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318 results for “Data mining”
Invasion Biology WikiProject Scientific Papers: Text Data Mining and LLM-based Information Extraction of Species, Locations, Habitats, and Ecosystems
<p>This dataset contains the abstract and full-text for publication DOIs from the Invasion Biology WikiProject (DOI: <a href="https://www.doi.org/10.5281/zenodo.12518036">10.5281/zenodo.12518036</a>). The data was retrieved using the <a href="https://ask.orkg.org/">ask.orkg.org</a> <a href="https://api.ask.orkg.org/docs#tag/Semantic-Neural-Search/operation/explore_documents_index_explore_get">API</a>. For the <a href="https://github.com/jd-coderepos/invasion-biology-IE/blob/main/scripts/ask-doi-list-fulltext-search.py">script</a> used to obtain the data, refer to the accompanying GitHub repository: <a href="https://github.com/jd-coderepos/invasion-biology-IE/" target="_blank" rel="noopener">https://github.com/jd-coderepos/invasion-biology-IE/</a>.</p> <p>The resulting CSV file includes the following fields: <code>"ASK ID"</code>, <code>"DOI"</code>, <code>"Title"</code>, <code>"Abstract"</code>, and <code>"Full-text"</code>.</p> <p>Of the 49,438 queried DOIs, the ASK database provided:</p> <ul> <li><strong>Total DOIs processed:</strong> 12,636</li> <li><strong>DOIs with neither abstract nor full-text:</strong> 36 (abstract token count was less than 10)</li> <li><strong>DOIs with abstracts but no full-text:</strong> 12,636</li> <li><strong>DOIs with both abstract and full-text:</strong> 2,834</li> </ul> <p>The second part of the dataset contains structured information extracted from the publications using the GPT-4o Large Language Model. This structured data is included in the zipped folder <code>structured-publications.zip</code>.</p> <p>The accompanying GitHub repository provides access to the code and scripts used at various stages of the information extraction (IE) process.</p> <p><strong>Theme of the Study:</strong><br>"Mining for Species, Locations, Habitats, and Ecosystems from Scientific Papers in Invasion Biology: A Large-Scale Exploratory Study with Large Language Models."</p>
Supplementary material for "Song et al., Modelling Simul. Mater. Sci. Eng., 2021: Data-mining of dislocation microstructures: concepts for coarse-graining of internal energies"
<p>This zip archive contains supplementary material in the form of datasets and jupyter notebooks that are used in the following publication:</p> <ul> <li>authors: Hengxu Song, Nina Gunkelmann, Giacomo Po, and Stefan Sandfeld</li> <li>journal: Modelling Simul. Mater. Sci. Eng.</li> <li>year: 2021</li> <li>title: Data-mining of dislocation microstructures: concepts for coarse-graining of internal energies</li> </ul>
Endosymbiont mining from Lepidoptera genomics data
<p>Alignments and assembly files from the manuscript: 'One’s trash is someone else’s treasure: Sequence read archives from Lepidoptera genomes provide material for genome reconstruction of their endosymbionts'</p>
COALMOD-World 2.0 data, results, figures for: Stranded assets and early closures in global coal mining under 1.5°C
<p>This dataset contains all COALMOD-World 2.0 data for Hauenstein (2023): Stranded assets and early closures in global coal mining under 1.5°C (doi.org/10.1088/1748-9326/acb0e5) </p> <p>With the input data files and the GAMS scenario file the model (https://doi.org/10.5281/zenodo.7077678) can be run to reproduce the model results.</p> <p>Furthermore, the output.zip folder contains the results file, the R code to compile the figures, and PDFs of the figures.</p>
Global mining deforestation footprint data from 2000 to 2019
