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13,587 results for “specification”
GERONTE H2020 project - GERDAT006 - Dataset of symptoms and proms for specific cancer types and gender
<p>This dataset describes a series of symptoms, potentially indicative of treatment-related complications, destabilised comorbidity or functional decline, to be used in the Geronte project for symptoms monitoring in older patients with multimorbidity during and after their cancer treatment</p>
Phase separation of hnRNP A1 upon specific RNA-binding observed by magnetic resonance
<p>Experimental data, <a href="https://mmmx.info">MMMx</a> restraint and ensemble analysis files (.mcx), restraint data, raw ensembles, and ensemble lists with populations (.ens) pertaining to the manuscript "Phase separation of hnRNP A1 upon specific RNA-binding observed by magnetic resonance" <a href="https://www.biorxiv.org/content/10.1101/2022.03.21.485092v1">available at bioRxiv</a> and submitted to a peer-reviewed journal.</p>
Auxiliary Euro-Calliope datasets: QTDIAN storyline-specific spatial data to represent a European energy system model at several spatial resolutions
<p>Custom output generated with the <a href="https://github.com/brynpickering/possibility-for-electricity-autarky/tree/custom-regions">custom-region possibility-for-electricity-autarky</a> workflow.</p> <p>This output provides similar data to <a href="https://zenodo.org/record/6600619">https://zenodo.org/record/6600619</a> (technically eligible land area for renewables and other spatially disaggregated energy system data), but with three additional land area scenarios.</p> <p>These scenarios are in line with three storylines from the <a href="https://zenodo.org/record/5834010">QTDIAN toolbox</a> and are based on updating the `possibility-for-electricity-autarky` workflow configuration to include the following parameters (also included in `config.yaml`):</p> <p> </p> <pre><code> scenarios: people-powered: use-of-protected areas: false pv-on-farmland: true share-farmland-used: 0.2 # agro pv share-forest-areas-used: 0.1 share-other-land-used: 1.0 share-offshore-used: 0.1 share-rooftop-used: 1.0 government-directed: use-of-protected areas: false pv-on-farmland: true share-farmland-used: 1.0 share-forest-areas-used: 0.1 share-other-land-used: 1.0 share-offshore-used: 1.0 share-rooftop-used: 1.0 market-driven: use-of-protected areas: true pv-on-farmland: true share-farmland-used: 1.0 share-forest-areas-used: 1.0 share-other-land-used: 1.0 share-offshore-used: 1.0 share-rooftop-used: 1.0</code></pre> <p> </p> <p>This dataset includes different spatial resolutions of land availability. For more information on the `ehighways` resolution, see <a href="https://zenodo.org/record/6600619">https://zenodo.org/record/6600619</a>.</p> <p>This dataset is used as an input to the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope workflow</a>.</p> <p> </p>
Crop-specific salinity and irrigation data for river sub-basin water scarcity analyses in the US and AU
<p>This dataset contains observed monthly and annual salinity (EC) data for surface water (river) respectively groundwater, spatially averaged over sub-basins within the Central Valley, CA and the Murray Darling basin, AU, used for salinity-inclusive water scarcity assessments. The data also includes crop-specific irrigated area, irrigation withdrawals and salinity thresholds and other parameters specified, as well as example codes for analyses related to the manuscript: Thorslund et al., <em>Salinity impacts on irrigation water-scarcity in food bowl regions of the US and Australia.</em></p>
Data files: Single-cell RNA profiling of Plasmodium vivax-infected hepatocytes reveals parasite- and host- specific transcriptomic signatures and therapeutic targets
<p>Scripts, preprocessed count matrices, and single-cell data objects generated in <strong>“Single-cell RNA profiling of <em>Plasmodium vivax</em><em>-</em>infected hepatocytes reveals parasite- and host- specific transcriptomic signatures and therapeutic targets” </strong></p>
Infant N290 event-related potentials and stimulus-specific adaptation to face stimuli
