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566 results for “Data Spaces”
Data for device simulation in the article "Analysing the impact of the hole transport layer on the space charge distribution and hysteresis in perovskite solar cells using capacitance-voltage profiling"
<p>This repository contains the data used to perform device simulation in the article "Analysing the impact of the hole transport layer on the space charge distribution and hysteresis in perovskite solar cells using capacitance-voltage profiling", submitted in September 2024 to the journal Sustainable Energy and Fuels.</p> <p><br>The authors of this data and the article are E. Regalado-Pérez, Evelyn B. Díaz-Cruz, and J. Villanueva-Cab </p> <p><br>The scripts (.m) and input files (.csv) hosted here are based on the files created by the authors of the Driftfusion code, which can be found in the GitHub repository "barnesgroupICL/Driftfusion" at https://github.com/barnesgroupICL/Driftfusion.</p> <p> </p>
Polar UVI Images (Intensity and Boundary) & Space Physical Parameters Joint Data
<p><span>The ultraviolet imager on board the Polar satellite is capable of capturing information about the polar and equatorial boundaries of the auroral oval, the overall morphology of the auroral oval, and the spatial distribution of auroral oval intensity. The intensity of the auroral oval are related to the amount of energy injected from the solar wind and magnetosphere into the polar ionosphere. And the position and size of the auroral oval boundary is also closely linked to changes in the space environment. In order to mitigate the influence of daylight on auroral images, we selected a total of 4215 images of both full, gap and incomplete aurora ovals. We utilized corresponding Interplanetary Magnetic Field (IMF), solar wind parameters, and geomagnetic index data from the NASA OMNI database to build a jointly dataset consist of the space physical parameters and the intensity and boundaries of the aurora oval.</span></p>
Wikidata and DBpedia Space Travel Data Comparison with ABECTO
<p>This is an <a href="https://github.com/fusion-jena/abecto">ABECTO</a> execution plan to compare space travel data from <a href="https://www.wikidata.org">Wikidata</a> and <a href="https://www.dbpedia.org">DBpedia</a> and the according results.</p> <p>The generated result data are derived from the compared knowledge graphs, which are licensed as follows:</p> <ul> <li><a href="https://www.dbpedia.org">DBpedia</a> by DBpedia Association (<a href="http://en.wikipedia.org/wiki/Wikipedia:Text_of_Creative_Commons_Attribution-ShareAlike_3.0_Unported_License">CC BY-SA 3.0</a>)</li> <li><a href="https://wikidata.org">Wikidata</a> (<a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0 1.0</a>)</li> </ul>
Data from: All-season space use by non-native resident Mandarin Ducks (Aix galericulata) in northeastern Germany
<p>Data from article "All-season space use by non-native resident Mandarin Ducks (<em>Aix galericulata</em>) in northeastern Germany", accepted for publication in Journal of Ornithology in 2021.</p> <p><strong>Article abstract:</strong></p> <p>Patterns of space use are often subject to large temporal and individual-level variation, due to seasonality in behaviour and environmental conditions as well as age- or sex-specific needs. Especially in temperate regions, seasonality likely influences space use even in non-migratory birds. In waterfowl of the family <em>Anatidae</em>, however, few studies have analyzed space use of the same individuals across the full annual cycle. We used a resident population of Mandarin Ducks (<em>Aix galericulata</em>) in northeast Germany to study their year-round space use in relation to season, sex, and age. We marked 172 birds with colour rings and surveyed relevant water bodies for re-encounters for several years. As space-use patterns we derived home ranges from minimum convex polygons and the number of water bodies used by individual birds. Our analysis revealed that individuals shifted their space use between seasons, in particular extending their home ranges during the non-breeding season. Between years, in contrast, birds tended to show season-specific site fidelity. Sex differences were apparent during both breeding and non-breeding season, male consistently having larger home ranges and using slightly more water bodies. No difference was found between first-year and adult birds. Our study demonstrates that mark-resighting can provide valuable information about space use in species with suitable behaviour and readily accessible habitat. In such cases, it may be a valid alternative to more expensive GPS-tracking or short-term manual radio telemetry, particularly within citizen-science projects.</p> <p><strong>Data description:</strong></p> <p>Mark-resight data on Mandarin ducks in the Potsdam/Berlin region, Germany. Each sighting (including capture and ringing event) contains information about:</p> <p>- individual with sex and age specification </p> <p>- date, season, sub-season as defined in article</p> <p>- coordinates of sighting location: Gauss-Krüger coordinate system with right (R; Rechtswert) and high (H; Hochwert) value. For distinction between "fine" and "coarse" coordinates, see article. Water body names refer to the "coarse" site specification. </p> <p> </p> <p> </p>
