Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,721
datasets available to search
ShareScore release 0.7.1
Dataset results
2,721 results for “Connectivity”
Jornada Basin and Experimental Range Mesquite Herbicide Project (JERHM) Connectivity Modifier Data, 2023
This dataset includes plant community composition, plant litter, soil depth, and shrub interspace fetch distance data collected in 2023 as part of the Jornada Experimental Range Herbicide Mesquite Project (JERHM). Data were collected to characterize plant community and ecosystem resource (plant litter, soil depth) responses to the use of Connectivity Modifiers (ConMods), which are used to reduce bare ground connectivity, alongside herbicide application to reduce honey mesquite (Neltuma glandulosa [=Prosopis glandulosa]) encroachment. ConMod and control (rebar-only) arrays were installed on 16 paired, 5-hectare plots, with one plot within each pair randomly selected to receive herbicide treatment in 2021 to reduce N. glandulosa abundance, or left untreated for comparison. Approximately 6 months after herbicide application (spring 2022), 12-unit ConMod and control (rebar-only) arrays were installed on experimental plots within 8 randomly selected N. glandulosa shrub interspaces (n = 8 arrays per plot, 4 per array type). Data were collected in the fall of 2023 at the end of the second growing season following array installation. There are no immediate plans to continue data collection.
Canopy Trimming Experiment (CTE) Litter decomposition and Connectivity basket data
This experiment was designed to decouple the effects of canopy opening from those of increased detrital inputs on rates of detrital processing and resultant community and ecosystem processes. In a study initiated after massive inputs of organic matter from Hurricane Georges in 1998, the forest floor returned to prehurricane values very quickly, within 2-10 months (Ostertag et al. 2003). However, it was unclear to what extent this homeostasis was caused by increased rates of decomposition. Furthermore, if accelerated decomposition was implicated in rapid recovery, the relative contributions of environmental and resource changes wrought by canopy opening versus green leaf deposition on the forest floor were unclear because these factors are confounded in hurricane damage. A full factorial design was therefore used to tease apart the separate and combined effects of simulated storm damage on rates of mass loss in pre-weighed senesced and green litter cohorts inserted into litter decomposition baskets following application of canopy trimming and debris deposition treatments. Natural litter cohorts (i.e., organic forest floor material and subsequent natural litterfall separated into 3-month cohorts) were also weighed when replicate baskets were harvested at approximately 3-month intervals. In addition to obtaining mass and percent moisture of litter cohorts, the extent of fungal connections between litter cohorts was quantified. Fungal connections between partly decomposed and fresh leaf litter have been shown to be important in importation of phosphorus (the most limiting major nutrient in decomposition of tabonuco forest litter) into the freshly fallen leaves in order to rapidly build fungal biomass and associated acceleration of decomposition (Lodge 1993, 1996). The thickest of these fungal colonization and translocation organs (rhizomorphs, cords and hyphal strands) are primarily basidiomycete fungi, which have an almost unique capacity to cause white-rot by breaking
Ascending arousal network connectivity during recovery from traumatic coma
Open the record for dataset details and reuse information.
