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3,480 results for “value”
Research data supporting "Impact of global heterogeneity of renewable energy supply on heavy industrial production and green value chains"
<p>Research data supporting the peer-reviewed article "Impact of global heterogeneity of renewable energy supply on heavy industrial production and green value chains" by the same authors.</p>
Hourly values of an advanced human-biometeorological index for diverse populations from 1991 to 2020
<p>The presented human thermal bioclimate dataset was created in the frame of the <a href="https://theheatalarm.wordpress.com/">HEAT-ALARM</a> ("Development of a heat-health warning system in Greece") research project.</p> <p><strong>Initially developed for Greece</strong>, it consists of hourly values of population-weighted mPET (modified physiologically equivalent temperature), simulated by the RayMan Pro model for the period 1991-2020 and for 10 population subsets in 72 regional units and combinations thereof, which are based on the NUTS-3 (Nomenclature of Territorial Units for Statistics-3) classification in Greece, using the Copernicus European Regional Reanalysis (CERRA) at 5.5 km spatial resolution. The dataset also includes the main environmental drivers of mPET (e.g. temperature) at the same spatiotemporal resolution.</p> <p>In the framework of <strong>replicating</strong> the original dataset, the current version includes population-weighted values of mPET and its environmental drivers for six populations in five districts of <strong>Cyprus</strong> at the LAU-1 (Local Administrative Units-1) level, covering the period from 1991 to 2020. </p> <p>The code used to produce the presented data is available at: <a href="https://doi.org/10.5281/zenodo.10793067">https://doi.org/10.5281/zenodo.10793067</a>. It can be used to replicate the dataset not only directly in Greece, but also in any other country included in the CERRA domain after appropriate adjustments, as in the case of Cyprus above.</p> <p><em>Compared to the previous version of the dataset for Greece, this version includes vapor pressure (VP) instead of relative humidity (see README.txt for more details), as VP is more relevant for human-biometerological and health-related studies.</em><em> </em></p> <p><strong>References</strong></p> <p>Giannaros, C., Agathangelidis, I., Galanaki, E. <em>et al.</em> Hourly values of an advanced human-biometeorological index for diverse populations from 1991 to 2020 in Greece. <em>Sci Data</em> <strong>11</strong>, 76 (2024). <a href="https://doi.org/10.1038/s41597-024-02923-y">https://doi.org/10.1038/s41597-024-02923-y</a> </p>
Chronic Ethanol Exposure Produces Sex-Dependent Impairments in Value Computations in the Striatum
<div> <div>These datasets and scripts are organized by figures. All data are stored as .mat format and can be open and manipulated using MATLAB. Scripts are all written in MATLAB and can be ran in MATLAB.</div> <div>There are two ways to run the code to reproduce each figures and statistics.</div> <div>1. Run RUN_ME.m. In this case, the file will automatically excute scripts to load corresponding data and figures.</div> <div>2. Open individual script to load corresponding data and generate statistics and figures.</div> <br> <div>All scripts here have been validated and tested. The system and coding environment is:</div> <div>- Windows 11 24H2</div> <div>- MATLAB 2023a</div> <br> <div>Matlab dependent package (not all are required but those are installed in my environment):</div> <div>- Bioinformatics Toolbox v4.17</div> <div>- Communications Toolbox v8.0</div> <div>- Computer Vision Toolbox v10.4</div> <div>- Curve Fitting Toolbox v3.9</div> <div>- Data Acquisition Toolbox v4.7</div> <div>- Database Toolbox v11.0</div> <div>- Deep Learning HDL Toolbox v1.5</div> <div>- Deep Learning Toolbox v14.6</div> <div>- DSP HDL Toolbox v1.2</div> <div>- Econometrics Toolbox v6.2</div> <div>- Financial Toolbox v6.5</div> <div>- Fixed-point Designer v7.6</div> <div>- Image Processing Toolbox v11.7</div> <div>- MATLAB Coder v5.6</div> <div>- MATLAB Compiler v8.6</div> <div>- MATLAB Compiler SDK v7.2</div> <div>- MATLAB Report Generator v5.14</div> <div>- MATLAB Support for MinGW-w64 C/C++ Compiler v23.1.0</div> <div>- Optimization Toolbox v9.5</div> <div>- Parallel Computing Toolbox v9.5</div> <div>- FR Toolbox v4.5</div> <div>- Signal Integrity Toolbox v1.3</div> <div>- Simulink v10.7</div> <div>- Statistics and Machine Learning Toolbox v12.5</div> <div>- Symbolic Math Toolbox v9.3</div> <div>- Text Analytics Toolbox v1.10</div> <div>- Wavelet Toolbox v6.3</div> </div>
RGB pixels VALUES FOR APPLES/LETTUCE AI OPTICAL RECOGNITION - 5 categories of Freshness
