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9,152 results for “Networking”
Screening for Chagas disease using a deep neural network
<p>Pre-trained models accompanying the source code at https://github.com/carji475/ecg-chagas</p>
A Dataset of Patients Reviews on Doctissimo Forum of the 2017 Levothyrox Scandal in France for the Early Detection of Safety Signals from Social Networks
<p>In France, the Levothyrox case is an extremely mediated pharmacovogilance case which received a lot of media coverage in August 2017 following the change, in March of the same year, of the pharmaceutical formulation of the specialty (modification of the type and content of excipients at the initial request of the french's national agency of drug safety - ANSM). Many patients have experienced adverse effects related to fluctuating thyroid function when they were already treated and stabilized with Levothyrox (3 million french patients with thyroid pathologies).</p> <p>This dataset presents patients posts collected from the French health forum <a href="http://forum.doctissimo.fr">Doctissimo</a>, Thyroid and Endocrine Problems sub-forum using a web scraping algorithm. The main idea is to find out whether the result of the processing of the data exchanged on the Doctissimo forum can be used in pharmacovigilance and whether it would have allowed better reactivity from the public authorities and responsible laboratories.</p> <p><br> Doctissimo was chosen since it is the most used health forum in France by drug consuming patients (ranking first with 61\% of users). Other sources of information could have been chosen such as Twitter which is the most used website in the world by drug consuming patients. Twitter brings together 52% of these patients against 27% for all discussion forums combined. However, access to Twitter data is chargeable, which is why Doctissimo has been selected.</p> <p>The extraction was performed on the ``Thyroide et Problemes Endocriniens (Thyroid and Endocrine Problems)'' sub-forum with the keyword ``levothyrox''. The choice of extracting information from this forum using the particular relevant keyword ``levothyrox'' was to limit the amount of extracted data. Indeed, during data extraction, Doctissimo blocks the scraping task when reaching a limit of 8,000 extracted discussion threads, since it detects an automatic machine activity.</p> <p>We collected the messages written between years 2000 and 2020. This resulted in a total of 110,260 comments written by a total of 7650 subjects. For each of the comments, we extract the date, pseudo of the person who wrote the comment, the comment's text, and URL link. </p> <p> </p>
SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning Over Traffic Events
<p><strong>SUTD-TrafficQA</strong> is a dataset that takes the form of <strong>Video</strong> <strong>QA</strong> based on 10,080 in-the-wild videos and annotated 62,535 QA pairs, for benchmarking the cognitive capability of causal inference and event understanding models in complex traffic scenarios. Specifically, the dataset proposes 6 challenging reasoning tasks corresponding to various traffic scenarios, so as to evaluate the reasoning capability over different kinds of complex yet practical traffic events.</p>
Synthetic electricity distribution networks from UK for flexibility analysis - ATTEST project
<p>The dataset contains information about two realistic 6.6 kV weakly meshed synthetic UK distribution networks with 38 buses and 38 lines, and with 124 buses and 124 lines. The network files (MatPower format) include a “snapshot” or “steady-state” at a given time. The systems are ideal for testing flexible resources integration. The first network has buses with high demand (lower voltages) and distributed generation (higher voltages). In the second network there are potential voltage issues and congestions. Both systems can also be used for analysing network reconfiguration effects.</p>
Figure datafiles for Network of hotspot interactions cluster tau amyloid folds
<p>Datafiles used to produce the figures for</p> <p>Network of hotspot interactions cluster tau amyloid folds</p>
Dataset about sharks in French Polynesia extracted from the ORP citizen science network