Livorno, Urban driving, Connected vehicle detects fallen bicyle
<p><strong>Scenario description</strong>:</p> <p>Test session for fallen bicycle with connected but not automated vehicle</p> <p><strong>Session description</strong>:</p> <p>The fallen bicycle use case aims to demonstrate the possibility for a vehicle to detect in advance, using V2X communication, the presence of a fallen bicycle on the road. In case of fall, the bicycle signals its presence to the other vehicles using DENM messages.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Driving in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno.</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.</p>
Livorno, Highway pilot, only connected cars
<p><strong>Scenario description</strong>:</p> <p>Precondition:</p> <p>A vehicle is driving in the first lane of a “smart highway” at 90 km/h with all the devices working correctly and connected to all services needed.</p> <p>Actions or events:</p> <p>1 The puddle monitoring system of the highway triggers a puddle hazard warning for a specific extended zone.</p> <p>2 The AD car receives the information by IoT based services and sets a speed limitation according to the area interested by hazard conditions: it smoothly decelerates in order to enter in the area at the proper speed.</p> <p>3 At the end of the dangerous area, as notified by the “smart road”, the vehicle will recover the legally allowed cruise speed.</p> <p>Relevant situations: How the AD function interacts with different IoT input: from oneM2M platform (advisory speed limit due to puddles); from I2V (DENM, puddle hazard warning); from V2V (CAM with info from other vehicles).</p> <p><strong>Session description</strong>:</p> <p>Test session with only connected cars, lap of 12,3 km on the highway. The goal is to check all the systems and data management before the next test session with AD cars.</p> <p><strong>Datasets description</strong>:</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_V2X_all</strong>: V2V messages during the Highway Pilot sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno.</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.</p>
Livorno, Highway pilot, data management connected car
<p><strong>Scenario description</strong>: Dynamic speed adaptation due to puddle on the road</p> <p>Precondition:</p> <p>A vehicle is driving in the first lane of a “smart highway" at 90 km/h with all the devices working correctly and connected to all services needed.</p> <p>Actions or events:</p> <p>1 The puddle monitoring system of the highway trigger a puddle hazard warning for a specific extended zone.</p> <p>2 The AD car receives the information by IoT based services and sets a speed limitation according to the area interested by hazard conditions: it smoothly decelerates in order to enter in the area at the proper speed.</p> <p>3 At the end of dangerous area, as notified by the «smart road», the vehicle will recover the legally allowed cruise speed.</p> <p>Relevant situations: How the AD function interacts with different IoT input: from oneM2M platform (advisory speed limit due to puddles); from I2V (DENM, puddle hazard warning); from V2V (CAM with info from other vehicles).</p> <p><strong>Session description</strong>:</p> <p>pre-test session with only connected cars, lap of 12,3 km on the highway. Goal is to check all the system and data management.</p> <p><strong>Datasets description</strong>:</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_V2X_all</strong>: V2V messages during the Highway Pilot sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno.</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.</p>
Livorno, Highway pilot, one automated car, two connected cars, smart highway
<p><strong>Scenario description</strong>:</p> <p>Precondition:</p> <p>1. AD cars with C-eHorizon and V2X OBU devices on board travels on the highway. The highway is equipped with IoT G5 RSUs. All the devices publish and share the information by the oneM2M platform in the cloud.</p> <p>Actions or events: </p> <p>1 The Traffic Control Center publishes the presence of roadway works to the OneM2M platform.</p> <p>2 The RSU (subscribed to the OneM2M platform) receives the information and it broadcasts to the vehicles the DENM message containing information about available lanes, speed limits, geometry, alternative routes etc.