<p>The data in this repository is available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/<br><br>This repository includes two datasets. The first is a collection of polygons covering mines globally and the associated forest cover loss from 2000 to 2019. The polygons were derived by merging the "global-scale mining polygons version 2" (Maus et al., 2022) and mining and quarry polygon features extracted from the OpenStreetMap database (OpenStreetMap contributors, 2017). To remove double counting of areas the overlaps between the datasets were resolved by uniting intersecting features into single polygon features, i.e. keeping only the external borders of intersecting features. A random visual check was conducted, and a few small manual editing of polygons was performed where errors were identified.</p> <p>The resulting dataset is encoded as a Geopackage in the file 'global_mining_polygons.gpkg'. The GeoPackage includes a single layer with 192,584 entries called 'mining_polygons' with the following attributes:</p> <ul> <li><em>id<string></em> unique feature identifier</li> <li><em>isoa3<string></em> ISO 3166-1 alpha-3 country codes</li> <li><em>country<string></em> country names</li> <li><em>area<double></em> area of the polygon in squared kilometres</li> <li><em>geom<polygon></em> the geometry of the features in geographical coordinates WGS84</li> </ul> <p>The second dataset provides annual time series of global tree cover loss within mines from 2000 to 2019, covering all polygons in the above dataset. The area of tree cover loss for each polygon was calculated from the Global Forest Change database (Hansen et al., 2013). Each polygon also has additional string attributes with biomes derived from <em>Ecoregions 2017 <sup>© Resolve </sup></em>(Dinerstein et al., 2017) and the level of protection derived from The World Database on Protected Areas (UNEP-WCMC and IUCN, 2022).</p> <p>This dataset is encoded in CSV format in the file 'global_mining_forest_loss.csv', which includes 416,412 entries and 53 variables, such that:</p> <ul> <li><em>id<string></em> unique feature identifier</li> <li><em>id_hcluster<string></em> unique feature identifier</li> <li><em>list_of_commodities<string></em> a comma-separated list of commodities</li> <li><em>isoa3<string></em> ISO 3166-1 alpha-3 country codes</li> <li><em>country<string></em> country names</li> <li>ecoregion<em><string></em> ecoregion name</li> <li><em>biome<string></em> biome name</li> <li><em>year<numeric></em> the year</li> <li>area_forest_loss_XXX_YYY<double> the area of forest cover loss within a polygon per year in squared kilometres.</li> </ul> <p>The values of tree cover loss are disaggregated per initial percentage of tree cover (XXX) and per protection level (YYY).</p> <ul> <li>XXX can take one of: <ul> <li>000: total tree cover loss independently from the initial tree cover</li> <li>025: tree cover loss on pixels with initial tree cover between 0 and 25%</li> <li>050: tree cover loss on pixels with initial tree cover between 25 and 50%</li> <li>075: tree cover loss on pixels with initial tree cover between 50 and 75%</li> <li>100: tree cover loss on pixels with initial tree cover between 75 and 100%</li> </ul> </li> <li>YYY can take one of: <ul> <li>la: tree cover loss within strict nature reserve</li> <li>Ib: tree cover loss within wilderness area</li> <li>II: tree cover loss within national park</li> <li>III: tree cover loss within natural monument or feature</li> <li>IV: tree cover loss within habitat/species management area</li> <li>V: tree cover loss within protected landscape/seascape</li> <li>VI: tree cover loss within PA with sustainable use of natural resources</li> <li>p: tree cover loss within any type of protection, including not applicable, not assigned, or not reported</li> <li>none: when YYY is omitted, total tree cover loss within the polygon</li> </ul> </li> </ul> <p>For details about the protection levels definition see the UNEP-WCMC and IUCN (2022). The <em>id</em> can be used to link polygons to forest loss data.</p>
RDF version of the data from Hagar I. Labouta et al. Meta-Analysis of Nanoparticle Cytotoxicity via Data-Mining the Literature. NanoImpact (2019)
<p>This is an RDFied version of the dataset published by Hagar I. Labouta et al. Meta-Analysis of Nanoparticle Cytotoxicity via Data-Mining the Literature. NanoImpact (2019).</p> <p>The original dataset publication DOI: <a href="https://doi.org/10.1021/acsnano.8b07562">https://doi.org/10.1021/acsnano.8b07562</a></p> <p>The Original publication authors: Hagar I. Labouta, Nasimeh Asgarian, Kristina Rinker, and David T. Cramb</p>
