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2,212 results for “space”
Super-resolving ocean dynamics from space with computer vision algorithms: training datasets
<p>We provide here the datasets used for the development of the dilated Adaptive Residual Network for the super-resolution of ocean Absolute Dynamic Topography described in <em>Buongiorno Nardelli et al.</em> (2022). The model is designed to combine satellite altimetry and thermal observations and provides super-resolved dynamic topography. The training/test datasets have been built starting from the data originally prepared for an Observing System Simulation Experiment carried out in the framework of the European Space Agency CIRCOL project [<em>Ciani et al.</em>, 2021]. They consist of one year of synthetic daily Absolute Dynamic Topography (ADT), surface geostrophic currents and sea surface temperature data obtained from Copernicus Marine Service Mediterranean Forecasting System (MFS) (Product ID: MEDSEA-ANALYSIS- FORECAST-PHY-006-013) [<em>Clementi et al. 2021</em>]. Synthetic Altimeter-derived ADT maps were obtained by first sampling the model output along the actual tracks of a synthetic constellation composed of 4 Radar Altimeters: Jason-3, Sentinel-3A, SARAL/Altika, and Cryosat-2 missions (this step is achieved by running the SWOT simulator software [<em>Gaultier et al.</em>, 2016]) and successively applying the DUACS (<em>Data Unification and Altimeter Combination System)</em> mapping method. The original input images cover the entire Mediterranean domain at 1/24° spatial resolution, leading to an individual image size of 380x1000 pixels. Here, we have randomly chosen 40 dates (~11% of the total) to be kept aside as fully independent test data, and successively re-sampled the original images extracting much smaller tiles (76x100), which are used as input to the network training. The tiles are extracted by going through a double loop on latitude and longitude, imposing a spatial overlap of 50%. Full details on data pre-processing (e.g.normalization strategies) are given in the paper:</p> <ul> <li>Buongiorno Nardelli, B.; Cavaliere, D.; Charles, E.; Ciani, D. Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms. <em>Remote Sens.</em>, <strong>2022</strong>, 14, 1159. https://doi.org/10.3390/rs14051159</li> </ul>
A dataset of 150000 terminal weighted projective spaces
<p><strong>Weighted projective spaces with at worst terminal singularities</strong></p> <p>A dataset of 150000 randomly generated weighted projective spaces with at worst terminal singularities, in dimensions 1 to 10.</p> <p>The data consists of the plain text files "rank_1_dim_N.txt" where N, which is the dimension of the weighted projective space, is in the range 1 to 10. Each line of the file is a sequence of weights of length N+1. For example, the first line of "rank_1_dim_4.txt" is:</p> <p>[1,2,5,14,21]</p> <p>and this corresponds to the 4-dimensional weighted projective space P(1,2,5,14,21).</p> <p>For details, see the paper:</p> <p>"Machine learning the dimension of a Fano variety", Tom Coates, Alexander M. Kasprzyk, and Sara Veneziale, <em>Nature Communications</em>, <strong>14:</strong>5526 (2023). doi:10.1038/s41467-023-41157-1</p> <p>Magma code capable of generating this dataset is in the file "generate_rank_1.m".</p> <p>If you make use of this data, please cite the above paper and the DOI for this data:</p> <p>doi:10.5281/zenodo.5790079</p>
Lake mask and distance to land dataset of 2024 lakes for the European Space Agency Climate Change Initiative Lakes v2
