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242 results for “Spatial Dataset”
GPRChinaSPEI1km: High spatial resolution and century-long SPEI datasets for China from 1901 to 2020 generated by machine learning
<p>The high spatial resolution and century-long Standardized Precipitation Evapotranspiration Index (SPEI) dataset with a spatial resolution of 0.0083 degrees (~1 km) was spatially downscaled from the global SPEI data with a 0.5 degrees spatial resolution (https://spei.csic.es/database.html) based on machine learning integrated with high spatial resolution climatic and topographic variables. The 1-km SPEI datasets are across the land areas of China from January 1901 to December 2020, including 1-month, 3-month, 6-month and 12-month SPEIs. The unit of the data is 0.01. The dataset was evaluated using the root zone soil moisture and the historical drought events, and the evaluation indicated that the high spatial resolution SPEI dataset is reliable.</p> <p>Data Information: </p> <p>GPRChinaSPEI1km: High spatial resolution and century-long SPEI datasets over China from 1901 to 2020 generated by machine learning</p> <p>Publication: </p> <p><span>He, Q., Wang, M., Liu, K., & Wang, B. (2025). High-resolution Standardized Precipitation Evapotranspiration Index (SPEI) reveals trends in drought and vegetation water availability in China. <em>Geography and Sustainability</em>, <em>6</em>(2), 100228. https://doi.org/10.1016/j.geosus.2024.08.007</span></p> <p></p> <p>----------------------------------------------------data description---------------------------------------------</p> <p>This is a gridded dataset for the Standardized Precipitation Evapotranspiration Index (SPEI) at a spatial resolution of 1 km over the main terrestrial lands of China for each month during 1901-2020, which is generated using the Gaussian process regression (GPR) based on the Global SPEI database (https://spei.csic.es/database.html) integrated with high spatial resolution climatic and topographic variables. Four timescales of SPEI were generated: 1-month (SPEI-1), 3-month (SPEI-3), 6-month (SPEI-6) and 12-month (SPEI-12). The details are as follows:</p> <p>Region: China</p> <p>Temporal Extent: January 1901 to December 2020</p> <p>Spatial resolution: 0.0083° (~1 km)</p> <p>Temporal resolution: month</p> <p>Timescales: 1-month, 3-month, 6-month and 12-month</p> <p>Data format: GeoTIFF</p> <p>Unit: unitless (0.01)</p> <p>Geographic coordinate system: WGS 1984</p> <p>---------------------------------------------------dataset filename---------------------------------------------</p> <p>The file name specifically shows the data information.</p> <p>For example,</p> <p>“SPEI_1_2020_1.tif” means “1-month SPEI of January 2020”.</p> <p>“SPEI_3_2020_1.tif” means “3-month SPEI of January 2020”.</p> <p>All the file names are formatted in “SPEI_timescale_year_month”</p> <p>timescale: 1, 3, 6 and 12 indicate 1-month, 3-month, 6-month and 12-month, respectively</p> <p>year: from 1901 to 2020</p> <p>month: from 1 to 12</p> <p>--------------------------------------------------storage information-------------------------------------------</p> <p>The high-resolution SPEI dataset is stored in TIFF format using WGS 1984 coordinate system. The data type is int16 with a scale factor of 0.01. The nodata value is -32768. The dataset requires multiplication by 0.01 during application to obtain the actual value ranges.</p> <p>The data were compressed into .rar format every 10 years for each timescale SPEI.</p>
Spatial transcriptomics images for papillary and anaplastic thyroid cancer IRIBHM dataset
