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575 results for “Quality assessment”
Quality assessment of the included papers to assess the pooled prevalence of iodine deficiency among school-age children in Ethiopia, 2023 by using Newcastle-Ottawa Scale adapted for cross-sectional studies.
<p>This is a quality assessment data of the studies included to assess the pooled prevalence of iodine deficiency and associated factors among school-age children in Ethiopia</p>
An evidence map of research assessing the effects of timber harvesting on water quality, biotic and biodiversity indicators in running waters: Data and R code
<p>These are the data and R code that accompany the Forest Ecology and Management publication titled "An evidence map of research assessing the effects of timber harvesting on water quality, biotic and biodiversity indicators in running waters". </p>
Replication Package for "Automating Code Review: Using Deep Learning to Assess the Quality of Code Contributions and Recommend Changes"
<p>Replication Package for "Automating Code Review: Using Deep Learning to Assess the Quality of Code Contributions and Recommend Changes"</p>
Quality-assessed Antarctic coastline products
<p>Radarsat-1 coastline products are quality-assessed and validated in this product. </p>
Dataset - No Reference Image Quality assessment Scores for Humanities Online Repositories
<p>The dataset contains data on No-Reference Image Quality Assessment (NR-IQA) scores for online repositories in the humanities.</p>
Case report for Assessment and impact in quality-of-life post radiotherapy in breast cancer patients treated at Acharya Vinoba Bhave Rural Hospital (AVBRH), Sawangi, 2023 - 2024
<p>Case report for Assessment and impact in quality-of-life post radiotherapy in breast cancer patients treated at Acharya Vinoba Bhave Rural Hospital (AVBRH), Sawangi, 2023 - 2024</p>
QOL questionnaires for Assessment and impact in quality-of-life post radiotherapy in breast cancer patients treated at Acharya Vinoba Bhave Rural Hospital (AVBRH), Sawangi, 2023 - 2024
<p>QOL questionnaires for Assessment and impact in quality-of-life post radiotherapy in breast cancer patients treated at Acharya Vinoba Bhave Rural Hospital (AVBRH), Sawangi, 2023 - 2024</p>
Dataset for External quality assessment (EQA) of Neisseria gonorrhoeae antimicrobial susceptibility testing in primary laboratories in Germany
<p>This dataset is used for the publication "External quality assessment (EQA) of Neisseria gonorrhoeae antimicrobial susceptibility testing in primary laboratories in Germany" and contains data on performed Neisseria gonorrhoeae AMR tests in sentinel Laboratories.</p>
Experiment Data - 952 Assessments of 8 Vision Videos Regarding Overall Video Quality and 15 Individual Quality Characteristics
<p>In 2018, we conducted a within-subjects experiment to investigate how individual quality characteristics of vision videos relate to the overall quality of vision videos from a developer's point of view. 139 undergraduate students who had the role of a developer and actively developed software in projects with real customers at the time of the experiment participated in the experiment. The subjects can be considered as developers due to their experience at the time of the experiment. The subjects were put in the situation that they join an ongoing project in their familiar role as a developer. In this context, we showed the 8 vision videos (one after the other) always with the intent to share the vision of the particular project with the subjects. The undergraduate students subjectively assessed the overall quality and 15 individual quality characteristics of the 8 vision videos by completing an assessment form for each video. After data cleaning, the final data set contains 952 complete assessments of 119 subjects for the 8 vision videos.</p> <p>Each entry of the data set consists of:</p> <ul> <li>Entry ID: The ID of the entry in the dataset.</li> <li>Subject ID: The ID of the subject.</li> <li>Video ID: The ID of the vision video assessed.</li> <li>Overall quality: The subject's assessment of the overall quality of the vision video.</li> <li>Image quality: The subject's assessment of the visual quality of the image of the vision video.</li> <li>Sound quality: The subject's assessment of the auditory quality of the sound of the vision video.</li> <li>Video length [s]: The duration of the vision video in seconds.</li> <li>Focus: The subject's assessment of the compact representation of the vision which is presented in the vision video.</li> <li>Plot: The subject's assessment of the structured presentation of the content of the vision video.</li> <li>Prior knowledge: The subject's assessment of the presupposed prior knowledge to understand the content of the vision video.</li> <li>Clarity: The subject's assessment of the intelligibility of the aspired goals of the vision which is presented in the vision video.