<p>This dataset contains the distance to land and the lake identifiers as a global netcdf file for all the water pixels at 1km (1/120 deg) lat/lon resolution of 2024 lakes distributed globally. It contains also the list of lakes as a csv file with information such as the lake center as defined in [1], and the coordinate of a box to easily locate the like in the global netcd file. The mask excludes islands on lakes and it has been derived from the GloboLakes high resolution limnology dataset [2]. The dateset have been further harmonized with the lake maximum extent lake polygons by PML [3]. The lake list with the plot of the mask and the polygons is available as a html file accessible also from the lake website at the University of Reading: http://www.laketemp.net/home_CCI/LMPolygons.php</p> <p>This dataset accompanies the <strong>ESA CCI Lakes v2 dataset</strong> [4].</p> <p> </p> <p>[1] Carrea, L.; Embury, O.; Merchant, C.J. (2015): High-resolution datasets related to in-land water for limnology and remote sensing applications: distance-to-land, distance-to-water, water-body identifier and lake-centre co-ordinates - Geoscience Data Journal, 2 (2). pp. 83-97. ISSN 2049-6060 doi: https://doi.org/10.1002/gdj3.32</p> <p>[2] Carrea, L.; Embury, O.; Merchant, C.J. (2015): GloboLakes: high-resolution global limnology dataset v1. Centre for Environmental Data Analysis. doi:10.5285/6be871bc-9572-4345-bb9a-2c42d9d85ceb. <a href="http://dx.doi.org/10.5285/6be871bc-9572-4345-bb9a-2c42d9d85ceb">http://dx.doi.org/10.5285/6be871bc-9572-4345-bb9a-2c42d9d85ceb</a></p> <p>[3] Simis, S.; Mata, A.; Selmes, N.; Carrea, L. (2021) Lake polygons dataset accompanying Calimnos v1.4.0 and ESA CCI Lakes Climate Research Data Package v2.0. zenodo https://doi.org/10.5281/zenodo.4899250</p> <p>[4] Carrea, L.; Crétaux, J.-F.; Liu, X.; Wu, Y.; Bergé-Nguyen, M.; Calmettes, B.; Duguay, C.; Jiang, D.; Merchant, C.J.; Mueller, D.; Selmes, N.; Simis, S.; Spyrakos, E.; Stelzer, K.; Warren, M.; Yesou, H.; Zhang, D. (2022): ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 2.0.1. NERC EDS Centre for Environmental Data Analysis <a href="https://catalogue.ceda.ac.uk/uuid/03c935c6890c4b2ebf4aae4d84cd9472">https://catalogue.ceda.ac.uk/uuid/03c935c6890c4b2ebf4aae4d84cd9472</a></p>
Dataset for simulations of a beamline that controls longitudinal phase space whilst transporting LWFA electrons to an undulator
<p>This dataset relates to a design for a particle accelerator beamline. The beamline transports particles (electrons) from a laser wakefield accelerator (LWFA) source to an undulator. The unique design allows the 'chirp' or 'longitudinal phase space' of the electron distribution to be sheared during transport.<br> The dataset contains: 1) Initial bunch distributions created by the ASTRA generator program and conversion to MAD8 program format; 2) a working MAD8 batch file; 3) Three set-ups of the beamline to provide positive, negative or no shear; 4) Tracking simulation input files to track the initial distributions through each beamline set-up; 5) Output electron distributions that result from each tracking simulation.</p>
Test-Retest qt-dMRI datasets for "Non-Parametric GraphNet-Regularized Representation of dMRI in Space and Time"
<p>We release these four diffusion MRI data sets as part of our recent journal publication; Fick, Rutger H.J., et al. "Non-Parametric GraphNet-Regularized Representation of dMRI in Space and Time." <em>Medical Image Analysis</em> (2017). More detailed information about the use of these data sets can also be found in the publication.</p> <p>We acquired test-retest diffusion MRI spin echo sequences from two C57Bl6 wild-type mice on an 11.7 Tesla Bruker scanner. The test and retest acquisition were taken 48 hours from each other. The data consists of 80x160x5 voxels of size 110x110x500<span class="math-tex">\(\mu\)</span>m. Each data set consists of 515 Diffusion-Weighted Images (DWIs) spread over 35 acquisition shells. The shells are spread over 7 gradient strength shells with a maximum gradient strength of 491 mT/m, 5 pulse separation shells between [10.8 - 20.0]ms, and a pulse length of 5ms. We manually created a brain mask and corrected the data from eddy currents and motion artifacts using FSL's eddy. We then drew a region of interest in the middle slice in the corpus callosum, where the tissue is reasonably coherent.</p> <p>- The diffusion MRI data are contained in the files with 'dwis' in the name.<br> <br> - The corpus callosum masks are contained in the files with 'mask' in the name.</p> <p>- The acquisition parameters are contained in the .txt files.</p>