<p>Data for publication: "Neural specialization to human faces at the age of 7 months".</p> <p>Further details will be available at: https://github.com/yrttiahoS/ssa/</p> <p> </p> <p>Metadata:</p> <p>I. Event-related potentials</p> <p>The current study investigated both cortical sensitivity and categorical specificity through event-related potentials (ERPs) previously implicated in face processing in 7-month-old infants (N290) and adults (N170). Using a category-specific repetition/adaptation paradigm, cortical specificity to human faces, or control stimuli (cat faces), was operationalized as changes in ERP amplitude between conditions where a face probe was alternated with categorically similar or dissimilar adaptors. In adults, increased N170 for human vs. cat faces and category-specific release from adaptation for face probes alternated with cat adaptors was found. In infants, a larger N290 was found for cat vs. human probes. Category-specific repetition effects were also found in infant N290 and the P1-N290 peak-to-peak response where latter indicated category-specific release from adaptation for human face probes resembling that found in adults.</p> <p>*Dataset files: N290_P1_infant, N170_P1_adult.sav</p> <p> </p> <p>*EEG data in EEGLAB’s format,</p> <p>Filenames indicate type of data with:</p> <p>1) Initial numerical code indicating (anonymized) participant number, "adult/infant" indicating participant group</p> <p>2) f1/c2/f3/c3 indicating stimulus conditions probe:face, adaptor:face / probe:cat, adaptor:cat / probe:face, adaptor:cat / probe:cat,adaptor:face, respectively</p> <p> 3) Final number _1_ or _2_ indicates block number for adult participants (in the order of presentation)</p> <p>Files with only codes 1) and 2) are continuous data with all triggers included</p> <p> </p> <p>*video-based Quality Control is listed in file: anon_vQC2022-09-22.xlsx*video-based Quality Control is listed in file: anon_vQC2022-09-22.xlsx</p> <p> </p> <p>*Analysis syntax: https://github.com/yrttiahoS/ssa/</p> <p> </p> <p>*Dataset updates: TBA at https://github.com/yrttiahoS/ssa/</p> <p> </p> <p> </p> <p>II. Temperament questionnaire</p> <p>*Data collection: Temperament data were collected from participants of an infant event-related potential (ERP) study as potential correlate of outcome variables and as a descriptor of the participant group. Participants were 7-month-old infants from families volunteering in the brain research study, which were contacted through information from a population registry sample. Infant temperament was assessed by parent-reported IBQ-R short form (Putnam, S. P., Helbig, A., Gartstein, M.A., Rothbart, M.K. & Leerkes, E. M. (2014). <em>Development and assessment of short and very short forms of the Infant Behavior Questionnaire-Revised.</em> Journal of Personality Assessment, 96, 445-458. Finnish translation: Professor Katri Räikkönen-Talvitie and the Developmental Psychology Research Group University of Helsinki, Finland)</p> <p>*Authors of the dataset: Santeri Yrttiaho, Mikko Peltola, Anneli Kylliäinen, Tiina, Parviainen, Jari Hietanen</p> <p>*Site of data collection: Human Information Processing laboratory, Tampere University, Finland</p> <p>*Funding: Emil Aaltonen Foundation, Tampere University, Academy of Finland</p> <p>*Participant demographics: Participant group is described by age of M(SD) = 30.5(0.5) weeks. Participants were from Tampere metropolitan area, Finland. Data were collected during Fall 2020 (October 16th – December 7th, 2020).</p> <p>*Data content. Initial data will contain group level statistics and the individual data will be made available as additional files after publication of study results in a peer-reviewed article. Data is anonymized. Individual data contains IBQ-R short form items, scales, and factors as well as participant gender and age in weeks. Data also includes variable indicating whether the participant was included in the final ERP analysis after EEG quality control.</p> <p>*variable names: see 'Scale abbreviations.docx' and for full discussion the original article by Putnam et al. (2014).</p> <p>*Dataset files</p> <p>1. IBQ-R-short-Finnish_dataset.sav / data of items, scales, and factors</p> <p>2. SSA_infant_ibq-r_summary.sps / syntax for computing scores</p> <p>3. IBQ-R-short-Finnish_descriptives.spv / output of descriptive statistics</p> <p>4. Scale abbreviations.docx / explanation of variable names</p> <p> </p> <p> </p> <p> </p> <p> </p>