<p><span>Observers of the Polynesian Shark Observatory (ORP), a citizen science network organized mainly through the Polynesian dive centers, collected an unprecedented amount of data in 43% of the islands of French Polynesia between July 8, 2011, and April 11, 2018, during 13,916 dives. The objective of such a data collection, not accessible to standard research resources, was to provide a unique dataset, offering the opportunity to explore the specific diversity, distribution, seasonality and abundance of many elasmobranch species spread out throughout the overall French Polynesia territory. Indeed, since the data are based on random citizen observation, the spatial distribution was biased toward the most frequented sites and islands where the scuba diving activity is mostly developed. Overall, the increase in observed abundance of rays and sharks observed in French Polynesia and the three most sampled islands, as well as the high specific diversity recorded for the region, provide the first evidence of the effectiveness of the Shark Sanctuary established in 2006. These data, collected randomly by the volunteers, also give insights about potential movement patterns and site fidelity of some species more often seen. While not leading to final conclusion, the network of volunteers filling with information to the Polynesian Shark Observatory is essential in giving preliminary results and research perspectives and directions for future projects on sharks and rays in French Polynesia.</span></p>
Edge disturbance shapes liana diversity and abundance but not liana-tree interaction network patterns in moist semi-deciduous forests, Ghana
<p>Edge disturbance can drive liana community changes and alter liana-tree interaction networks, with ramifications for forest functioning. Understanding edge effects on liana community structure and liana-tree interactions is therefore essential for forest management and conservation. We evaluated the response of liana community structure and the patterns of liana-tree interaction structure to forest edge in two moist semi-deciduous forests in Ghana (Asenanyo and Suhuma Forest Reserves: AFR and SFR, respectively). Liana community structure and liana-tree interactions were assessed in 24 50 × 50 m randomly located plots in three forest sites in each forest: edge, interior and deep-interior established at 0-50 m, 200 m and 400 m from edge. Edge effects positively and negatively influenced liana diversity in forest edges of AFR and SFR, respectively. There was a positive influence of edge disturbance on liana abundance in both forests. We observed anti-nested structure in all the liana-tree networks in AFR, while no nestedness was observed in the networks in SFR. The networks in both forests were less connected, and thus more modular and specialised than their null models. Many liana and tree species were specialised, with specialisation tending to be symmetrical. The plant species played different roles in relation to modularity. Most of the species acted as peripherals (specialists), with only a few species having structural importance to the networks. The latter species group consisted of connectors (generalists) and hubs (highly connected generalists). Some of the species showed consistency in their roles across the sites, while the roles of other species changed. Generally, liana species co-occurred randomly on tree species in all the forest sites, except edge site in AFR where lianas showed positive co-occurrence. Our findings deepen our understanding of the response of liana communities and liana-tree interactions to forest edge disturbance, which are useful for managing forest edge.</p>
Telecommunication Networks as Knowledge Graph Research Datasets
<p><strong>Telecommunication Networks as Knowledge Graph Research Datasets.</strong></p> <p>All the datasets were created using the PC with following parameters: Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz/16.00 GB RAM 1TB SSD</p> <p>All the software which is used for the datasets creation is available here: https://github.com/kulikovia/TN_KG_research<strong> </strong></p> <p>The datasets and supplementary files description of follow:</p> <table> <tbody> <tr> <td> <p><strong>#</strong></p> </td> <td> <p><strong>File</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>1.</p> </td> <td> <p>Computational_complexity_v5_ (ENG).pdf</p> </td> <td> <p>SPARQL performance tests report</p> </td> </tr> <tr> <td> <p>2.</p> </td> <td> <p>Synthesis_performance_tests_results_v1.pdf</p> </td> <td> <p>Inductive and deductive synthesis performance tests report</p> </td> </tr> <tr> <td>3.</td> <td>Computational_complexity_Parallel_v2_ (ENG).pdf</td> <td>Comparision of SPARQL performanse using multi-level KG structure approach and execution using distributed RDF storege </td> </tr> <tr> <td>4.</td> <td>Synthesis_additional_experiments_v4.pdf</td> <td>Additional Inductive and deductive synthesis performance tests report (with different elements distribution by levels)</td> </tr> <tr> <td> <p>5.</p> </td> <td> <p>Dataset_10k_hierarchical.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 10k hierarchical model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>6.