<p>This dataset includes the image files for spatial transcriptomics data associated with the publication "Idiosyncratic and generic single nuclei and spatial transcriptional patterns in papillary and anaplastic thyroid cancers".</p>
CosMx Spatial transcriptome dataset of human gastric mucosa
<p>This dataset contains the spatial transcriptome dataset of human gastric mucosa obtained by CosMx</p> <p>A zip file contains the following folders and files:</p> <p><strong>Folders</strong></p> <p>- CellComposite folder: the composite immunofluorescent images of each FOV.</p> <p>- CellLabels folder: the cell definitions images for each FOV determined during cell segmentation.</p> <p>- CellOverlay folder: the cell boundary images for each FOV determined during cell segmentation.</p> <p>- CompartmentLabels folder: the subcellular compartment images for each FOV determined during cell segmentation. The compartment types are as follows: 0. Extracellular, 1. Nuclear, 2. Membrane, 3. Cytoplasmic</p> <p>- RawMorphologyImages folder: raw morphological TIF images for each FOV</p> <p> </p> <p><strong>Files</strong></p> <p>- Run5458_{sample_name}_exprMat_file.csv: cell expression matrix.</p> <p>- Run5458_{sample_name}_fov_positions_file.csv: each FOV relative position within global structure.</p> <p>- Run5458_{sample_name}_metadata_file.csv: the metadata of each cell.</p> <p>- Run5458_{sample_name}_tx_file.csv: the transcript file for each target gene and its position.</p> <p>- Run5458_{sample_name}-polygons.csv: the segmentation polygon file.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset for your research, please cite our paper.</p> <p>Ayumu Tsubosaka, Daisuke Komura, Miwako Kakiuchi, Hiroto Katoh, Takumi Onoyama, Asami Yamamoto, Hiroyuki Abe, Yasuyuki Seto, Tetsuo Ushiku, Shumpei Ishikawa, Stomach encyclopedia: Combined single-cell and spatial transcriptomics reveal cell diversity and homeostatic regulation of human stomach, Cell Reports, Volume 42, Issue 10, 2023, 113236, https://doi.org/10.1016/j.celrep.2023.113236.</p>
High-spatial-resolution monthly precipitation dataset over China during 1901–2017
<p>The dataset with 0.5 arcminute (~1 km) was spatially downscaled from CRU TS v4.02 based on Delta downscaling method, including monthly precipitation from 1901.1 to 2017.12. The dataset covers the main land area of China. The dataset was evaluated by 496 national weather stations across China, and the evaluation indicated that the downscaled dataset is reliable for the investigations related to climate change across China.</p> <p>Another data download site is Loess plateau Scientific Data Center (http://loess.geodata.cn/). This is a Chinese website. This website publishes the updated histrorical dataset and future downscaled monthly precipitation under multiple SSP Scenarios and GCMs, with 1 km spatial resolution.</p> <p>/*************/ The dataset is updated yearly. Now, the period of the dataset is from 1901.1 to 2020.12.</p> <p>/*************/ The future 1km dataset from 2021-2100 is published.</p> <p><br> The data provider recommended the below publication as the reference.<br> Peng Shouzhang, Ding Yongxia, Liu Wenzhao, Li Zhi. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017. Earth System Science Data, 2019, 11, 1931–1946, https://doi.org/10.5194/essd-11-1931-2019.</p>
Data from: Monitoring temporal and spatial trends of illegal and legal fishing in Canada's marine conservation areas using vessel tracking datasets
Open the record for dataset details and reuse information.
Dataset for: An optimisation approach for designing wildlife corridors with ecological and spatial considerations
Open the record for dataset details and reuse information.
