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566 results for “Data Spaces”
Traversing Chemical Space with Active Deep Learning for Low-data Drug Discovery
<p>Raw and processed data from LitPCBA used in the paper "Traversing Chemical Space with Active Deep Learning for Low-data Drug Discovery"</p>
Data for: Exploring Transition States of Protein Conformational Changes via Out-of-Distribution Detection in the Hyperspherical Latent Space
<p>This contains all the TS-DART training results as well as the raw MD simulation data reported in the preprint "Exploring Transition States of Protein Conformational Changes via Out-of-Distribution Detection in the Hyperspherical Latent Space".</p>
Data from: Data-driven analysis of oscillations in Hall thruster simulations & Data-driven sparse modeling of oscillations in plasma space propulsion
<p>Data from: Data-driven analysis of oscillations in Hall thruster simulations</p> <p> </p> <p>- Authors: Davide Maddaloni, Adrián Domínguez Vázquez, Filippo Terragni, Mario Merino</p> <p>- Contact email: <a href="mailto:dmaddalo@ing.uc3m.es">dmaddalo@ing.uc3m.es</a></p> <p>- Date: 2022-03-24</p> <p>- Keywords: higher order dynamic mode decomposition, hall effect thruster, breathing mode, ion transit time, data-driven analysis</p> <p>- Version: 1.0.4</p> <p>- Digital Object Identifier (DOI): <a href="https://doi.org/10.5281/zenodo.6359505">10.5281/zenodo.6359505</a></p> <p>- License: This dataset is made available under the <a href="http://opendatacommons.org/licenses/by/1.0/">Open Data Commons Attribution License</a></p> <p> </p> <p>Abstract</p> <p> </p> <p>This dataset contains the outputs of the HODMD algorithm and the original simulations used in the journal publication:</p> <p>Davide Maddaloni, Adrián Domínguez Vázquez, Filippo Terragni, Mario Merino, "Data-driven analysis of oscillations in Hall thruster simulations", 2022 <em>Plasma Sources Sci. Technol.</em> 31:045026. Doi: <a href="https://iopscience.iop.org/article/10.1088/1361-6595/ac6444">10.1088/1361-6595/ac6444</a>.</p> <p>Additionally, the raw simulation data is also employed in the following journal publication:</p> <p>Borja Bayón-Buján and Mario Merino, "Data-driven sparse modeling of oscillations in plasma space propulsion", 2024 <em>Mach. Learn.: Sci. Technol.</em> 5:035057. Doi:<a href="https://iopscience.iop.org/article/10.1088/2632-2153/ad6d29"> 10.1088/2632-2153/ad6d29</a></p> <p> </p> <p>Dataset description</p> <p> </p> <p>The simulations from which data stems have been produced using the full 2D hybrid PIC/fluid code <a href="https://ep2.uc3m.es/assets/docs/pubs/conference_proceedings/domi19b.pdf">HYPHEN</a>, while the HODMD results have been produced using an adaptation of the original <a href="https://doi.org/10.1137/15M1054924">HODMD algorithm</a> with an improved <a href="https://doi.org/10.1063/1.4863670">amplitude calculation routine</a>.</p> <p>Please refer to the relative article for further details regarding any of the parameters and/or configurations.</p> <p> </p> <p>Data files</p> <p> </p> <p>The data files are in standard Matlab .mat format. A recent version of <a href="https://www.mathworks.com/products/matlab.html">Matlab</a> is recommended.</p> <p>The HODMD outputs are collected within 18 different files, subdivided into three groups, each one referring to a different case. For the file names, "case1" refers to the nominal case, "case2" refers to the low voltage case and "case3" refers to the high mass flow rate case. Following, the variables are referred as:</p> <ul> <li>"n" for plasma density</li> <li>"Te" for electron temperature</li> <li>"phi" for plasma potential</li> <li>"ji" for ion current density (both single and double charged ones)</li> <li>"nn" for neutral density</li> <li>"Ez" for axial electric field</li> <li>"Si" for ionization production term</li> <li>"vi1" for single charged ions axial velocity</li> </ul> <p>In particular, axial electric field, ionization production term and single charged ions axial velocity are available only for the first case. Such files have a cell structure: the first row contains the frequencies (in Hz), the second row contains the normalized modes (alongside their complex conjugates), the third row collects the growth rates (in 1/s) while the amplitudes (dimensionalized) are collected within the last row. Additionally, the time vector is simply given as "t", common to all cases and all variables.