</p> </td> <td> <p>Dataset_10k_one-level.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 10k one-level model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>7.</p> </td> <td> <p>Dataset_1k_hierarchical.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 1k hierarchical model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>8.</p> </td> <td> <p>Dataset_1k_one-level.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 1k one-level model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>9.</p> </td> <td> <p>Dataset_200k_hierarchical.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 200k hierarchical model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>10.</p> </td> <td> <p>Dataset_200k_one-level.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 200k one-level model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>11.</p> </td> <td> <p>Dataset_500k_hierarchical.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 500k hierarchical model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>12.</p> </td> <td> <p>Dataset_500k_one-level.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 500k one-level model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>13.</p> </td> <td> <p>Synthesis_Hierarchical_RDF-XML.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: RDF/XML datasets for hierarchical models (1k, 10k, 200k, 500k)</p> </td> </tr> <tr> <td> <p>14.</p> </td> <td> <p>Synthesis_Linear_RDF-XML.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: RDF/XML datasets for hierarchical models (1k, 10k, 200k, 500k)</p> </td> </tr> <tr> <td> <p>15.</p> </td> <td> <p>Hierarchy_model_results_Exp_10M.zip</p> </td> <td> <p>SPARQL performance tests: Datasets for 10M, 3-5-levels, exponential distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> <tr> <td>16.</td> <td>Hierarchy_model_results_Exp_15M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 15M, 3-5-levels, exponential distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>17.</td> <td>Hierarchy_model_results_Exp_200k.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 200k, 3-5-levels, exponential distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>18.</td> <td>Hierarchy_model_results_Linear_10M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 10M, 3-5-levels, linear distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>19.</td> <td>Hierarchy_model_results_Linear_15M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 15M, 3-5-levels, linear distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>20.</td> <td>Hierarchy_model_results_Linear_200k.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 200k, 3-5-levels, linear distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>21.</td> <td>Hierarchy_model_results_Quadro_10M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 10M, 3-5-levels, quadratic distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>22.</td> <td>Hierarchy_model_results_Quadro_15M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 15M, 3-5-levels, quadratic distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>23.</td> <td>Hierarchy_model_results_Quadro_200k.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 200k, 3-5-levels, quadratic distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>24.</td> <td>Hierarchy_model_results_Uniform_10M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 10M, 3-5-levels, uniform distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>25.</td> <td>Hierarchy_model_results_Uniform_15M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 15M, 3-5-levels, uniform distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>26.</td> <td>Hierarchy_model_results_Uniform_200k.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 200k, 3-5-levels, uniform distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td> <p>27.</p> </td> <td> <p>Linear_model_resuts.zip</p> </td> <td> <p>SPARQL performance tests: Datasets for 200k, 10M, 15M, one-level model in RDF/XML format</p> </td> </tr> </tbody> </table>
PAGER: constructing PAGs and new PAG–PAG relationships for network biology
