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13,276 results for “IT management”
Data from: Seasonal bee communities vary in their responses to resources at local and landscape scales: Implication for land managers
<p><strong>Context</strong>:<em> </em>There is great interest in land management practices for pollinators; however, a quantitative comparison of landscape and local effects on bee communities is necessary to determine if adding small habitat patches can increase bee abundance or species richness. The value of increasing floral abundance at a site is undoubtedly influenced by the phenology and magnitude of floral resources in the landscape, but due to the complexity of measuring landscape-scale resources, these factors have been understudied.</p> <p><strong>Objectives</strong>: To address this knowledge gap, we quantified the relative importance of local versus landscape scale resources for bee communities, identified the most important metrics of local and landscape quality, and evaluated how these relationships vary with season.</p> <p><strong>Methods</strong>: We studied season-specific relationships between local and landscape quality and wild-bee communities at 33 sites in the Finger Lakes region of New York, USA. We paired site surveys of wild bees, plants, and soil characteristics with a multi-dimensional assessment of landscape composition, configuration, insecticide toxic load, and a spatio-temporal evaluation of floral resources at local and landscape scales.</p> <p><strong>Results</strong>:<em> </em>We found that the most relevant spatial scale and landscape factor varied by season. Early-season bee communities responded primarily to landscape resources, including the presence of flowering trees and wetland habitats. In contrast, mid to late-season bee communities were more influenced by local conditions, though bee diversity was negatively impacted when sites were embedded in highly agricultural landscapes. Soil composition had complex impacts on bee communities, and likely reflects effects on plant community flowering. </p> <p><strong>Conclusions</strong>:<em> </em>Early-season bees can be supported by adding flowering trees and wetlands, while mid to late-season bees can be supported by local addition of summer and fall flowering plants. Sites embedded in landscapes with a greater proportion of natural areas will host a greater bee species diversity.</p>
Investigating the Utility of Potato (Solanum tuberosum L.) Canopy Temperature and Leaf Greenness Responses to Water-Restriction for the Improvement of Irrigation Management Data
<p><span>Traits that rapidly respond to stress in important agricultural crops have the potential to provide growers with actionable feedback. E.g., traits that respond to water-restriction could inform irrigation systems by identifying crop water status and requirements in real-time. This would be particularly useful for potato, which is extremely susceptible to drought. We conducted two pot experiments and one field experiment to evaluate the utility of two traits, canopy temperature and leaf greenness, for informing irrigation management in potatoes. We also evaluated the efficacy of Phenospex PlantEye F500 sensors for the remote sensing of leaf greenness. We found that canopy temperatures of the cvs. Maris Piper (Spring Pot Experiment, +0.8°C; Autumn Pot Experiment, +5.3°C) and Désirée (Autumn Pot Experiment, +2.5°C) increased with water-restriction and that the canopy temperatures of Maris Piper return to baseline within three days after the resumption of well-watered conditions. We also found that these responses varied between cultivars, with predictable outcomes based on reported and corroborated drought tolerance ratings. We found inconclusive evidence of leaf greenness increasing due to water-restriction (Spring Pot Experiment, +0.8°C; Autumn Pot Experiment, +5.3°C) and found no evidence that post-drought recovery periods return this trait to baseline. However, leaf greenness measurements from the Phenospex PlantEye F500 were moderately to strongly correlated with SPAD values, suggesting this tool might be useful in the screening of drought-tolerant cultivars in the future.</span></p>
Generation of a network slicing dataset: the foundations for AI-based B5G resource management
