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566 results for “Data Spaces”
Data and codes related to the article: Renard et al. A Hidden Climate Indices Modeling Framework for Multi-Variable Space-Time Data. Water Resources Research.
<p>This package contains data and codes related to the article:</p> <p>B. Renard, M. Thyer, D. McInerney, D. Kavetski, M. Leonard and S. Westra. A Hidden Climate Indices Modeling Framework for Multi-Variable Space-Time Data. <em>Water Resources Research</em>.</p> <p><strong>R scripts</strong></p> <p>The main computations of the paper have been performed using a computing code named <a href="https://github.com/STooDs-tools">STooDs</a>, which is called using the bash script launchpad.sh.</p> <p>The R scripts in this package only perform pre-processing (create configuration files) and post-processing (analyze results) steps.</p> <ul> <li>Funk.R: a set of functions called by other scripts.</li> <li>1_defineModel.R: define the model to be inferred and create STooDs configuration files in <em>dataset_XXX/runs.</em></li> <li>2_analyzeResults.R: analyze the outputs of STooDs runs.</li> <li>3_crossValidation.R: analyze the outputs of cross-validation experiments in <em>dataset_XV</em> and <em>dataset_XV_1971-1990</em>.</li> </ul> <p><strong>Data</strong></p> <p>Data for the 3 cases (full dataset and 2 cross-validation experiments) are located in folders <em>dataset_XXX/data</em>.</p> <ul> <li>dat.txt: raw dataset in text format.</li> <li>dataset.RData: dataset in RData format.</li> <li>DMI.txt, NINO.txt, SAM.txt: 3 standard climate indices.</li> <li>spaceP.txt, spaceQ.txt, spaceT.txt: properties of Precipitation (P), Streamflow (Q) and Temperature (T) stations.</li> <li>[only for cross-validation experiments] validation.RData: left-out data used for validation.</li> </ul> <p> </p> <p> </p>
Landscape composition and life-history traits influence bat movement and space use: analysis of 30 years of published telemetry data
<p>Using temperate bats, a group of particular conservation concern, we investigated how morphological traits, habitat specialization and environmental variables affect home range sizes and daily foraging movements, using a compilation of 30 years of published bat telemetry data in Northern America and Europe for the period 1988 – 2016.</p> <p>We compiled data on home range size and mean daily distance between roosts and foraging areas at both colony and individual levels from 166 studies of 3,129 radiotracked individuals of 49 bat species. We calculated multi-scale habitat composition and configuration in the surrounding landscapes of all studied roosts. Using mixed models, we examined the effects of habitat availability and spatial arrangement on bat movements, while accounting for body mass, aspect ratio, wing loading and habitat specialization.</p> <p>We found a significant effect of landscape composition on home range size and mean daily distance at both colony and individual levels. On average, home ranges were up to 42% smaller in the most habitat-diversified landscapes while mean daily distances were up to 30% shorter in the most forested landscapes. Bat home range size significantly increased with body mass, wing aspect ratio and wing loading, and decreased with habitat specialization.</p>
Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space
<p><strong>Description</strong></p> <p>This dataset contains remote sensing data from the ESA Copernicus missions Sentinel-2 and Sentinel-5P (tropsopheric NO2 column density) in the 2018-2020 timespan. The satellite measurements each cover ~3100 locations in Europe and ~100 on the US Westcoast, each with a size of 1.2x1.2km. The locations are selected such that each measurement is centered at the location of an air quality measurement station on the ground (from the European Environment Agency or the US Environmental Protection Agency, measuring NO2). This makes it possible to analyze spatiotemporally aligned remote sensing and ground-based measurements.</p> <p> The 13 Sentinel-2 bands are upsampled (bilinear) to 10m resolution and cropped to 120x120 pixel. For some locations multiple Sentinel-2 images are available. The images are stored as binary numpy `.npy` files organized into directories based on their locations. </p> <p>The Sentinel-5P data was pre-processed by mapping the measurements from consecutive satellite overpasses onto a common rectangular grid of 0.05×0.05◦(∼5×5km) across Europe. To harmonize the Sentinel-2 (10m to 60m, upscaled to 10m) and Sentinel-5P (5×3.5km, rescaled to 5×5km) imaging resolutions, the Sentinel-5P data is linearly interpolated to 10m resolution and cropped to 120×120 pixel around the locations of interest. Additionally, all measurements with a QA flag (qa_value) below 75 were discarded, following ESA recommendations. The Sentinel-5P data are stored as `.netcdf` file, organized by location. For each location, three such files are available, containing averaged Sentinel-5P measurements at different temporal frequencies (2018-2020, quarterly, monthly).