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6,782 results for “project”
GRILLIX simulation data for TCV-X21 divertor validation project
<p><strong>GRILLIX simulation data for TCV-X21 divertor validation project</strong></p> <p>This dataset contains simulation data from the GRILLIX high-performance edge simulation software. The simulation data is for the TCV-X21 divertor validation case.</p> <p>It contains two large-file types. These are stored separately from the main TCV-X21 repository, to reduce the repository size for the base validation repository.</p> <p>The first are "work files", which allow you to check the time-resolved dynamics of the GRILLIX TCV-X21 results.<br> The second are "checkpoints", the complete simulation state at a single time-point (as well as input files), which might be helpful if you are starting new TCV-X21 simulations.</p> <p>To use these files, you can use the TCV-X21 processing routines. For more details about the project, please check the arXiv version, available at https://arxiv.org/abs/2109.01618</p>
Overview of the BLR Project funded by Arcadia Fund
<p><strong>The project includes the BLR repository and the TB data processing platform. The workflow includes all the tools for automated processing from discovering articles, to downloading, to extracting data, QC and dissemination. DOI: <a href="https://doi.org/10.5281/zenodo.5507005">10.5281/zenodo.5507005</a>, <a href="https://zenodo.org/record/5499090">https://zenodo.org/record/5499090</a></strong></p>
METADATA: Marie Skłodowska-Curie Action, DADA2GT, Project Number: 841780
<p>This repository contains the data generated through the Marie Skłodowska-Curie Action DADA2GT (Project Number: 841780) entitled "Development of gene therapy and genome editing strategies to treat adenosine deaminase 2 deficiency." All the data are stored as .cvs files. The results of the Action have been included in the publication entitled "Lentiviral correction of enzymatic activity restrains macrophage inflammation in adenosine deaminase 2 deficiency" published in the open-access journal Blood Advances and available at https://doi.org/10.1182/bloodadvances.2020003811 and Zenodo.</p>
A unified genealogy of modern and ancient genomes: Unified, inferred tree sequences of 1000 Genomes, Human Genome Diversity, and Simons Genome Diversity Projects with ancient samples
<p>Unified, inferred tree sequences built from the 1000 Genomes phase 3, Human Genome Diversity, and Simons Genome Diversity Projects with high coverage sequenced ancient samples. The ancient samples are the Altai, Chagyrskaya, and Vindija Neanderthals, the Denisovan, and a high-coverage family of four from the Afanasievo Culture.</p> <p>Each tree sequence is the arm of an autosome (the short arm of acrocentric chromosomes are not included). Tree sequences were inferred with <a href="https://tsinfer.readthedocs.io/">tsinfer</a> version 0.2.1 and <a href="https://tsdate.readthedocs.io/en/latest/">tsdate</a> version 0.1.4, as described in <a href="http://www.biorxiv.org/content/10.1101/2021.02.16.431497v2">Wohns et al. (2021)</a>. The files were compressed using <a href="https://tszip.readthedocs.io/en/stable/">tszip</a>. All data is in GRCh38.</p> <p>The full data pipeline used to generate these tree sequences and associated metadata is available on <a href="https://github.com/awohns/unified_genealogy_paper">GitHub</a>. A description can be found in the Supplementary Material of <a href="https://www.biorxiv.org/content/10.1101/2021.02.16.431497v2">Wohns et al. (2021)</a>.</p> <p>Tree sequences can be decompressed as follows:</p> <pre><code>$ tsunzip hgdp_tgp_sgdp_high_cov_ancients_chr1_p.dated.trees.tsz</code></pre> <p>Once decompressed, trees files can be loaded and processed in Python using <a href="https://tskit.readthedocs.io/">tskit</a>. </p> <pre><code>import tskit ts = tskit.load("hgdp_tgp_sgdp_high_cov_ancients_chr1_p.dated.trees") # ts is an instance of tskit.TreeSequence print("The short arm of chromosome 1 contains {} trees".format(ts.num_trees))</code></pre> <p>Accessing variant sites in the tree sequence provides the position and id of variants:</p> <pre><code>import json site = ts.site(1000) site_metadata = json.loads(site.metadata) print("The position of site 1000 is {} and its ID is {}.".format(site.position, site_metadata["ID"]))</code></pre> <p>Metadata associated with individuals and populations was derived from the original sources (<a href="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20130606_sample_info/20130606_g1k.ped">TGP</a>, <a>HGDP</a>, and <a href="https://sharehost.hms.harvard.edu/genetics/reich_lab/sgdp/SGDP_metadata.279public.21signedLetter.samples.txt">SGDP</a>) and