LOFAR Observation (MS file) from the Boötes Field and the Toothbrush cluster used in the paper: "Looking beyond pixels with continuous-space EstimAtion of Point sources"
<p>The dataset contains the measurement sets (MS file) of the LOFAR observations from the Boötes field and the Toothbrush cluster. The dataset was used in the experiments of the paper: </p> <blockquote> <p>LEAP: Looking beyond pixels with continuous-spaceEstimAtion of Point sources</p> <p>Pan, H., Simeoni, M., Hurley, P., Blu, T. & Vetterli, M. In: Astronomy & Astrophysics, in press, 2017</p> </blockquote> <p>The data was provided as a collaboration between ASTRON and IBM within the DOME project. The data was acquired for a LOFAR sky survey of the Boötes field:</p> <blockquote> <p>LOFAR 150-MHz observations of the Boötes field: Catalogue and Source Counts</p> <p>Williams, W. L. , Hardcastle, M. J. & 33 others In: Monthly Notices of the Royal Astronomical Society. 460, 3, p. 2385–2412</p> </blockquote> <p>and the Toothbrush cluster (RX J0603.3+4214):</p> <blockquote> <p>Simulating the toothbrush: evidence for a triple merger of galaxy clusters</p> <p>Brüggen, M., van Weeren, R. J., Röttgering, H. J. A. In: Monthly Notices of the Royal Astronomical Society: Letters. 425, 1, p. L76--L80</p> </blockquote> <p>In case of questions concerning the measurement set, please contact the original authors for details.</p> <p> </p> <p>We have also included the three catalogs used in the experiments, which are converted from their original FITS table to Numpy arrays:</p> <ul> <li>skycatalog.npz is the catalog of the Boötes field: https://academic.oup.com/mnras/article-lookup/doi/10.1093/mnras/stw1056</li> <li>TGSSADR1_7sigma_catalog.npz is the TGSS ADR1 source catalog: http://tgssadr.strw.leidenuniv.nl/catalogs/TGSSADR1_7sigma_catalog.fits</li> <li>NVSS_CATALOG.npz is the NRAO/VLA Sky Survey: ftp://nvss.cv.nrao.edu/pub/nvss/CATALOG/</li> </ul>
Third harmonic generation images of the lacuno-canalicular network in bone femoral diaphysis of mice from the BionM1 project (space flight)
<p>Data set for 11 samples in 3 groups of Control, Space Flight and Synchro (ground control with space flight housing and feeding conditions). Contains THG images in tif format of 2D mosaic of selected samples and 3D stacks in selected anatomical regions of interest. See readme file for more information.</p>
Confocal fluorescence microscopy images of the lacuno-canalicular network in bone femoral diaphysis of mice from the BionM1 project (space flight)
<p>This data set provides complementary measurements to a separate THG data set of the same study: doi: 10.5281/zenodo.1475906</p> <p>Data set for 1 sample of each of the 3 groups: Control, Space Flight and Synchro (ground control with space flight housing and feeding conditions). Contains confocal fluorescence microscopy images in tif format of 2D mosaic of selected samples and 3D stacks in selected anatomical regions of interest. See readme file for more information.</p>
Built Open spaces
<p>ESM data subset, generated by extracting band 30 as buildings with the next information:</p> <p>gid integer geom geometry(Polygon,EPSG:3035) albedo real transmissivity real vegetation_shadow real run_off_coefficient real building_shadow smallint hillshade_building real</p> <p>This data is an input for local effects calculation.</p>
Potential of the three-terminal heterojunction bipolar transistor solar cell for space applications
<p>In this video we repeat the presentation we gave at the European Space Power Conference (2019), in JeanLes Pins, describing the potential of the three-terminal heterojunction bipolar transistor solar cell for space applications. There is a paper published in the proceedings of the Conference were you can find more details.</p>
Dataset for the paper "Designs in finite classical polar spaces"