</p> <p>The <p>samples_{frequency}_{area}.csv</p> files provide a list of observations with the corresponding file paths to a (cloud-free) Sentinel-2 image, the Sentinel-5P measurement, and the average NO2 concentration measurement by the EEA or EPA ground station. These files can be used for easy data-loading.</p> <p><strong>Content</strong></p> <p>The data is organized into the following files:</p> <ul> <li>README.md - this file</li> <li>sentinel-2-eea.tar.gz [33.1GB]</li> <li>sentinel-5p-eea.tar.gz [80.1GB]</li> <li>samples_2018_2020_eea.csv </li> <li>samples_quarterly_eea.csv</li> <li>samples_monthly_eea.csv</li> <li>sentinel-2-epa.tar.gz [0.15GB]</li> <li>sentinel-5p-epa.tar.gz [1.8GB]</li> <li>samples_2018_2020_epa.csv</li> <li>samples_quarterly_epa.csv</li> <li>samples_monthly_epa.csv</li> </ul> <p><strong>Acknowledgement</strong></p> <p>If you use this data set, please cite our publication:</p> <p><em>Scheibenreif, L., Mommert, M., Borth, D., "</em>Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space<em>", Tackling Climate Change with Machine Learning workshop at ICML 2021.</em></p> <p>Please refer to this publication for additional information on the data set.</p> <p>This data set contains modified Copernicus Sentinel data acquired in 2018-2020, processed by ESA.</p> <p> </p> <p><strong>Responsible Author</strong></p> <p>Linus Scheibenreif<br> University of St. Gallen, Institute of Computer Science<br> Chair Artificial Intelligence and Machine Learning<br> linus.scheibenreif ( at ) unisg.ch</p>
Questionnaire for surveys on Urban Green Space use and survey raw data for Brussels (Belgium), Luxembourg-city (Luxembourg) and Rouen (France)
<p>The repository contains the xml files of survey questionnaires on the use of urban green spaces. All survey files are translated into three languages (English, French and German).</p> <p>At the time of this publication, these questionnaires have already been used for conducting face-to-face surveys in 2016 in Brussels (Belgium), in 2017 in Luxembourg-city (Luxembourg) and in 2017 in Rouen (France).</p> <p>The results of these surveys are provided in raw data format (csv files), after anonymisation (home and workplace locations have been removed).</p> <p>Please feel free to contact us for any supplementary info.</p>
Tracking magma spine extrusion from space: Implications for conduit and topography complexity at Shiveluch volcano, Kamchatka - Photogrammetric data repository
<p>This is a dataset relevant for a paper on lava spine extrusion at Shieveluch volcano, Kamchatka. Data was used to show that the spine elongates along a previously identified fracture line and bends to a preferred northerly direction. By repeated morphology analysis and feature tracking, we constrain a spine diameter of ~300 m, extruding at a velocity of 1.7 m/day and discharge rate of 0.3-0.7 m³/s. Results are relevant for understanding the growth and collapse hazards of spines and provide unique insights into the hidden magma-conduit architecture.</p> <p>The data consists of three parts. First, we provide the filtered and corrected three dimensional point clouds generated from Pleiades tristereo data. These 3D point clouds were co-aligned and now allow analysing subtle changes. Point clouds are provided in .las format. Second, we provide the filtered and corrected digital elevation models generated from the point cloud data, these DEMs are provided in geotiff format. The name of the files indicates the dates of their acquisition. Third and lastly, we provide an orthomap stack used to estimate displacements by tracking offsets.</p> <p> </p>
Data from: Iridescence untwined - Honey bees can separate hue variations in space and time
<p><span>Iridescence is a phenomenon whereby the hue of a surface changes with viewing or illumination angle. Many animals display iridescence but it currently remains unclear whether relevant observers process iridescent color signals as a complex collection of colors (spatial variation), or as moving patterns of colors and shapes (temporal variation). This is important as animals may use only the spatial or temporal component of the signal, although this possibility has rarely been considered or tested. Here, we investigated whether honey bees could separate the temporal and spatial components of iridescence by training them to discriminate between iridescent disks and photographic images of the iridescent patterns presented by the disks. Both stimuli therefore contained spatial color variation, but the photographic stimuli do not change in hue with varying angle (no temporal variation). We found that individual bee observers could discriminate the variable patterns of iridescent disks from static photographs during unrewarded tests. Control experiments showed that bees reliably discriminated iridescent disks from control silver disks, showing that bees were processing chromatic cues. These results suggest that honey bees could selectively choose to attend to the temporal component of iridescence signals to make accurate decisions. </span></p>