Sequence data for 'Machine-driven parameter-space exploration of biochemical reactions'
<p>The development of complex, multi-step <em>omics</em> methods in molecular biology is a laborious, costly, iterative and often intuition-bound process where an optimum is sought in a parameter space through step-by-step optimisations. The the difficulty of miniaturising assays and the cost of the experiments limit the dynamic range and the number of parameters that can be explored. However, because of non-linearities of the response of biochemical systems to their reagent concentrations, a broad dynamic range is necessary. Here we demonstrate the use of a high-performance nanoliter handling platform (Labcyte Echo 525) and computer generation of liquid transfer programs to explore in quadruplicates more than 600 combination of 4 parameters of a biochemical reaction, which lead us to uncover non-linear responses, parameter interactions and novel mechanical insights. With the increased availability of « <em>cloud biology</em>» computer-driven laboratory platforms, our results participate in changing methods development for biotechnology towards reproducible, computer-aided exhaustive characterisation of biochemical systems.</p> <p>This dataset contains the raw sequencing data produced with an Illumina MiSeq instrument for this project. FASTQ files and sample sheets are found in the usual location (Data/Intensities/BaseCalls). The "Thumbnail_Images" and "L001" directories were deleted to save space.</p> <p>Run IDs: 171227_M00528_0321_000000000-B4GLP, 180403_M00528_0348_000000000-B4GP8, 180517_M00528_0364_000000000-BRGK6, 180123_M00528_0325_000000000-B4PCK, 180411_M00528_0351_000000000-BN3BL, 180606_M00528_0367_000000000-BN3FG, 180326_M00528_0346_000000000-B4GJR, 180501_M00528_0359_000000000-B4PJY, 180607_M00528_0368_000000000-BN9KM</p> <p> </p>
Data from Lamb et al.: "Hanging out at the club: breeding status and territoriality affect individual space use, multi-species overlap, and pathogen transmission risk at a seabird colony"
<p>This dataset consists of two files:</p> <p><strong>ams_sku_all provides</strong> GPS locations from tracked skuas.</p> <p><strong>Skua_GPS_Metadata</strong> provides information on tracked skuas. The file consists of two workseets, the data table ("skua_gps_metadata", and a key providing descriptions of the column names and values ("Key")</p>
Data on learning about green space management by upper secondary school students
<p>A workshop survey -dataset detailing how upper secondary school students learn about green space management.</p>
Data files for Atmospheric Gravity Wave and Instability Observations from the International Space Station using the Near InfraRed Airglow Camera (NIRAC)
<p>The files in this set are data obtained from the NIRAC airglow imager on the International Space Station. The files are named for a JGR paper by J. Hecht et al. entitled Atmospheric Gravity Wave and Instability Observations from the International Space Station using the Near InfraRed Airglow Camera (NIRAC). These files are for plots in Figures 5,7,10,11,17,18, and 19 in the submitted paper. The files are published here so as to be available for review. This paper should appear in JGR Atmospheres sometime in late 2023 or early 2024. The files that are text files are meant to be read with IDL as discussed in the readme file. </p>
Raw data for "μeV electron spectromicroscopy using free-space light"
<p>Raw data set related to the article "μeV electron spectromicroscopy using free-space light", currently on arXiv (arXiv: 2212.12457)</p>
Data from: Latent generative landscapes as maps of functional diversity in protein sequence space