converted to JSON form. For example, to access individual metadata we can use:</p> <pre><code>ind = ts.individual(0) metadata_dict = json.loads(ind.metadata)</code></pre> <p>The metadata_dict variable will now contain all the metadata for the individual with ID 0 as a dictionary. Metadata associated with populations can be found in a similar way. Population IDs are associated with individuals via their constituent nodes. For example,</p> <pre><code>pop_metadata = [json.loads(pop.metadata) for pop in ts.populations()] ind_node = ts.node(ind.nodes[0]) ind_pop_metadata = pop_metadata[ind_node.population]</code></pre> <p>After this, the ind_pop_metadata variable will contain the population level metadata for individual ID 0.</p>
DCNDS Project Dataset - P2P Botnet Detection Using Enhanced Peer Hunter
<p>This dataset represents the Enhanced Peer Hunter implementation that was deployed on two NREN within the AsiaConnect project in Malaysia and Indonesia. Based on a 30 days measurement from Saturday, June 19, 2021 4:00:00 PM GMT - Monday, July 19, 2021 3:59:55 PM GMT, the list of detections and all flows associated with the detected machines are listed in the dataset (2 files per country). The dataset would allow any future p2p botnet detection mechanisms to leverage this (flow) data to train and evaluate their detection mechanisms.</p> <p> Take note that all IP addresses have been anonymized using a hash function with salt to avoid leakage of information.</p> <p> </p>
MED-GOLD Indicators for the Wine pilot over Douro Valley based on High Resolution Climate projections
<p>Indicators of interest for the Wine sector over the Douro Valley using high resolution climate projections.</p> <ol> <li>GDD (Growing Degree Days) - summation of daily differences between daily temperature averages and 10 for the period April-October</li> <li>GST (Growing Season Temperature) - average of daily average temperatures for the period April-October</li> <li>SprR (Spring Rain) - Precipitation accumulated between 21st April-to 21st June,</li> <li>HarvR (Harvest Rain)-Precipitation accumulated between 21 August and 21 October</li> <li>SU35 -number of days with temperature higher than 35°C for the period April-October,</li> <li>WSDI (Warm Spell Duration Index) -days with at least 6 consecutive days when the daily temperature maximum exceeds its 90th percentile for the period April-October.</li> </ol> <p>The results are based on an sub-ensemble of five RCMs from the EURO-CORDEX modelling experiment which have been statistically downscaled to 1km x1km horizontal resolution using the PTHRES gridded dataset as the reference dataset. More details can be found in Raül Marcos-Matamoros, (2018). Report on the methodology followed to implement the wine pilot services. Zenodo. https://doi.org/10.5281/zenodo.4543337</p> <p>Datasets computed by National Observatory of Athens, in collaboration with SOGRAPE VINHOS S.A. in the framework of the European MED-GOLD project, funded from the European Union's Horizon 2020 Research and Innovation programme under Grant agreement No.776467</p> <p> </p> <p> </p>
Pandanaceae Plastome Phylogenetics Project Files
<p>RAxML Tree: PartitionedMLPlastome.tree</p> <p>BEAST Tree: med_Wojahn_Pandanaceae_Plastome_2024.tree</p> <p>Alignment: AlignedRevCompGood.fasta</p>
Expert opinion survey on habitat-threat parameters for the PEM-Sul ocean zoning project
<p>This is an ongoing dataset.</p> <p> </p>
Barriers & Facilitators of People With Disabilities in Accepting & Adopting Autonomous Shared Mobility Services (Project A5)
<p>Enclosed you will find the data collected during our STRIDE Phase II Extension research project (A5) and a data dictionary.</p>
Progeny Project NAMD data for MW1 and MW2 surfactants
<p>Data of the excitation energy relaxation in MW1 and MW2 surfactants of Progeny project from non-adiabatic molecular dynamics simulations</p> <p>See Readme.txt for more details.</p> <p> </p> <p> </p>
Progeny Project DFT and NAMD data for periodic 2D surfactant
<p>Crystalline unit cell, band structure, and electron-hole recombination dynamics from non-adiabatic molecular dynamics for the periodic 2D surfactant of the Progeny.</p> <p>See Readme.txt for more details.</p>
Progeny Project DFT and TD-DFT data for MW1 and its variants with different side chain length
<p>DFT and TD-DFT data, i.e. geometries, electronic excited states for the MW1 and its variants in vacuum with different length of side chains.</p> <p>See Readme.txt for more details.</p>
Mastodon project containing the cell tracks of a developing Tribolium Castaneum embryo.