</p> <p>3 At the same time the CONTI cloud is subscribed to the oneM2M platform; it receives and share with the FCA cloud the information of the road works, updating dynamically the maps of the Connected e-Horizon installed onboard the CRF AD car</p> <p>4 The in-vehicle application fusing the information from the OBU, the C-eHorizon and on-board sensors, performs speed adaptation and lane change maneuvers</p> <p>Relevant situations: How the AD function interacts with different IoT input: from I2V (DENM, Roadwork position and extension); from V2V (CAM with info from other vehicles).</p> <p><strong>Session description</strong>:</p> <p>Test session with one AD+connected car and two connected cars, lap of 11,6 km on the highway.</p> <p><strong>Datasets description</strong>:</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno.</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.</p>
Livorno, Highway pilot, one automated and connected car and one connected car
<p><strong>Scenario description</strong>:</p> <p>Precondition:</p> <p>1. AD cars with C-eHorizon and V2X OBU devices on board travels on the highway. The highway is equipped with IoT G5 RSUs. All the devices publish and share the information by the oneM2M platform in the cloud.</p> <p>Actions or events:</p> <p>1 The Traffic Control Center publishes the presence of roadway works to the OneM2M platform.</p> <p>2 The RSU (subscribed to the OneM2M platform) receives the information and it broadcasts to the vehicles the DENM message containing information about available lanes, speed limits, geometry, alternative routes etc.</p> <p>3 At the same time the CONTI cloud is subscribed to the oneM2M platform; it receives and share with the FCA cloud the information of the road works, updating dynamically the maps of the Connected e-Horizon installed onboard the CRF AD car</p> <p>4 The in-vehicle application fusing the information from the OBU, the C-eHorizon and on-board sensors, performs speed adaptation and lane change maneuvers</p> <p>Relevant situations: How the AD function interacts with different IoT input: from I2V (DENM, Roadwork position and extension); from V2V (CAM with info from other vehicles).</p> <p><strong>Session description</strong>:</p> <p>Test session with one AD+connected car and one connected car, lap of 11,6 km on the highway.</p> <p><strong>Datasets description</strong>:</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno.</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.</p>
Data for: Brain structural connectivity predicts brain functional complexity
<p>Data used in analyses for "Brain structural connectivity predicts brain functional complexity: DTI derived centrality accounts for variance in fractal properties of fMRI signal"</p>
Fig. 4 in Connecting systematic and ecological studies using DNA barcoding in a population survey of Drosophilidae (Diptera) from Mt Oku (Cameroon)
Fig. 4. Phylogenetic analysis of the subgenus Sophophora and Lissocephala aff. diola Tsacas & Lachaise, 1979. Conventions as for Fig. 3.
Fig. 2 in Connecting systematic and ecological studies using DNA barcoding in a population survey of Drosophilidae (Diptera) from Mt Oku (Cameroon)
Fig. 2. Percent divergence of the morphospecies DNA barcode from the closest neighbor found in the barcode database.
Fig. 3 in Connecting systematic and ecological studies using DNA barcoding in a population survey of Drosophilidae (Diptera) from Mt Oku (Cameroon)
Fig. 3. Phylogenetic analysis of the genus Zaprionus and Microdrosophila aff. mamaru (Burla, 1954). This tree is the neighbor-joining tree. The maximum likelihood tree gives the same topology. Nodes with a bootstrap value lower than 50% were merged. Bootstrap values were calculated over 1000 repeats. Above nodes: bootstrap values for maximum likelihood using a GTR + G + I model. Below nodes: bootstrap values for neighbor-joining using the Kimura-2p distance.
BIDS wildtype data selection from "Dysfunctional Autism Risk Genes Cause Circuit-Specific Connectivity Deficits With Distinct Developmental Trajectories"
<p>Data package selecting wildtype animals from the “Dysfunctional Autism Risk Genes Cause Circuit-Specific Connectivity Deficits With Distinct Developmental Trajectories” article, formatted corresponding to the Brain Imaging Data Structure. The relevant publication can be found via DOI <a href="https://doi.org/10.1093/cercor/bhy046">10.1093/cercor/bhy046</a> .</p>
Hippocampal hub neurons maintain distinct connectivity throughout their lifetime