<p>Variational autoencoders are unsupervised learning models with generative capabilities, when applied to protein data, they classify sequences by phylogeny and generate de novo sequences which preserve statistical properties of protein composition. While previous studies focus on clustering and generative features, here, we evaluate the underlying latent manifold in which sequence information is embedded. To investigate properties of the latent manifold, we utilize direct coupling analysis and a Potts Hamiltonian model to construct a latent generative landscape. We showcase how this landscape captures phylogenetic groupings, functional and fitness properties of several systems including Globins, β-lactamases, ion channels, and transcription factors. We provide support on how the landscape helps us understand the effects of sequence variability observed in experimental data and provides insights on directed and natural protein evolution. We propose that combining generative properties and functional predictive power of variational autoencoders and coevolutionary analysis could be beneficial in applications for protein engineering and design.</p>
Supplementary data S1 associated with the manuscript "Limited climatic space for alternative ecosystem states in Africa"
<p>Site locations and meta-data used in this study. The original sources of the data and how they were filtered for this study are described in the methods section of the manuscript.</p>
Data set from "A free-space interferometer design for optical frequency dissemination and out-of-loop characterization below the 10^{-21}-level"
<p>The data set contains the data underlying the improved out-of-loop interferometer layout performance evaluation published in Photonics Research (<a href="https://doi.org/10.1364/PRJ.485899">https://doi.org/10.1364/PRJ.485899</a>). The experimental setup and the methodology used is explained in that publication.</p> <p>The data is stored in the Matlab(R)-native file format. This proprietary file format is also readable by other numerical computing environments.</p> <p><br> The files 'data_i_*_S*.mat' contain the timeseries of the analysed continuous measurement runs in configuration S*. Each of these files includes the following variables:</p> <p>year, month, day, hour, minute, second: date at which the measurment point was acquired<br> rem_float, rem: observed 1s Lambda-averaged out-of-loop frequency deviation in Hz as float and string, respectively<br> p: measured air pressure in hPa<br> T: measured laboratory temperature inside the cover close to the interferometer in °C<br> pressure_phase_OOL: phase variations of the out-of-loop signal estimated from the measured pressure variations<br> temp_phase_OOL: phase variations of the out-of-loop signal estimated from the measured temperature variations</p> <p> </p> <p>Different barometers have been used for the pressure mesaurements. In the measurement runs for the S1 and S2PM configurations, a barometer placed in a neighboring laboratory in the same building at PTB was used to characterize pressure fluctuations. For the measurement in the S2 configuration, we used air pressure data from the climate station of the department of Hydrology and River Basin Management of the Technical University Braunschweig, which is ≈6km apart. At times of overlapping operation, we have observed matching pressure instabilities of both barometers for averaging times 𝜏>1000s, which shows that on these averaging times the exact placement of the barometer is of lesser importance.</p>
Analysis of the Ground Level Enhancement GLE 60 on April 15, 2001, and its Space Weather Effects: Comparison with Dosimetric Measurements - Data
<p>Computed data that was used within the "Analysis of the Ground Level Enhancement GLE 60 on April 15, 2001, and its Space Weather Effects: Comparison with Dosimetric Measurements" paper. Computations of cones were done by OTSO using TSY89 + IGRF13 magnetic field parameters. Contains the atmospheric yield functions used for radiation computation as well as the global radiation map at 35kft for GLE60. Data is provided in .csv format.</p>
HeatResilientCity II - work package 2.3: Interactions between buildings and open space adaptation measures – Meteorological input data for building performance simulation
<p>This repository contains <strong>meteorological</strong> <strong>data</strong> from urban climate simulations that were carried out in districts of the cities of Dresden and Erfurt as part of the <a href="http://heatresilientcity.de/">HeatResilientCity II</a> project. The data was extracted at specific points (receptors) of the urban climate model. In addition to the data, a <strong>script </strong>is attached that can be utilized to generate a time series for IDA ICE building performance simulations using IceWeather.exe. Therefore, a Microsoft Windows operating system is required. To create a time series, simply use the function <em>createIdaIceInput()</em> at the end of the script <em>createTimeSeries.py</em>. Further explanations can be found at the beginning of the script. Information about the ENVI-met data used to create the IDA ICE input can be found in <em>README_RawENVImetOutput_DD.txt</em> and <em>README_RawENVImetOutput_EF.txt</em>.