Datasets of high spatial resolution scans of the airborne ultrasound field at the front, back and left side of an ultrasonic welding machine
<p>This dataset contains measuring data which are the result of investigations of the airborne ultrasound field of an ultrasonic welding machine. These investigations have been conducted within the scope of the EMPIR project 15HLT03: “Ears II - Metrology for modern hearing assessment and protecting public health from emerging noise sources”. In the context of “Ears II”, they served gaining knowledge for occupational safety and health. Here, the aim was to investigate the structure of the airborne ultrasound field at ultrasound related workplaces. Therefore, a reference workstation was set up in the laboratory of the Physikalisch-Technische Bundesanstalt (PTB). The airborne ultrasound field of a typical industrial source of airborne ultrasound (an ultrasonic welding machine) was measured with a scanning microphone system with a high spatial resolution.</p> <p>The dataset contains data of 6 measurements:</p> <ul> <li>At the back of the ultrasonic welding machine at a vertical surface with dimensions 168 cm x 150 cm (width x height)</li> <li>At the left side of the ultrasonic welding machine at a vertical surface with dimensions 168 cm x 150 cm (width x height)</li> <li>At the front of the ultrasonic welding machine at a horizontal surface with dimensions 168 cm x 75 cm (width x depth)</li> <li>At the front of the ultrasonic welding machine at a vertical surface with dimensions 168 cm x 150 cm (width x height) with a distance of 24 cm to the sonotrode</li> <li>At the front of the ultrasonic welding machine at a vertical surface with dimensions 168 cm x 150 cm (width x height) with a distance of 99 cm to the sonotrode</li> <li>At the front of the ultrasonic welding machine in a volume with dimensions 58 cm x 33 cm x 50 cm (width x depth x height) </li> </ul> <p>The measuring data is the square of the absolute value of the discrete Fourier transform of the signal voltage re 1 V<sup>2</sup> represented in the frequency domain, converted by fast Fourier transform, in the frequency range 0 – 100 kHz with a frequency resolution of 15.625 Hz. The data can be converted to sound pressure levels (SPL) using the calibration values published as a supplementary dataset.</p>
The Pacific lamprey genomic divergence, association mapping, temporal Willamette Falls, spatial rangewide datasets
<p>High rates of dispersal can breakdown coadapted gene complexes. However, concentrated genomic architecture (i.e., genomic islands of divergence) can suppress recombination to allow evolution of local adaptations despite high gene flow. Pacific lamprey (<em>Entosphenus tridentatus</em>) is a highly dispersive anadromous fish. Observed trait diversity and evidence for genetic basis of traits suggests it may be locally adapted. We addressed whether concentrated genomic architecture could influence local adaptation for Pacific lamprey. Using two new whole genome assemblies and genotypes from 7,716 single nucleotide polymorphism (SNP) loci in 518 individuals from across the species range, we identified four genomic islands of divergence (on chromosomes 01, 02, 04, and 22). We determined robust phenotype-by-genotype relationships by testing multiple traits across geographic sites. These trait associations likely explain genomic divergence across the species' range. We genotyped a subset of 302 broadly distributed SNPs in 2,145 individuals for association testing for adult body size, sexual maturity, migration distance and timing, adult swimming ability, and larval growth. Body size traits were strongly associated with SNPs on chromosomes 02 and 04. Moderate associations also implicated SNPs on chromosome 01 as being associated with variation in female maturity. Finally, we used candidate SNPs to extrapolate a heterogeneous spatiotemporal distribution of these predicted phenotypes based on independent datasets of larval and adult collections. These maturity and body size results guide future elucidation of factors driving regional optimization of these traits for fitness. Pacific lamprey is culturally important and imperiled. This research addresses biological uncertainties that challenge restoration efforts.</p>
Dataset for Angular Dependence and Spatial Distribution of Jupiter's Centimeter-Wave Thermal Emission from Juno's Microwave Radiometer