<p>The temporal embryonic origins of cortical GABA neurons are critical for their specialization. In the neonatal hippocampus, GABA cells born the earliest (ebGABAs) operate as ‘hubs’ by orchestrating population synchrony. However, their adult fate remains largely unknown. To fill this gap, we have examined CA1 ebGABAs using a combination of electrophysiology, neurochemical analysis, optogenetic connectivity mapping as well as ex vivo and in vivo calcium imaging. We show that CA1 ebGABAs not only operate as hubs during development, but also maintain distinct morpho-physiological and connectivity profiles, including a bias for long-range targets and local excitatory inputs. In vivo, ebGABAs are activated during locomotion, correlate with CA1 cell assemblies and display high functional connectivity. Hence, ebGABAs are specified from birth to ensure unique functions throughout their lifetime. In the adult brain, this may take the form of a long-range hub role through the coordination of cell assemblies across distant regions.</p>
Genetic diversity and connectivity of southern right whales (Eubalaena australis) found in the Brazil and Chile–Peru wintering grounds and the South Georgia (Islas Georgias del Sur) feeding ground
<p></p><p>As species recover from exploitation, continued assessments of connectivity and population structure are warranted to provide information for conservation and management. This is particularly true in species with high dispersal capacity, such as migratory whales, where patterns of connectivity could change rapidly. Here we build on a previous long-term, large-scale collaboration on southern right whales (Eubalaena australis) to combine new (nnew) and published (npub) mitochondrial (mtDNA) and microsatellite genetic data from all major wintering grounds and, uniquely, the South Georgia (Islas Georgias del Sur: SG) feeding grounds. Specifically, we include data from Argentina (npub mtDNA/microsatellite = 208/46), Brazil (nnew mtDNA/microsatellite = 50/50), South Africa (nnew mtDNA/microsatellite = 66/77, npub mtDNA/microsatellite = 350/47), Chile–Peru (nnew mtDNA/microsatellite = 1/1), the Indo-Pacific (npub mtDNA/microsatellite = 769/126), and SG (npub mtDNA/microsatellite = 8/0, nnew mtDNA/microsatellite = 3/11) to investigate the position of previously unstudied habitats in the migratory network: Brazil, SG, and Chile–Peru. These new genetic data show connectivity between Brazil and Argentina, exemplified by weak genetic differentiation and the movement of 1 genetically identified individual between the South American grounds. The single sample from Chile–Peru had an mtDNA haplotype previously only observed in the Indo-Pacific and had a nuclear genotype that appeared admixed between the Indo-Pacific and South Atlantic, based on genetic clustering and assignment algorithms. The SG samples were clearly South Atlantic and were more similar to the South American than the South African wintering grounds. This study highlights how international collaborations are critical to provide context for emerging or recovering regions, like the SG feeding ground, as well as those that remain critically endangered, such as Chile–Peru.</p><p></p>
The Corona Connection: How LabHive and Open Science is Helping Scientists Solve COVID-19
<p><strong>Episode Summary: </strong></p> <p>The Coronavirus pandemic has led to many new initiatives to help scientists share resources and data. We talked to, Tobias Opialla and Lisa Rieble who have created a new platform called LabHive. We discussed what LabHive is and how it got started, as well as how Open Science principles and practices relate to the new normal and how communication is key. </p> <p><strong>Episode Links: </strong></p> <p><a href="https://labhive.de/#/">LabHive</a></p> <p><a href="https://wirvsvirus.org/">WirVsVirus</a></p> <p><a href="https://berlin.impacthub.net/">Impact Hub Berlin</a></p> <p><strong>Bonus links regarding Drosten, the Teachers and Kindergarteners and the BILD:</strong></p> <p>Original tweet from Drosten:<br> <a href="https://twitter.com/c_drosten/status/1264934434756755456">https://twitter.com/c_drosten/status/1264934434756755456</a> </p> <p>Replies from improperly quoted reviewers:<br> <a href="https://twitter.com/jdoeschner/status/1264948078790029313">https://twitter.com/jdoeschner/status/1264948078790029313</a> <br> <a href="https://twitter.com/domliebl/status/1264935266185293826">https://twitter.com/domliebl/status/1264935266185293826</a> <br> <a href="https://twitter.com/polenz_r/status/1264946109719379970">https://twitter.com/polenz_r/status/1264946109719379970</a> <br> <a