<div> <div><span>This repository contains the designs from the paper </span><span>"Designs in finite classical polar spaces" by Michael Kiermaier, Kai-Uwe Schmidt, and Alfred Wassermann, </span><span>in Designs, Codes, and Cryptography. </span></div> <div> </div> <div> <div> <div><span>All designs in this repository are simple designs.</span></div> <br> <div><span>The file format is JSON. Each file contains the designs in a fixed finite polar space </span><span>for a pair of parameters t and k. </span></div> <div> </div> <div><span>The file README.md contains more detailed information about the file format.</span></div> </div> </div> </div>
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>
Detecting small changes in tropical forests from space... data and code for thesis chapter 4
<p>SAR and UAV-LiDAR data used in chapter 4 of my thesis <em>Detecting small changes in tropical forests from space: experiments using synthetic aperture radar. </em>This content has also been submitted for peer review in Frontiers in Remote Sensing.</p> <p>DEM_timeseries_3m contains phase height and coherence from TanDEM-X InSAR high-resolution spolight images, processed by Jose-Luis Bueso-Bello at DLR. NetCDF format, dimensions latitude, longitude, time.</p> <p>TDX_descending_intensity contains intensity from the same TanDEM-X time series, covering an area of the Madre de Dios region in Peru. These data were processed by Harry Carstairs using ESA's SNAP software.</p> <p>UAV_change_1m_mask is a raster showing the change in canopy height at the study site between June 2019 and July 2021, according to two UAV LiDAR campaigns, with 1m pixels, and with areas with low point density masked out.</p> <p>CODE.zip contains python scripts and notebooks used to collate the data, create change detection metrics, develop SAR models of canopy height, and produce the figures.</p> <p>Funded by European Research Council (ERC) grant to the Tropical Forest Degradation Experiment (FODEX).</p>
Data from Simulations of a Magnetic Shielding System to Deflect Background-Inducing Secondary Electrons away from Space-Based X-ray Detectors
<p>Data from simulations examining the effect of cosmic rays and secondary particles generated by them on background induced in an X-ray astronomy space telescope, and the effectiveness of a surrounding magnetic field at reducing this background.</p> <p>These simulations were performed for the paper “Effectiveness of a dual solenoid magnetic shield at reducing X-ray-like background in silicon-based X-ray detectors” (2023) published in the Journal of Astronomical Telescopes Instruments and Systems. The paper can also be found at https://openresearch.surrey.ac.uk/esploro/outputs/journalArticle/The-Effectiveness-of-a-Dual-Solenoid/99777566602346/filesAndLinks?forceView=true&mode=quickaccess&index=0 .</p> <p>This data was also used for the simulations and analysis described in the thesis "The Simulation, Composition and Shielding of Radiation-Induced X-ray-like Background in Space-Based X-ray Astronomy Missions" (2021), which can be found at <a href="https://doi.org/10.21954/ou.ro.00012e1f">https://doi.org/10.21954/ou.ro.00012e1f</a> .</p>
Data on different types of green spaces and their accessibility in the seven largest urban regions in Finland
<p>This repository contains data described in the article "Data on different types of green spaces and their accessibility in the seven largest urban regions in Finland" (Heikinheimo et al. 2023) and used in the research article "Associations of neighborhood-level socioeconomic status, accessibility, and quality of green spaces in Finnish urban regions" (Viinikka et al. 2023). <br> <br> This repository contains data on green space quality and path distances to different types of green spaces. The path distances represent green space accessibility using active travel modes (walking, cycling). The path distances were calculated using the pedestrian street network across the seven largest urban regions in Finland. We derived the green space typology from the Urban Atlas Data that is available across functional urban areas in Europe and enhanced it with national data on water bodies, conservation areas and recreational facilities and routes from Finland. We extracted the walkable street network from OpenStreetMap and calculated shortest paths to different types of green spaces using open-source Python programming