<p>This dataset comprises all processed data (in HDF5 format) used in figures and discussion in the paper "Angular Dependence and Spatial Distribution of Jupiter's Centimeter-Wave Thermal Emission from Juno's Microwave Radiometer".</p>
Dataset for "Sorption, anomalous water transport and dynamic porosity in cement paste: A spatially localised 1H NMR relaxation study and a proposed mechanism"
<p>This record is the dataset for Figures 4, 5, 6, 7, 8 and 9 in the journal article "Sorption, anomalous water transport and dynamic porosity in cement paste: A spatially localised 1H NMR relaxation study and a proposed mechanism" published in Cement and Concrete Research, Volume 133, July 2020, 106045, https://doi.org/10.1016/j.cemconres.2020.106045.</p> <p>Abstract: The link between anomalous water sorption and dynamic porosity in cement pastes is explored using spatially resolved GARField1H nuclear magnetic resonance (NMR) relaxation analysis. A model is developed in which the effective capillary diffusion coefficient is dependent on the instantaneous pore size distribution. This and earlier data show changes in pore size distribution resultant from changes in saturation that do not occur instantaneously with changes in degree of saturation. Therefore, it is assumed that the pore size distribution is always relaxing exponentially towards a (saturation dependent) equilibrium. It follows that the diffusivity is sample history (i.e. time) dependent as well as saturation dependent. This is sufficient to explain anomalies in rapid capillary water sorption. The same concepts are applied to slow drying. In this case, porosity changes occur on a timescale much shorter than drying so the system is always in dynamic equilibrium and anomalies are therefore not seen.</p>
Integrated spatial genomics dataset
<p>Supplementary processed data. The raw microscopy data are not uploaded owing to their large size (2.9 Tb), but are available upon reasonable request (Long Cai: lcai@caltech.edu).</p> <p>These supplementary processed data contain additional files for RNA seqFISH, DNA seqFISH+ and sequential immunofluorescence.</p> <p>DNAseqFISH+.zip contains DNA seqFISH+ pixel locations for 3,600 loci in single cells (pixel sizes are 103 nm for x and y, and 250 nm for z). Please refer to Supplementary Table 1 at the journal website for genomic coordinates of the targeted loci.</p> <p>RNA seqFISH.zip contains RNA seqFISH expression profiles for mRNAs and introns in single cells. Intron data also contain pixel coordinates for intron spot locations. Cells were filtered out if keep1 column is 0 due to the laser illumination bias.</p> <p>Excel file contains additional information for sequential immunofluorescence experiments. The file contains target names with corresponding hybridization cycle round and fluorescent channel information per each experiment.</p> <p>Zip files for IF binned data contain IF intensity information in each pixel coordinate (either bin1 or bin2). Please refer to the included excel file to match each target to each hybridization and fluorescent channel.</p>
A spatial dataset of forest mensuration collected in black pine plantations in central Italy
<p>This dataset has been developed during the A2 Action of the SelPiBio LIFE project (www.selpibio.eu). The main aim of this project is demonstrate the effects of two thinning regimes, selective and from below, on soil biodiversity in young black pine stands.<br> To evaluate the effect of thinning on analysed pinewoods, eighteen monitoring areas of 1 ha each were designed and realized across two study areas in Tuscany (Central Italy), the Pratomagno mountain chain and the Monte Amiata. In each study area, 27 circular plots of 15 metres of radius were geo-referenced (3 for each monitoring area). All the included trees were measured and data were collected between 2015 and early 2016 to characterize the horizontal and vertical structure of studied forests.</p>
Characterizing Spatially Continuous Variations in Tissue Microenvironment through Niche Trajectory Analysis - Dataset
<p><span>Recent technological developments have made it possible to map the spatial organization of a tissue at the single-cell resolution. However, computational methods for analyzing spatially continuous variations in tissue microenvironment are still lacking. Here we present ONTraC as a strategy that constructs niche trajectories using a graph neural network-based modeling framework. Our benchmark analysis shows that ONTraC performs more favorably than existing methods for reconstructing spatial trajectories. Applications of ONTraC to public spatial transcriptomics datasets successfully recapitulated the underlying anatomical structure, and further enabled detection of tissue microenvironment-dependent changes in gene regulatory networks and cell-cell interaction activities during embryonic development. Taken together, ONTraC provides a useful and generally applicable tool for the systematic characterization of the structural and functional organization of tissue microenvironments.</span></p>