<p>Mastodon project containing the cell tracks of a developing <em>Tribolium Castaneum </em>embryo. </p> <p>To open it in Mastodon, download both the <em>.mastodon </em>and the <em>.xml </em>files and put them in the same folder.</p> <p>The image data is stored on a BigDataServer in the Institut Pasteur, Paris, provided by the <a href="https://research.pasteur.fr/en/team/image-analysis-hub/">Image Analysis Hub</a>. When you open this <em>.mastodon </em>file with Mastodon, it will stream the image from the server without you having to download it. The mastodon file contains just the tracks and the <em>.xml </em>file contins the address of the server from where the image data will be streamed.</p> <p>Check the <a href="https://mastodon.readthedocs.io/">Mastodon scientific software</a> for information on how to open and use this file for scientific purposes.</p> <p>The image data is the 'TRIF' training video 02 from the <a href="http://www.celltrackingchallenge.net/">CellTrackingChallenge.net</a>, with authorization. The original dataset provider is Dr. A. Jain. Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.</p> <ul> <li>Microscope: Zeiss LightSheet LZ.1</li> <li>Objective lens: Plan-Apochromat 20x/1.0 (water)</li> <li>Voxel size (microns): 0.38 x 0.38 x 0.38</li> <li>Time step (min): 1.5</li> </ul>
List of websites with CS projects information
<p>The dataset contains the list of wesbites from where TIDE-UPF extracted the CS projects information. </p>
Plant Communities at the Eden Project, UK, derived from soil eDNA
<p>The project seeks to understand the potential for the use of eDNA collected from soil to characterise plant communities. To do so, soils were sampled at the Eden Project in Cornwall UK, within the two covered biomes where we have a good understanding of the structure and composition of plant communities (further quantified with above ground plant coverage inventories). 32 plots were established across 10 different plant assemblages, each of which experiences subtle differences in soil chemistry and microclimate. Each plot consists of a 2 x 2 m quadrat, with four soil aggregates collected at each corner. </p> <p>eDNA was then extracted and amplified following the methods detailed in Zinger et al. (2016) and Donald et al. (2021). The primers used targeted the P6 loop of thechloroplastic trnL intron [primer_fwd: GGGCAATCCTGAGCCAA, primer_rev: CCATTGAGTCTCTGCACCTATC] (Taberlet et al. 2007). 16 Extraction, 54 Sequencing, and 16 PCR controls are included so as to account for potential errors generated during the processing of samples, with a mock community (4 positive controls) of 10 known plant sequences also included to guide filtering thresholds. PCR products were pooled and sequencing libraries were constructed using the Illumina TruSeq NanoPCRFree kit following the supplier’s instructions (Illumina Inc., San Diego, California, USA), except that the ligation product was not PCR amplified to limit tag-jump biases (Taberlet et al 2018). The libraries were then sequenced on an Illumina Hiseq platform (San Diego, CA, USA).</p> <p>Sequencing was conducted by the GenoToul bioinformatics platform (Toulouse, France), with the OBITOOLS package (Boyer et al. 2016). Here, the produced sequence data was processed using the following steps. First, ‘illuminapairedend’ was used to assemble paired-end reads. This algorithm is based on an exact alignment algorithm that considers the quality scores at all positions during the assembly process. Subsequently, we used the ‘ngsfilter’ command to identify and remove the primers and tags on each read, and assign reads to their respective samples (NGS filter file provided: <strong>ngsfilter_TRNL_PLANTS_EDEN_PROJECTb.txt</strong>). This program was used with its default parameters tolerating two mismatches for each of the two primers and no mismatch for the tags. Following this, sequencing reads were dereplicated using the ‘obiuniq’ command. The produced <strong>data.uniq.fasta</strong> file