<p>The Datasets include RGB color pallete per pixel values for optical recognition on apples/lettuce and freshness categorized using AI Algorithm . Those Datasets are for AI Training projects . It will be used on the stage of creation, verification or optimization for new optical AI models. The tables can be used direclty on the AI tools, inserted and using the pixels colors number for every category. The freshness categories are 5, from the highest- crop day (5) to the lowest - not for eating (1).</p> <p> </p>
Supplementary Datafile for "A reform of value-added taxes on foods can have health, environmental and economic benefits in Europe"
<p>The dataset contains the results of the study "A reform of value-added taxes on foods can have health, environmental and economic benefits in Europe" by Marco Springmann, Eugenia Dinivitzer, Florian Freund, Jørgen Dejgård Jensen, and Clara G Bouyssou. </p> <p>It contains VAT rates on foods across Europe, as well as the results of reforming VAT rates for foods, including increasing rates for meat and dairy and reducing rates for fruits and vegetables. The set of results include changes in food demand, changes in environmental impacts (greenhouse gas emissions, land use, water use, and eutrophication potential), changes in diet-related mortality, changes in costs (revenues, climate change costs, costs of illness). </p>
A stakeholder-centered determination of High-Value Data sets: the use-case of Latvia
<p>The data in this dataset were collected in the result of the survey of Latvian society (2021) aimed at identifying high-value data set for Latvia, i.e. data sets that, in the view of Latvian society, could create the value for the Latvian economy and society.<br> The survey is created for both individuals and businesses.<br> It being made public both to act as supplementary data for "Towards enrichment of the open government data: a stakeholder-centered determination of High-Value Data sets for Latvia" paper (author: Anastasija Nikiforova, University of Latvia) and in order for other researchers to use these data in their own work.</p> <p>The survey was distributed among Latvian citizens and organisations. The structure of the survey is available in the supplementary file available (see Survey_HighValueDataSets.odt)</p> <p>***Description of the data in this data set: structure of the survey and pre-defined answers (if any)***<br> 1. Have you ever used open (government) data? - {(1) yes, once; (2) yes, there has been a little experience; (3) yes, continuously, (4) no, it wasn’t needed for me; (5) no, have tried but has failed}<br> 2. How would you assess the value of open govenment data that are currently available for your personal use or your business? - 5-point Likert scale, where 1 – any to 5 – very high<br> 3. If you ever used the open (government) data, what was the purpose of using them? - {(1) Have not had to use; (2) to identify the situation for an object or ab event (e.g. Covid-19 current state); (3) data-driven decision-making; (4) for the enrichment of my data, i.e. by supplementing them; (5) for better understanding of decisions of the government; (6) awareness of governments’ actions (increasing transparency); (7) forecasting (e.g. trendings etc.); (8) for developing data-driven solutions that use only the open data; (9) for developing data-driven solutions, using open data as a supplement to existing data; (10) for training and education purposes; (11) for entertainment; (12) other (open-ended question)<br> 4. What category(ies) of “high value datasets” is, in you opinion, able to create added value for society or the economy? {(1)Geospatial data; (2) Earth observation and environment; (3) Meteorological; (4) Statistics; (5) Companies and company ownership; (6) Mobility}<br> 5. To what extent do you think the current data catalogue of Latvia’s Open data portal corresponds to the needs of data users/ consumers? - 10-point Likert scale, where 1 – no data are useful, but 10 – fully correspond, i.e. all potentially valuable datasets are available<br> 6. Which of the current data categories in Latvia’s open data portals, in you opinion, most corresponds to the “high value dataset”? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies}<br> 7. Which of them form your TOP-3? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies}<br> 8. How would you assess the value of the following data categories?<br> 8.1. sensor data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable<br> 8.2. real-time data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable<br> 8.3. geospatial data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable<br> 9. What would be these datasets? I.e. what (sub)topic could these data be associated with? - open-ended question<br> 10. Which of the data sets currently available could be valauble and useful for society and businesses? - open-ended question<br> 11. Which of the data sets currently NOT available in Latvia’s open data portal could, in your opinion, be valauble and useful for society and businesses? - open-ended question<br> 12. How did you define them? - {(1)Subjective opinion; (2) experience with data; (3) filtering out the most popular datasets, i.e. basing the on