Pressure data used in 'Surface-to-space atmospheric waves from Hunga Tonga-Hunga Ha'apai eruption' (Wright et al., 2022)
<p>Pressure data used in 'Surface-to-space atmospheric waves from Hunga Tonga-Hunga Ha’apai eruption' (Wright et al., 2022). </p> <p> </p> <p><strong>Phase speed estimates by station:</strong></p> <p>Author: <em>Fred Prata, AIRES Pty Ltd</em></p> <p>Description:<em> distances, locations, arrival times and phase speed estimates for the Hunga Tonga Lamb wave from pressure stations used in our study.</em></p> <p> </p> <p> </p> <p><strong>Pressure time series data (19 stations):</strong></p> <p><strong>Lauder (1 station):</strong></p> <p>Author: <em>Dan Smale/NIWA, State Highway 85, Omaku, New Zealand</em></p> <p>Description: <em>Data sourced from a CO2 eddy-covariance instrument operated and maintained by NIWA. Values were provided as an image file of pressure anomaly versus time (NZST) which was digitized at approximately 90 s time resolution and 0.1 hPa.</em></p> <p><strong>Mt Eliza / HRO (1 station):</strong></p> <p>Author: <em>Fred Prata/AIRES Pty Ltd, 116 Humphries Road, Mount Eliza, Vic 3930, Australia</em></p> <p>Description: <em>Data derived from an ecowitt weather station (Easyweather-WIFIA 19E) operated and maintained by AIRES Pty Ltd. The measurements are logged every 5 minutes with a pressure resolution of 0.1 hPa.</em></p> <p><strong>Tonga (1 station):</strong></p> <p>Author:<em> Malo e Leilei Taaniela/Fua'amotu Domestic Airport, Tonga and Shane Cronin/University of Auckland, School of Environment, New Zealand.</em></p> <p>Description: <em>Data derived from a barometer operated by the Tongan meteorological office located at Nukualofa port (met.gov.to). Sampling interval is 1 minute and the pressure resolution is 0.1 hPa</em></p> <p><strong>Weatherlink (3 stations):</strong></p> <p>Author: <em>Fred Prata/AIRES Pty Ltd, 116 Humphries Road, Mount Eliza, Vic 3930, Australia</em></p> <p>Description: <em>Data downloaded from http://weatherlink.com The time resolution is 5 minutes for Davis and Boston and 15 minutes for Travis. The pressure resolution is 0.01 in Hg.</em></p> <p><strong>PurpleAir (13 stations): </strong></p> <p>Author: <em>citizen science project - https://map.purpleair.com/ (free for non-commercial use)</em></p> <p>Description: <em>PNG images of pressure traces from each station: American Samoa, Anchorage, Auckland, Brisbane, Colorado Springs, Concepcion, Glenn Dale, Kahuko, Manhattan Beach, Papeete, Solvang, Sydney, Tokyo. See table, described above, for latitude/longitude of each site.</em></p> <p> </p> <p><strong>Other pressure data used in the paper already archived elsewhere, and associated licensing (11 stations):</strong></p> <p><strong>AIMS (10 stations)</strong>: https://apps.aims.gov.au/metadata/search?term=Weather%20Stations (CC BY 3.0 AU)</p> <p><strong>Wegenernet (1 station)</strong>: https://wegenernet.org/portal/v7.1/2021/1 ("openly available to all and free of charge except for commercial usage")</p> <p> </p> <p> </p> <p><strong>Not included (6 stations):</strong></p> <p>Due to licensing terms, we do not include 6 pressure time series obtained from the Australian Bureau of Meteorology in their raw form, specifically those at <em>Mt Isa Aero, Learmonth Airport, Broome Airport, Alice Springs Airport, Adelaide Airport and Perth Airport</em>. Derived products made from these data are permitted to be shared, and accordingly phase speed estimates from these stations are included in the table described above. A graphical representation of the data from <em>Broome</em> is also included in the scientific paper these data support as Extended Data Figure 1e.</p> <p> </p> <p> </p>
Inhabiting Extraterrestrial Space - Data set of 477 images
