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2,797 results for “specialization”
Soil biogeochemical measurements from the Antarctic Specially Protected Area No. 131 (ASPA 131), McMurdo Dry Valleys Antarctica, December 2022
These data include soil biological properties (16S ASV community sequences, invertebrate community counts, ash-free dry mass, pigment concentrations), physical properties (location, gravimetric soil moisture, pH, electrical conductivity, remote detection of soil moisture change), chemical properties (dissolved inorganic nitrogen, extractable sulfate ions, extractable Cl ions) from soils collected within the Antarctic Specially Protected Area No. 131 (ASPA-131) surrounding Canada Stream in the McMurdo Dry Valleys of Antarctica. Collection sites were associated with a warming event that occurred on March 22, 2022, and include the following remotely-sensed categories: V - validation sites representing arid soils with little soil moisture and minimal detectable change in liquid water, S - significant sites that had a significant increase in liquid water, and N - nonsignificant sites that had detectable moisture but did not experience a significant increase in liquid water. These data aid in our understanding of how landscape heterogeneity and hydroclimate variability influence soil biota communities sensitive to changes in liquid water availability in a polar desert.
Sample of 100 place names from 'special maps' by Charles Perthées (1:225,000, 1783-1804)
<p>The dataset contains a sample of 100 place names from ‘special maps’ by Charles Perthées (1:225,000, 1783-1804). Place names are supplemented with possible matches from the “National Register of Geographical Names” (NRGM) or the "Historical Atlas of Poland (HAP). There are 19 266 matches for 100 place names. 85 of them have 'certain' or 'uncertain' matches and 15 remained unmatched and, therefore, not geocoded.</p>
Nutritional value of edible insects: special emphasis on their micro and macro nutrients
<p>The data set was prepared by collecting the existed information regarding the nutritional value of insects from relevant published papers. For each column, variables or descriptors were expressed in the same unit. The information from each insect in a raw were followed by a reference of the paper which the information is taken from with the DOI which makes it easily accessible for the readers.</p>
NeuroMET - SPECIAL MRS Reproducibility
<p>Magnetic Resonance Spectroscopy Data acquired in 9 healthy volunteers using a SPECIAL Localization with three different adiabatic inversion pulses (hyperbolic secant, WURST, GOIA) at a 7T Magnetom whole-body system (Siemens Healthineers, Erlangen, Germany). Every volunteer was examined 4 times (twice on day one including a repositioning between the measurements, and twice on day two a week later without repositioning between alike measurements) to investigate the repeatability and reproducibility. Please find more details in L.T. Riemann, C.S. Aigner, S.L.R. Ellison, R. Brühl, R. Mekle, S. Schmitter, O. Speck, G. Rose, B. Ittermann, A. Fillmer, "Assessment of Measurement Precision in Single Voxel Spectroscopy at 7 T: Towards Minimal Detectable Changes of Metabolite Concentrations in the Human Brain In-Vivo", Magn Reson Med DOI: DOI: 10.1002/mrm.29034 (in press).</p> <p>Correspondence: layla.riemann@ptb.de, ariane.fillmer@ptb.de</p> <p>Code to generate the restricted maximum-likelihood estimation (REML) and the Bland-Altman (BA) plots of the spectral shape can be found under https://gitlab1.ptb.de/LRiemann/repeatability_reproducibility.git</p> <p>Following data sets are provided:</p> <p>1. ConcentrationsPaper-mrm29034_20211012_Riemann-Fillmer.xls</p> <p>Data that is used with the R code to obtain the REML analysis of the metabolite concentrations. Note that the zeros indicate that the metabolite concentration could not be quantified.</p> <p>2. RawData-mrm29034_20211012_Riemann-Fillmer.zip folder</p> <p>Raw spectral data from Magnetom Siemens 7 T scanner<br> file names have the following structure: "SubjectNumber_Session_ScanBlock_Pulse.dat", e.g. "p1_T1_1_GOIA.dat"</p> <p>3. npy-files-mrm29034_20211012_Riemann-Fillmer.zip folder</p> <p>Spectral data in a python format to generate the BA plots with the shared code;<br> data names have the following structure: "SubjectNumber_PulseSessionScanBlock", e.g. "p1_GOIAT11.npy" for Subject p1, GOIA Pulse (AHS for hyperbolic secant), Session T1, ScanBlock 1</p> <p>4. AnonymizedVolunteerData-mrm29034_20211012_Riemann-Fillmer.xlsx</p> <p>Anonymized measurement data for each individual volunteer and measurement:<br> age, gender, transmit voltage, linewidth, peak voltages for all three pulses (HS, GOIA, WURST), CSF -, gray and white matter fraction in the scanned voxel for each subject.</p>