Data-Mining of In-Situ TEM Experiments: Towards Understanding Nanoscale Fracture
<p>Datasets for the publication in the "Computational Materials Science". This is essentially a snapshot of the gitlab repository https://gitlab.com/computational-materials-science/public/publication-data-and-code/2022-data-mining-of-in-situ-tem-experiments that might contain additional updates and scripts. A version of the manuscript can also be found at https://arxiv.org/abs/2206.11355</p>
Processed data for "Model identification of neural encoding (MINE)" publication
<p>This dataset contains mouse and zebrafish data processed by MINE. These datafiles were used to generate the publication figures for the mouse cortical dataset [m<em>usall.hdf5</em>] (Figure 5) and the zebrafish whole-brain [<em>main_analysis.hdf5</em>] (Figures 6 and 7) and reticulospinal datasets [r<em>s_analysis.hdf5</em>] (Figure 6).</p> <p> </p> <p><em>Musall.hdf5 </em>contains reordered data from "Musall, S., Kaufman, M.T., Juavinett, A.L. <em>et al.</em> Single-trial neural dynamics are dominated by richly varied movements. <em>Nat Neurosci</em> <strong>22</strong>, 1677–1686 (2019)."</p> <p>The contents of each dataset are described in <em>DataContent_xxx.pdf</em></p>
Supporting data for "CoVEffect: Interactive System for Mining the Effects of SARS-CoV-2 Mutations and Variants Based on Deep Learning"
<p>This repository contains the datasets created and extracted for the paper:</p> <p>Giuseppe Serna García, Ruba Al Khalaf, Francesco Invernici, Stefano Ceri, and Anna Bernasconi. 2022.<br> "<strong>CoVEffect</strong>: Interactive System for Mining the <strong>Effects of SARS-CoV-2 Mutations and Variants</strong> Based on Deep Learning". (Available online at http://gmql.eu/coveffect)</p> <p>--------------------------------------------------------------------------------<br> LIST OF FILES WITH DESCRIPTION:<br> --------------------------------------------------------------------------------</p> <p>AdditionalFile1-effects-taxonomy:<br> Descriptions of legal values for the 'Effect' field, based on a categorized taxonomy.</p> <p>AdditionalFile2-levels-taxonomy:<br> Descriptions of legal values for the 'Level' field.</p> <p>AdditionalFile3-training_dataset_target:<br> List of target tuples (manually annotated) of 221 abstracts considered for training the model. For each abstract, target tuples follow the schema ID, DOI, title, entity, effect, level, type (mutation or variant), tuples_count (>1 when an effect/level is shared by multiple entities, #abstracts containing the same effect described in the tuple).</p> <p>AdditionalFile4-validation_dataset_target:<br> List of target tuples (manually annotated) of 50 abstracts considered for validating the prepared prediction model.<br> For each abstract, target tuples follow the schema defined for AdditionalFile3.</p> <p>AdditionalFile5-validation_dataset_highlighted:<br> Textual abstracts of the 50 manuscripts considered for validation; the text used to support the manual target annotations has been highlighted in yellow.</p> <p>AdditionalFile6-validation_dataset_prediction:<br> List of predicted annotations of 50 abstracts considered for validating the prepared prediction model. The file is split in 4 TSV, respectively for entity (a), effect (b), level (c), and whole tuple predictions (d).</p> <p>AdditionalFile7-keywords_query_list:<br> Keyword-based search run on the CORD-19 dataset to extract a relevant subset of abstracts regarding the scope of interest of CoVEffect. The Boolean logic used to combine keywords is explained in the section 'Annotations of the biology-related CORD-19 cluster'.</p> <p>AdditionalFile8-CORD-19_batch_dataset_metadata:<br> Metadata of the 7,230 papers extracted by the keyword-based query in AdditionalFile7.<br> These abstracts have been annotated by the prediction framework.</p> <p>AdditionalFile9-CORD-19_batch_dataset_prediction:<br> List of predicted annotations of 7,230 abstracts extracted from the biology-related cluster of CORD-19.</p> <p>AdditionalFile10-test_dataset_target:<br> List of target tuples (manually annotated) of 100 abstracts randomly selected from the 7,230 extracted as in AdditionalFile8.<br> For each abstract, target tuples follow the schema defined for AdditionalFile3.</p> <p>AdditionalFile11-test_dataset_prediction:<br> List of predicted annotations of 100 abstracts considered for testing the prediction model on a subset of the CORD-19 biology-related cluster. As AdditionalFile6, it is split in 4 TSV, respectively for entity (a), effect (b), level (c), and whole tuple predictions (d).</p>