<p>Pathway and Annotated-list Gene-signature Electronic Repository (PAGER) is an online systems biology tool for constructing and visualizing gene and PAG networks from multiple PAG genesets.</p> <p>PAG stands for Pathway, Annotated-list and Gene-signature. A PAG geneset represents a set of genes that share a common feature, whether they belong within the same pathway, share common biological processes, are co-differentially expressed under a particular condition of an experiment or clinical sample, or share a common downstream target or function.</p> <p><strong>Available at:</strong> <a href="http://discovery.informatics.uab.edu/PAGER">http://discovery.informatics.uab.edu/PAGER</a></p> <p><strong>Acknowledgements:</strong></p> <ul> <li> <p>Zongliang Yue, Qi Zheng, Michael T Neylon, Minjae Yoo, Jimin Shin, Zhiying Zhao, Aik Choon Tan, Jake Y Chen, PAGER 2.0: an update to the pathway, annotated-list and gene-signature electronic repository for Human Network Biology, Nucleic Acids Research, Volume 46, Issue D1, 4 January 2018, Pages D668–D676,<a href="https://doi.org/10.1093/nar/gkx1040">https://doi.org/10.1093/nar/gkx1040</a></p> </li> </ul> <p> </p> <p> </p> <p> </p> <p><strong>File description</strong></p> <p>The GMT file format is a tab delimited file format that describes gene sets. In the GMT format, each row represents a PAG. The first column is PAG identifier, the second column is PAG link and the third and left columns are PAG genes. </p>
Supplementary material 1 from: Barton DN (2023) Value 'generalisation' in ecosystem accounting - using Bayesian networks to infer the asset value of regulating services for urban trees in Oslo. One Ecosystem 8: e85021. https://doi.org/10.3897/oneeco.8.e85021
Bayesian Belief Network
EuroSAT processed with quantum convolutional neural network
<p>This dataset contains all the <a href="https://github.com/phelber/EuroSAT">EuroSAT</a> images processed with a quanvolutional neural network with rx rotation gates and a random layer.</p>
VIOLA JONES ALGORITHM WITH CAPSULE GRAPH NETWORK FOR DEEPFAKE DETECTION
<p><strong>dataset of this paper VIOLA JONES ALGORITHM WITH CAPSULE GRAPH NETWORK FOR DEEPFAKE DETECTION</strong></p>
Are neural network potentials trained on liquid states transferable to crystal nucleation? A test on ice nucleation in the mW water model
<p>Dataset used to train the neural network potential for reproducing mW nucleation.</p>
Automated Earthquake Catalogue for SIEMBRA & ESP networks in South America (Pampean Flat Slab)
<p>This repository includes:</p> <p>1. A gzipped automated catalogue of earthquake locations with P and S arrival times for the SIEMBRA and ESP networks in South America</p> <p>2. A README explaining the information in each column </p>
PI Cargo Network Cargo Flows
<p> PI Cargo Network Cargo Flows</p>
Study of terminological subsystems of modern school textbooks in Russian with the help of word embedding models Word2Vec and neural networks
<p>The reported study was funded by RFBR, project number 19-29-14032 mk.</p>
Data release for the Open Biomedical Network Benchmark
<p> </p> <div> </div>
Replication package for the Docker Inheritance network analysis
<p><strong>Container Image Inheritance on DockerHub: Empirical Analysis and Insights</strong></p> <p>This repository represents a replication package for our SCAM paper on DockerHub inheritance network.</p> <p>This replication package requires Python 3.5+ to be installed.</p> <p>These experiments were executed on a Linux Ubuntu OS.</p> <p>This replication package contains four folders:<br> - data: contains all datasets required.<br> - scripts: contains all scripts needed to collect the data.<br> - notebooks: contains notebooks where we analyze data. <br> - figures: contains figures saved from the notebooks.</p> <p>To obtain the analysis used in the paper, one should execute ``jupyter notebook`` at the root of this replication package, and open the notebook contained in ``notebooks``.</p> <p>The data is under the Creative Commons Attribution Share-Alike 4.0 license. The source code is under the GNU General Public License.</p>
Metadata supporting the AFDB90v4 annotated sequence similarity network
<p>Driven by the development and upscaling of fast genome sequencing and assembly pipelines, the number of protein-coding sequences deposited in public protein sequence databases is increasing exponentially. Recently, the dramatic success of deep learning-based approaches applied to protein structure prediction has done the same for protein structures. We are now entering a new era in protein sequence and structure annotation, with hundreds of millions of predicted protein structures made available through the AlphaFold database. These models cover most of the catalogued natural proteins, including those difficult to annotate for function or putative biological role based on standard, homology-based approaches. In this work, we quantified how much of such "dark matter" of the natural protein universe was structurally illuminated by AlphaFold2 at a high predicted accuracy and modelled this diversity as an interactive sequence similarity network that can be navigated at <a href="https://uniprot3d.org/atlas/AFDB90v4">https://uniprot3d.org/atlas/AFDB90v4</a>. The dataset deposited here corresponds to the metadata generated, and that makes the base of the similarity network constructed and its interpretation. These files are either generated or processed using the code available at <a href="https://github.com/ProteinUniverseAtlas/AFDB90v4">https://github.com/ProteinUniverseAtlas/AFDB90v4</a>.