COSI-Article matrix: linking ISCB Communities of Special Interest to Wikipedia
<p>Wikipedia is regarded as one of the most important channels for the public communication of science; English Wikipedia has around 1,500 articles relating to computational biology, which are frequently accessed as an educational resource. Joint efforts between the International Society for Computational Biology (ISCB) and the Computational Biology taskforce of WikiProject Molecular Biology (a group of expert Wikipedia editors) have considerably improved computational biology representation on Wikipedia in recent years. However, there is still an urgent need for further quality improvement, primarily while comparing to related scientific fields such as genetics and medicine. Facilitating the involvement of members from ISCB COSIs (Communities of Special Interest) would improve a vital open educational resource in computational biology, additionally allowing COSIs to provide a quality educational resource particular to their subfield.</p> <p>This first version of the COSI-Article matrix is a binary matrix identifying relevant ISCB COSIs for all Wikipedia articles relating to computational biology, defining a domain-specific open educational resource for each COSI. In addition, quality and importance ratings for each article allow identification of areas where domain experts could improve computational biology representation.</p>
Range shifts of overwintering birds depend on habitat type, snow conditions and habitat specialization
<p>Data and R code accompanying the publication "Range shifts of overwintering birds depend on habitat type, snow conditions and habitat specialization"</p> <p>Bosco L, Xu Y, Deshpande P, Lehikoinen A</p> <p>2022</p> <p>---------</p> <p>The data and code to calculate range shifts based on the center of gravity are provided here.</p> <p>The RData files contains raw data from the winter bird counts with added average snow depth values downloaded from open source databases (described in the paper), 100x100km grid info (grid ID, centroid coordinates and average (geographical) coordinates).</p> <p>The csv file contains the route lengths from the winter bird count transects per habitat type.</p> <p>The R file contains the R code used to clean the data (see methods in the publication) and calculate the habitat specific center of gravity (based on bird densities) which were used to calculate shift direction and distance.</p>
Student's logs and perceptions of an automated assessment tool in a software engineering MOOC specialization
<p>Our dataset contains students' perceptions and usage of an automated assessment tool (MOOCauto) for obtaining formative feedback in software engineering assignments that are part of a MOOC specialization at Universidad Politécnica de Madrid (Spain), delivered by the MiriadaX platform. The dataset has previously been used in a study to evaluate students' perceptions of the tool and to analyze their usage patterns using Growth Mixture Models <a href="https://www.computer.org/csdl/magazine/so/5555/01/10196480/1P9AhkBLYXK">(López-Pernas et al., 2023)</a>. The code of each of the assignments is available on Github: <a href="https://github.com/ging-moocs">https://github.com/ging-moocs</a>.</p> <p>Our dataset contains two files:</p> <h2>MOOCauto usage logs</h2> <p>The first file is called<strong> moocauto_logs.csv </strong>and it contains 9,108 anonymized logs of students' use of the automated assessment tool in the MOOC specialization assignments. The columns of the dataset are as follows:</p> <ul> <li><strong>MOOCid</strong>: Unique numeric identifier for the MOOC (1-4)</li> <li><strong>MOOC: </strong>Name of the MOOC: Frontend Development, Backend Development, Git & Github, Fullstack Development</li> <li><strong>AssignmentName</strong>: Name of the assignment.