Tree ring, leaf mining, climate, and remote sensing data from aspen leaf miner survey sites: III - Climate, leaf mining, and NDVI data
This dataset contiains annual site-level measurements from 2004 - 2015 of growing season climate moisture index ( GS CMI; summed CMI from May - September), average site level leaf mining, and mean July - August normalized difference vegetation index (NDVI) derived from Landsat, GIMMS3g, MODIS Aqua, and MODIS Terra
Umfrageergebnisse "Bedarf und Anforderungen an Ressourcen für Text und Data Mining"
<p>Daten der Umfrage "Bedarf und Anforderungen an Ressourcen für Text und Data Mining" der Schwerpunktinitiative "Digitale Information" der Allianz der deutschen Wissenschaftsorganisationen, Arbeitsgruppe Text und Data Mining, vom Mai 2015</p>
Data: Evolution of anatomical concept usage over time: Mining 200 years of biodiversity literature
<p>This data package contains data and results corresponding to the paper titled "Evolution of anatomical concept usage over time: Mining 200 years of biodiversity literature". </p>
Comprehensive Ethereum Execution Data for Object-Centric Process Mining of Decentralized Applications (DApps)
<p>The dataset pertains to the collection and analysis of blockchain execution data, particularly from Ethereum-based Decentralized Applications (DApps). This data includes transactions, transaction receipts, and detailed transaction traces, documenting the execution steps performed by the Ethereum Virtual Machine (EVM). Such traces are essential for understanding the interaction between smart contracts and accounts, including Contract Accounts (CAs) and Externally Owned Accounts (EOAs).</p> <p>A blockchain is an append-only ledger that chronologically records data in blocks. Each block contains transactions that signify state transitions, and transaction receipts that provide a hashed result of these transitions to ensure uniform results across different executions. The dataset includes a classification of Ethereum accounts, detailing the functions and interactions between EOAs and CAs, where CAs deploy and execute smart contract code.</p> <p>The dataset captures the granular operational data of blockchain transactions, such as function calls, contract creations, and log entries generated by smart contracts. These details are crucial for creating object-centric event logs, aiding in process mining and analysis to bridge the gap between theoretical process models and actual execution.</p> <p>Contract creations and function calls are fundamental components of the dataset. The former documents the deployment of smart contracts, including the mechanics of contract updates and additions through various design patterns. Function calls between accounts are also extensively logged, providing insights into the flow of Ethereum's native token, Ether, and other transactional data within the blockchain.</p> <p>Delegated calls and log entries represent more specialized interactions within Ethereum, where delegated calls allow contracts to use code from other contracts to manipulate their own state, supporting upgradeable contract designs. Log entries, specified within smart contract code, facilitate the communication of contract execution details to external systems.</p> <p>To handle the diverse and dynamic nature of blockchain data, the dataset employs the Object-Centric Event Log (OCEL) format. This format accommodates multiple object types in a single log, addressing issues such as event divergence and convergence, typical of traditional single-case logs. The latest version, OCEL 2.0, supports documenting dynamic object roles and relationships, improving the fidelity of logs in capturing blockchain operations.</p> <p>In summary, the dataset is structured to support a comprehensive analysis of blockchain behaviors, particularly focusing on Ethereum DApps. It is tailored to assist researchers and practitioners in understanding and analyzing the decentralized execution of smart contracts and the associated data flows within the blockchain environment.</p>