<p><span>This paper introduces a comprehensive network slicing dataset designed to empower artificial intelligence (AI), and other data-based resource management and network performance prediction applications, in 5G and beyond (B5G) networks. The dataset, generated through a packet-level simulator, captures the complexities of network slicing considering the three main network slice types defined by 3GPP: Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Internet of Things (mIoT). It includes a wide range of network scenarios with varying topologies, slice instances, and traffic flows. The included scenarios consist of transport networks, excluding the RAN infrastructure.</span></p> <p><span>Each sample consists of pairs of (network scenario, performance metrics). The network configuration includes network topology, traffic characteristics, routing configurations, while the performance metrics are the delay, jitter, and loss for each flow. The dataset is generated with a custom network slicing admission control module, enabling the simulation of realistic scenarios without violating SLAs.</span></p> <p><span>This network slicing dataset is a valuable asset for the research community, unlocking opportunities for innovations in 5G and B5G networks.</span></p>
Open dataset of 20 interviews with senior service designers and managers for the Empathy Business research project
<p>The aim of the Empathy Business research project (2023-2024) led by the University of Lapland focused on how to digitalize services and business prototyping through creativity. The research named challenges as well as design methods for developing digital tools for meeting the future needs of service design and business development.<br><br>In total, 20 interviews among senior service designers and managers located in Europe, Latin America and Asia were conducted during spring 2023. The interviews provide perspectives related to the future of service design as a practise, the skills required and further issues of relevance for professionals in the field. Based on affinity diagramming eight main clusters were named: Sustainability, Business compatibility, New tools, Designer’s skills, Art-based methods, People in the centre, Online workshops, and Physical workshops.<br><br>This data set includes an anonymized list of the interviewees, affinity diagram post-it notes of the interviews, short descriptions of the main clusters and an internet link to the online affinity diagram on Miro board.<br><br>The materials provided initial insights for developing Proof-of-Concepts for digitized interfaces, such as suitable plugins, 3D-based photorealistic solutions, or an application with the potential to be used in service design and service prototyping contexts as well as in other development processes within and across organizations.</p>
Dataset of management and growth curve derivatives to predict first lactation milk yield
<p>This dataset presents management and modeled variables of 78 Holstein-Friesian heifers. <span>Spreadsheets consist of seven sets of training and testing data randomly split with replacement.</span></p> <p></p> <p><span>In each spreadsheet, the included variables are:</span></p> <table> <tbody> <tr> <td><strong>Variable name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>ID</td> <td>Heifer ID tag</td> </tr> <tr> <td>Mes</td> <td>Month of birth of the heifer</td> </tr> <tr> <td>Estacion</td> <td>Birth season, according to Mes</td> </tr> <tr> <td>L_305_F_2x2</td> <td>First lactation milk yield is calculated as the integral from day 1 to 305 of a Fourier model representing daily milk yield (kg)</td> </tr> <tr> <td>Edad_IA</td> <td>Heifer's age at effective artificial insemination</td> </tr> <tr> <td>servicios</td> <td>Number of inseminations</td> </tr> <tr> <td>Edad_1P</td> <td>Heifer's age at first calving</td> </tr> <tr> <td>P_parto</td> <td>Heifer's weight at first calving (kg)</td> </tr> <tr> <td>B_parto</td> <td>Heifer's condition score at first calving</td> </tr> <tr> <td>Sexo_C</td> <td>Calf sex</td> </tr> <tr> <td>Peso_C</td> <td>Calf weight at birth (kg)</td> </tr> <tr> <td>wt_3._F, wt_6._F, wt_9._F, wt_12._F, wt_15._F, wt_18._F, wt_21._F</td> <td>Heifer's weight at various months after birth, as per a Fourier model of Heifer's growth curve (kg)</td> </tr> <tr> <td>X1STd_3._F, X1STd_6._F, X1STd_9._F, X1STd_12._F, X1STd_15._F, X1STd_18._F, X1STd_21._F</td> <td>First derivative at several months after birth, based on a Fourier model of Heifer's growth curve</td> </tr> <tr> <td>X2STd_3._F, X2STd_6._F, X2STd_9._F, X2STd_12._F, X2STd_15._F, X2STd_18._F, X2STd_21._F</td> <td>Second derivative at several months after birth, based on a Fourier model of Heifer's growth curve</td> </tr> <tr> <td> <p>wt_3._L to wt_21._L, X1STd_3._L to X1STd_21._L, </p> <p>X2STd_3._P to X2STd_21._P</p> </td> <td>Variables generated from a Power model</td> </tr> </tbody> </table>