<p><strong>Data set of 477 images on Space Habitat</strong></p> <p>Abstracted from the research project « Habiter l’espace extraterrestre »</p> <p>HEAD-Genève<br> Partenaire scientifique : L’Observatoire de l’Espace, le laboratoire culturel du CNES (Paris)<br> Projet financé par le Fonds national suisse de la recherche scientifique (FNS)</p> <p>website : habitat-extraterretre.ch</p> <p> </p> <p><em><strong>Abstract of the project</strong></em></p> <p>Objects conceived and realized to inhabit extraterrestrial space are arousing a strong resurgence of interest with regards to the ecological questions as well as the economic issues they cover. Thus, "Leaving earth" has become a contemporary reality often mentioned but whose understanding is, however, rarely based on scientific research dedicated to space or extraterrestrial habitats and remains most often far from a specific basis, whether historical, material or cultural.</p> <p>The two historical and cultural lines followed by this research start from the same point: the implementation of a corpus of space research images made up of documents produced within the framework of authenticated works, supported by state institutions at an international level (Space Agencies) belonging to an official voted project and specifying one or more elements of an inhabited space object. At the margins of this framework, we have opened this corpus of documents to "pioneer" engineers who worked before the advent of the space age in 1957 and whose influence will be lasting on its future developments. </p> <p>The corpus of images from the fields of space communication, architecture, cinema and visual arts, are based on proven links between the objects and images they gather and those of space research. They are also opened to other images and objects, whose connections stem from these first connections.</p> <p>The relationships of these images and objects can be queried through descriptors applying to all the fields of the database. They ensure a given research to have transversal or multidisciplinary dimensions. Each corpus generated by these researches is a scientific material of knowledge related to the cultural history of space habitat. This research main goal is to enable researchers from the human sciences as well as artists to seize these corpuses and to contribute in the writing of this history.</p> <p> </p> <p><em><strong>Research team</strong></em></p> <p>Christophe Kihm, Associate Professor, HEAD - Geneva, HES-SO (principal applicant)</p> <p>Floriane Germain, Doctor, PHD in museum studies, mediation, heritage, space archives expert (scientific assistant),</p> <p>Jill Gasparina, Assistant Professor, HEAD - Geneva, HES-SO (scientific collaborator)</p> <p>Anne-Lyse Renon, Senior lecturer at the Laboratory of practices and theory of contemporary art, University of Rennes 2 (scientific assistant)</p> <p>With the cllaboration of Gérard Azoulay, director of the Observatoire de l'Espace, the cultural laboratory of the CNES (Paris).</p> <p> </p> <p><em><strong>General information </strong></em></p> <p>Images in the database are classified according to five fields:</p> <p>- Space research</p> <p>- Space communication</p> <p>- Architecture</p> <p>- Cinema</p> <p>- Visual arts</p> <p>The following dataset gives access to the 477 image files of the database. Each image file includes descriptors and metadata linking the image to other images in the database such as:</p> <p>Field, Subfield, Title, Author(s), Date, Sponsor(s), Country of Origin, Type of document, Dimensions, Creation Techniques , Medium of the Original, Conservation Place of the Original, Diffusion Medium of the Document, Inhabitants, Type of Habitats, Situation in Space, Dedicated Activitie(s), Localisation (Associated Space), Related Material Object(s), Situation in the Research Process, Type of Collaboration, Collaborator(s), Related Theme(s), Comments, Related Project, Related Object.</p>
Simulation data for "Nonlinear electron phase-space dynamics in spontaneous excitation of falling-tone chorus" submitting to Geophysical Research Letters
<p>Simulation data for "Nonlinear electron phase-space dynamics in spontaneous excitation of falling-tone chorus" submitting to Geophysical Research Letters.</p> <p>Including the simulation input parameter file and the necessary output data for analysis described in the article. The output data consists of waveform data, wave intensity profile, binned phase space distribution, etc. A detailed guide to load the output data is included in the zipped file as well. </p>
Data related to NAHAYO et al (2022), to appear in AGU Space Weather Journal (2022SW003092)
<p>Geomagnetic data related to the publication by Nahayo, et al. (2022).</p> <p>The file ending in "event1.csv" is for the first event (October 2003) and the second file, filename ending "event2.csv" for the second event discussed in the paper (March 2015).</p> <p> </p>
Data for the analysis of aquifer-system deformation in the Doñana Natural Space (Spain) using unsupervised cloud-computed InSAR data and wavelet analysis
