-judged records were correct, 64.3% [57.6, 70.5] (95% CI).","medium band: 80 of 104 hand-judged records were correct, 76.9% [68.0, 84.0] (95% CI).","high band: 73 of 76 hand-judged records were correct, 96.0% [89.0, 98.7] (95% CI).","A fitted calibrated probability was built, measured and withdrawn: it overstated held-out accuracy and reorders nothing."],"claim":"Confidence band and evidence score","gloss":"The confidence band carries a measured accuracy interval; 0.85 represents relative rank strength.","detail":"Confidence on this run is a BAND plus its measured accuracy interval. The number stored alongside each record is a monotone evidence score that orders records; 0.85 represents relative rank strength. A fitted probability was withdrawn after it overstated held-out accuracy. Threshold on the band.","status":"measured","severity":"medium"},{"code":"three_way_split_is_diagnostic","basis":["The corpus is published at the two-way decision: automatable or not-automatable. The three-way split (data_only / physical / mixed) is published as a diagnostic with its measured reliability attached, not as a claim, because the rule engine's `mixed` call cannot be defended.","The engine called 22 record(s) mixed; 10 matched the human judgement (45.5%, 95% CI 26.9-65.3).","The engine's `mixed` call is right on 10/22 hand-judged records (45.5%, 95% CI 26.9%-65.3%); a constant `physical` guess scores 36.4%.","The engine's `mixed` call does not reliably beat a constant `physical` guess on the hand-judged records it labels mixed.","The sign of that difference flips between two independent samples, so the three-way refinement is not demonstrably informative.","The mixed stratum contributes a disproportionate share of errors relative to its size.","At title-only depth the physical/mixed split collapses: a title says that work was done, not whether it was also analysed."],"claim":"The published decision is two-way","gloss":"The three-way split is shown for diagnosis only. The published split is two ways.","detail":"Count and filter the corpus on automatable versus not-automatable. The data_only / physical / mixed split remains an internal engine diagnostic because the mixed call has lower measured reliability.","status":"measured","severity":"medium"},{"code":"automatable_flag_unproven","basis":["6 of 29 assay families are marked automatable now: meta_analysis, machine_learning, survival_analysis, pca, molecular_docking, differential_expression.","The flag is a rule over assay family and work type; validation begins with the first executed replication.","It inherits the 75.2% data_only agreement rate, because the flag requires the record to be analysis-only."],"claim":"Automatable now","gloss":"This flag marks assay families the rules classify as data-only. The first executed replication will validate it.","detail":"This flag marks families the rules consider replicable from data alone. Its validity depends on the analysis-only classification and will be measured through an executed replication.","status":"unverified","severity":"medium"}],"confidence_policy":"band_with_measured_interval","confidence_contract":{"policy":"band_with_measured_interval","summary":"Confidence is reported as a BAND with a measured accuracy interval. The raw evidence score supplies monotone ordering. A fitted point estimate was withdrawn after it overstated held-out accuracy.","evidence_score":"A monotone evidence score in [0,1] built from cue mass, axis separation and text coverage. Use it to order records. Interpret 0.85 as relative rank strength.","confidence_band":"low | medium | high. Consumers threshold on this value.","band_accuracy":"Wilson 95% interval on the measured agreement of the hand-judged records in this band.","calibrated_confidence":"WITHDRAWN. Reintroduction requires held-out evidence demonstrating reliable estimates across every bin."},"band_accuracy":{"low":{"n":210,"low":0.576,"band":"low","high":0.7046,"agree":135,"accuracy":0.6429},"high":{"n":76,"low":0.8903,"band":"high","high":0.9865,"agree":73,"accuracy":0.9605},"medium":{"n":104,"low":0.6795,"band":"medium","high":0.8397,"agree":80,"accuracy":0.7692}},"provider":"rule_based_v1+lex99e1195c7d6f","lexicon_id":"99e1195c7d6f","contract_published_at":"2026-10-23T12:25:29.022232+00:00","confidence_is_band":true,"confidence_is_probability":false,"decision_taxonomy":"two_way","decidable_fraction_two_way":0.8854,"decidable_fraction_two_way_low":0.8565,"decidable_fraction_two_way_high":0.9143,"decidable_fraction_three_way":0.8586,"decidable_fraction_three_way_low":0.8287,"decidable_fraction_three_way_high":0.8884,"decidable_population":5529,"validation_sample_size":438,"three_way_cost_fraction":0.0268,"three_way_cost_records":148,"decision_counts":{"automatable":230,"not_automatable":5299},"decision_split":{"automatable":{"count":230,"share":0.04159884246699222},"not_automatable":{"count":5299,"share":0.9584011575330078}},"diagnostic_work_type_counts":{"mixed":24,"physical":5275,"data_only":230}}}