Supporting data for managing fire-prone forests in a time of decreasing carbon carrying capacity
<p>These data and code include surface fuels and prescribed fire emissions data from the Teakettle Experimental Forest in the Sierra Nevada, California, USA. These data include transect data of surface fuels and the emissions from a 2017 prescribed burn. Emissions from the prescribed burn were calculated using a stock change approach by subtracting pre-burn surface fuels from post-burn surface fuels. We used these data and a Monte Carlo simulation approach to estimate the frequency of prescribed burning required to reduce surface fuels following a widespread overstory tree mortality event.</p>
Bottom of DR1 tank, the sides of the module are fitted with glass panels which allow natural light from a window to enter the tank, and the observer to view the behaviour of the broodstock. This device ensures easy viewing and checking of the broodstock, facilitates management of feeding and allows effective monitoring of reproduction. in Reproduction of Zingel asper (Linnaeus, 1758) in controlled conditions: an assessment of the experiences realized since 2005 at the Besançon Natural History Museum
Bottom of DR1 tank, the sides of the module are fitted with glass panels which allow natural light from a window to enter the tank, and the observer to view the behaviour of the broodstock. This device ensures easy viewing and checking of the broodstock, facilitates management of feeding and allows effective monitoring of reproduction.
Curated dataset for analysis for the paper "Decision Support Systems Adoption in Pesticide Management"
<p>Dataset created from farmer responses to a survey on the decision support systems adoption for intergrated pest management in the framework of the EU funded project IPM Decisions.</p>
Figure 8 in Unraveling distributional patterns and life-history traits of a deep-water shrimp Plesionika edwardsii (Decapoda, Pandalidae) under unexploited virgin conditions: a benchmark for fisheries management
Figure 8. Hypothesized life cycle of Plesionika edwardsii in the Azorean region. After the incubation period of shrimp eggs, (1) larvae are released into the water column and (2) juveniles develop in shallow waters. Mature females and males are distributed up to 600 m with a sexual segregation by depth: (3) non-ovigerous females are mainly found up to 200 m, (4) ovigerous females between 200 and 300 m, and (5) males from 400 to 500 m deep. Females are bigger than males, and ovigerous females are bigger than nonovigerous females. A bigger-deeper trend is observed up to 400 m. (6) Long larval stages of P. edwardsii increases its potential for dispersal (Landeira et al., 2009), favoring connectivity and stock homogeneity between adjacent areas.
Figure 5 in Unraveling distributional patterns and life-history traits of a deep-water shrimp Plesionika edwardsii (Decapoda, Pandalidae) under unexploited virgin conditions: a benchmark for fisheries management
Figure 5. Sex ratio of Plesionika edwardsii by depth stratum in the Azorean region during the period 1999–2000.
Figure 2 in Unraveling distributional patterns and life-history traits of a deep-water shrimp Plesionika edwardsii (Decapoda, Pandalidae) under unexploited virgin conditions: a benchmark for fisheries management
Figure 2. Seasonal predicted mean catch per unit effort (CPUE, g trap-1) by depth stratum for males, non-ovigerous and ovigerous females of Plesionika edwardsii in the Azorean region for the period 1999–2000. Light-colored symbols represent raw data. Detailed parameter estimates are in Tab. S4.
Figure 7 in Unraveling distributional patterns and life-history traits of a deep-water shrimp Plesionika edwardsii (Decapoda, Pandalidae) under unexploited virgin conditions: a benchmark for fisheries management
Figure 7. Size at which 50 % of the shrimps are mature (L 50) estimated for Plesionika edwardsii in the Azorean region fitting a logistic curve to the proportion of ovigerous females. Logistic curve was estimated combining all data obtained during the period 1999–2000.
Figure 4 in Unraveling distributional patterns and life-history traits of a deep-water shrimp Plesionika edwardsii (Decapoda, Pandalidae) under unexploited virgin conditions: a benchmark for fisheries management
Figure 4. Seasonal predicted mean cephalothorax length (CL) by depth stratum for males, non-ovigerous and ovigerous females of Plesionika edwardsii in the Azorean region for the period 1999–2000. Light-colored symbols represent raw data. Detailed parameter estimates are in Tab. S4.
