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992 results for “substances”
Dataset for "Modeling Dipolar Nonprotogenic Solvents with PC-SAFT-Type Equations of State: Pure Substance Properties"
<p>Dipolar nonprotogenic solvents (DNS) are important chemical substances used across a wide range of applications, including renewable green solvent media, sustainable energy sources, and as efficient solvents for fabricating and processing semiconductive materials used in organic photovoltaics. Therefore, for efficient solvent screening or process design, a description and prediction of the thermodynamic properties of DNS using thermodynamic models is essential. This dataset contains calculation results of four different modeling strategies within the PC-SAFT equation of state for pure-substance properties of six DNSs: gamma-valerolactone, propylene carbonate, acetonitrile, dihydrolevoglucosenone, 1-methyl-2-pyrrolidone, and sulfolane. The modeling strategies differ in the treatment of the strong dipolar interactions of DNSs. The pure-substance properties include liquid density, vapor pressure, enthalpy of vaporization, and residual isobaric liquid heat capacity. The PC-SAFT performance was analyzed and evaluated based on the calculated data. Additionally, the dataset includes input files for quantum mechanical calculations of optimal molecular geometries and dipole moments of the considered DNSs using Gaussian 16 software.</p>
Inter-Chemical Correlation results for the study: HHEARx2017-1967 (Perfluoroalkyl and Polyfluroalkyl Substances (PFAS), Protein Biomarkers, Adiposity and Cardiometabolic Risk Factors in a 3-year Cohort of Low-Income Latino Children with Overweight and Obesity from the Stanford GOALS Randomized Controlled Trial)
Title: Perfluoroalkyl and Polyfluroalkyl Substances (PFAS), Protein Biomarkers, Adiposity and Cardiometabolic Risk Factors in a 3-year Cohort of Low-Income Latino Children with Overweight and Obesity from the Stanford GOALS Randomized Controlled Trial <br>Species: Homo sapiens <br>Number of samples: 1085 <br>Number of named analytes: 8 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=36 <br>
S90 | ZEROPMBOX1 | ZeroPM Box 1 Substances
<p>This is the collection associated with list S90 ZEROPMBOX1 ZeroPM Box 1 Substances on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>This list contains representative PFAS, triazoles and triazines, the "Box 1" substances to kick-off the H2020 project ZeroPM (<a href="https://zeropm.eu">https://zeropm.eu</a>)</p> <p> </p>
S36 | UBAPMT | Prioritised PMT/vPvM substances in the REACH registration database
<p><strong>Prioritised PMT/vPvM substances in the REACH registration database</strong></p> <p>This is the 2022 update (first update) of the UBA list of prioritised persistent, mobile and toxic/very persistent and very mobile (PMT/vPvM) substances in the REACH registration database. All substances are registered under REACH (EC No 1907/2006) and meet the <a href="https://www.umweltbundesamt.de/publikationen/protecting-the-sources-of-our-drinking-water-the">PMT/vPvM criteria as proposed by UBA in 2019</a>. Compared to the first version from 2019, this 2022 update of the UBA list adds new substances and improved the PMT/vPvM assessment. This UBA list is published as UBA TEXTE xxx /2022. It is indicated if a substance would also meet the less stringent PMT/vPvM criteria as published by the European Commission (EC) in September 2021, which are currently under discussion for inclusion in the Classification, Labelling and Packaging (<a href="https://echa.europa.eu/guidance-documents/guidance-on-clp">CLP</a>) regulation (EC No 1272/2008).</p> <p><em>Reference: </em>Hans Peter H Arp, Sarah E Hale, Ivo Schliebner and Michael Neumann (2022). Prioritised PMT/vPvM substances in the REACH registration database, Texte | XXX/2022, edited by Michael Neumann and Ivo Schliebner, IV 2.3 Chemicals, German Environment Agency (UBA), Dessau-Roßlau, Germany. ISBN: 1862-4804 xxx pages</p> <p><em>Acknowledgement: </em>Environmental Research of the Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV) Project No. (FKZ) 3719 65 408 0 and Report No. (to be announced)</p> <p><em>Previous version:</em></p> <p>The first version of this UBA list from 2019 was published as a <a href="https://www.umweltbundesamt.de/publikationen/reach-improvement-of-guidance-methods-for-the">technical note (UBA TEXTE 126/2019)</a>.</p> <p><em>Reference: </em>Hans Peter H Arp and Sarah E Hale (2019). REACH: Improvement of guidance and methods for the identification and assessment of PMT/vPvM substances, Texte | 126/2019, German Environment Agency (UBA), Dessau-Roßlau, Germany. ISBN:1862-4804, 131 pages</p> <p><em>Acknowledgement: </em>Environmental Research of the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety Project No. (FKZ) 3716 67 416 0 and Report No. FB000142/ENG.</p> <p>This collection is associated with list S36 UBAPMT on the NORMAN Suspect List Exchange (<a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a>).</p>