</li> <li>Essence: The subject's assessment of the amount of important core elements, e.g., persons, locations, and entities, which are to be presented in the vision video.</li> <li>Clutter: The subject's assessment of the amount of disrupting and distracting elements, e.g., background actions or noises, that can be inadvertently recorded in the vision video.</li> <li>Completeness: The subject's assessment of the coverage of the three contents of a vision which is presented in the vision video, i.e., the considered problem, the proposed solution, and the improvement of the problem due to the solution.</li> <li>Pleasure: The subject's assessment of the enjoyment of watching the vision video.</li> <li>Intention: The subject's assessment of how well the vision video is suitable for the intended purpose of the given scenario.</li> <li>Sense of responsibility: The subject's assessment of the compliance of the vision video with legal regulations.</li> <li>Support: The subject's assessment of his or her level of acceptance of the vision which is presented in the vision video.</li> <li>Stability: The subject's assessment of the consistency of the vision which is presented in the vision video.</li> </ul> <p>This dataset includes the following files:</p> <ul> <li>"Dataset_Assessments.xlsx" contains the anonymized 952 assessments of the 119 subjects for the 8 vision videos</li> <li>"Assessment_form.docx" contains the assessment form which was used to assess each of the 8 vision videos</li> <li>"Assessment_form.pdf" contains the assessment form which was used to assess each of the 8 vision videos</li> </ul> <p>The 8 vision videos are not included in this dataset since we do not have the explicit consent of the actors to distribute the vision videos.</p> <p>This experiment was designed, conducted, and analyzed by Oliver Karras (<a href="https://twitter.com/KarrasOliver">@KarrasOliver</a>), Kurt Schneider, and Samuel A. Fricker (<a href="https://twitter.com/samuelfricker">@samuelfricker</a>).</p>
QAVA-DPC: Eye-Tracking Based Quality Assessment and Visual Attention Dataset for Dynamic Point Cloud in 6 DoF
Open the record for dataset details and reuse information.
Translanguaging experimental research: list of eligible studies and quality assessment data
Open the record for dataset details and reuse information.
Quality Assessment of YUNYAO GNSS-RO Refractivity Data in the Neutral Atmosphere
Open the record for dataset details and reuse information.
Towards the Migration from JavaScript to TypeScript: Strategies and Quality Assessment
<p>Data used for the paper: "Towards the Migration from JavaScript to TypeScript: Strategies and Quality Assessment"</p>
High quality figures of "Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations"
<p>This repository provides the figures for the publication "Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations" in their original resolution, ensuring clarity the high-quality visual representations for readers.</p>
Data from: Measuring couple relationship quality in a rural African population: validation of a couple functionality assessment tool in Malawi
Available data suggest that individual and family well-being are linked to the quality of women's and men's couple relationships, but few tools exist to assess couple relationship functioning in low and middle-income countries. In response to this gap, Catholic Relief Services has developed a Couple Functionality Assessment Tool (CFAT) to capture valid and reliable data on various domains of relationship quality. This tool is designed to be used by interventions which aim to improve couple and family well-being as a means of measuring the effectiveness of these interventions, particularly related to couple relationship quality. We carried out a validation study of the CFAT among 401 married and cohabiting adults (203 women and 198 men) in rural Chikhwawa District, Malawi. Using psychometric scales, the CFAT addressed six domains of couple relationship quality (intimacy, partner support, sexual satisfaction, gender roles, decision-making, and communication and conflict management), and included questions on intimate partner violence. We used exploratory factor analysis to assess scale performance of each domain and produce a shortened Relationship Quality Index (RQI) composed of items from five relationship quality domains. This article reports the performance of the RQI. Internal reliability and validity of the RQI were found to be good. Regression analyses examined the relationship of the RQI to outcomes important to health and development: intra-household cooperation, positive health behaviors, intimate partner violence, and gender-equitable norms. We found many significant correlations between RQI scores and these couple- and family-level development issues. There is a need to further validate the tool with use in other populations as well as to continue to explore whether the observed linkages between couple functionality and development outcomes are causal relationships.