</li> <li><strong>AssignmentId</strong>: Unique identifier for each assignment (1-17)</li> <li><strong>user: </strong>Unique identifier of the student (it varies per assignment)</li> <li><strong>timestamp: </strong>Time in which the assessment was performed</li> <li><strong>score</strong>: Score obtained (0-10)</li> </ul> <h2>Students' perceptions of MOOCauto</h2> <p>The second file is called <strong>moocauto_questionnaire.csv</strong> and it contains 213 students' responses to the questionnaire conducted at the end of each MOOC in order to evaluate their opinion of the tool and perception on usefulness, ease of use, and other aspects related to the Technology Acceptance Model (TAM). The questions were as follows:</p> <ul> <li><strong>What is your general opinion of MOOCauto?</strong> (1 Horrible - 5 Excellent)</li> <li><strong>Indicate your level of agreement with the following statements </strong>(1 Strongly disagree - 5 Strongly agree) <ul> <li>MOOCauto has been easy to install</li> <li>MOOCauto has been easy to use</li> <li>The feedback provided by MOOCauto was easy to understand</li> <li>The feedback provided by MOOCauto was useful</li> <li>The feedback provided by MOOCauto helped me improve my assignments</li> <li>The documentation Of MOOCauto was useful</li> <li>MOOCauto has increased my motivation to work on the assignments</li> <li>I prefer the feedback from MOOCauto than from peer assessment</li> <li>I would like to have a bot like MOOCauto in other MOOCs</li> </ul> </li> <li><strong>How useful do you perceive the following features of MOOCauto?</strong> (1 Useless - 5 Very useful) <ul> <li>It works locally on my computer</li> <li>It allows to run the test suite as many times as I want</li> <li>It provides instantaneous feedback every time the test suite is executed</li> <li>It has documentation that explains its use and available options</li> </ul> </li> </ul>
BOP_RODENT - Rodent specialized birds of prey (Circus, Asio, Buteo) in Flanders (Belgium)
<p><em>BOP_RODENT - Rodent specialized birds of prey (Circus, Asio, Buteo) in Flanders (Belgium)</em> is a bird tracking dataset published by the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a>. It contains animal tracking data collected by the LifeWatch GPS tracking network for large birds (<a href="http://lifewatch.be/en/gps-tracking-network-large-birds">http://lifewatch.be/en/gps-tracking-network-large-birds</a>) for the project/study <strong>BOP_RODENT</strong>, using trackers developed by Ornitela (<a href="https://www.ornitela.com">https://www.ornitela.com</a>). The study has been operational since 2020. In total 35 individuals of 5 bird of prey species have been tagged at several locations in Flanders (Belgium), mainly to study their habitat use and migration behaviour. Data are automatically synced with Movebank and from there periodically archived on Zenodo (see <a href="https://github.com/inbo/bird-tracking">https://github.com/inbo/bird-tracking</a>).</p> <h2>Files</h2> <p>Data in this package are exported from Movebank study <a href="https://www.movebank.org/cms/webapp?gwt_fragment=page=studies,path=study1278021460">1278021460</a>. Fields in the data follow the <a href="http://vocab.nerc.ac.uk/collection/MVB">Movebank Attribute Dictionary</a> and are described in <code>datapackage.json</code>. Files are structured as a <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Package</a>. You can access all data in R via <code>https://zenodo.org/records/12567894/files/datapackage.json</code> using <a href="https://frictionlessdata.github.io/frictionless-r/">frictionless</a>.</p> <ul> <li><strong>datapackage.json:</strong> technical description of the data files.</li> <li><strong>BOP_RODENT-reference-data.csv</strong>: reference data about the animals, tags and deployments.</li> <li><strong>BOP_RODENT-gps-yyyy.csv.gz</strong>: GPS data recorded by the tags, grouped by year.</li> </ul> <h2>Acknowledgements</h2> <p>This dataset was collected using infrastructure provided by INBO and funded by Research Foundation - Flanders (FWO) as part of the Belgian contribution to LifeWatch. Additional funding was provided by Agentschap voor Natuur en Bos (ANB).</p>
The SPECIAL Policy Log Vocabulary