<p>This are the data necessary to correlate InSAR and hydrogeological information through wavelet analysis, by WaSAR Python script (Jiménez-González & Guardiola-Albert, 2022, http://doi.org/10.5281/zenodo.6334996). The structure and information about the data is the following:</p> <p>PSBAS: Processed Interferometric Synthetic Aperture Radar (InSAR) data from the European Space Agency (ESA) Sentinel-1 satellites to estimate line-of-sight (LOS) ground motion in the period 2014-2020 in the Doñana area (SW Spain). These images have been processed using the P-SBAS approach (Parallel Small BAseline Subset), which is the parallel computing solution for the SBAS processing chain at the ESA Geohazards Exploitation Platform (GEP) by CNR-IREA.</p> <p>Aggregates deformation: Former InSAR information aggregated in polygons</p> <p>Climate: rainfall and ET information in the Doñana area for the 2014-2020 period. Daily records of evapotranspiration and precipitation have been obtained from the agroclimatic stations belonging to the Junta de Andalucía (https://www.juntadeandalucia.es/agriculturaypesca/ifapa/riaweb/web/).</p> <p>Piezometry: piezometry information in Doñana area for the 2014-2020 period. Groundwater level information was provided by the piezometric networks of the Guadalquivir Hydrographic Confederation and the Geological and Mining Institute of Spain.</p> <p>Pump rates: estimated pumping rate time series in the Matalascañas touristic resort</p>
Research data for "Exploring the configurational space of amorphous graphene with machine-learned atomic energies"
<p>This dataset supports the paper: "Exploring the configurational space of amorphous graphene with machine-learned atomic energies" (<a href="https://doi.org/10.1039/D2SC04326B">https://doi.org/10.1039/D2SC04326B</a>).</p> <p>Trajectory data for the 200-atom structures (Fig. 3) and the final configurations for the 612-atom structures as well as the GAP-17-optimised 610-atom structure from Toh et al are provided (Fig. 4). Additionally, the structures used for data analysis in Fig. 5 are given.</p> <p>The files are in extended xyz (.xyz) format and contain the raw data for coordinates, forces, and atomic energies (labelled 'c_1'). The files also contain the atomic energies relative to pristine graphene, labelled "Energy_per_atom", and the locally averaged energy relative to pristine graphene, labelled "NN_Energy_per_atom". Topological information is included at the end of the .xyz file for the 612-atom structures ('fig_4'/) and for the structures in 'fig_5/'.</p> <p>All raw atomic energies were computed using LAMMPS default settings and were output with six significant figures, with the exception of the Toh et al. structure (for which ASE was used, outputting a higher number of significant figures). </p> <p>The data can be read using, for example, the Atomic Simulation Environment (ASE), or visualised using Ovito.</p> <p> </p>
Data from: Migratory singers dynamically overlap the signal space of a breeding warbler community
<p>Migratory species inhabit many communities along their migratory routes. Across taxa, these species repeatedly move into and out of communities, interacting with each other and locally breeding species and competing for resources and niche space. However, their influence is rarely considered in analyses of ecological processes within the communities they temporarily occupy. Here, we explore the impact of migratory species on a breeding community using the framework of acoustic signal space, a limited resource in which sounds of species within communities co-exist. Migrating New World warblers (Parulidae, hereafter referred to as migrant species) often sing during refueling stops in areas and at times during which locally breeding warbler species (hereafter breeding species) are singing to establish territories and attract mates. We used eBird data to determine co-occurrence of 19 migrant and 11 breeding warbler species across spring migration in SW Michigan, generated a signal space from song recordings of these species, and examined patterns of signaling overlap experienced by breeding species as migrants moved through the community. Migrant species were present for two-thirds of the breeding season of local species, including periods when breeding species established territories and attracted mates. Signaling niche overlap experienced by individual breeding species was idiosyncratic and varied over time, yet niche overlap between migrant and breeding species occurred more commonly than between breeding species or between migrant species. Nevertheless, the proportion of niche overlap between migrant and breeding warblers was similar to overlap among breeding species. Our findings showed that singing by migrant species overlapped the signals of many breeding species, suggesting that migrants could have unexplored impacts on communication in breeding species, potentially affecting song detection and song evolution. Our study contributes to a growing body of research documenting impacts of migratory species on communities and ecosystems.</p>