How Tertiary Studies perform Quality Assessment of Secondary Studies in Software Engineering - Replication Package
<p>Replication Package for the paper:</p> <p>D. Costal, C. Farré, X. Franch, C. Quer. 2021. How Tertiary Studies perform Quality Assessment of Secondary Studies in Software Engineering. CIbSE 2021.</p> <p>Please refer to the above paper if you want to cite/use this data.</p>
Supplementary Material for: 'An impedance pneumography signal quality index: design, assessment and application to respiratory rate monitoring'
<p>This supplementary material accompanies:</p> <p>Charlton P.H. <em>et al.</em>, "<a href="https://doi.org/10.1016/j.bspc.2020.102339">An impedance pneumography signal quality index for respiratory rate monitoring: design, assessment and application</a>", <em>Biomedical Signal Processing and Control</em>, 65, 102339, 2021.</p> <p>The Impedance Pneumography Signal Quality Index (SQI) dataset and accompanying scripts (in Matlab format) are provided to facilitate reproduction of the analyses using data from the MIMIC III dataset in this publication.</p> <p><strong>Summary of Publication</strong></p> <p>In this article we developed and assessed the performance of a signal quality index (SQI) for the impedance pneumography signal.<br> The SQI was developed using data from the <a href="http://peterhcharlton.github.io/RRest/listen_dataset.html">Listen dataset</a>, and assessed using data from the <a href="http://peterhcharlton.github.io/RRest/listen_dataset.html">Listen dataset</a> and MIMIC III datasets.<br> The SQI was found to accurately classify segments of impedance pneumography signal as either high or low quality. Furthermore, when it was coupled with a high performance RR algorithm, highly accurate and precise RRs were estimated from those segments deemed to be high quality. In this study performance was assessed in the critical care environment - further work is required to deteremine whether the SQI is suitable for use with wearable sensors. Both the dataset and code used to perform this study are publicly available.</p> <p><strong>Reproducing this Publication</strong></p> <p>The work relating to the MIMIC dataset in this publication can be reproduced as follows:</p> <p><strong> - Reproducing the analysis</strong><br> These steps can be used to quickly reproduce the analysis using the curated and annotated dataset.</p> <p>* Download the curated and annotated dataset from <a href="https://doi.org/10.5281/zenodo.3973770">Zenodo</a> using this <a href="https://zenodo.org/record/3973771/files/mimic_imp_sqi_data.mat?download=1">direct download link</a>.<br> * Run the analysis using the <a href="https://zenodo.org/record/3973771/files/run_imp_sqi_mimic.m?download=1"><em>run_imp_sqi_mimic.m</em></a> script.</p> <p><strong> - Full reproduction</strong><br> These steps include downloading the raw data files, extracting data from these files, collating the dataset, manually annotating the data, and performing the analysis.</p> <p>* Use the <a href="https://zenodo.org/record/3973771/files/ImP_SQI_mimic_data_importer.m?download=1"><em>ImP_SQI_mimic_data_importer.m</em></a> script to download raw MIMIC data files from PhysioNet, and collate them into a single Matlab file.<br> * Prepare the dataset for manual annotation by running the <a href="https://zenodo.org/record/3973771/files/run_imp_sqi_mimic.m?download=1"><em>run_imp_sqi_mimic.m</em></a> script.<br> * Manually annotate the signals by running the <a href="https://zenodo.org/record/3973771/files/run_imp_sqi_mimic.m?download=1"><em>run_mimic_imp_annotation.m</em></a> script - the annotations are stored in separate files (the original annotation files are available <a href="https://zenodo.org/record/3974113/files/2019_annotations.zip?download=1">here</a>).<br> * Import the manual annotations into the collated data file by re-running the <a href="https://zenodo.org/record/3973771/files/ImP_SQI_mimic_data_importer.m?download=1"><em>ImP_SQI_mimic_data_importer.m</em></a> script.<br> * Run <a href="https://zenodo.org/record/3973771/files/run_imp_sqi_mimic.m?download=1"><em>run_imp_sqi_mimic.m</em></a> to perform the analysis described in the publication.</p> <p><strong> - Submitted manuscript</strong></p> <p>The submitted manuscript is available <a href="https://zenodo.org/record/5211463/files/Impedance%20SQI%20manuscript%20-%20Oct%202020%20revision.docx?download=1">here</a>.</p> <p>The scripts are also stored (alongside details of how to use them) are available in the <a href="http://peterhcharlton.github.io/RRest/">RRest GitHub repository</a> at: <a href="https://github.com/peterhcharlton/RRest/tree/master/RRest_v3.0/Publication_Specific_Scripts/ImP_SQI">https://github.com/peterhcharlton/RRest/tree/master/RRest_v3.0/Publication_Specific_Scripts/ImP_SQI</a></p> <p>License: The dataset (<a href="https://zenodo.org/record/3974113/files/mimic_imp_sqi_data.mat?download=1"><em>mimic_imp_sqi_data.mat</em></a>) is distributed under the terms specified in the accompanying LICENSE file. The scripts are distributed under the GNU General Public Licence (as specified towards the start of each file).</p> <p>Version 1.0: This version includes the submitted manuscript.</p> <p> </p>