Figure 1 in Unraveling distributional patterns and life-history traits of a deep-water shrimp Plesionika edwardsii (Decapoda, Pandalidae) under unexploited virgin conditions: a benchmark for fisheries management
Figure 1. Sampling areas of Plesionika edwardsii in the mid-North Atlantic Ocean, Azorean region (ICES Subdivision 10a2) between 1999 and 2000. Orange dots represent each site sampled by a trap.
Figure 6 in Unraveling distributional patterns and life-history traits of a deep-water shrimp Plesionika edwardsii (Decapoda, Pandalidae) under unexploited virgin conditions: a benchmark for fisheries management
Figure 6. Sex ratio of Plesionika edwardsii by size class in the Azorean region during the period 1999–2000.
Figure 3 in Unraveling distributional patterns and life-history traits of a deep-water shrimp Plesionika edwardsii (Decapoda, Pandalidae) under unexploited virgin conditions: a benchmark for fisheries management
Figure 3. Size frequency distribution of males, non-ovigerous and ovigerous females Plesionika edwardsii in the Azorean region during the period 1999-2000.
Eye Tracking based Learning Style Identification for Learning Management Systems
<h2>Abstract: </h2> <p>In recent years, universities have been faced with increasing numbers of students dropping out. This is partly due to the fact that students are limited in their ability to explore individual learning paths through different course materials. However, a promising remedy to this issue is the implementation of adaptive learning management systems. These systems recommend customised learning paths to students - based on their individual learning styles. Learning styles are commonly classified using questionnaires and learning analytics, but both methods are prone to error. Questionnaires may yield superficial responses due to time constraints or lack of motivation, while learning analytics ignore offline learning behaviour. To address these limitations, this study aims to integrating Eye Tracking for a more accurate classification of students' learning styles. Ultimately, this comprehensive approach could not only open up a deeper understanding of subconscious processes, but also provide valuable insights into students' unique learning preferences.</p> <h3>Research: </h3> <p>As an example of a possible analysis of the eye-tracking stimuli and eye movement recordings available here, as well as the corresponding ILS questionnaire responses, we refer to the following research works, which should also be referred to if necessary: </p> <ul> <li>Bittner, D., Nadimpalli, V. K., Grabinger, L., Ezer, T., Hauser, F., & Mottok, J. (2024, June), Uncovering Learning Styles through Eye Tracking and Artificial Intelligence, <em>In 2024 Symposium on Eye Tracking Research and Applications.</em> ETRA.</li> <li>Bittner, D. (2024), Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence. Master’s Thesis, Regensburg University of Applied Sciences (OTH), Regensburg, Germany</li> <li>Bittner, D., Ezer, T., Grabinger, L., Hauser, F., & Mottok, J. (2023). Unveiling the secrets of learning styles: decoding eye movements via machine learning. In <em>ICERI2023 Proceedings</em> (pp. 5153-5162). IATED.</li> <li>Bittner, D., Hauser, F., Nadimpalli, V. K., Grabinger, L., Staufer, S., & Mottok, J. (2023, June). Towards eye tracking based learning style identification. In <em>Proceedings of the 5th European Conference on Software Engineering Education</em> (pp. 138-147). ECSEE.</li> </ul> <p>The following descriptions and the previous abstract are part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. and have to be cited accordingly. </p> <h3>Experimental Setup:</h3> <p>In the following section, crucial notes on the circumstances and the experiment itself as well as the equipment are given. <br>In order to reduce the external influence on the experiment, variables such as:</p> <ul> <li>order, number, and presentation of the stimuli,</li> <li>instruction to the participant prior to the experiment,</li> <li>position of the participant in respect to the Eye Tracking equipment,</li> <li>environment such as illuminance and ambient noise for the participant,</li> <li>Eye Tracking equipment, software, settings such as sampling frequency and latency as well as calibration</li> </ul> <p>were attempted to keep constant and consistent throughout the experiment. </p> <h3>Equipment: </h3> <p>In this study, the <strong>Tobii Pro Fusion</strong> (<a href="https://go.tobii.com/tobii-pro-fusion-user-manual">https://go.tobii.com/tobii-pro-fusion-user-manual</a>) eye tracker is utilized without a chin rest along with the <strong>Tobii IVT filter</strong> for fixation detection and <strong>Tobii Pro Lab</strong> software for data collection. The Tobii Pro Fusion is categorised as a video-based combined pupil and corneal reflection technology. This tracker provides several advantages, such as the collection of comprehensive data, comprising gaze, pupil, and eye-opening metrics. The eye tracker captures up to 250 images per second (250Hz), enhancing its precision and eye movement analysis. In addition, Tobii Pro Fusion is capable of performing under different lighting conditions, thus making this portable device ideal for off-site studies.