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13,021 results for “locality”
MetFrag Local CSV: CompTox (7 March 2019 release) Wastewater MetaData File
<p>This is the CSV file that can be used as a local database in MetFrag (<a href="https://msbi.ipb-halle.de/MetFrag/">https://msbi.ipb-halle.de/MetFrag/</a>), for those who wish to integrate this into the command line version.</p> <p>Note that this file is TOO LARGE to be uploaded via the web interface, this is already integrated in the web interface.</p> <p>This file is based off the "SelectMetaData" CompTox MetFrag file from the 7 March 2019 release, available from:</p> <p>ftp://newftp.epa.gov/COMPTOX/Sustainable_Chemistry_Data/Chemistry_Dashboard/MetFrag_metadata_files</p> <p>The Wastewater MetaData file contains the following fields, in addition to the regular (basic) CompTox data fields:</p> <p>Suspect Lists (1=presence, 0=absence):</p> <p>- ITNANTIBIOTIC, STOFFIDENT, REACH2017, ZINC15PHARMA and PFASMASTER</p> <p>Suspect Lists with scores from KEMI (see details on <a href="https://www.norman-network.com/nds/SLE/">NORMAN-SLE</a> and hyperlinks below):</p> <p>- <a href="https://zenodo.org/record/2628787">KEMIMARKET_EXPO</a>, <a href="https://zenodo.org/record/2628787">KEMIMARKET_HAZ</a>, <a href="https://zenodo.org/record/2653567">KEMIWW_WDUIndex</a>, <a href="https://zenodo.org/record/2653567">KEMIWW_StpSE</a>, <a href="https://zenodo.org/record/2653567">KEMIWW_SEHitsOverDL</a></p>
TDA4ContextualEmbeddings - Public - Debug Data for the codebase of the publication "Local Topology Measures of Contextual Language Model Latent Spaces With Applications to Dialogue Term Extraction"
<p>Debug dataset for testing the <a href="https://gitlab.cs.uni-duesseldorf.de/general/dsml/tda4contextualembeddings-public">codebase</a> of the paper <a href="https://doi.org/10.18653/v1/2024.sigdial-1.31">“Local Topology Measures of Contextual Language Model Latent Spaces With Applications to Dialogue Term Extraction”</a> published at the 25th Meeting of the Special Interest Group on Discourse and Dialogue, Kyoto, Japan (SIGDIAL 2024).</p>
Local Governance in Ukraine during the full-scale Russian invasion. – Merged data from online surveys of local self-government authorities by the Congress of Local and Regional Authorities of the Council of Europe in 2022 and Kyiv School of Economics in 2024.
The dataset includes responses from two waves of online surveys targeting local self-government representatives in Ukraine, with a focus on crisis governance during the ongoing Russian war. The first wave was conducted from August 30 to September 20, 2022, by the Congress of Local and Regional Authorities of the Council of Europe, yielding 241 responses (16% of all Ukrainian local communities). The second wave was conducted by Kyiv School of Economics from January 1 to March 12, 2024, with 181 responses (14% of government-controlled municipalities). Data formats include CSV and SAV files, along with an XSL codebook for both waves. The merged dataset comprises 442 responses from small, medium, and large municipalities under varied security conditions, with a total file size of approximately 4 MB.