Supplementary material for: "Typology of Dementia-Specific Care Units: A Nationwide Survey Study in Germany"
<p>This is a dataset and R code of statistical software R version 4.2.1 (2022-06-23) to develop a typology focusing on dementia-specific care units in German nursing homes.</p> <p>The dataset is based on a national survey. For this purpose, 2020, a stratified, randomized sample of 134 care units was included. A telephone interview with facility managers and a standardized questionnaire were used to collect 28 organization-specific variables, which were analyzed as constituent variables for a typology. In addition, the dataset contains seven variables on nonpharmacological interventions (Drugs, Pain, Behavior, Training, Expert, DCM, Music), a variable on "Provider of the nursing home" (Provider) and the variable (sub-question) about "Admission criteria for residents contractually regulated with cost bearers" (Criteria) that were queried for the care units to analyze associations related to typology.</p>
A complete map of specificity encoding for a partially fuzzy protein interaction
<p>All data required to run analyses for "A complete map of specificity encoding for a partially fuzzy protein interaction". Please see <a href="https://github.com/lehner-lab/fuzzy_specificity">https://github.com/lehner-lab/fuzzy_specificity</a> for instructions. </p>
RNA-seq dataset for Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism
<p>Data to be used along with <a href="https://github.com/neelroop/asd-development-coexpression-2013">code</a> from 2013 paper that was originally on a site hosted at UCLA, but may no longer be accessible.</p>
Data for a publication "Argon plasma-modified bacterial nanocellulose: Cell-specific differences in the interaction with fibroblasts and endothelial cells"
<p>A dataset containing data for the published article "Argon plasma-modified bacterial nanocellulose: Cell-specific differences in the interaction with fibroblasts and endothelial cells".</p> <p> </p> <p>For more details, please read the <strong>README - Description of data and analysis informations.txt</strong> file.</p> <p><strong>Dataset versions:</strong></p> <p><strong>V1:</strong> The first dataset containing a majority of the data.</p> <p><strong>V2:</strong> Dataset contains all the data mentioned in the article in the appropriate file formats for long-term preservation and accessibility.</p>
Region-specific sourcing of lignocellulose residues as renewable feedstocks for a net-zero chemical industry
<p><span>data_1_lignocellulose_residue_grid_all_years_scenarios – datasets presenting the <em>theoretical</em>, <em>ecological</em>, and <em>available</em> potential of various lignocellulose residues on the GLOBIOM grid level (200 km </span><span>×</span><span> 200 km)</span></p> <p><span>data_2_lignocellulose_feedstock_potential_impacts_country_level_all_scenarios – datasets presenting the <em>theoretical</em>, <em>ecological</em>, and <em>available</em> potential of various lignocellulose residues on the country level and their corresponding climate-change impacts, water stress, and land-use-related biodiversity loss impacts</span></p> <p><span>LUID_CTY – shapefile for the global map with GLOBIOM grids</span></p> <p> </p>
Liquid-Jet Photoemission Spectroscopy as a Structural Tool: Site-Specific Acid-Base Chemistry of Vitamin C