public opinion; (4) other (open-ended question)}<br> 13. How high could be the value of these data sets value for you or your business? - 5-point Likert scale, where 1 – not valuable, 5 – highly valuable<br> 14. Do you represent any company/ organization (are you working anywhere)? (if “yes”, please, fill out the survey twice, i.e. as an individual user AND a company representative) - {yes; no; I am an individual data user; other (open-ended)}<br> 15. What industry/ sector does your company/ organization belong to? (if you do not work at the moment, please, choose the last option) - {Information and communication services; Financial and ansurance activities; Accommodation and catering services; Education; Real estate operations; Wholesale and retail trade; repair of motor vehicles and motorcycles; transport and storage; construction; water supply; waste water; waste management and recovery; electricity, gas supple, heating and air conditioning; manufacturing industry; mining and quarrying; agriculture, forestry and fisheries professional, scientific and technical services; operation of administrative and service services; public administration and defence; compulsory social insurance; health and social care; art, entertainment and recreation; activities of households as employers;; CSO/NGO; Iam not a representative of any company<br> 16. To which category does your company/ organization belong to in terms of its size? - {small; medium; large; self-employeed; I am not a representative of any company}<br> 17. What is the age group that you belong to? (if you are an individual user, not a company representative) - {11..15, 16..20, 21..25, 26..30, 31..35, 36..40, 41..45, 46+, “do not want to reveal”}<br> 18. Please, indicate your education or a scientific degree that corresponds most to you? (if you are an individual user, not a company representative) - {master degree; bachelor’s degree; Dr. and/ or PhD; student (bachelor level); student (master level); doctoral candidate; pupil; do not want to reveal these data}</p> <p>***Format of the file***<br> .xls, .csv (for the first spreadsheet only), .odt</p> <p>***Licenses or restrictions***<br> CC-BY</p> <p> </p> <p> </p>
Effects of Mouthrinsing and Gargling to CT Values of SARS CoV-2 DATASET
<p><strong>Background:</strong> Coronavirus disease 2019 can spread rapidly. Surgery in the oral cavity poses a high risk of transmission of severe acute respiratory syndrome coronavirus 2. The American Dental Association and the Centers for Disease Control and Prevention recommend the use of mouthwash containing 1.5% hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) or 0.2% povidone iodine (PI) to reduce the viral load in the upper respiratory tract and decrease the risk of transmission. The aim of the present study was to analyze the effect of mouthrinsing and gargling with mouthwash containing 1% PI, 0.5% PI, 3% H<sub>2</sub>O<sub>2</sub>, or 1.5% H<sub>2</sub>O<sub>2</sub> and water on the cycle threshold (CT) value.</p> <p><strong>Methods:</strong> In total, 69 subjects recruited from Persahabatan General Hospital who met the inclusion criteria were randomly assigned to one of four treatment groups or the control group. The subjects were instructed to gargle with 15 mL of mouthwash for 30 s in the oral cavity followed by 30 s in the back of the throat three times per day for 5 days. CT values were collected on postprocedural days 1, 3, and 5.</p> <p><strong>Results:</strong> The results of the Friedman test significantly differed among the groups. The CT values increased from baseline (day 0) to postprocedural days 1, 3, and 5.</p> <p><strong>Conclusions:</strong> Mouthrinsing and Gargling with mouthwash containing 1% PI, 0.5% PI, 3% H<sub>2</sub>O<sub>2</sub>, or 1.5% H<sub>2</sub>O<sub>2</sub> and water increased the CT value.</p> <p><strong>Background:</strong> Coronavirus disease 2019 can spread rapidly. Surgery in the oral cavity poses a high risk of transmission of severe acute respiratory syndrome coronavirus 2. The American Dental Association and the Centers for Disease Control and Prevention recommend the use of mouthwash containing 1.5% hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) or 0.2% povidone iodine (PI) to reduce the viral load in the upper respiratory tract and decrease the risk of transmission. The aim of the present study was to analyze the effect of mouthrinsing and gargling with mouthwash containing 1% PI, 0.5% PI, 3% H<sub>2</sub>O<sub>2</sub>, or 1.5% H<sub>2</sub>O<sub>2</sub> and water on the cycle threshold (CT) value.</p> <p><strong>Methods:</strong> In total, 69 subjects recruited from Persahabatan General Hospital who met the inclusion criteria were randomly assigned to one of four treatment groups or the control group. The subjects were instructed to gargle with 15 mL of mouthwash for 30 s in the oral cavity followed by 30 s in the back of the throat three times per day for 5 days. CT values were collected on postprocedural days 1, 3, and 5.</p> <p><strong>Results:</strong> The results of the Friedman test significantly differed among the groups. The CT values increased from baseline (day 0) to postprocedural days 1, 3, and 5.</p> <p><strong>Conclusions:</strong> Mouthrinsing and Gargling with mouthwash containing 1% PI, 0.5% PI, 3% H<sub>2</sub>O<sub>2</sub>, or 1.5% H<sub>2</sub>O<sub>2</sub> and water increased the CT value.</p>