href="https://twitter.com/christoph_rothe/status/1265344225979314177">https://twitter.com/christoph_rothe/status/1265344225979314177</a> <br> <a href="https://twitter.com/christoph_rothe/status/1264930677306413058">https://twitter.com/christoph_rothe/status/1264930677306413058</a> </p> <p>The xkcd regarding preprints:<br> <a href="https://xkcd.com/2304/">https://xkcd.com/2304/</a> </p> <p>Regarding renewed interest in Testing:<br> Drosten wanting to test schools and Kindergartens<br> <a href="https://www.ndr.de/nachrichten/info/39-Welche-Chancen-neue-Tests-bieten,podcastcoronavirus206.html">https://www.ndr.de/nachrichten/info/39-Welche-Chancen-neue-Tests-bieten,podcastcoronavirus206.html</a> </p> <p>Press Release from Berlin Senate regarding test strategy:<br> <a href="https://www.berlin.de/rbmskzl/aktuelles/pressemitteilungen/2020/pressemitteilung.935676.php">https://www.berlin.de/rbmskzl/aktuelles/pressemitteilungen/2020/pressemitteilung.935676.p</a>df </p>
Heat pump connected to floor heating
<p>Heat pump connected to floor heating.</p>
Data from: Sporadic genetic connectivity among small insular populations of the rare geoendemic plant Caulanthus amplexicaulis var. barbarae (Santa Barbara Jewelflower)
Globally, a small number of plants have adapted to terrestrial outcroppings of serpentine geology, which are characterized by soils with low levels of essential mineral nutrients (N, P, K, Ca, Mo) and toxic levels of heavy metals (Ni, Cr, Co). Paradoxically, many of these plants are restricted to this harsh environment. Caulanthus ampexlicaulis var. barbarae (Brassicaceae) is a rare annual plant that is strictly endemic to a small set of isolated serpentine outcrops in the coastal mountains of central California. The goals of the work presented here were to 1) determine the patterns of genetic connectivity among all known populations of Caulanthus ampexlicaulis var. barbarae, and 2) estimate contemporary effective population sizes (Ne), in order to inform ongoing genomic analyses of the evolutionary history of this taxon, and to provide a foundation upon which to model its future evolutionary potential and long-term viability in a changing environment. Eleven populations of this taxon were sampled, and population-genetic parameters were estimated using 11 nuclear microsatellite markers. Contemporary effective population sizes were estimated using multiple methods and found to be strikingly small (typically Ne < 10). Further, our data showed that a substantial component of genetic connectivity of this taxon is not at equilibrium, and instead showed sporadic gene flow. Several lines of evidence indicate that gene flow between isolated populations is maintained through long-distance seed dispersal (e.g. > 1 km), possibly via zoochory.
The effects of dexamphetamine on the resting state electroencephalogram and functional connectivity
<p>This upload comprises supplementary material and data for the paper "The effects of dexamphetamine on the resting state electroencephalogram and functional connectivity" Albrecht et al. (2015), Human Brain Mapping DOI: 10.1002/hbm.23052</p> <p>1) The cleaned and group ICA resting state data in EEGLAB format.</p> <p>2) Basic demographics for the participants. Drug order 1 = placebo first, then dexamphetamine second. Drug order 2 = dexamphetamine first, then placebo second. Gender 1 = Female, Gender 2 = Male.</p> <p>3) Bayesian hierarchical modelling functions for R and Stan (through rstan). See paper for more details.</p>
Leveraging on Digital Signage Networks to Bring Connectivity to IoT Devices
<p>This dataset contains the open data related to the research paper:</p> <p><br /> J. David de Hoz, Jose Saldana, Julián Fernández-Navajas, José Ruiz-Mas, Rebeca Guerrero Rodríguez, Félix de Jesús Mar Luna, Raúl Iván Herrera González, "Leveraging on Digital Signage Networks to Bring Connectivity to IoT Devices," Telcon UNI 2015, Lima, Peru, Oct. 2015.</p> <p><br /> This work has been partly financed by CONACYT (PEI 682/2014); Servicios d TI de Durango S.A. de C.V.; Ateire S.A.C., and de ER H2020 Wi 5 project (Grant Agreement no: 644262). </p> <p><br /> The name of each of the files indicates the figure of the paper: for example, "figure_15.csv" includes the information used to generate the figure 15. In some cases, ".csv" and ".xlsx" files are provided, but they include the same information.</p> <p><br /> In the "measurements" folder, the results are provided, and also the scripts used to obtain them.</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.