Probing Aqueous Ions with Non-local Auger Relaxation - data
<p>Data set pertaining to the article "Probing aqueous ions with non-local Auger relaxation" | Physical Chemistry Chemical Physics, <strong>24</strong>, 8661-8671 (2022). doi: <a href="http://dx.doi.org/10.1039/D2CP00227B">10.1039/D2CP00227B</a>.</p> <p>Files with extension .h5 are hdf5-files structured according to the NeXus standard v2022.06, see<br> https://www.nexusformat.org/<br> https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html<br> NeXus data files can be opened with any software capable of opening hdf5-files. The following viewers are adapted to the specifics of the NeXus data format:<br> * nexpy (distributed with python)<br> * https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)</p> <p>In each NeXus file-entry, two types of spectra are shown:<br> 1. Sweep-averaged spectra integrated over the non-dispersive coordinate of our detector ('data').<br> 2. As-measured data ('raw').</p> <p><br> The following files are provided:</p> <p>Photoemission data pertaining to ICD measurements, and to 1s spectra shown in Supplementary Fig. S2 (Na, Al):<br> ICD_data.na.h5<br> ICD_data.mg.h5<br> ICD_data.al.h5<br> Photon energy corrections are applied as explained in the article and Supplementary Material, kinetic energy correction is applied to the dataset 'data'.</p> <p>Calibration data:<br> calibration_data.p04.h5 : Mostly photon energy calibration for ICD spectra.<br> calibration_data.bessy.mg.h5 : Spectra measured at BESSY for MgCl2 Mg 1s binding energy calibration.<br> calibration_data.bessy.al.h5 : Spectra measured at BESSY for AlCl3 Al 1s binding energy calibration.<br> calibration_data.p04.add.h5 : Additional spectra for cross-checking binding energy calibration, measured at DESY P04.<br> All calibration spectra are included as-measured. A binding energy axis, shown for some spectra, is derived as implied from the uncalibrated photon and kinetic energies.</p> <p> </p> <p>Contact: Uwe Hergenhahn, uhe@fhi.mpg.de .</p> <p>v2 release notes<br> A number of minor errors in the metadata and .hdf5-structure of the v1 dataset were corrected. The data themselves are unaffected.<br> * Names of NXdata-groups now agree to NXmpes naming-convention,<br> * incorrect value of photon energy correction of Al ICD data fixed (ICD_data.al.h5),<br> * proposal numbers added to metadata,<br> * measurements on pure water solution designated as calibration.</p>
TBPos: Dataset for Large-Scale Precision Visual Localization (database files)
<p>Large-scale dataset for visual localization, provided in the format of the well-known InLoc dataset (Taira et al, 2018). Contains co-registered RGB point clouds and a script for generating the rest of the 'database' files for visual localization by the InLoc algorithm. Note: query images are provided in a separate repository.</p>
Super-Resolved FRET Imaging by Confocal Fluorescence-Lifetime Single-Molecule Localization Microscopy
<p>FRET-based methods are a special tool for detecting interactions between (bio)molecules and their immediate environment. The spatial distribution of molecular interactions and functional states can be seen using FLIM (Fluorescence Lifetime IMaging) and FRET imaging. The spatial information, accuracy, and dynamic range of the observed signals are, however, constrained by the fact that conventional FLIM and FRET imaging only provides average information over an ensemble of molecules within a diffraction-limited volume. On the other hand, conventional Single Molecule Localization Microscopy (SMLM) relies on highly sensitive multi-pixel detectors (e.g. sCMOS or EM-CCD) whose time resolution is not suitable for fluorescence lifetime measurements.</p> <p>Here, we demonstrate a method for obtaining super-resolved FRET imaging using confocal fluorescence-lifetime single-molecule localization microscopy. The proof of concept was carried out using a DNA origami sample for performing DNA-PAINT measurements in combination with fluorogenic probes for reducing background signal. With this method, We show that FRET events separated by sub-diffraction distances can be distinguished based on lifetime modifications.</p>
Transparency in agricultural land lease by local government
<p>In this research, the focus was on analysing transparency aspect of government and public administration, i.e. how transparent tenders for the allocation and disposition of state-owned agricultural land are conducted. The main objective of this work was to investigate and critically examine the practices of publishing tenders for the lease of state agricultural land in the local units of six selected counties in the Republic of Croatia.</p>