</p> <p>Ensuring consistent quality across all experiment participants is crucial. Prior to each individual experiment, eye trackers are calibrated, aiming for a maximum reproduction error of less or equal than 0.2 degree during calibration to minimize deviations. The calibration is excluded from the experiment recording. Each participant is given the same instructions for their single trial of the experiment. The stimuli is displayed on a 24-inch monitor in a 16:9 format, positioned approximately 65cm away from the participants' eyes. Any effect related to the characteristics of the participants, such as age, visual acuity, eye colour, pupil size, etc., are considered in the experiment design. </p> <h3>Procedure: </h3> <p>Initially, the participants are requested to confirm their ability to conduct the experiment based on their current condition. Subsequently, the participant must be positioned comfortably and accurately in relation to the eye tracker. The eye tracker calibration is carried out for each participant to ensure a suitable experimental configuration. Once a successful calibration is achieved, the Eye Tracking experiment begin with introductions prior to each task. The stimuli presentation is unrestricted by time constraints, and no prior knowledge of the stimuli contents is necessary. Employing a within-subject design, each stimulus is exposed to each subject. Following completion of the experiment, participants anonymously answer the ILS questionnaire. To prevent any impact on the experiment, it is important that the questionnaire only be seen and completed after the experiment. </p> <h3>Stimuli: </h3> <p>The specially designed stimuli shown to participants during the study are illustrated in the left-hand column of the figure in the PDF file "[Documentation]stimuli_preview.pdf", which is part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. For this research, only specific regions of a stimulus, referred to as AOI, are taken into consideration. The size of the AOI depends on both stimulus information and distance between multiple AOIs. Adequate results are ensured by not overlapping AOIs and appropriate spacing. The AOIs of the various stimuli employed in this research are illustrated in the right-hand column of the figure in the PDF file "[Documentation]stimuli_preview.pdf", which is part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. The stimuli are presented in German language, ensuring reliable Eye Tracking measurements without any interference from language barriers. Each stimulus comprises diverse learning materials to engage students with varying learning styles, with some general information about the quantitative research cycle. Some stimuli feature identical type of material, e.g. <em>illustrations</em> or <em>key words</em>, but with different contexts and positions on the stimuli. Rearranging the identical material reduces the influence of reading style and enhances the impact of the learning style, producing a more reliable experiment. These identical types of material or AOIs on different stimuli can be grouped together, identified by the same colour and title, and referred to as AOI groupings.<br>There are ten different AOI groupings in total, as illustrated in the figure in the "[Documentation]stimuli_preview.pdf" file, where each grouping consists of several AOIs. <br>In detail, the AOI grouping regarding:</p> <ul> <li><em>table of contents and summary contain only a single AOI each,</em></li> <li><em>illustrations</em>, <em>key words</em>, <em>theory</em>, <em>exercise</em>, <em>example</em> and <em>additional material</em> contain three AOIs each,</li> <li><em>supporting text</em> and <em>multiple choice question</em> contain two AOIs each.</li> </ul> <h3>Research data management: </h3> <p>To ensure the transparency and reproducibility of this study, effective management of research data is essential. This section provides details on the management, storage and analysis of the extensive dataset collected as part of the study. Importantly, this research, the study and its processes adhered to ethical guidelines at all times, including informed consent, participant anonymity and secure data handling. The data collected will only be kept for a specific period of time as defined in the research project guidelines. The collection itself involves the recording of participants' eye movements during the ET study and the collection of their demographic data and responses to the ILS questionnaire. </p>