is supplied here. Sequences were then further filtered to remove sequences of low quality (containing Ns or with paired-end alignment scores below 50), and sequences represented by only one read (singletons) using the ‘obigrep’ command. To remove PCR/sequencing errors as well as intraspecific variability, we built OTUs (Operational Taxonomic Units) using the ‘sumaclust’ clustering algorithm (Mercier et al. 2013), which considers the most abundant sequence of each cluster as the cluster representative. OTUs were set at a sequence similarity threshold of 95%. To assign a taxon to plant OTUs, we built a reference sequence database using the ecoPCR programme (Ficetola et al. 2010) on the European Molecular Biology Laboratory (EMBL; release 141). OTUs were then assigned a taxonomy, using OBITOOL’s ecotag programme (Boyer et al. 2016), which performs a global alignment of each OTU sequence (the query) against each reference. The reference taxon assigned to each OTU corresponds to the Last Common Ancestor of all the best-match sequences for the query. </p> <p><br> Datasets were subsequently filtered to remove contaminants as well as artefacts such as PCR chimeras and remaining sequencing errors, using routines implemented in the metabaR R package (Zinger et al 2021), in R version 3.6.1 (R Development Core Team, 2013). The filtering process consisted of four steps: (i) a negative control-based filtering. OTUs whose maximum abundance was found in extraction/PCR negative controls were removed from the dataset, as they were likely to be reagent/aerosol contaminants, better amplified in the absence of competing DNA fragments as it is the case in biological samples. (ii) a reference-based filtering. OTUs which are too dissimilar from sequences available in reference databases are potential chimeras generated during sequencing and amplification. In this study, we chose to set similarity thresholds at 100%. (iii) an abundance-based filtering. This procedure targets incorrect assignment of a few numbers of sequences corresponding to true OTUs occurring to the wrong sample, a phenomenon called “tag-switching”. It consists in setting OTUs abundances to 0 in samples where their abundance represents < 0.03% of the total OTU abundance in the entire dataset. (iv) Finally, we conducted a PCR-based filtering by considering any PCR reaction that yielded less than 1000 reads as non-functional, and removed them from the dataset. The script used for implementing this is provided (<strong>metabaR_Eden_Plants_100sim.html)</strong>, with sequence data processed to remove contaminants, OTUs of low taxonomic resolution, and PCRs with too low a read count. The clean data is provided (<strong>eden_plant_postclean_100sim.rds</strong>).</p> <p>References:</p> <p>Boyer, F. <em>et al.</em> (2016) ‘obitools: a unix-inspired software package for DNA metabarcoding’, <em>Molecular Ecology Resources</em>, 16(1), pp. 176–182. doi:<a href="https://doi.org/10.1111/1755-0998.12428">10.1111/1755-0998.12428</a>.</p> <p>Donald, J. <em>et al. (2021) '</em>‘Multi-taxa environmental DNA inventories reveal distinct taxonomic and functional diversity in urban tropical forest fragments.‘ <em>Global Ecology and Conservation</em> 29 (2021): e01724.</p> <p>Mercier, C. <em>et al.</em> (2013) ‘SUMATRA and SUMACLUST: fast and exact comparison and clustering of sequences’, in <em>Programs and Abstracts of the SeqBio 2013 workshop. Abstract</em>. Citeseer, pp. 27–29.</p> <p>Taberlet, P. <em>et al.</em> (2007) ‘Power and limitations of the chloroplast trn L (UAA) intron for plant DNA barcoding’, <em>Nucleic Acids Research</em>, 35(3), pp. e14–e14. doi:<a href="https://doi.org/10.1093/nar/gkl938">10.1093/nar/gkl938</a>.</p> <p>Taberlet, P. <em>et al.</em> (2018) <em>Environmental DNA: For Biodiversity Research and Monitoring</em>. Oxford University Press.</p> <p>Team, R.C. (2013) <em>R: A language and environment for statistical computing</em>. Vienna, Austria.</p> <p>Zinger, L. <em>et al.</em> (2016) ‘Extracellular DNA extraction is a fast, cheap and reliable alternative for multi-taxa surveys based on soil DNA’, <em>Soil Biology and Biochemistry</em>, 96, pp. 16–19.</p> <p>Zinger, L. et al. (2021) ‘metabaR: An r package for the evaluation and improvement of DNA metabarcoding data quality’, Methods in Ecology and Evolution. DOI: <a href="https://doi.org/10.1111/2041-210X.13552">https://doi.org/10.1111/2041-210X.13552</a></p>