FRUC multiple sensor forest dataset including absolute, map-referenced localization
<p><strong>FRUC Datasets (Forest environment dataset)</strong></p> <p>This dataset was collected as part of the work conducted by the Forestry Robotics @ University of Coimbra team (<a href="https://www.youtube.com/@forestryroboticsuc">https://www.youtube.com/@forestryroboticsuc</a>; part of the Institute of Systems and Robotics, <a href="https://www.isr.uc.pt/">https://www.isr.uc.pt/</a>) within the scope of the Safety, Exploration and Maintenance of Forests with Ecological Robotics (SEMFIRE, ref. CENTRO-01-0247-FEDER-03269; <a href="http://semfire.ingeniarius.pt/">http://semfire.ingeniarius.pt/</a>) and the Semi-Autonomous Robotic System for Forest Cleaning and Fire Prevention (SafeForest, CENTRO-01-0247-FEDER-045931) research projects. Its purpose is to allow researchers in forestry robotics to have an in-depth analysis of a florests environment; obtain an a priori map for robot operations (e.g. path plannning, landscaping, etc…) and to train segmentation algorithms;</p> <p> </p> <p>The dataset in question includes data from multiple sensors and absolute, map-referenced localization which can be used to register the sensor data to a fixed coordinate system. It was collected at the <a href="https://www.google.com/maps/place/Mata+Nacional+do+Choupal/@40.2208522,-8.4429989,842m/data=!3m1!1e3!4m6!3m5!1s0xd22f91d7cec3b95:0xb02aedc4d8380d48!8m2!3d40.2222536!4d-8.4438944!16s%2Fm%2F026jw89?hl=pt-PT">Choupal National Woods, Coimbra, Portugal</a> (40<sup>◦</sup>13′13.3′′N;8<sup>◦</sup>26′38.1′′W). The dataset was collected during a partly clouded day in a forest environment by performing <strong>two circular loop</strong> laps amounting to a total distance of approximately <strong>800m,</strong> with a total duration of <strong>14 minutes and 22 seconds</strong>. The scenario is rich in features relevant to forestry robotics applications, including trees, bushes, tree trunks, etc. To better handle the multimodal nature of the acquired data, the dataset is bundled into <a href="http://wiki.ros.org/rosbag">rosbags</a>, a file format used by the <a href="http://wiki.ros.org/">ROS (Robot Operating System)</a> to record and play back data.</p> <p><strong>More specifically, the datasets include:</strong></p> <ul> <li><strong>RGB Images</strong> from an Intel Realsense D435i</li> <li>Aligned <strong>Depth Images</strong> from an Intel Realsense D435i</li> <li>Left and Right Mono Images from a Mynt Eye s1030</li> <li><strong>Point Clouds</strong> from a Livox Mid-70 LiDAR</li> <li>Unfiltered <strong>acceleration, gyroscopic and magnetic</strong> data from a Xsens MTi IMU</li> <li>Unfiltered <strong>acceleration, gyroscopic </strong>data from an Intel Realsense D435i</li> <li><strong>GNSS Fix data</strong> from a Xiaomi Mi Mix 3 device</li> </ul> <p><strong>Description of files:</strong></p> <ol> <li>The dataset is contain in <strong>choupal.bag</strong>.</li> <li>The <strong>rosbag_info.txt </strong>contains the information of each rosbag;</li> <li>The <strong>sensor_box.urdf </strong>contains all the required transforms;</li> <li>The <strong>sensor_box.stl</strong> contains the 3D model of the apparatus;</li> <li>The <strong>choupal.launch </strong>publishes the sensor transforms and plays the dataset;</li> <li>The <strong>localization.bag</strong> contains the final graph of poses extracted with Cartographer republished as nav_msgs/odom at 4.98Hz.</li> <li>The <strong>localization_15Hz.bag</strong> contains a map-referenced localization extracted with Cartographer at a higher frequency, but the poses are interpolated. If you don't require a high frame rate, please use the <strong>localization.bag</strong> instead.</li> </ol> <p><strong>Usage:</strong></p> <ol> <li>Extract the <em>fruc_dataset_choupal_launch.zip </em>into a catkin workspace</li> <li>Install the necessary dependencies of the package: <ol> <li> <pre><code class="language-bash">cd [/path/to/catkin_ws]</code></pre> <p> </p> </li> <li> <pre><code class="language-bash">rosdep install --from-paths src --ignore-src -y -r</code></pre> </li> </ol> </li> <li>Copy the <strong>rosbags </strong>into the <em>fruc_dataset_choupal_launch/rosbag/</em></li> <li>Edit the <em>fruc_dataset_choupal_launch/launch/choupal.launch </em>file to your use case: <ol> <li>Change the <em>file_path </em>argument if the rosbags are not in the default location;</li> <li>Set <em>localization_file</em> to <em> </em>the path of the desired localization bag, leave it empty to run the dataset without localization.</li> </ol> </li> <li>Compile the package and source the environment: <ol> <li> <pre><code class="language-bash">catkin_make [/your_catkin_workspace/]</code></pre> <p> </p> </li> <li> <pre><code class="language-bash">source [/your_catkin_workspace/devel/setup.bash]</code></pre> <p> </p> </li> </ol> </li> <li>Launch the files: <pre><code class="language-bash">roslaunch fruc_dataset_choupal_launch choupal.launch</code></pre> </li> </ol>