<p>This documents specifies <em>splog</em>, a vocabulary to log data processing and sharing events that should comply with a given consent provided by a data subject. We also model the consent actions related to consent giving and revocation.</p> <p> </p> <p>See more at: <a href="http://purl.org/specialprivacy/splog">http://purl.org/specialprivacy/splog</a></p>
Data and code for: Genomic changes underlying host specialization in the bee gut symbiont Lactobacillus Firm5
<p>This dataset contains data and code underlying the comparative genomics, amplicon sequencing, and statistical analysis of the research article "Genomic changes underlying host specialization in the bee gut symbiont Lactobacillus Firm5”. Genome sequences and short read datasets are available under NCBI Bioproject accession PRJNA392822.</p> <p>The dataset contains tar-balls for the main workflows of the analysis. Dowload and unpack to view the contents (tar -zxvf filename.tar.gz). For each tar-ball, a README.txt file describes the contents of the directory. The analyses require certain open-source software packages to be installed. These are not provided here.</p>
RE-Lab-Projects/TRY_DE_2015_2045: Test Reference Years (TRY) for 15 typical regions in germany with special regards on realisitc radiation data on a 1min timescale
<p>Test Reference Years (TRY) for 15 typical regions in germany with special regards on realisitc radiation data on a 1min timescale</p> <p><strong>Summary:</strong></p> <p>The data set contains the updated test reference years (TRY) of the German Weather Service (DWD). By subdividing into 15 TRY regions, each postcode area can be assigned a representative weather data set. It should be emphasized that in addition to a mean, current test reference year for a region, there is also a year with extreme summer and extreme winter weather. To take climate change into account, there is then a time series for the year 2045 for each test reference year based on the IPCC climate models. This means that a total of 90 weather data sets are available with a one-hour time resolution.</p> <p>In order to use the data in simulations with a temporal resolution of 1min or 15min, the data set was extended by linear interpolation. While this approach is justifiable for air pressure and temperature, for example, it does not depict high fluctuations in solar radiation. Therefore, based on the one-minute open data measurement data set of the Baseline Surface Radiation Network, with an algorithm by Hofmann et. al. the time series of global radiation are newly generated for all test reference years. Another algorithm by Hofmann et. al. was used to calculate the corresponding diffuse radiation times series.</p> <p><strong>Sources:</strong></p> <ul> <li>Raw data from DWD: <a href="https://kunden.dwd.de/obt/">https://kunden.dwd.de/obt/</a> -> <code>1_raw-data</code></li> <li>Synthetic 1min radiation data: <a href="http://pvmodelling.org/">http://pvmodelling.org/</a> -> <code>2_synthetic-radiation</code></li> </ul> <p><strong>How to use or recreate the final dataset:</strong></p> <ol> <li>clone/download this repository</li> <li>unzip the files from the data.zip file <ol> <li><a href="https://github.com/RE-Lab-Projects/TRY_DE_2015_2045/releases/download/v1.4.0/data.zip">https://github.com/RE-Lab-Projects/TRY_DE_2015_2045/releases/download/v1.4.0/data.zip</a></li> </ol> </li> <li>Use or recreate the final dataset <ol> <li>use: Final datasets are then located in -> <code>3_processed-data</code></li> <li>recreate: run the <code>process-data.py</code></li> </ol> </li> </ol> <p><strong>Test reference stations / regions</strong></p> <p>No. | lon | lat | station | region<br> 1 | 53.5591 | 8.5872 | Bremerhaven | Nordseeküste<br> 2 | 54.0878 | 12.1088 | Rostock | Ostseeküste<br> 3 | 53.5299 | 10.0078 | Hamburg | Nordwestdeutsches Tiefland<br> 4 | 52.3938 | 13.0651 | Potsdam | Nordostdeutsches Tiefland<br> 5 | 51.4562 | 7.0568 | Essen | Niederrheinisch-westfälische Bucht und Emsland<br> 6 | 550.6461 | 7.9426 | Bad Marienburg | Nördliche und westliche Mittelgebirge, Randgebiete<br> 7 | 51.3334 | 9.4725 | Kassel | Nördliche und westliche Mittelgebirge, zentrale Bereiche<br> 8 | 51.7239 | 10.6069 | Braunlage | Oberharz und Schwarzwald (mittlere Lagen)<br> 9 | 50.8233 | 12.9181 | Chemnitz | Thüringer Becken und Sächsisches Hügelland<br> 10 | 50.3226 | 11.9124 | Hof | Südöstliche Mittelgebirge bis 1000 m<br> 11 | 