S35 | INDOORCT16 | Indoor Environment Substances from 2016 Collaborative Trial
<p>This is the collection associated with list S35 INDOORCT16 on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>S35 INDOORCT16 <strong>Indoor Environment Substances from 2016 Collaborative Trial</strong></p> <p>Lists of GC-MS and LC-MS compounds and DSFP output, plus merged files from the Indoor Dust Collaborative Trial, 2016 provided by Peter Haglund (UMU) and Pawel Rostkowski (NILU). Details in Rostkowski <em>et al</em>. 2019 DOI: <a href="https://link.springer.com/article/10.1007/s00216-019-01615-6">10.1007/s00216-019-01615-6</a></p> <p>Update 6 Feb 2020: two NA SMILES removed in CSV for PubChem upload. 17/7/2022: NA and N/A SMILES removed from CSV and XLSX, most replaced with structures; some are representative structures for classes.</p>
S54 | EFSAPRI | European Food Safety Authority Priority Substances
<p>This is the dataset associated with list S54 EFSAPRI on the NORMAN Suspect List Exchange:</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p>
Named Entity Corpus for Occupational Substance Exposure Assessment
<p>This is a corpus consisting of selected sections (i.e., <em>Abstract, Methods</em> and <em>Results</em>) of scientific research articles concerning occupational exposures to two different types of substance, i.e., diesel exhaust (51 articles) and respirable crystalline silica (RCS) (50 articles). The article sections have been annotated by experts in the field with 6 categories of named entities (NEs) relevant to the assessment of occupational substance exposures, particularly in the context of Job Exposure Matrices (JEMs).</p> <p>The corpus is available in two different formats, <a href="https://brat.nlplab.org/standoff.html">brat standoff format</a> and JSON. </p> <p>The corpus and associated NER models are described in more detail in the following atricle, which should be cited if you use the corpus: </p> <p>Thompson, P., Ananiadou, S., Basinas I., Brinchmann, B. C., Cramer, C., Galea, K. S., Ge, C., Georgiadis, P., Kirkeleit, J., Kuijpers, E., Nguyen, N., Nuñez, R., Schlünssen, V., Stokholm, Z. A., Taher, E. A., Tinnerberg, H., Van Tongeren, M. and Xie, Q. (2024).<a href="https://doi.org/10.1371/journal.pone.0307844"> </a><a href="https://doi.org/10.1371/journal.pone.0307844">Supporting the working life exposome: annotating occupational exposure for enhanced literature search</a>. PLoS ONE 19(8): e0307844</p>
S63 | UBADWGW | REACH Registered Substances Detected in Drinking (DW) or Groundwater (GW)
<p><strong>REACH Registered Substances Detected in Drinking (DW) or Groundwater (GW)</strong></p> <p>This is a literature review and a subsequent persistent, mobile and toxic/very persistent and very mobile (PMT/vPvM) assessment published by the German Environment Agency (UBA) in 2019. All substances are reported in literature to have been detected in drinking water and groundwater. This list contains only those substance that are registered under REACH (EC No 1907/2006). It is assessed whether a substance meets the PMT/vPvM criteria as proposed by the German Environment Agency (UBA) in 2019 <a href="https://www.umweltbundesamt.de/publikationen/reach-improvement-of-guidance-methods-for-the">(UBA TEXTE 126/2019)</a>.</p> <p><em>Reference</em>: Hans Peter H Arp and Sarah E Hale (2019). REACH: Improvement of guidance and methods for the identification and assessment of PMT/vPvM substances, Texte | 126/2019, German Environment Agency (UBA), Dessau-Roßlau, Germany. ISBN: 1862-4804, 131 pages.</p> <p><em>Acknowledgement:</em> Environmental Research of the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety Project No. (FKZ) 3716 67 416 0 and Report No. FB000142/ENG</p> <p>This is the collection associated with list S63 UBADWGW on the <a href="https://www.norman-network.com/nds/SLE/">NORMAN Suspect List Exchange</a>.</p>
S2 | STOFFIDENT | HSWT/LfU STOFF-IDENT Database of Water-Relevant Substances
<p>This is the collection associated with list S2 STOFFIDENT<strong> HSWT/LfU STOFF-IDENT Database of Water-Relevant Substances</strong> on the NORMAN Suspect List Exchange</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>The database enables the search for exact masses from target or unknown lists and the automatic use of a Retention Time Index. See: <a href="https://www.lfu.bayern.de/stoffident/#!home">https://www.lfu.bayern.de/stoffident/#!home</a> (single search for free; batch search after free registration).</p> <p>Nov 17 2019 update: added CSV for PubChem upload.<br> Jun 18, 2020 update: fixed synonym for CAS=1245526-82-2 after feedback from PubChem (Jeff).<br> Jan 18, 2022 update: added IUPAC name instead of anakinra & deleted incorrect CAS (reported by Leon)</p>