Heat Cost Allocator Dataset for the Reconcycle Project
<p>The dataset consists of images of the Kalo 1.5 heat cost allocator (HCA) and the Qundis HCA. The dataset has been created for the Reconcycle project. Find information at reconcycle.eu. The objects are positioned in different areas of the Reconcycle workcell, designed by JSI.</p> <p>The dataset has the following properties:</p> <ul> <li> <p>1577 images with resolution: 1450x1450 pixels (Basler camera )</p> </li> <li> <p>57 images with resolution: 848 x 480 pixels (Realsense D435 camera)</p> </li> <li> <p>The images have segmentation annotations labelled using the labelme software.</p> </li> <li> <p>The original labelme annotations are present and exported to COCO dataset format.</p> </li> <li> <p>The annotations are in the form of polygon segmentations.</p> </li> <li> <p>The included COCO train/test split is a 90/10 split.</p> </li> </ul> <p>The images have been annotated with the following labels:</p> <ul> <li> <p>hca_front</p> </li> <li> <p>hca_back</p> </li> <li> <p>hca_side1</p> </li> <li> <p>hca_side2</p> </li> <li> <p>battery</p> </li> <li> <p>pcb</p> </li> <li> <p>internals</p> </li> <li> <p>pcb_covered</p> </li> <li> <p>plastic_clip</p> </li> </ul>
Research & Innovation Digitalization Scoreboard for the SEA-EU alliance and its member universities in the output D2.2 Digital transformation of research and innovation roadmap of the reSEArch-EU project
<p>This file is a detailed Research & Innovation Digitalization Scoreboard for the SEA-EU alliance and its member universities, used in the output D2.2 Digital transformation of research and innovation roadmap of the Horizont project reSEArch-EU, implemented by the SEA-EU university alliance.</p>
InteropEHRate project dataset1
<p>Data Collected in InteropEHRate pilot 1 activities</p>
[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects - Graph Files
<p><strong>Explanation/Overview:</strong></p> <p>Corresponding graph files of the extracted Zooniverse networks described in D3.3 (can be found here), which are the result of our research that culminated into the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: <a href="https://link.springer.com/book/10.1007/978-3-031-20218-6">Collaboration Technologies and Social Computing</a> and published as part of the <a href="https://link.springer.com/bookseries/558">Lecture Notes in Computer Science</a> book series (LNCS,volume 13632) <a href="https://link.springer.com/chapter/10.1007/978-3-031-20218-6_5">here</a>. Usernames have been anonymised.</p> <p>The graph files are in <code>.gexf</code> (graph exchange XML format) and <code>.gml</code> (graph modeling language) formats which can be used by common graph/network-analysis and visualisation tools such as Gephi.</p> <p><strong>Purpose:</strong></p> <p>The purpose of this dataset is to provide the basis for possible further examinations of the network structure, involving additional (not yet analysed) features such as the content of the comments etc.</p> <p><strong>Relatedness:</strong></p> <p>The data of the different projects was derived from the forums of 7 Zooniverse projects based on similar discussion board features. The projects are: 'Galaxy Zoo', 'Gravity Spy', 'Seabirdwatch', 'Snapshot Wisconsin', 'Wildwatch Kenya', 'Galaxy Nurseries', 'Penguin Watch'.