Data release for "Rapid pre-merger localization of binary neutron stars in third generation gravitational wave detectors"
<p>We publish skymap files in fits format of the simulation in our work "Rapid pre-merger localization of binary neutron stars in third generation gravitational wave detectors". There are 68000 BNS events, and results of different negative latencies are zipped in different tar files. An example jupyter notebook for using the data is provided.</p> <p> </p> <p> </p>
Data - Low-Noise Phase-Sensitive Optical Parametric Amplifier with Lossless Local Pump Generation using a Digital Dither Optical Phase-Locked Loop
<p>This dataset contains measurement data and processing code for the results published in "Low-Noise Phase-Sensitive Optical Parametric Amplifier with Lossless Local Pump Generation using a Digital Dither Optical Phase-Locked Loop". The Pyrpl code change used in the work is also attached.</p> <p>This work was funded by the Swedish Research Council (grant VR-2015-00535).</p>
Regional and local variation in chemical, structural, and physical leaf traits for tree species in the northeastern United States, 2016-2023.
This dataset is a compilation of leaf trait measurements for 25 different Northern American tree species in the northeastern United States collected between 2016 and 2023 by the Terrestrial Ecosystems Analysis Lab at the University of New Hampshire. Currently, this dataset contains measurements for 2,006 samples across 18 chemical, physical, and structural traits. Measured traits include stable isotopes for carbon (C) and nitrogen (N), chlorophyll estimates, leaf and petiole dimensions, and leaf and petiole water content. Traits have been measured at plots spanning a wide range of latitude, longitude, elevation, and forest types. A simple table containing these plot descriptions has been included. Additional leaf physiological and optical traits have been measured concurrently on many of these samples and have been or will be published separately. This is a continuous dataset that will be updated on an as needed basis.
Local water years for 4-digit hydrologic unit areas across the conterminous United States
Quantifying and predicting precipitation and water flow, and their influence on ecosystems is challenged by the dynamic relationships between and timing of precipitation and water fluxes. To help with these challenges, scientists use “water year” to examine and predict the impacts of precipitation and relevant extreme climatic and hydrological events on ecosystems. However, traditional water year definitions used in the U.S. have limited considerations of areal variations in climate and hydrology, which need to be considered when studying ecosystems at regional or national scales. We developed local water year (LWY) values that consider spatial variation using existing definitions whereby the water year begins in the month with the lowest or highest average monthly streamflow. We employed a spatial interpolation technique to assign the start and end months of two LWY timeframes to 202 subregions across the conterminous U.S. that range from 4,384 to 134,755 km2. This dataset can be linked with diverse climate, terrestrial, and aquatic data for broad-scale studies.
CCE LTER process cruise, in the California Current region, event log records including date, time, position and activity for use in post-cruise data integration based on co-sampling indexes. From 2006 to 2019 CCE LTER used a locally developed event logging system. During P2107, CCE LTER started to utilize the R2R Event Logger on UNOL ships, 2006 - 2024 (ongoing).
The event logger program developed and maintained by the California Cooperative Oceanic Fisheries Investigations, SIO, program is used aboard CCE LTER process cruises to create indexes with temporal, spatial and activity information for post-cruise data integration. The event log is configured aboard the ship for the recording of sampling events by both ship crew personnel on the bridge, and research personnel in the lab. The event log is processed post-cruise to correct for various errors.