50.4312 | 12.9522 | Fichtelberg | Erzgebirge, Böhmer- und Schwarzwald oberhalb 1000 m<br> 12 | 49.4902 | 8.4637 | Mannheim | Oberrheingraben und unteres Neckartal<br> 13 | 48.2432 | 12.5286 | Mühldorf | Schwäbisch-fränkisches Stufenland und Alpenvorland<br> 14 | 48.6536 | 9.8666 | Stötten | Schwäbische Alb und Baar<br> 15 | 47.4945 | 11.1046 | Garmisch Partenkirchen | Alpenrand und -täler</p> <p><strong>Content</strong></p> <ul> <li><strong>files</strong>: 90 test reference years (TRY) <pre><code>15 test reference regions x 3 reference conditions (average year, extreme summer, extreme winter) x 2 reference projections (year 2015 and year 2045) </code></pre> </li> <li><strong>columns per file</strong>: <pre><code>datetime [yyyy-MM-dd hh:mm:ss+01:00/02:00] temperature [degC] pressure [hPa] wind direction [deg] wind speed [m/s] cloud coverage [1/8] humidity [%] direct irradiance [W/m^2] diffuse irradiance [W/m^2] synthetic global irradiance [W/m^2] synthetic diffuse irradiance [W/m^2] clear sky irradiance [W/m^2] </code></pre> </li> <li><strong>length</strong>: 1 year</li> <li><strong>time increment</strong>: 60s / 900s / 3600s</li> </ul> <p><strong>Important hints</strong>:</p> <ul> <li>all files in <code>3_processed-data</code> were calculated with the skript <code>process-data.py</code></li> <li><em>A value with, for example, a timestamp 12:00:00 represents the mean value from this timestamp until the following timestamp.</em></li> <li><em>datetime column is in CET / CEST</em></li> </ul>
Raw data: Specialized metabolites accumulation pattern in buckwheat is strongly influenced by accession choice and co-existing weeds
<p>Screening suitable allelopathic crops and crop genotypes that are competitive with weeds can be a sustainable weed control strategy to reduce the massive use of herbicides. In this study, three accessions of common buckwheat <em>Fagopyrum esculentum</em> Moench. (Gema, Kora, and Eva) and one of Tartary buckwheat <em>Fagopyrum tataricum</em> Gaertn. (PI481671) were screened against the germination and growth of the herbicide-resistant weeds <em>Lolium rigidum </em>Gaud. and <em>Portulaca oleracea</em> L. The chemical profile of the four buckwheat accessions was characterised in their shoots, roots, and root exudates in order to know more about their ability to sustainably manage weeds and the relation of this ability with the polyphenol accumulation and exudation from buckwheat plants. Our results show that different buckwheat genotypes may have different capacities to produce and exude several types of specialized metabolites, which lead to a wide range of allelopathic and defence functions in the agroecosystem to sustainably manage the growing weeds in their vicinity. The ability of the different buckwheat accessions to suppress weeds was accession-dependent without differences between species, as the common (Eva, Gema, and Kora) and Tartary (PI481671) accessions did not show any species-dependent pattern in their ability to control the germination and growth of the target weeds. Finally, Gema appeared to be the most promising accession to be evaluated in organic farming due to its capacity to sustainably control target weeds while stimulating the root growth of buckwheat plants.</p>
Specialized vitamin D databases- European collection and USDA SR26 collection
<p>These two data sets are collections of best quality data on vitamin D content in foods, available in Europe (hosted by EuroFIR<sup>TM</sup>) and in US (hosted by USDA- in SR26). Data was collected in period of 2014-2015. Search criteria included analytical and manufactures' data sources, and these are sorted in food groups and vitamin D forms- vitamin D total (expressed in ug/100g or IU, and converted to ug), vitamin D3 (ug/100g), vitamin D2 (ug/100g), vitamin 25OHD (ug/100g). </p> <p>A manuscript describing creation process of these two dataset is uploaded as well. </p>
FIG. 2. — A in Annotated checklist of the Hemiptera Heteroptera of the Site of Community Importance and Special Area of Conservation "Alpi Marittime" (NW Italy)
FIG. 2. — A, La Perla small lake; B, La Perla Valley; C, Pastures of the "Colle dell'Arpione"; D, Palanfrè forest; E, Pastures of Palanfrè; F, Pian della Casa and Gias della Casa small ponds; G, Pian del Valasco; H, Vej del Bouc lake. Photos: M. Norbiato.