</p> <p><strong>Content:</strong></p> <p>The dataset contains distinct graph files for each of the analysed projects. For each graph file, there are <em>nodes </em>and <em>edges</em> and their associated attributes (i.e., each edge can have an attribute). For the edges, apart from source and target, we have as attributes:</p> <ul> <li><code>weight</code></li> <li><code>project_title</code></li> <li><code>body </code>(i.e., text)</li> <li><code>created_at</code></li> <li><code>userRoles</code></li> <li><code>discussion_title</code></li> <li><code>discussion_id</code></li> <li><code>user_id</code></li> <li><code>board_title</code></li> <li><code>relation</code></li> <li><code>target_role</code></li> </ul> <p>For the nodes, the attributes are:</p> <ul> <li><code>user_id</code></li> <li><code>userRoles</code></li> <li><code>degree_reply </code>(i.e., degree for the reply relation)</li> <li><code>in_degree_reply</code></li> <li><code>out_degree_reply</code></li> <li><code>degree_comment</code></li> <li><code>in_degree_comment</code></li> <li><code>out_degree_comment</code></li> <li><code>degree_total</code></li> <li><code>in_degree_total</code></li> <li><code>out_degree_total</code></li> <li><code>target_role</code></li> </ul> <p><strong>Grouping:</strong></p> <p>Each graph file represents all the comments for the respective project across its lifespan irrespective of any time slices. Edges represent the comments and users represent the nodes. While the different boards are still contained within the data, all boards occur in the data.</p> <p> </p>
Identifying learning dimensions in CS project descriptions
<p>For this study, we conducted a qualitative content analysis of a random sample of 94 English-language project descriptions stored in the CS Track database with the goal of determining which dimensions of learning are reflected most prominently in these texts. Using a slightly modified version of the model of individual learning outcomes developed by Phillips et al. in 2018 as a coding rubric, two members of the research team independently coded all project descriptions by manually assigning phrases, sentences and short paragraphs to eight main categories and 21 subcategories. From these text snippets, distinctive and frequently occurring keywords were extracted, which have since been used in follow-up studies. </p> <p>Our analysis revealed that some learning dimensions (such as data collection or using technology) are very prominently discussed in the project descriptions we studied, while others (e.g. experimenting, study design, community action) are clearly underrepresented. In other words, the project descriptions analysed only partially reflect the educational potential of participation in CS. Based on these findings, we suggested possible explanations and ways in which the issue could be addressed on the level of both project design and project communication.</p> <p>This study profited immensely from the kind support of Tina Phillips and her colleagues, who agreed to share parts of their coded dataset with us.</p> <p>Details related to the analysis procedure are provided in a paper which is currently under review (on the date of submission of this deliverable - November 2022), no link to a repository is available. Contact the main authors if you have interest to receive further information).</p> <p>References: Phillips, T., Porticella, N., Constas, M., & Bonney, R. (2018). A framework for articulating and measuring individual learning outcomes from participation in citizen science. Citizen Science: Theory and Practice, 3(2).</p> <p>More information on this research can be found in D2.2 section 5.1.</p> <p><strong>Content and grouping: </strong></p> <ul> <li> <p>coding rubric used (Main category, Subcategory, Definition (Draft) and Inclusion/exclusion criteria) columns)</p> </li> </ul> <ul> <li> <p>complete coding (i.e. including CS Track projects title)</p> </li> <li> <p>calculation of rate of agreement between coders</p> </li> <li> <p>list of keywords extracted</p> </li> <li> <p>list of projects titles and the website URL from where we extracted the information</p> </li> </ul>
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.