Mining and Extractivism Records Data for Bibliometric Analysis (Scopus database 1992-2020)
<p>The dataset file export from scopus database and the dataset file export as bibliometrix file on excel format from biblioshiny.</p>
Fatiando a Terra Data: Osborne Mine, Australia - Airborne total-field magnetic anomaly
<p>This is a section of a survey acquired in 1990 by the Queensland Government, Australia. The data are good quality with approximately 80 m terrain clearance and 200 m line spacing. The anomalies are very visible and present interesting processing and modelling challenges, as well as plenty of literature about their geology.</p> <p><strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please <strong>cite the original authors</strong> when using this dataset.</p> <p><strong>Changes made: </strong>Change the horizontal datum from GDA94 to WGS84. Convert terrain clearance to flight height using an SRTM grid. Keep only the coordinates, AWAGS leveled magnetic anomaly, and flight line ID. Cut to a smaller region containing only the 2 anomalies of interest.</p> <p><strong>Source: </strong>Geophysical Acquisition & Processing Section 2019. MIM Data from Mt Isa Inlier, QLD (P1029), magnetic line data, AWAGS levelled. Geoscience Australia, Canberra. <a href="http://pid.geoscience.gov.au/dataset/ga/142419">http://pid.geoscience.gov.au/dataset/ga/142419</a></p> <p><strong>Source license: </strong><a href="http://pid.geoscience.gov.au/dataset/ga/142419">CC-BY</a></p> <p><strong>Repository: </strong><a href="https://github.com/fatiando-data/osborne-magnetic">https://github.com/fatiando-data/osborne-magnetic</a></p>
Data for 'Formalizing Artisanal and Small-scale Gold Mining: a Grand Challenge of the Minamata Convention'
<p>Signatories to the Minamata Convention on Mercury with ‘more than insignificant’ artisanal and small-scale gold mining (ASGM) sectors are required to develop and implement National Action Plans (NAPs) to reform their ASGM sectors in line with Annexe C of the Convention. We compiled the budgets of available NAPs for reducing mercury emissions from ASGM sectors. As of 2021-12-31, these were available for 16 countries from: www.mercuryconvention.org/en/parties/national-action-plans. We used these data to estimate the approximate costs of expanding such approaches globally.</p>
Dataset: Applications of Educational Data Mining and Learning Analytics on Data From Cybersecurity Training
<p>This repository contains supplementary materials for the following journal paper:</p> <p>Valdemar Švábenský, Jan Vykopal, Pavel Čeleda, Lydia Kraus.<br> <em>Applications of Educational Data Mining and Learning Analytics on Data From Cybersecurity Training.</em><br> In Springer Education and Information Technologies. 2022.<br> <a href="https://doi.org/10.1007/s10639-022-11093-6">https://doi.org/10.1007/s10639-022-11093-6</a></p> <p>Preprint available at: <a href="https://arxiv.org/abs/2307.08582">https://arxiv.org/abs/2307.08582</a></p> <ul> </ul> <p><strong>How to cite</strong></p> <p>If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link).</p> <pre><code>@article{Svabensky2022applications, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and Vykopal, Jan and \v{C}eleda, Pavel and Kraus, Lydia}, title = {{Applications of Educational Data Mining and Learning Analytics on Data From Cybersecurity Training}}, journal = {Education and Information Technologies}, publisher = {Springer}, volume = {27}, year = {2022}, issn = {1360-2357}, url = {https://doi.org/10.1007/s10639-022-11093-6}, doi = {10.1007/s10639-022-11093-6}, }</code></pre> <p><strong>Attached content</strong></p> <p>The files included in the ZIP archive are:</p> <ul> <li>`All-discovered-papers.bib` -- a BibTeX export of the Mendeley database of all considered papers discovered by the automated search.