</p> <p>Some input <strong>data files have already been generated</strong><strong> </strong>and can be directly used for<strong> thermal building performance simulations with IDA ICE</strong>. These files can be found in the folder <em>0.3_Input_Timeseries (Climate) for IDA ICE</em>.</p> <p>The <strong>naming convention</strong> of the final input data files for IDA ICE is as follows:</p> <ul> <li>TOWN_SCENARIO_RECEPTOR_AVERAGING_INTERFACE_LATITUDE_LONGITUDE_VERSION</li> <li>TOWN: Choose between 'Erfurt' and 'Dresden'</li> <li>SCENARIO: See further information in <em>README_RawENVImetOutput_DD.txt</em> and <em>README_RawENVImetOutput_EF.txt</em></li> <li>RECEPTOR: Location in the modelled area (ENVI-met simulation) where data was extracted.</li> <li>AVERAGING: Information about averaging the hourly values of the urban climate simulation (see <em>createTimeSeries.py and READMEs)</em></li> <li>INTERFACE: Information on how single days were joined together (see <em>createTimeSeries.py</em>).</li> <li>LATITUDE: Default values for Dresden and Erfurt are set in the script. Add additional values in the function <em>setIceWeatherParams()</em> if you are using other cities/custom ENVI-met simulation data.</li> <li>LONGITUDE: Default values for Dresden and Erfurt are set in the script. Add additional values in the function <em>setIceWeatherParams()</em> if you are using other cities/custom ENVI-met simulation data.</li> <li>VERSION: The version number can be set in the script.</li> </ul> <p>Example: <em>Dresden_2y_A1_a_timeSeries_24-24_51.0468_13.6707_v11.prn</em></p> <p><strong>Folder overview:</strong></p> <ul> <li>The ENVI-met raw data is stored in <em>0.1_Input_RawENVImetOutput</em>.</li> <li>The script is stored in <em>0.2_Input_ScriptsToCreateTimeSeries</em>.</li> <li>The final datasets ready for simulation with IDA ICE are stored in <em>0.3_Input_Timeseries(Climate)ForIDAICE</em>. This folder also contains some weather data time series that have already been created and can be used for IDA ICE (subfolders Erfurt_v11 and Dresden_v11).</li> </ul>
VNMPF-LIS: Validation Network Multiplatform Precipitation Feature (VNMPF) Dataset with International Space Station Lightning Imaging Sensor (ISS LIS) Data
<p>The Multiplatform Precipitation Feature (MPF) database combines ground- and space-based precipitation observations and retrievals from the Global Precipitation Measurement (GPM) mission Validation Network (VN) with space-based lightning measurements from the Lightning Imaging Sensor on board the International Space Station (ISS LIS). The data are synthesized in a thunderstorm-like, feature-based framework that encapsulates the microphysical, kinematic, and electrical properties of the observed storm.<br> <br> A VNMPF includes:</p> <p>- Radar information, GPM orbit, and ISS orbit <br> - Time/date information<br> - Geographical information<br> - Radar reflectivity characteristics<br> - Lightning energetic and identification information (where there is lightning)<br> - 3-dimensional wind information (where radars in dual-Doppler configuration are available)<br> <br> Version 1: 2017-2020</p> <p>Version 2: 2017-2022, updated VN winds </p>
Space Tumbling Attitude Data Generation (STAG)
<p>The STAG experiment aims to provide data on the motion and attitude of tumbling objects in microgravity. The data (acceleration, rotation rate, pressure, temperature, and monocular imagery) is collected during parabolic flight performed with gliders. The dataset can be used for validation of attitude estimation techniques based on monocular imagery, and for dynamics analysis and simulation of non-cooperative objects. The dataset will continue to grow as more flights become available, testing different object shapes and inertias.</p>
Data from: Differential use of nest materials and niche space among avian species within a single ecological community
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Data from: Migratory singers dynamically overlap the signal space of a breeding warbler community
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Data for: Ecological pathways connecting drought to stream invertebrate community shifts across space and time
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Data for: Age, sex, and temperature shape within- and among-individual space use in Black-capped Chickadees
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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.