FIG. 10 in A new hyporheic Monchenkocyclops Karanovic, Yoo & Lee, 2012 (Crustacea: Copepoda) from Turkey with special emphasis on antennulary homology
FIG. 10. — Scanning electron micrographs of Monchenkocyclops mehmetadami n. sp., paratype ♂: A, antennule, ventral view; B, C, inset showing detail of modified seta on segment 11 (ancestral segment (XVI) and 12 (ancestral segment XVII), ventral view; D, segment 14 (ancestral segment (XIX-XX), ventral view; E, segment 15 (ancestral segment (XXI-XXIII), ventral view; F, segments 16 and 17, ventral view; G, segment 17, posterior view. Scale bars: A, 10 µm; B, C, 1 µm; D, F, G, 2 µm; E, 4 µm.
FIG. 6. — Monchenkocyclops mehmetadami n in A new hyporheic Monchenkocyclops Karanovic, Yoo & Lee, 2012 (Crustacea: Copepoda) from Turkey with special emphasis on antennulary homology
FIG. 6. — Monchenkocyclops mehmetadami n. sp., holotype ♀: A, urosome, ventral view; B, urosome, dorsal view; C, P6, lateral view; D, furcal ramus, ventral view. Roman numerals indicating terminology proposed by Huys et al. (1996). Symbol: *, indicating insert of seta IV and V. Scale bars: A, B, 100 µm; C, 25 µm, D, 50 µm.
FIG. 1. — Monchenkocyclops mehmetadami n in A new hyporheic Monchenkocyclops Karanovic, Yoo & Lee, 2012 (Crustacea: Copepoda) from Turkey with special emphasis on antennulary homology
FIG. 1. — Monchenkocyclops mehmetadami n. sp.: A, holotype ♀, habitus, dorsal view; B, allotype ♂, habitus, dorsal view; C, holotype ♀, anal somite and furca, dorsal view, setae are indicated by Roman numerals, following Huys et al. (1996). Not all integumental pore and sensilla of the prosomites are drawn as they are extremely difficult to observe even under 100 × magnification, but in general similar to that of M. changi Karanovic, Yoo & Lee, 2012. Scale bars: 50 µm.
Figure 2 in Economically Beneficial Ground Beetles. The specialized predators Pheropsophus aequinoctialis (L.) and Stenaptinus jessoensis (Morawitz): Their laboratory behavior and descriptions of immature stages (Coleoptera: Carabidae: Brachininae)
Figure 2. Frequency of numbers of eggs laid daily by S. jessoensis females in February 1987 (Σ observations = 145 excluding records of zero).
Figure 3 in Economically Beneficial Ground Beetles. The specialized predators Pheropsophus aequinoctialis (L.) and Stenaptinus jessoensis (Morawitz): Their laboratory behavior and descriptions of immature stages (Coleoptera: Carabidae: Brachininae)
Figure 3. Frequency of numbers of eggs laid daily by P. aequinoctialis females in February-April 1987 (Σ observations = 145 excluding records of zero).
Figure 4 in Economically Beneficial Ground Beetles. The specialized predators Pheropsophus aequinoctialis (L.) and Stenaptinus jessoensis (Morawitz): Their laboratory behavior and descriptions of immature stages (Coleoptera: Carabidae: Brachininae)
Figure 4. Scanning Electron Micrograph of egg of S. jessoensis: a, complete egg; b, apical micropore; c, surface texture; d, microperforations.
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.