<p>Dataset pertaining to the article "Liquid-Jet Photoemission Spectroscopy as a Structural Tool: Site-Specific Acid-Base Chemistry of Vitamin C", submitted to Physical Chemistry Chemical Physics. Here, we demonstrate how liquid-jet photoemission spectroscopy can be systematically used for chemical analysis, probing tautomeric forms and deprotonation sites in aqueous vitamin C. We also present a fast and reliable computational protocol to model the spectra.</p> <p> </p> <p>Files with extension .h5 are hdf5-files structured according to the NeXus standard v2022.07, see<br>https://www.nexusformat.org/<br>https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html<br>NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:<br>* nexpy (distributed with python)<br>* https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)</p> <p><br>The following files are provided:</p> <p><strong>Photoemission data pertaining to vitamin C PES measurements:</strong></p> <p>vitamin-C.h5</p> <p> </p> <p><strong>Computational data for Figures 3, 5, and 6: binding energy values (plain text) from which the spectra were modeled, optimized molecular geometries (XYZ coordinates, distances in angstroms) used in the calculations, and a sample input for the binding energy calculation. All data for each figure are packed in a zip file.</strong></p> <p>COMPUTATIONAL-DATA-Fig3.zip<br>COMPUTATIONAL-DATA-Fig5.zip<br>COMPUTATIONAL-DATA-Fig6.zip<br><br><br></p> <p><strong>Numeric representations of the traces shown in the article's figures (space-separated or comma-separated ascii-files):</strong></p> <p><strong>Figure 3:</strong><br>EXPT.dat<br>TAUTOMER-A.dat<br>TAUTOMER-B.dat<br>C2.dat<br>C3.dat<br>C5.dat<br>C6.dat<br>C2+C3.dat<br>C2+C5.dat<br>C2+C6.dat<br>C3+C5.dat<br>C3+C6.dat<br>C5+C6.dat</p> <p><strong>Figure 5:</strong><br>EXPT.dat<br>TAUTOMER-A.dat<br>C3.dat<br>C2+C3.dat</p> <p><strong>Figure 6:</strong><br>EXPT.dat<br>C3.dat<br>C3-UFF-ensemble.dat<br>C3-Bondi-single.dat<br>C3-Bondi-ensemble.dat<br>TAUTOMER-A.dat<br>TAUTOMER-A-ensemble.dat<br>C2+C3.dat<br>C2+C3-ensemble.dat</p> <p><strong>Figure S1:</strong><br>EXPT.dat<br>EXPT-850eV.dat</p> <p> </p> <p>Version history:<br>4: Data from version 2 reuploaded<br>3: Experimental NeXus-data added<br>2: Computational data and figure traces added<br>1: Initial upload</p> <p>Contact person for questions regarding this data set: Lukas Tomanik, tomanikl@vscht.cz . If you use these data for your scientific work we are curious to learn about it.</p>
Data set for "State-dependent cell-type-specific membrane potential dynamics and unitary synaptic inputs in awake mice"
<p>Data set for: Pala A, Petersen CCH (2018) State-dependent cell-type-specific membrane potential dynamics and unitary synaptic inputs in awake mice. eLife 7: e35869. DOI: https://doi.org/10.7554/eLife.35869.</p> <p>There are 12 files in this data upload:</p> <p>1. '2018_Pala_eLife.pdf' - this is a pdf version of the online publication: Pala & Petersen (2018).</p> <p>2. 'data.mat' - this is a Matlab data structure, which contains all the data for the publication.</p> <p>3. 'DataViewer.m' - this is a Matlab code for viewing the data.</p> <p>4. 'DataViewer.fig' - this is a Matlab figure file, which is the GUI layout for 'DataViewer.m'.</p> <p>5. 'PalaPetersen_Plot.m' - this is a Matlab code, which plots the figures for Pala & Petersen (2018).</p> <p>6. 'PalaPetersen_Analysis.m' - this is a Matlab code, which analyses the data for the figures of Pala & Petersen (2018).</p> <p>7. 'blankAPs.m' - this is a Matlab code, which blanks action potentials from the membrane potential trace.</p> <p>8. 'lowpassfilt.m' - this is a Matlab code, which low pass filters the LFP.</p> <p>9. 'medianFiltAPs.m' - this is a Matlab code, which median filters the membrane potential trace to remove action potentials.</p> <p>10. 'remTrialswithAPs.m' - this is a Matlab code, which removes trials with action potentials.</p> <p>11. 'retrieveSegDur.m' - this is a Matlab code, which retrieves chunks of the recording of a given length.</p> <p>12. 'suptitleAP.m' - this is a Matlab code, which puts titles above subplots.</p>
Estimation of the variation in specific discharge over large depth using Distributed Temperature Sensing (DTS) measurements of the heat pulse response