SBC LTER: Beach: Local and regional kelp wrack inputs to sandy beaches
These data describe the inputs of giant kelp (Macrocystis pyrifera) to sandy beach ecosystems in the Santa Barbara Channel. The local dataset details the average number of kelp plants deposited in 100 m wide segments of coastline over the course of 66 months, from August 2015 through July 2021. Data are presented as the overall average as well as the seasonal averages for each segment. The coordinates of each segment are provided as well as the overall average dry beach width and the beach orientation for each segment. The regional dataset details the average wrack cover from cross-shore transects and the average number of kelp plants deposited on the 1 km stretch of beach for 24 sandy beach sites in a 100 km long region of coastline along the Santa Barbara Channel. The coordinates of each site are provided as well as the dry beach width and the beach orientation. Data are contained in two tables: 1) the local 25 km dataset with 250 total segments, and 2) the 100 km regional dataset with 24 total study beaches.
Seismic monitoring of Hans glacier (Svalbard) using dedicated local network
<p>Seismic dataset registered during monitoring of Hans glacier (Svalbard) using dedicated local network in Hornsund 10/2017-04/2018 carried by Wojciech Gajek and coworkers financed by an internal grant of Institute of Geophysics Polish Academy of Sciences.</p> <p>Dataset can be used for analyzing the glacier seismicity. More on that topic in Svalbard can be find in Seismology chapter of SESS 2019 report <a href="https://sios-svalbard.org/SESS_Issue2">https://sios-svalbard.org/SESS_Issue2</a></p> <p>Project log in ResearchGate:</p> <p><a href="https://www.researchgate.net/project/Seismic-monitoring-of-Hans-glacier-Svalbard-using-dedicated-local-network">https://www.researchgate.net/project/Seismic-monitoring-of-Hans-glacier-Svalbard-using-dedicated-local-network</a></p> <p> </p> <p>The data includes seismic records (3C) from the temporary seismic network. It is advised to take into the processing also the permanent station HSPB.<br> Data is packed as a zip archive. Its structure is SDS, compatible with ObsPy query system.<br> The structure includes HSPB but HSPB data is not there due to limited file space here (its publicly available eg in Orpheus).</p> <p> </p> <p>Other files are:<br> coordinates,<br> map<br> data availability chart<br> my presentation from ESC Malta with preliminary results<br> photos from field installation<br> data conditioning report</p> <p>Have fun.</p> <p>You can contact me via researchgate:</p> <p><a href="https://www.researchgate.net/profile/Wojciech_Gajek">https://www.researchgate.net/profile/Wojciech_Gajek</a></p>
Data for 'Local food crop production can fulfil demand for less than one-third of the population'
<p><strong>This dataset is supplement to the following publication (<em>please cite that when using the data</em>):</strong></p> <p>Kinnunen et al. 2020. Local food crop production can fulfil demand for less than one-third of the population. Nature Food 1: 229–237. http://doi.org/10.1038/s43016-020-0060-7</p> <p> </p> <p><strong>Data description</strong></p> <p><strong><em>Distance to food:</em></strong> Globally optimized distance between crop production and consumption. The optimization creates a theoretical food allocation set-up that minimizes travel time cost from crop production to consumption. Data is in two formats: NetCDF (dist_food_netcdf.zip) and multi-band geotiff (dist_food_tif.zip).