Supporting information - A value creation model from science-society interconnections: Components and archetypes
<p>Data protocol and datasets used for the study entitled 'A value creation model from science-society interconnections: Components and archetypes'. </p> <p><strong>Abstract of the paper:</strong></p> <p>The interplay between science and society takes place through a wide range of intertwined relationships and mutual influences that shape each other and facilitate continuous knowledge flows. Stylised consequentialist perspectives on valuable knowledge moving from public science to society in linear and recursive pathways, whilst informative, cannot fully capture the broad spectrum of value creation possibilities. As an alternative we experiment with an approach that gathers together diverse science-society interconnections and reciprocal research-related knowledge processes that can generate valorisation. Our approach to value creation attempts to incorporate multiple facets, directions and dynamics in which constellations of scientific and societal actors generate value from research. The paper develops a conceptual model based on a set of nine value components derived from four key research-related knowledge processes: production, translation, communication, and utilization. The paper conducts an exploratory empirical study to investigate whether a set of archetypes can be discerned among these components that structure science-society interconnections. We explore how such archetypes vary between major scientific fields. Each archetype is overlaid on a research topic map, with our results showing that different archetypes correspond to distinctive topic areas. The paper finishes by discussing the significance and limitations of our results and the potential of both our model and our empirical approach for further research.</p>
Harvesting the Value of Data: A Data Architectural Smart Solutions Approach for Enabling Digital Water - Dataset
<p>This database includes the test data used to produce the results for the following article:</p> <p>Harvesting the Value of Data: A Data Architectural Smart Solutions Approach for Enabling Digital Water by S. Seshan, D. Vries, M. Zandvoort, A. W. C. van der Helm, J. Poinapen, Smart Water - WaterAge Magazine, February 16-23</p>
Dataset: "Balancing consumer and business value of recommender systems: A simulation-based analysis"
<p>The data files in this directory contain to the results of the simulations reported in the paper: "Balancing Consumer and Business Value of Recommender Systems: A Simulation-based Analysis" published in Electronic Commerce Research and Applications. The paper is available here: <a href="https://doi.org/10.1016/j.elerap.2022.101195">https://doi.org/10.1016/j.elerap.2022.101195</a></p> <p> </p>
IPBES Assessment of the diverse values and valuation of nature - Figures presented in Chapter 2
<p>These figures are an integral part of Chapter 2 of the Methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. To see the full document visit the related links. </p>
IPBES Assessment of the diverse values and valuation of nature - Figures presented in Chapter 1
<p>These figures are an integral part of Chapter 1 of the Methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. To see the full document visit the related links. </p>
IPBES Assessment of the diverse values and valuation of nature - Figures presented in Chapter 3
<p>These figures are an integral part of Chapter 3 of the Methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. To see the full document visit the related links. </p>
IPBES Assessment of the diverse values and valuation of nature - Figures presented in Chapter 6
<p>These figures are an integral part of Chapter 6 of the Methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. To see the full document visit the related links. </p>
IPBES Assessment of the diverse values and valuation of nature - Figures presented in Chapter 5
<p>These figures are an integral part of Chapter 5 of the Methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. To see the full document visit the related links. </p>
IPBES Assessment of the diverse values and valuation of nature - Figures presented in Chapter 4
<p>These figures are an integral part of Chapter 4 of the Methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. To see the full document visit the related links. </p>
Learned value modulates the access to visual awareness during continuous flash suppression