SEAKNOT - SEvere Accident Research and KNOwledge ManagemenT for LWRs
<p>Video presented at the <a href="https://snetp.eu/2023/04/14/read-the-coordinators-hub-day-summary/">SNETP Coordinators’ hub day</a>. This initiative took place in Brussels on March 14th, 2023 as part of the SNETPFORWARD project. The event was co-organized by SNETP. </p>
Data: Managing European Alpine forests with close-to-nature forestry to improve climate change mitigation and multifunctionality
<p><strong>The repository contains the data supporting the findings of the study: <em>Managing European Alpine forests with close-to-nature forestry to improve climate change mitigation and multifunctionality</em></strong></p> <p><strong>Abstract:</strong></p> <p>Close-to-nature forestry (CNF) has a long tradition in European Alpine forest management, playing a crucial role in ensuring the continuous provision of biodiversity and forest ecosystem services, including protection against natural hazards. However, climate change is causing huge uncertainties about the future applicability of CNF in the Alpine region. The question arises as to whether current CNF practices are still suitable for adapting forests to climate change impacts while also meeting the increasing societal demands regarding Alpine forests, including their potential contribution to climate change mitigation.</p> <p>To answer this question, we simulated forest development using the ForClim forest model at two Alpine study sites, together representing a large biogeographic gradient from high-elevation inner Alpine forests (Switzerland) to lower-elevation south-eastern Alpine forests (Slovenia). The simulations considered three climate scenarios (historical climate, SSP2‑4.5 and SSP5-8.5) and six alternative management strategies, including both current CNF management practices and climate-adapted versions. Using a multi-criteria decision analysis framework, we assessed the joint impacts of climate and management on biodiversity and key ecosystem services of the investigated regions, including carbon sequestration (CS) inside and outside the forest ecosystem boundary. </p> <p>The joint effects of climate change and CNF varied, both among and within the study sites along the biogeographical gradient. While CS was more resistant to climate change under current CNF at the south-eastern Alpine site, it was more sensitive at the inner Alpine site, where CS potentials decreased at lower elevations. This adverse effect could be partly mitigated by fostering the use of climate-adapted tree species. However, current CNF and adaptations of it did not meet multiple management objectives equally well: while protection from gravitation hazards and timber production also benefited from this silvicultural practice, biodiversity benefited from CNF variants with low-intensity or no management. </p> <p>In conclusion, CNF has a high potential to continue fulfilling its crucial role in European Alpine forests. A differentiated approach will be needed in the future, however, to identify forest stands where adaptive measures are required, especially at sites particularly vulnerable to climate change. In combination with less intensively managed or unmanaged areas, CNF provides a management portfolio that will help European Alpine forests to meet the demands of future society.</p> <p><strong>Data:</strong></p> <p>There is one folder for each case study, including: </p> <ul> <li>simulated biodiverstiy and ecosystem service indicators</li> <li>forest stand metadata</li> <li>normlized utility values for indicators</li> <li>partial utility values for biodiversity and ecosystem service groups</li> </ul> <p>This study was conducted as part of the <strong>ONEforest project</strong>, which received funding from the <strong>European Union's Horizon 2020</strong> research and innovation programme under the <strong>grant agreement Nº 101000406</strong>.</p>
Figure 2 in The Importance Of Natura 2000 Sites And Their Management For The Conservation Of Freshwater Fish, Lamprey And Crayfish In Latvia
Figure 2. Occurrence of threatened freshwater fish, lamprey and crayfish species (n=15) in N2000 and outside N2000 at GC5, grouped by 0–10 %; 10–25 %; 25–50 %;>50 % with species numbers 0–1; 2–3; 4–7; 8–12.
ScienceDex guides
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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.