</p> <p>This repository further contains the detailed, individual sequence similarity networks (in CLANS format) generated for the 3 example protein (super)families described in the text.</p> <p>The full content of this repository includes:</p> <ol> <li> <p><strong>AFDBv4_pLDDT_diggestion_UniRef50_2023-02-01.csv</strong>: table listing all uniref50 clusters in UniProt, including information on structural representatives from AFDB. Each column provides different annotations, including functional brightness, median and best pLDDT, brightness and structural representatives, etc.</p> </li> <li> <p><strong>AFDBv4_DUF_dark_diggestion_UniRef50_2023-02-06.csv</strong>: table listing all uniref50 clusters in UniProt and whether they include proteins mapped to known domains of unknown function (DUF).</p> </li> <li> <p><strong>AFDBv4_90.fasta</strong>: fasta file with the sequences of all UniRef50 clusters selected, and used for the all-against-all mmseqs searches that make the base of the network.</p> </li> <li> <p><strong>AFDB90v4_data.csv</strong>: the subset of file (1) that corresponds to the AFDB90v4 dataset, including columns such as functional brightness, median and best pLDDT, brightness and structural representatives, etc.</p> </li> <li> <p><strong>AFDB90v4_data_with_graph_labels.csv</strong>: table listing each individual uniref50 cluster included in the AFDB90v4 dataset, together with their mapping to communities, and connected components.</p> </li> <li> <p><strong>AFDB90v4_cc_data.csv</strong>: table of uniref50 clusters in connected components, including their annotations, and the columns in file (5).</p> </li> <li> <p><strong>AFDB90v4_cc_data_uniprot_community_taxonomy_map.csv</strong>: mapping of each uniprotAC entry to their corresponding component, community and taxonomy.</p> </li> <li> <p><strong>AFDB90v4_subgraphs_summary.csv</strong>: table summarising the properties of individual connected components, including the average brightness, the number of members, the number of unique protein sequences, the median length, and the number of communities.</p> </li> <li> <p><strong>communities_summary.csv</strong>: table summarising the properties of individual communities, including average brightness, the number of members, the number of unique protein sequences, the median length, the most common superkingdom represented, the average structure outlier score, etc.</p> </li> <li> <p><strong>communities_edge_list-coordinates.csv</strong>: the coordinates of each community in the graphical representation. Singleton communities or singleton UniRef50 clusters are not included.</p> </li> <li> <p><strong>communities_edge_list_no_duplicates.csv</strong>: list of edges making the graph.</p> </li> <li> <p><strong>node_class.json</strong>: map between each Uniref50 cluster and its corresponding component and community.</p> </li> <li> <p><strong>subgraphs.tar.gz</strong>: tar file containing gml files for each individual connected component.</p> </li> <li> <p><strong>AFDB90v4_outlier_scores.tsv</strong>: table containing the outlier scores for each community representative.</p> </li> <li> <p><strong>AFDB90v4_dark_galaxies_summary.csv</strong>: table containing the summary of all dark connected components, including average brightness, median length, representatives, number of communities, etc.</p> </li> <li> <p><strong>AFDB90v4_uniprot_naming_assessment_counts.csv</strong>: table listing the per-component semantic diversity scores, as well as the major source of the titles of the proteins included and their count.</p> </li> <li> <p><strong>uniprot_naming_assessment.tar.gz</strong>: tar file containing the per community assessment of predicted protein names in UniProt as of February 2023.</p> </li> <li> <p><strong>CLANS_files.tar.gz</strong>: stores the 3 sequence similarity networks, in CLANS format, constructed for the analysis of the sequence diversity and sequence similarities of the proteins in components, 27, 159 and 3314. These CLANS files make the base of panel A in all figures 3 and 4 and extended data figure 5.</p> </li> </ol> <p> </p>
Data set for Finding Equivalence of Layout Independent Water Distribution Network for Expansion/Reorganization Using Nonlinear Multi-Port Thevenin Theorem
<p>This is the dataset for our manuscript titled "Finding Equivalence of Layout Independent Water Distribution Network for Expansion/Reorganization Using Nonlinear Multi-Port Thevenin Theorem".</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.