<p>The data contains measurements and derived values that are used for the manuscript "Estimation of the variation in specific discharge over large depth using Distributed Temperature Sensing (DTS) measurements of the heat pulse response, [Paper # 2018WR024171]" Currently under review at the Water Resources Research journal.</p> <p>The data is stored in netCDF files with xarray (Python), and should be readable with any other netCDF reader. </p> <ul> <li>TEMP is the measured temperature in degrees Celsius relative to the background temperature</li> <li>tempinfty is one of the calibration parameters. Represents the steady state temperature increase</li> <li>A is one of the calibration parameters. Represents the timescale in days</li> <li>b is one of the calibration parameters. Represents the scaled distance to the heat source</li> <li>err_alpha is one of the calibration parameters. Represents the autoregressive parameter</li> <li>TEMPmodel is the best fit temperature response in degrees Celsius relative to the background temperature</li> <li>Innovation is termed the noise in the article, in degrees Celsius.</li> <li>q is the estimated specific discharge in meters per day</li> <li>q_MC_XX are the confidence intervals of the estimated specific discharge calculated with Monte Carlo as presented in the article</li> <li>q_lmfit_XX are the confidence intervals of the estimated specific discharge calculated with LMFIT. Is a rough estimate for q_MC_XX calculated by lmfit (Python package).</li> </ul> <p>Time is measured in days with respect to when the heating cable is turned on.</p> <p>Additionally, a Jupyter notebook is supplemented to the article. It demonstrates the calibration routine and the calculation of the confidence interval for the temperature response at a single depth.</p>
CellSIUS provides sensitive and specific detection of rare cell populations from complex single cell RNA-seq data: Codes and processed data
<p>Codes and processed data to reproduce the analysis discussed in: </p> <p>Wegmann <em>et Al.</em>,<strong> CellSIUS provides sensitive and specific detection of rare cell<br> populations from complex single cell RNA-seq data</strong>, Genome Biology 2019 (Accepted)<br> </p>
Site-Specific MCER Response Spectra for Los Angeles Region based on 3-D Numerical Simulations and the NGA West2 Equations
<p><strong>ABSTRACT</strong></p> <p>The Utilization of Ground Motion Simulation (UGMS) committee of the Southern California Earthquake Center (SCEC) developed site-specific, risk-targeted Maximum Considered Earthquake (MCER) response spectra for the Los Angeles region. The long period (T ≥ 2-sec) MCER response spectra were computed as the weighted average of MCER spectral accelerations derived from (1) 3-D numerical ground-motion simulations using the CyberShake computational platform, and (2) empirical ground-motion prediction equations (GMPEs) from the Pacific Earthquake Engineering Research (PEER) Center NGAWest2 project. The short period (T < 2- sec) MCER response spectra were computed exclusively from the NGAWest2 GMPEs. A web-based lookup tool was also developed so users can obtain the MCER response spectrum for a specified latitude and longitude and for a specified site class or 30-m average shear-wave velocity, VS30. The tool provides acceleration ordinates of the MCER response spectrum at 21 natural periods in the 0 to 10-sec band.</p> <p>This dataset includes a Java application to run queries. It serves as the backend data source for the web-based tool that can be found at: <a href="https://data2.scec.org/ugms-mcerGM-tool_v18.4/">https://data2.scec.org/ugms-mcerGM-tool_v18.4/</a>.</p> <p>For more information, please see <a href="https://www.scec.org/research/ugms">https://www.scec.org/research/ugms</a>.</p> <p><strong>DISCLAIMER</strong></p> <p>The UGMS MCER Tool is provided "as is" and without warranties of any kind. While SCEC and the UGMS Committee have made every effort to provide data from reliable sources or methodologies, SCEC and the UGMS Committee do not make any representations or warranties as to the accuracy, completeness, reliability, currency, or quality of any data provided herein. SCEC and the UGMS Committee do not intend the results provided by this tool to replace the sound judgment of a competent professional, who has knowledge and experience in the appropriate field(s) of practice. By using this tool, you accept to release SCEC and the UGMS Committee of any and all liability.</p> <p>Please note: The site-specific, design response spectral acceleration, Sa, returned by this tool for user-specified inputs, must be compared to the minimum Sa requirement described in Section 21.3 of ASCE 7-16 (second and third paragraphs). This minimum Sa is computed as 80% of the design response spectrum derived from the SDS, SD1, and TL values obtained from the ASCE tool at https://asce7hazardtool.online/. The larger of the site-specific Sa and the 80% minimum Sa at each period, T, is the final design response spectral acceleration. This final Sa x 1.5 is the final MCER response spectral acceleration.</p>