Integrated Harmonized Dataset Adolescent Substance Use, Psychosocial Constructs, and Demographics
<p>This dataset contains final analysis cases used in our paper Psychosocial Constructs Related to Alcohol, Cigarette, and Marijuana Use: An Integrated and Harmonized Analysis. We assembled raw data from 25 longitudinal research projects. We collected data from our own research projects (7 projects) as well data provided by 18 researchers. Datasets included epidemiological studies and prevention studies. For the latter, only control group and pretest data were included. All data, including surveys and projects have been de-identified.</p>
Data used in: 'Atmospheric impacts of chlorinated very short-lived substances over the recent past – Part 1: Stratospheric chlorine budget and the role of transport' by Bednarz et al. (2022)
<p>Data used in: 'Atmospheric impacts of chlorinated very short-lived substances over the recent past – Part 1: Stratospheric chlorine budget and the role of transport' by Bednarz et al. (2022), which has been accepted for publication in Atmospheric Chemistry and Physics.</p> <p> </p>
Data repository for the publication "Economic Interests Cloud Hazard Reductions in the European Regulation of Substances of Very High Concern"
<p>This repository contains the data and scripts associated with the article “Economic Interests Cloud Hazard Reductions in the European Regulation of Substances of Very High Concern“, written by Jessica Coria, Erik Kristiansson and Mikael Gustavsson.</p>
S14 | KEMIPFAS | PFAS Highly Fluorinated Substances List: KEMI
<p>This is the collection associated with list S14 KEMIPFAS on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>Appendix 2 from Swedish Chemicals Agency <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/report-7-15-occurrence-and-use-of-highly-fluorinated-substances-and-alternatives.pdf">KEMI Report 7/15</a>. Provided by Stellan Fischer, KEMI. Registration and mapping to <a href="https://comptox.epa.gov/dashboard/">CompTox Dashboard</a> by Antony Williams, US EPA. Nov 17 2019 update: added CSV files for PubChem upload.</p>
S120 | DUSTCT2024 | Substances from Second NORMAN Collaborative Dust Trial
<p>This is the collection associated with list S120 DUSTCT2024 Substances from Second NORMAN Collaborative Dust Trial on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>List of substances detected via GC-MS and LC-MS from the second NORMAN collaborative dust trial initiated in 2020, including classification and detection information as described in Haglund et al (2024) DOI: <a href="https://doi.org/10.1016/j.scitotenv.2024.177639">10.1016/j.scitotenv.2024.177639</a>. </p> <p> </p>
Witchcraft trials in the Czech lands: Data on persons, places, charges, punishments, and material substances
<p>This is the most comprehensive digital dataset of witchcraft trials in the Czech lands since the earliest case in 1491 until as late as 1785. The dataset covers 257 cases. It records the year, the suspects' names, their sex, places of residence (including geographic coordinates), charges, results of the trial, places of interrogation (including geographic coordinates), and the material substances they were charged of using to perform magic. The dataset allows researchers to conduct systematic and quantitative research into early modern withcraft trials, crime and punishment, as well as the imagery of magic and evil-doing. It also allows to include the Czech lands in broader quantitative studies of European witchcraft trials on a large temporal and geographic scale. Five B.A. theses have been written at Masaryk University, Brno, Czech Republic on the basis of this dataset.</p> <p>The dataset does not cover all known trials. Based on data availability, we did our best to cover what was published and reasonably accessible, but we did not perform original archival research. Our very rough estimate is that up to 100 further specific witchcraft trials in the Czech lands could be identified in published material, and archival work could reveal further ca. 250-800 cases.</p>
Extracellular polymeric substances are closely related to land cover, microbial communities, and enzyme activity in tropical soils
<p>These are datasets and R codes linked to the paper: Extracellular polymeric substances are closely related to land cover, microbial communities, and enzyme activity in tropical soils. </p>
Valuation of peers' safe choices is associated with substance-naivete in adolescents'
Open the record for dataset details and reuse information.