Data and code for: Predicting rapid adaptation in time from adaptation in space: a 30-year field experiment in marine snails
<p>Scripts and data used in the research study <strong>Predicting rapid adaptation in time from adaptation in space: a 30-year field experiment in marine snails</strong>.</p> <p><a href="https://doi.org/10.1101/2023.09.27.559715" target="_blank" rel="noopener">https://doi.org/10.1101/2023.09.27.559715</a></p>
Structure and dynamics of plasma irregularities over the equatorial ionospheric region: A study using spaced receiver technique employing geostationary satellites' radio signals-Data set
<p>The study investigates the characteristic features of the ionospheric irregularities using spaced receiver technique. In the spaced receiver technique, we have used a trio of receivers separated by 40 and 100 m from each other. These receivers monitor scintillations patterns of the L1 signals transmitted by the geostationary satellites. The cross-correlation of the signals and the power spectral analysis yields the measure of characteristic features of the irregularities. The data folder contains the S4 index, drift velocity of the irregularities, powerspectral slopes and size of the irregularities observed on four days. The folder also contains the gnuscript used for plotting. </p> <p> </p> <p> </p>
Data and the simulation script for "Through-Bond and Through-Space Radiofrequency Amplification by Stimulated Emission of Radiation"
<p>The dataset includes NMR spectra and the MatLab script for the simulations of RASER triggered via Nuclear Overhauser Effect for the "Through-Bond and Through-Space Radiofrequency Amplification by Stimulated Emission of Radiation" paper (under revision at the moment of the dataset publication).</p>
Space time cube for precipitation derived from 24h hours of metereological radar data
<p>The animation depicts a 24h space-time cube derived from radar-metereologic data recorded by the x-band radar station of the South African Weather Service for the Liebenbergvlei in the Freestate, South Africa on December 31 2001. The temporal resolution (z) is 5 Minutes, starting on 2001-12-31 00:00:00 hours, ending on 2001-12-31 23:55:00 hours. Spatial resulution please see. The spatial resolution (xy) is 1km, covering a radius of 200km from the radar station. Tempospatial zones of weak precispitation are colored in blue, zones of severe precipitation are colored in yellow.</p> <p>Data processing was done in GRASS v6.x, visualisation was done in Paraview.</p>
Data, plotting scripts, and figures for "A Projective Method for Solving the Single-Group Space-Time Neutron Kinetics Equations with Precursor Advection"
<p>Contains all the files necessary for figure reproduction.</p>
Data sets for "MELISSA: System description and spectral features of pre‐ and post‐midnight F‐region echoes. Journal of Geophysical Research: Space Physics" by Rodrigues et al.
<p>Observations used in the study "Rodrigues, F. S., Zhan, W., Milla, M. A., Fejer, B. G., de Paula, E. R., Neto, A. C., et al ( 2019). MELISSA: System description and spectral features of pre‐ and post‐midnight <em>F</em>‐region echoes. <em>Journal of Geophysical Research: Space Physics</em>, 124. <a href="https://doi.org/10.1029/2019JA027445">https://doi.org/10.1029/2019JA027445</a>."</p> <p>The uploaded files include the RTI maps measured by the MELISSA radar system between 2014 and 2018 (.tif files). They also include values of SNR versus local time and height and the spectra presented in the manuscript (.mat files).</p> <p>Please, see README.txt for additional details.</p>
Data for manuscript: The Conformational Space of the SARS-CoV-2 Main Protease Active Site Loops is Determined by Ligand Binding and Interprotomer Allostery
<div>The data is provided as a part of the manuscript "<strong>The Conformational Space of the SARS-CoV-2 Main Protease Active Site Loops is Determined by Ligand Binding and Interprotomer Allostery</strong>". This repository includes an archive with folders:</div> <div> </div> <div><strong>md_data </strong></div> <div> <ul> <li>a directory with MD data for all simulation systems considered in the manuscript. Initial and final conformations are provided.</li> </ul> </div> <div> </div> <div><strong>fig_data</strong></div> <div> <ul> <li>a directory with the data underlying all the main text in the manuscript. </li> </ul> </div> <div> </div> <div>Videos S1-S3 are also included.</div>
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.