tools. Network distances were calculated up to ten kilometers from each green space edge and the distances were aggregated into a 250 m x 250 m statistical grid that is interoperable with various statistical data from Finland. The geospatial data files representing the different types of green spaces, network distances across the seven urban regions, as well as the processing and analysis scripts are shared in an open repository. These data offer actionable information about green space accessibility in Finnish city regions and support the integration of green space quality and active travel modes into further research and planning activities.</p> <p> </p> <p><strong>Data description article: </strong></p> <p>Heikinheimo, V., Tiitu, M., & Viinikka, A. (2023). Data on different types of green spaces and their accessibility in the seven largest urban regions in Finland. <em>Data in Brief</em>, <em>50</em>, 109458. <a href="https://doi.org/10.1016/j.dib.2023.109458">https://doi.org/10.1016/j.dib.2023.109458</a></p> <p><strong>Related research article:</strong> </p> <p>Viinikka, A., Tiitu, M., Heikinheimo, V., Halonen, J. I., Nyberg, E., & Vierikko, K. (2023). Associations of neighborhood-level socioeconomic status, accessibility, and quality of green spaces in Finnish urban regions. <em>Applied Geography</em>, <em>157</em>, 102973. <a href="https://doi.org/10.1016/j.apgeog.2023.102973">https://doi.org/10.1016/j.apgeog.2023.102973</a></p>
Results files for Land-free Bioenergy From Circular Agroecology -- A Diverse Option Space and Trade-offs
<p>This is the open data repository to support and reproduce results in the paper "<em>Land-free Bioenergy From Circular Agroecology -- A Diverse Option Space and Trade-offs</em>." There are <strong>three types </strong>of files here:</p> <p> </p> <p> </p> <p><strong>1. Ready-to-use final results files of all strategies and scenarios referred to in the paper. </strong>They can be downloaded and used directly without running any codes. They all have the same naming format for strategies/scenarios: `Org` = organic share, `ConcRed` = concentrate feeding reduction share, `WasteRed` = waste reduction share, and numbers refer to the share. E.g., `Org0_ConcRed50_WasteRed75` is a strategy with 0% organic share, 50% concentrate feeding reduction, and 75% waste reduction.</p> <p> </p> <ul> <li>`NationalAncillaryBioenergyPotential_EJ.csv`: The national potential of ancillary bioenergy in 2050 from all scenarios. (Units: EJ). Same in both pathways.</li> <li>`GlobalPotentialEnvironmentalImpacts_NutrientFirst.csv`: Environmental impacts of all scenarios from the pathway `<em>NutrientFirst</em>.` The first three rows refer to the combination of agroecological practices in places, which allow you to explore environmental impacts grouped by, e.g., different organic shares.</li> <li>`GlobalPotentialEnvironmentalImpacts_NegFirst.csv`: Same structure as the file above, but from another pathway, `<em>NegativeFirst</em>`.</li> </ul> <p> </p> <p><strong>2. `SOLmOutputs` contains all original output files from our model <a href="https://orgprints.org/id/eprint/38778/">SOLmV6</a>. </strong></p> <p> </p> <p><strong>3. `DataCleaningKit` has the Python codes and additional dataset of heat values to process 2. `SOLmOutputs` and spit 1. </strong>(Tip: One should adjust the `input_path` and `output_path` before running `DataCleaning.py.`)</p> <p> </p> <p> </p> <p>Fei Wu (fei.wu@usys.ethz.ch)</p> <p>Delft, August, 2023</p> <p> </p>
Activity budgets and space use for two common Pacific parrotfish (Chlorurus Sp.) at Palmyra Atoll National Wildlife Refuge