Assessing and predicting the quality of peer reviews: a text mining approach
<p>Dataset</p>
Processing steps to generate a Digital Surface Model based on SPOT-7 tri-stereo images published in the study "An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar inundation areas at volcan Copahue (Argentina & Chile)" in the Journal of South American Earth Sciences https://doi.org/10.1016/j.jsames.2022.104138
<p>The Digital Surface Model (DSM) was created from SPOT-7 tri-stereo images for the Copahue volcano between the border of Argentina and Chile. Two versions of the DSM are provided: an unfiltered product and a final, filtered product. The final product has a spatial resolution of 5-m and was used for lahar inundation modeling for the Copahue volcano (Viotto, Toyos, and Bookhagen 2022, <a href="https://doi.org/10.1016/j.jsames.2022.104138">https://doi.org/10.1016/j.jsames.2022.104138</a> : An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at Volcán Copahue (Argentina & Chile). <em>Journal of South American Earth Sciences</em> ). The dataset provided should be cited together with the article. </p> <p><strong>DSM processing </strong></p> <p>The source images were given by a SPOT-7 snow- and cloud-free triplet (Nadir, Backward and Forward) of 1.5 m spatial resolution from 19 April 2018 (SPOT Image, Airbus Defence and Space GmbH, distributed by CONAE; Dataset ID: <em>SEN_SPOT7_20180419_142955500_000</em>, delivered by CONAE as <em>DS_SPOT7_20180419</em>).</p> <p>The data were processed with the suite of digital photogrammetry tools AMES Stereo Pipeline ASP (Beyer et al., 2018). The procedure for the generation of the DSM is summarized by following steps: </p> <ol> <li> <p>The orbital parameters (RCP models) were adjusted using the bundle adjustment tool with no ground control points, since they were unavailable.</p> </li> <li> <p>The scenes were map-projected onto the NASADEM (spatial resolution of 30 m) elevation dataset, assisted by the results of the orbital adjustment in Step 1.</p> </li> <li>The stereo correlation of the map-projected scenes including the results of the adjusted orbital parameters, was performed three times, using as first scene (i.e., primary image) the nadir (N), backward (B), and forward (F) images . In each run, the order of images to perform the stereo correlation was: N-F-B, F-N-B, and B-N-F. Thus, three point clouds were generated. Specific ASP correlator settings (other than defaults parameters; for details see the provided stereo-default file) were set in the following way: <em>Correlation Kernel</em>: 15 x 15 pixels; <em>Sub-pixel Refinement Kernel</em>: 21 x 21 pixels; <em>Subpixel Refinement Mode</em>: 2 (Weighted Affine Adaptive Window Correlator EM)</li> <li> <p>The three point clouds were merged into one point cloud with a regular grid of 5 m (unfiltered product, known as <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em>).</p> </li> </ol> <p>The quality of the final point cloud was assessed by comparing the unfiltered DSM with a spatial resolution of 12-m against the WorldDEM<sup>TM</sup> elevation dataset (Collins et al., 2015). The WorldDEM was provided by Airbus Defence and Space GmbH under license for the scope of the Viotto et al., 2022 study. The comparison of the pixel-to-pixel heights above the ellipsoid (WGS84) between the two datasets resulted in a mean difference of 0.67 m and a standard deviation of +/- 4.82 m. </p> <p>Comprehensive details on the methodologies evaluated to create the dataset with ASP, can be found in the corresponding master's thesis “Topografía digital y modelado de lahares en el Volcán Copahue, Argentina-Chile” from S. Viotto (link: https://rdu.unc.edu.ar/handle/11086/15384). Recommended literature about processing DEMs from SPOT imagery is given by Mueting et al., 2021 (<a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330</a>). </p> <p><strong>Creation of the Final, Filtered DSM product</strong></p> <p>The corrections and improvements applied to the unfiltered product to create the final, filtered DSM (named DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif) are summarized by following steps. </p> <p> </p> <ol> <li> <p><em>Water Bodies Delineation</em></p> </li> </ol> <p>The delineation of the water bodies was based on a mask created from the free access water bodies datasets provided by the Instituto Geográfico Nacional of Argentina (<a href="https://www.ign.gob.ar/NuestrasActividades/InformacionGeoespacial/CapasSIG">https://www.ign.gob.ar/ NuestrasActividades/InformacionGeoespacia l/CapasSIG</a>) and by the Ministerio de Bienes Nacionales in Chile ( <a href="https://www.ide.cl/index.php/aguas-continentales/item/1508-catastro-de-lagos">https://www.ide.cl/index.php /aguas-continentales/item/1508-catastro-de-lagos</a>). A total of 45 lakes within the area of interest were considered. Lakes with areas below or equal to 25 m2 were smoothed with a median filter in the last step. Lakes with areas above this threshold were filled in with a constant value and their borders were smoothed with a median filter to provide smooth shorelines.