</p> <p>The data includes:</p> <ul> <li>baseline scenario (dist_food_baseline.nc / .tif)</li> </ul> <p>and three other scenarios where food availability is changed by</p> <ul> <li>decreasing food waste by half (dist_food_halfLoss.nc / .tif)</li> <li>halving the yield gap (dist_food_halfYieldGap.nc / .tif)</li> <li>both of these measures together (dist_food_halfLoss_halfYielGap.nc / .tif)</li> </ul> <p><em>The data covers six crop functional types</em>: maize, pulses, rice, temperate cereals, tropical cereals and tropical roots </p> <p><em>Dataset specifications:</em></p> <p>spatial extent: -180, 180, -90, 90 (xmin, xmax, ymin, ymax)</p> <p>spatial resolution: 0.5 degrees</p> <p>projection: long/lat WGS84</p> <p>layers: 1: maize, 2: pulses, 3: rice, 4: temp_cereals, 5: trop_cereals, 6: trop_roots </p> <p>no data value: -999</p> <p>unit: km</p> <p> </p> <p><em><strong>Foodsheds:</strong></em> The data contains global foodsheds which are areas that are connected by food flows between raster cells. The food flows are from a theoretical food allocation set-up that minimizes travel time cost from crop production to consumption. In addition to normal foodsheds (values>0), there are two special cases: ridge-cells (value: -99) and unconnected single cells (value: -50). Ridge-cells are raster cells connected to multiple foodsheds, while being able to satisfy their own demand locally. Unconnected single cells are not connected to any other foodshed. Each positivie value is a crop specific id, signifying a connected foodshed area. </p> <p>Data is in two formats: NetCDF (foodsheds_netcdf.zip) and multi-band geotiff (foodsheds_tif.zip).</p> <p>The data includes:</p> <ul> <li>baseline scenario (foodsheds_baseline.nc / .tif)</li> </ul> <p>and three other scenarios where food availability is changed by</p> <ul> <li>decreasing food waste by half (foodsheds_halfLoss.nc / .tif)</li> <li>halving the yield gap (foodsheds_halfYieldGap.nc / .tif)</li> <li>both of these measures together (foodsheds_halfLoss_halfYielGap.nc / .tif)</li> </ul> <p><em>The data covers six crop functional types</em>: maize, pulses, rice, temperate cereals, tropical cereals and tropical roots </p> <p><em>Dataset specifications:</em></p> <p>spatial extent: -180, 180, -90, 90 (xmin, xmax, ymin, ymax)</p> <p>spatial resolution: 0.5 degrees</p> <p>projection: long/lat WGS84</p> <p>layers: 1: maize, 2: pulses, 3: rice, 4: temp_cereals, 5: trop_cereals, 6: trop_roots </p> <p>no data value: -999</p> <p>unit: -</p> <p> </p>
Dynamic meta-analysis: a method of using global evidence for local decision making (supplementary materials)
<p>Dynamic meta-analysis: a method of using global evidence for local decision making (supplementary materials)</p>
GIIRS RTTOV coefficient file using local training profiles
<p>GIIRS RTTOV coefficient file using local training profiles </p> <p>Reference:</p> <p>Di, D., Jun Li, Han Wei, W. Bai, C. Wu, and W. Paul Menzel, 2018: Enhancing the fast radiative transfer model for FengYun-4 GIIRS by using local training profiles, <em>Journal of Geophysical Research - Atmospheres</em>, DOI: 10.1029/2018JD029089.</p>
Supplementary material for "A drop in immigration results in the extinction of a local woodchat shrike population"
<p>Data files and code for all analyses and figures presented in the paper. The three data files are provided either in ASCII format (WoodchatCount.txt, WoodchatReproduction.txt, WoodchatCMR.txt) or in csv format (WoodchatCount.csv, WoodchatReproduction.csv, WoodchatCMR.csv). The code file (WoodchatCode.txt) is a space delineated text file. The code file is written for R, but some models are run in JAGS from R. The code file also contains the description of the data files and code for data management.</p>
Local adaptation to light in Norway spruce
<p>Exome capture data of the 1654 trees involved in the study of local adaptation to light quality in Norway spruce:</p> <p>1. control_genes.vcf - Raw vcf file of the ten control genes that were not differentially expressed genes in response to SHADE (low R:FR light), between the southern and northern natural populations of Norway spruce in Sweden.</p> <p>2. degs.vcf - Raw vcf file of the 54 differentially expressed genes in response to SHADE (low R:FR light), between the southern and northern natural populations of Norway spruce in Sweden, that showed at least one missense SNP in coding region. Missense variations in coding regions of nine candidate genes followed a latitudinal cline in allele and genotype frequencies.</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.