Spatially Variant Super-Resolution (SVSR) benchmarking dataset
<p>The Spatially Variant Super-Resolution (SVSR) benchmarking dataset contains 1119 real low-resolution images that are degraded by complex noise of varying intensity and type and their corresponding real noise free X2 and X4 high-resolution counterparts, for evaluation of the robustness of real-world super-resolution methods. Additionally, the dataset is also suitable for evaluation of denoisers. The associated paper can be found here: <a title="https://openaccess.thecvf.com/content/WACV2024/papers/Aakerberg_PDA-RWSR_Pixel-Wise_Degradation_Adaptive_Real-World_Super-Resolution_WACV_2024_paper.pdf" href="https://openaccess.thecvf.com/content/WACV2024/papers/Aakerberg_PDA-RWSR_Pixel-Wise_Degradation_Adaptive_Real-World_Super-Resolution_WACV_2024_paper.pdf">https://openaccess.thecvf.com/content/WACV2024/papers/Aakerberg_PDA-RWSR_Pixel-Wise_Degradation_Adaptive_Real-World_Super-Resolution_WACV_2024_paper.pdf</a></p>
TroDSIF: an improved spatially downscaled solar-induced chlorophyll fluorescence product of TROPOMI dataset
<p>TroDSIF is an improved spatially downscaled solar-induced chlorophyll fluorescence product of TROPOMI dataset at far-red band (wavelength at 740 nm), with a spatial resolution of 500 m and a temporal resolution of 16 days under clear-sky condition.<br>The original TROPOMI SIF was retrieved by Guanter et al., which was available at https://doi.org/10.5270/esa-s5p_innovation-sif-20180501_20210320-v2.1-202104.</p>
Dataset for "Enhancing Cloud Detection in Sentinel-2 Imagery: A Spatial-Temporal Approach and Dataset"
<p>This dataset is built for time-series Sentinel-2 cloud detection and stored in Tensorflow TFRecord (refer to https://www.tensorflow.org/tutorials/load_data/tfrecord).</p> <p>Each file is compressed in 7z format and can be decompressed using Bandzip or 7-zip software.</p> <p><strong>Dataset </strong><strong> </strong><strong>Structure</strong>:</p> <p>Each filename can be split into three parts using underscores. The first part indicates whether it is designated for training or validation ('train' or 'val'); the second part indicates the Sentinel-2 tile name, and the last part indicates the number of samples in this file.</p> <p>For each sample, it includes:</p> <ol> <li>Sample ID;</li> <li>Array of time series 4 band image patches in 10m resolution, shaped as (n_timestamps, 4, 42, 42);</li> <li>Label list indicating cloud cover status for the center \(6\times6\) pixels of each timestamp;</li> <li>Ordinal list for each timestamp;</li> <li>Sample weight list (reserved);</li> </ol> <p>Here is a demonstration function for parsing the TFRecord file:</p> <pre><code>import tensorflow as tf # init Tensorflow Dataset from file name def parseRecordDirect(fname): sep = '/' parts = tf.strings.split(fname,sep) tn = tf.strings.split(parts[-1],sep='_')[-2] nn = tf.strings.to_number(tf.strings.split(parts[-1],sep='_')[-1],tf.dtypes.int64) t = tf.data.Dataset.from_tensors(tn).repeat().take(nn) t1 = tf.data.TFRecordDataset(fname) ds = tf.data.Dataset.zip((t, t1)) return ds keys_to_features_direct = { 'localid': tf.io.FixedLenFeature([], tf.int64, -1), 'image_raw_ldseries': tf.io.FixedLenFeature((), tf.string, ''), 'labels': tf.io.FixedLenFeature((), tf.string, ''), 'dates': tf.io.FixedLenFeature((), tf.string, ''), 'weights': tf.io.FixedLenFeature((), tf.string, '') } # The Decoder (Optional) class SeriesClassificationDirectDecorder(decoder.Decoder): """A tf.Example decoder for tfds classification datasets.""" def __init__(self) -> None: super().