</li> <li>`Candidate-papers-reviewer1.bib` -- a BibTeX export of the Mendeley database of the candidate papers suggested by the first investigator.</li> <li>`Candidate-papers-reviewer2.bib` -- a BibTeX export of the Mendeley database of the candidate papers suggested by the second investigator.</li> <li>`Selected-papers.bib` -- a BibTeX export of the Mendeley database of the 35 papers selected for the literature review.</li> <li>`Selected-papers.xlsx` -- an Excel spreadsheet with the extracted information about the selected papers.</li> <li>`Selected-papers.csv` -- a CSV equivalent of the Excel spreadsheet.</li> </ul>
Genome Mining for Data Carpentry lesson
<p>Data for Software Carpentry Genome Mining lesson. These data are part of the study<em> "Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: Implications for the microbial pan-genome" </em>(Hervé Tettelin et al 2005)</p>
Text-fig. 2. Tectocarya spp. a–n: Tectocarya grandis (E.REID et M.CHANDLER) comb. n. Holotype V.22968. a: Lateral view of broken endocarp, reflected light. b–d: Longitudinal views, surface renderings from micro-CT data. e: Translucent volume renderings. f: Apical view, surface rendering. g: View of transversely broken surface showing curved locule, reflected light. h–n: Successive digital transverse sections. Note septum in the dorsal infold (arrows). o, p: Tectocarya rhenana KIRCHH., Miocene of Germany, dorsal view and transverse section [Holotype of Mastixoidea tectocaryoides KIRCHH., Alfred Mine near Konzendorf, photo by Dieter Mai] (Synonym of T. rhenana MAI, 1993). q: T. rhenana transverse section. from Mine Alfred, Düren, Germany, coll. Claire A. Brown 1952, USNM 355632. r, s: Tectocarya sp. from late Eocene of Post, Oregon, USA, physical transverse section, reflected light. UF279-50014. [Surface views of same specimen shown in Manchester and McIntosh 2007: figs 62, 63]. Scale bars 1 cm in (a–r), 0.5 cm in (s). in Mastixioid Fruits (Cornales) From The Early Eocene London Clay Flora: Morphology, Anatomy And Nomenclatural Revision
Text-fig. 2. Tectocarya spp. a–n: Tectocarya grandis (E.REID et M.CHANDLER) comb. n. Holotype V.22968. a: Lateral view of broken endocarp, reflected light. b–d: Longitudinal views, surface renderings from micro-CT data. e: Translucent volume renderings. f: Apical view, surface rendering. g: View of transversely broken surface showing curved locule, reflected light. h–n: Successive digital transverse sections. Note septum in the dorsal infold (arrows). o, p: Tectocarya rhenana KIRCHH., Miocene of Germany, dorsal view and transverse section [Holotype of Mastixoidea tectocaryoides KIRCHH., Alfred Mine near Konzendorf, photo by Dieter Mai] (Synonym of T. rhenana MAI, 1993). q: T. rhenana transverse section. from Mine Alfred, Düren, Germany, coll. Claire A. Brown 1952, USNM 355632. r, s: Tectocarya sp. from late Eocene of Post, Oregon, USA, physical transverse section, reflected light. UF279-50014. [Surface views of same specimen shown in Manchester and McIntosh 2007: figs 62, 63]. Scale bars 1 cm in (a–r), 0.5 cm in (s).
Data format figures-DATA MINING LEARNING MODELS AND ALGORITHMS ON A SCADA SYSTEM DATA REPOSITORY
<p>The original data set included noisy, missing and inconsistent data. Data<br> preprocessing improved the quality of the data and facilitated e±cient data<br> mining tasks.<br> Before the experiment, we prepared data suitable to next operation as<br> following steps:<br> ² Delete or replace missing values;<br> ² Delete redundant properties (columns);<br> ² Data Transformation;<br> ² Data Discretization;<br> ² Export data to a required .ar® or .csv format ¯le [11].<br> The original and modi¯ed formats of data set are shown in Figure 1 and<br> Figure 2.<br> Data visualization is also a very useful technique because it helps to deter-<br> mine the di±culty of the learning problem. We visualized with Weka single<br> attributes (1-d) and pairs of attributes (2-d). The ¯gure 3 shows the variation<br> of the temperature in time.</p>
ScienceDex guides
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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.