<p>Data from Experiment 1 and Experiment 2 are reported in separate files. </p> <p>Each line contains the mean suppression time of a target grating under continuous flash suppression expressed in seconds for one participant. </p> <p>Each column refers to a different condition:<br> HREV = visual stimuli associated with high monetary reward<br> LREV = visual stimuli associated with low monetary reward<br> base = baseline measurements before associative learning<br> P1 = first measurement after associative learning<br> P2 = second measurement after associative learning<br> P3 = third measurement after associative learning</p> <p>For experiment 1, a short (20 trials) associative learning recall session was performed between P1 and P2 and between P2 and P3.</p> <p> </p> <p> </p> <p> </p>
Insolation values for the climate of the last 10 million years (Tables)
<p>Tables published in Berger A. and M. F. Loutre (1991), Insolation values for the climate of the last 10 million years, Quaternary Science Reviews, (10) 297 - 317 doi:10.1016/0277-3791(91)90033-Q and which constitute what is known in the community as the "BER90" solution. </p> <p>Orginal abstract: New values for the astronomical parameters of the Earth's orbit and rotation (eccentricity, obhqulty and precession) are proposed for paleochmatle research related to the Late Miocene, the Pliocene and the Quaternary They have been obtained from a numerical solution of the Lagrangian system of the planetary point masses and from an analytical solution of the Polsson equations of the Earth-Moon system. The analytical expansion developed in this paper allows the direct determination of the main frequencies with their phase and amplitude. Numerical and analytical comparisons with the former astronomical solution BER78 are performed so that the accuracy and the interval of time over which the new solution is valid can be estimated. The corresponding insolation values have also been computed and compared to the former ones. This analysis leads to the conclusion that the new values are expected to be reliable over the last 5 Ma In the time domain and at least over the last 10 Ma in the frequency domain. <br> </p>
Mean NDVI Values (1982-2018) and Future Predictions Using CHELSA Bioclim Variables for Türkiye
<p>This dataset contains mean Normalized Difference Vegetation Index (NDVI) values from 1982 to 2018 and their future predictions based on CHELSA bioclimatic variables, specifically for the region of Türkiye. The data is provided in .asc format and includes both historical and projected NDVI values under different climate scenarios.</p> <h4>Contents:</h4> <ul> <li><strong>Historical NDVI Data (1982-2018)</strong>: Mean NDVI values derived from remote sensing data.</li> <li><strong>Future NDVI Predictions</strong>: NDVI projections for the periods 2011-2040, 2041-2070, and 2071-2100 under three Shared Socioeconomic Pathways (SSPs): SSP1-2.6, SSP3-7.0, and SSP5-8.5.</li> </ul> <h4>Methodology:</h4> <ol> <li><strong>Model Training</strong>: <ul> <li>A Random Forest Regressor was used to model the relationship between NDVI and the selected bioclim variables.</li> <li>The model achieved an R² of 0.9341, Mean Absolute Error of 0.0275, and Root Mean Squared Error of 0.0499.</li> </ul> </li> <li><strong>Future Predictions</strong>: <ul> <li>Future NDVI values were predicted using the trained model and future CHELSA bioclim projections.</li> <li>Predictions were made for three future periods (2011-2040, 2041-2070, 2071-2100) under three SSPs (SSP1-2.6, SSP3-7.0, SSP5-8.5).</li> </ul> </li> </ol> <h4>Data Specifications:</h4> <ul> <li><strong>Extent</strong>: Covers the geographical area of Türkiye and adjacents.</li> </ul> <h4>Sources:</h4> <ul> <li><strong>NDVI Data</strong>: <ul> <li>Ma, Z., Dong, C., Lin, K., Yan, Y., Luo, J., Jiang, D., & Chen, X. (2022). A Global 250-m Downscaled NDVI Product from 1982 to 2018. Remote Sensing, 14(15), 3639.</li> </ul> </li> <li><strong>CHELSA Bioclim Data</strong>: <ul> <li>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017). Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122" target="_new" rel="noreferrer">https://doi.org/10.1038/sdata.2017.122</a></li> <li>Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M. Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4" target="_new" rel="noreferrer">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a></li> </ul> </li> </ul>
Production and trade data on cocoa value chain in Ghana
<p>This dataset covers upstream, midstream and downstream behavioural patterns that were used to identify precursors of vulnerabilities in Ghana’s cocoa value chain. Data were obtained via focus group discussions and individual interviews with cocoa farmers in Ghana and extracted from annual reports of the global cocoa industry published by the International Cocoa Organisation. Behavioural patterns were established from transcripts and reports using NVivo as the computer-assisted qualitative data analysis software.</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.