__init__() def decode(self, tid, ds): parsed = tf.io.parse_single_example(ds, keys_to_features_direct) encoded = parsed['image_raw_ldseries'] labels_encoded = parsed['labels'] decoded = tf.io.decode_raw(encoded, tf.uint16) label = tf.io.decode_raw(labels_encoded, tf.int8) dates = tf.io.decode_raw(parsed['dates'], tf.int64) weight = tf.io.decode_raw(parsed['weights'], tf.float32) decoded = tf.reshape(decoded,[-1,4,42,42]) sample_dict = { 'tid': tid, # tile ID 'dates': dates, # Date list 'localid': parsed['localid'], # sample ID 'imgs': decoded, # image array 'labels': label, # label list 'weights': weight } return sample_dict # simple function def preprocessDirect(tid, record): parsed = tf.io.parse_single_example(record, keys_to_features_direct) encoded = parsed['image_raw_ldseries'] labels_encoded = parsed['labels'] decoded = tf.io.decode_raw(encoded, tf.uint16) label = tf.io.decode_raw(labels_encoded, tf.int8) dates = tf.io.decode_raw(parsed['dates'], tf.int64) weight = tf.io.decode_raw(parsed['weights'], tf.float32) decoded = tf.reshape(decoded,[-1,4,42,42]) return tid, dates, parsed['localid'], decoded, label, weight t1 = parseRecordDirect('filename here') dataset = t1.map(preprocessDirect, num_parallel_calls=tf.data.experimental.AUTOTUNE) # </code></pre> <p><strong>Class Definition:</strong></p> <ul> <li>0: clear</li> <li>1: opaque cloud</li> <li>2: thin cloud</li> <li>3: haze</li> <li>4: cloud shadow</li> <li>5: snow</li> </ul> <p><strong>Dataset Construction:</strong></p> <p>First, we randomly generate 500 points for each tile, and all these points are aligned to the pixel grid center of the subdatasets in 60m resolution (eg. B10) for consistence when comparing with other products. <br>It is because that other cloud detection method may use the cirrus band as features, which is in 60m resolution. </p> <p>Then, the time series image patches of two shapes are cropped with each point as the center.<br>The patches of shape \(42 \times 42\) are cropped from the bands in 10m resolution (B2, B3, B4, B8) and are used to construct this dataset.<br>And the patches of shape \(348 \times 348\) are cropped from the True Colour Image (TCI, details see sentinel-2 user guide) file and are used to interpreting class labels.</p> <p>The samples with a large number of timestamps could be time-consuming in the IO stage, thus the time series patches are divided into different groups with timestamps not exceeding 100 for every group.</p>
Transcriptomic and spatial datasets of human ex vivo right atrial tissue in ischemic heart disease and heart failure
<p>This dataset contains raw counts and processed data and annotations for our transcriptomic and spatial dissection of human ex vivo right atrial tissue in ischemic heart disease and heart failure.</p> <p> </p> <p>snRNA.zip contains the snRNA-seq dataset for heart right atrial appendade and pericardial fluid.</p> <p>VISIUM.zip contains the Visium spatial transcriptomics data</p> <p>Molecular cartography.zip contains the Resolve Biosciences molecular cartography spatial transcriptomics data</p>
Temporal and spatial heterogeneity of atmospheric environmental and meteorological field in the urban street canyon——dataset
<p>Here are the datasets related to the article, "Temporal and spatial heterogeneity of atmospheric environmental and meteorological field in the urban street canyon". The datasets contain two part:</p> <ol> <li>The observation data and the analysis processing scripts (Folder observation).</li> <li>The data used for figures (Folder Fig_data).</li> </ol> <p>The CFD simulation configuration files can be found at https://doi.org/10.5281/zenodo.10841653.</p>
Processed datasets used in Almet et al. (2024), "Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics"
<p>These are the processed anndata objects used in Almet et al. (2024), "Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics".</p> <p>These datasets are stored as .h5ad files and are intended to be used with the <a href="https://scanpy.readthedocs.io/en/stable/api.html">Scanpy</a> package in Python. They contain all relevant cell type annotation, unnormalized and transformed gene expression counts, as well as the inferred intercellular fow networks inferred by FlowSig.</p>
Dataset for Can we predict kick force based solely on spatial-temporal variables? Applying long short-term memory model for predicting force values of turning and side kick of taekwon-do athletes
<p>This data set is created for a purpose of publication "<span>Can we predict kick force based solely on spatial-temporal variables? Applying long short-term memory model for predicting force values of turning and side kick of taekwon-do athletes". It contains of dataset of kicks and lstm models for predictions a force of kicks upon IMU data. Detailed description of file names are in readme file. Folders are divided into specific kicks - turning or side kick in sport or traditional versions.</span></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.