Data for the publication "Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer"
<p>This dataset encompasses all data needed to reproduce the analyses presented in <a href="https://www.nature.com/articles/s41591-019-0406-6">Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer</a></p> <p>You can also check the <a href="https://github.com/zellerlab/crc_meta">GitHub repository</a></p>
Data and Code for "Cell Type-specific Genome Scans of DNA Methylation Diversity Indicate an Important Role for Transposable Elements"
<p>This is a release of the gitlab repository "meta-methylome" (https://gitlab.com/okartal/meta-methylome.git) that, in addition to the code, also contains the resulting genomic data.</p> <p>Extract the directory on the command line using</p> <pre><code class="language-bash">$ tar -xhzvf meta-methylome.tar.gz</code></pre> <p>to preserve the symbolic links.</p>
A SAT Benchmark Suite for LTL Specification Sketching
<h1>LTL_Sketcher-SAT_Benchmark</h1> <p>This repository contains a set of formulas in Propositional Boolean Logic.<br>These formulas are generated during the execution of our <a href="https://github.com/rajarshi008/LTLSketcher/tree/master" target="_blank" rel="noopener">LTLSketcher tool</a>.<br>Given an LTL sketch (i.e., a partial LTL formula) and a sample (i.e., a set of program executions labeled desired and undesired), the tool solves the LTL sketching problem, i.e., complete the sketch to a specification consistent with the data. (feel freet to check out our <a href="https://link.springer.com/chapter/10.1007/978-3-031-45332-8_2" target="_blank" rel="noopener">paper</a> for more information on this problem)<br>In essence, this is done by reducing the problem to a series of formulas in Propositional Boolean Logic and checking their satisfiability.</p> <h2>Naming convention:</h2> <p>This repository contains each formula both in the DIMACS and SMTLib format.<br>Each file follows the same naming convention:</p> <p><em>type__sample-file__sketch__size__algorithm-configuration__satisifability</em></p> <p><em>type</em>: indicates whether the formula is stored in the DIMACS or SMTLib format<br><em>sample-file</em>: refers to the sample (cf., <a href="https://github.com/rajarshi008/LTLSketcher/tree/master/experiment_results/generated_files/final_benchmark" target="_blank" rel="noopener">here</a>) used by the LTLSketcher tool<br><em>sketch</em>: refers to the sketch (cf., See experimental evaluation of our <a href="https://link.springer.com/chapter/10.1007/978-3-031-45332-8_2" target="_blank" rel="noopener">paper</a>) used by the LTLSketcher tool<br><em>size</em>: refers to the size of the complete solution (i.e., the number of subformulas of the complete specification)<br><em>algorithm-configuration</em>: our algorithm can be extended by two heuristics (BMC and suffix), this indicates which combination of heuristics was used (none, either one of the two, both)<br><em>satisfiability</em>: indicates whether the formula is satisfiable or not</p>
Automotive Domain Property Specification Pattern Dataset
<p>This dataset contains 1000 requirements written in the Property Specification Pattern (PSP) format, specifically tailored for the automotive domain. The dataset serves as a valuable resource for researchers and practitioners working on the formal verification of automotive software, providing a comprehensive set of standardized requirements that can be used for validation, testing, and benchmarking of verification tools and methodologies.</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.