Table C.2: Mass spectra of confirmed substances uploaded to MassBank Europe (https://massbank.eu/MassBank)
<p>Table C.2 is part of the supporting information of the scientific article "Unraveling longitudinal pollution patterns of organic micropollutants in river by non-target screening and cluster analysis" (https://doi.org/10.1016/j.scitotenv.2020.138388). The table includes accecssion numbers on mass spectra of confirmed substances detected in river water. The mass spectra are uploaded to MassBank Europe (https://massbank.eu/MassBank) and can be found via the accession numbers provided in Table C.2.</p>
PharmaCoNER corpus: gold standard annotations of Pharmacological Substances, Compounds and proteins in Spanish clinical case reports
<p><strong>Intro:</strong></p><p>The PharmaCoNER corpus (divided into train, dev and test) is a Gold Standard manually annotated dataset used for the the PharmaCoNER shared task posed at BIONLP-ST (at EMNLP). In addition, we include here the PharmaCoNER background set. It contains the train, development and test sets of the two subtasks (subtask-1 and subtask-2) with Gold Standard annotations. In addition, it contains the documents of the background set, without annotations.</p><p>The PharmaCoNER corpus consists of:</p><ul><li>Manually classified clinical case sections derived from Open access Spanish medical publications, named the Spanish Clinical Case Corpus (SPACCC).</li><li>It was manually selected by a practicing oncologist and revised by a clinical documentalist to assure that records were relevant/representative and resembled structure and content relevant to process clinical records.</li><li>The final corpus: 1000 clinical cases 16,504 sentences</li><li>The corpus contains a total of 396,988 words, with an average of 396.2 words per clinical case.</li><li>It covers a range of medical disciplines including oncology, urology, cardiology, pneumology or infections diseases, etc.</li><li>The corpus has been annotated at the mention level by experts in medicinal chemistry and pharmacology following a granular annotation scheme covering four mention types:<ul><li><i>Entity type 1 (NORMALIZABLES)</i>: mentions of chemicals that can be manually normalized to a unique concept identifier (primarily SNOMED-CT).</li><li><i>Entity type 2 (NO_NORMALIZABLES)</i>: mentions of chemicals that could not be normalized manually to a unique concept identifier.</li><li><i>Entity type 3 (PROTEINAS)</i>: mentions of proteins/genes following an adaptation of the BioCreative GPRO track annotation guidelines (includes peptides, peptide hormones & antibodies).</li><li><i>Entity type 4 (UNCLEAR )</i>: cases of general substance class mentions of clinical relevance, including certain pharmaceutical formulations, general treatments, chemotherapy programs, and vaccines.</li><li>Mentions class "<i>UNCLEAR</i>" (not evaluated for the PharmaCoNER track)</li></ul></li></ul><p> </p><p> </p><p><strong>Please, cite: </strong></p><p>A. G. Agirre, M. Marimon, A. Intxaurrondo, O. Rabal, M. Villegas, M. Krallinger, Pharmaconer: Pharmacological substances, compounds and proteins named entity recognition track, in: Proceedings of The 5th Workshop on BioNLP Open Shared Tasks, 2019, pp. 1–10.</p><p> </p><p><strong>Annotation quality</strong></p><p>Inter-annotator agreement: 93% for annotation, 73% for mapping.</p><p>For more information, see the <a href="https://paperswithcode.com/paper/pharmaconer-pharmacological-substances">paper</a>.</p><p> </p><p><strong>Format</strong></p><p>For subtask 1 annotations are distributed in <i>Brat</i> format. (More info at Brat webpage https://brat.nlplab.org/standoff.html)</p><p>For subtask-2, codes are associated with each document are given in a <i>TSV</i> file with the following columns: </p><blockquote><p>filename code</p></blockquote><p> </p><p><strong>Shared task goal:</strong></p><p>In the two subtasks, the goal is to predict the annotations of the test files (either the ANN files or the TSV with the codes) given only the plain text files. </p><p> </p><p><strong>Resources:</strong></p><ul><li><a href="https://temu.bsc.es/pharmaconer/"><strong>Web</strong></a></li><li><a href="https://www.aclweb.org/anthology/D19-5701.pdf"><strong>Citation</strong></a><strong>: </strong>A. G. Agirre, M. Marimon, A. Intxaurrondo, O. Rabal, M. Villegas, M. Krallinger, Pharmaconer: Pharmacological substances, compounds and proteins named entity recognition track, in: Proceedings of The 5th Workshop on BioNLP Open Shared Tasks, 2019, pp. 1–10.