</p> <p><em>2 . Void Filling</em></p> <p>Voids (other than water bodies) were filled with the tool “Close Gaps” from Saga GIS software. </p> <p><em>3. Smoothing</em></p> <p>Finally, the elevation dataset was smoothed with a median filter using a 3 x 3 pixel window, excluding water bodies filled in the step 1. </p> <p><strong>Final Remarks and Suggestion</strong></p> <p>The quality assessment of the final version by visual inspection of the hillshades suggested an improvement of the signal to noise ratio. However, the void filling process may be improved.</p> <p><br> </p> <p><strong>Dataset Description</strong></p> <table align="center"> <caption> </caption> <tbody> <tr> <td>Digital Surface Models</td> <td> <p>No Data Value = -9999</p> <p>Format = float 32 bit</p> <p>File Format = GeoTiff</p> <p>Vertical Datum: WGS84</p> <p>Projection information: EPSG 32719 (UTM19S)</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>) </p> <p>Versions: </p> <ul> <li> <p>Unfiltered product: without corrections <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em></p> </li> <li> <p>Final, filtered product: smoothed and void filled <em>DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</em></p> </li> </ul> </td> </tr> <tr> <td>Water Bodies Mask</td> <td> <p>No Lake Value = 0</p> <p>Lakes Values = 1 to 45</p> <p>File Format= GeoTiff</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>)</p> <p>Projection information : EPSG 32719 (UTM19S)</p> <p><em>WB_mask_5m_UTM19S.tif</em></p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><strong>Repository structure</strong></p> <p>|__ 01_Scripts</p> <p> |+ run21_CopahueDSM_AMES_sviotto.sh</p> <p> |+ stereo.default</p> <p>|__ 02_DSMs</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_raw.tif</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</p> <p> |+ WB_mask_5m_UTM19S.tif</p> <p><strong>References</strong></p> <p>Beyer, R. A., Alexandrov, O., & McMichael, S. (2018). The Ames Stereo Pipeline: NASA's open source software for deriving and processing terrain data. <em>Earth and Space Science</em>, 5, 537– 548. <a href="https://doi.org/10.1029/2018EA000409">https://doi.org/10.1029/2018EA000409</a></p> <p>Collins, J., Riegler, G., Schrader, H., Tinz, M., 2015. Applying terrain and hydrological editing to TanDEM-X data to create a consumer-ready worlddem product. Int. Arch. Photogram. Rem. Sens. Spatial Inf. Sci. 40 (7), 1149. https://doi.org/10.5194/isprsarchives-XL-7-W3-1149-2015.</p> <p>Mueting, A., Bookhagen, B., & Strecker, M. R. (2021). Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina. <em>Journal of Geophysical Research: Earth Surface</em>, 126, e2021JF006330. <a href="https://doi.org/10.1029/2021JF006330">https://doi.org/10.1029/2021JF006330</a></p> <p>Viotto, S., Toyos, G., & Bookhagen, B. (2022). An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at volcán copahue (Argentina & Chile). Journal of South American Earth Sciences, 104138. https://doi.org/10.1016/j.jsames.2022.104138</p> <p> </p> <p> </p>
Data for: Assessment of quality of life (QoL) in cancer patients
<p><strong>Background</strong>: A cancer patient's quality of life (QoL) is the perception of their physical, functional, psychological, and social well-being as well as their mental and emotional state. QoL is one of the most important factors to consider when a person is being treated for cancer and during follow-up. The present study aimed to understand the status of QoL of cancer patients and determine the factors affecting it.</p> <p><strong>Methods</strong>: This cross-sectional study was conducted among 210 cancer patients attending the oncology unit of a medical college, within a 4-month consecutive time period in 2022. Data were collected by using the Bengali version of the European Organization for Research and Treatment of Cancer questionnaire.</p> <p><strong>Results</strong>: The present study reported a high number of female cancer patients (67.6%). Breast cancer was more common among females (31.43%) while lung and upper respiratory tract cancer was among males (19.05). Most of the patients in the present study were diagnosed with cancer in the past year (86.19%). The functional scales' overall mean scores varied from 54.92 for physical functioning to 38.89 for social functioning. The highest symptom scale score was for financial issues (63.02), while the lowest was for diarrhea (33.01). The overall QoL of cancer patients in the present study was 47.98 which was 45.71 for males and 49.10 for females respectively. </p> <p><strong>Conclusion</strong>: The overall QoL was poor in cancer patients in the present study compared to the developed countries. There was a low score for QoL for social and emotional function. Financial difficulty was the primary reason behind low QoL in the symptom scale. If the government supports cancer patients by providing subsidies for treatment and health insurance policies, cancer patients will benefit and QoL will improve.</p>
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.