Data was collected at Palmyra Atoll National Wildlife Refuge located in the Central Pacific in 2014, and the work focuses on two species of Parrotfish, Chlorurus microrhinos and Chlorurus spilurus (formerly Cholrurus sordidus). For C. microrhinos, the project was designed to collect fine-scale spatial behavior data, focusing on territory size, species interactions, and benthic impact (i.e. feeding behavior). Focal follow data was collected by one or two observer(s), either on snorkel or SCUBA, and recording focal activity down to the second. Simultaneously, the observer would be towing a GPS that was recording a location every 5 seconds and each location was then associated with a particular behavior. Throughout this study individual fish were identifiable and successive follows were possible on individuals. For C. spilurus the focus of the project was to collect behavioral time budget data on feeding, territorial defense, and spawning behavior. The ‘Chlorurus_Area_Palmyra_2014.csv’ data only covers C. Microrhinos and gives the 95% KUD area estimation for GPS towed tracks, as well as the 95% KUD area for locations where feeding was occurring. We also report the step length between successive points for the entire follow as well as where feeding was occurring. The ‘Chlorurus_Activity_Palmyra_2014.csv’ is for both C. Microrhinos and C. spilurus and reports the start and stop time of each focal follow as well as the start and stop time of each activity. Activity descriptions can be found in the metadata and the total length and phase for each individual in our study can be found in ‘Fish_Information_Chlorurus_Data_2014.csv.
EJR01 Foraging decisions underlying restricted space-use: effects of fire and forage maturation on large herbivore nutrient uptake on Konza Prairie
Recent models suggest that herbivores optimize nutrient intake by selecting patches of low to intermediate vegetation biomass. We assessed the application of this hypothesis to plains bison (Bison bison) in an experimental grassland managed with fire by estimating daily rates of nutrient intake in relation to grass biomass and by measuring patch selection in experimental watersheds in which grass biomass was manipulated by prescribed burning. Digestible crude protein content of grass declined linearly with increasing biomass, and the mean digestible protein content relative to grass biomass was greater in burned watersheds than watersheds not burned that spring (intercept; F1,251 = 50.57, P < 0.0001). Linking these values to published functional response parameters, ad libitum protein intake, and protein expenditure parameters, Fryxell's (Am. Nat., 1991, 138, 478) model predicted that the daily rate of protein intake should be highest when bison feed in grasslands with 400 - 600 kg/ha. In burned grassland sites, where bison spend most of their time, availability of grass biomass ranged between 40 and 3650 kg/ha, bison selected foraging areas of roughly 690 kg/ha, close to the value for protein intake maximization predicted by the model. The seasonal net protein intake predicted for large grazers in this study suggest feeding in burned grassland can be more beneficial for nutrient uptake relative to unburned grassland as long as grass regrowth is possible. Foraging site selection for grass patches of low to intermediate biomass help explain patterns of uniform space use reported previously for large grazers in fire-prone systems. This data set was used to test the forage maturation hypothesis in the Konza Prairie bison enclosure from 2012-2013. Our objectives were to quantify foraging site selection of Plains bison in order to determine if bison in a fire-prone grassland selected sites of low-to-intermediate forage biomass as posited by Fryxell’s (1991) forage mat
Short term accretion measured using marker horizon of feldspar at Space For Time locations.
This dataset provides a survey of short term accretion across the PIE LTER at both high and low marsh sites. A marker horizon plot was established and sampled annually to determine depth of accretion since deployment.
Soil core metrics taken at Space for Time plots across the PIE LTER.
Soil cores were collected and evaluated at three different depth intervals for bulk density, organic matter by loss on ignition (LOI), pore water ammonium, and pore water salinity. These biotic and abiotic factors differ across marsh types and elevations and help to parameterize other measurements of marsh structure and function, as well as provide insight into marsh heterogeneity within a single site.
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.