</li><li><a href="https://doi.org/10.5281/zenodo.4271908"><strong>Silver Standard corpus</strong></a></li><li><a href="https://doi.org/10.5281/zenodo.3763276"><strong>Annotation guidelines</strong></a></li><li><a href="https://github.com/TeMU-BSC/PharmaCoNER-Tagger"><strong>PharmaCoNER tagger</strong></a></li><li><a href="https://www.youtube.com/watch?v=B3ZzJl5OMkY"><strong>Youtube video(general setting)</strong></a></li><li><a href="https://www.slideshare.net/MartinKrallinger/pharmaconer-pharmacological-substances-compounds-and-proteins-named-entity-recognition-track-at-bionlpost-workshop-november-4-skycity-rm-2-hong-kong-emnlp2019"><strong>Slides PharmacoNER overview talk at BIONLP-ST / EMNLP </strong></a></li></ul><p>For further information, please visit <a href="https://temu.bsc.es/pharmaconer/">https://temu.bsc.es/pharmaconer/</a> or email us at encargo-pln-life@bsc.es</p><p>Copyright (c) 2018 Secretaría de Estado para el Avance Digital (SEAD)</p><p> </p><p><strong>License</strong></p><p>This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p><p> </p><p><strong>Contact</strong></p><p>If you have any questions or suggestions, please contact us at:</p><p><br>- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p><p><strong>Additional resources and corpora</strong></p><p>If you are interested in PharmaCoNER, you might want to check out these corpora and resources:</p><ul><li><a href="https://zenodo.org/records/7614764">DisTEMIST</a> (Corpus of disease mentions and normalization to SNOMED CT, same document collection)</li><li><a href="https://zenodo.org/records/8224056">MedProcNER </a>(Corpus of clinical procedure mentions and normalization to SNOMED CT, same document collection)</li><li><a href="https://zenodo.org/records/8413866">SympTEMIST</a> (Corpus of symptoms, signs and findings mentions and normalization, same document collection)</li><li><a href="https://zenodo.org/records/7116201">MEDDOPROF</a> (Corpus of mentions of professions, occupations and working status and normalization, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/8403498">MEDDOPLACE</a> (Corpus of mentions of place-related entity mentions, including departments, nationalities or patient movements etc.. and normalization, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/4279323">MEDDOCAN</a> (Corpus of mentions of Personal Health Identifiers (PHI), modified synthetic verions of the document collection)</li><li><a href="https://zenodo.org/records/3978041">CANTEMIST</a> (Corpus of cancer tumor morphology mentions and normalization, different document collection)</li><li><a href="https://zenodo.org/records/3837305">CodiESp</a> (Corpus of clinical case reportes with assigned clinical codes from ICD10, Spanish version, same document collection)</li><li><a href="https://zenodo.org/records/7684093">LivingNER</a> (Corpus of mentions of species, including human/family members, pathogens, food, etc.. and normalization to NCBI Taxonomy, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/2560344">SPACCC-POS</a> (Corpus of clinical case reports in Spanish annotated with POS-tags, same document collection)</li><li><a href="https://zenodo.org/records/2560338">SPACCC-TOKEN</a> (Corpus of clinical case reports in Spanish annotated with token-tags (word mention boundaries), same document collection)</li><li><a href="https://zenodo.org/records/2560338">SPACCC-SPLIT</a> (Corpus of clinical case reports in Spanish annotated with sentence boundary-tags, same document collection)</li><li><a href="https://zenodo.org/records/5602914">MESINESP-2</a> (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts, different document collection)</li></ul>
Studied disinfectant substances against SARS-CoV-2 and other coronaviruses
<p>This data-sheet covers those disinfectants tested against SARS-CoV-2 or other coronaviruses. Data were extracted from several research articles indicated in the reference row. The data-sheet comprises a total of 11 fields with info regarding the virus (virus and strain/isolate names), formulation (substance(s) and its concentration in percentage) and test characteristics (suspension or surface tested, kind of surface, use dilution before testing, disinfectant and inoculum volumes, organic load type and concentrations and contact time) as well as their results, normalized in terms of Log<sub>10 </sub>viral infectivity reduction. Data is included and comented in the following journal article: <a href="https://doi.org/10.3390/foods10020283">https://doi.org/10.3390/foods10020283</a> Please reference also to this publication if using the data-sheet.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.