</p> <p>The raw simulation data are collected within additional 15 variables, following the same nomenclature as above, with the addition of the suffix "_raw" to differentiate them from the HODMD outputs.</p> <p> </p> <p>Citation</p> <p> </p> <p>Works using this dataset or any part of it in any form shall cite it as follows.</p> <p>The preferred means of citation is to reference the publication associated to this dataset, as soon as it is available.</p> <p>Optionally, the dataset may be cited directly by referencing the DOI: 10.5281/zenodo.6359505.</p> <p> </p> <p>Acknowledgments</p> <p> </p> <p>This work has been supported by the Madrid Government (Comunidad de Madrid) under the Multiannual Agreement with UC3M in the line of ‘Fostering Young Doctors Research’ (MARETERRA-CM-UC3M), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation). F. Terragni was also supported by the Fondo Europeo de Desarrollo Regional, Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación, under grants MTM2017-84446-C2-2-R and PID2020-112796RB-C22.</p>
Data and R code used in Hennecke et al. "Plant species richness and the root economics space drive soil fungal communities"
<p>To investigate how plant diversity and root traits relate to soil fungal communities, in 2021 we collected trait data from plots in the Jena Experiment (https://the-jena-experiment.de; funded by the DFG FOR 5000) and characterized fungal communities by sequencing, respiration and lipid fatty acid quantification. </p>
Data from: Sympatric wren-warblers partition acoustic signal space and song perch height
Animals employing acoustic signals, such as birds, must effectively communicate over both background noise and potentially attenuating objects in the environment. To surmount these obstacles, animals evolve species-specific acoustic signals that do not overlap with sources of interference (such as songs of close relatives), and issue these songs from locations that maximize transmission. In multispecies assemblages of birds, the acoustic resource may thus be interspecifically partitioned along multiple axes, including song perch height and signal space. However, very few such studies have focused on open habitats, where differences in sound transmission patterns and limited availability of song perches may drive competition across multiple axes within signal space. Here, we demonstrate acoustic signal space partitioning in four sympatric species of wren-warbler (Cisticolidae, Prinia), in an Indian dry deciduous scrub-grassland habitat. We found that the breeding songs of the four species partition acoustic signal space, resulting in interspecific community organization. Within each species' signal space, we uncovered different intraspecific patterns in note diversity. Two species partitioned intraspecific signal space into multiple note types, whereas the other two varied note repetition rate to different extents. Finally, we found that the four species also partition song perch heights, thus exhibiting acoustic niche separation along multiple axes. We hypothesize that divergent song perch heights may be driven by competition for higher singing perches or other ecological factors rather than signal propagation. Acoustic signal partitioning along multiple axes may therefore arise from a combination of diverse ecological processes.
Experimantal data related to " Electron phase space control in on-chip laser-driven particle acceleration "
<p>Experimental and Simualtion data used to generate the Plots in the manusript of "Electron phase space control in on-chip laser-driven particle acceleration". Use Matlab file (R2019a or later) to generte plots.</p>
Data and code accompanying "'Safe spaces' and community building for climate scientists, exploring emotions through a case study", Haddaway and Duggan 2023
<p>Data and code accompanying "‘Safe spaces’ and community building for climate scientists, exploring emotions through a case study", Haddaway and Duggan 2023</p>
Welcome-Introduction to the Workshop " A first approach to an ELSA Curriculum for Data Scientists The FAIR Data Spaces Project as a Use Case
<p>Welcome-Introduction to the Workshop " A first approach to an ELSA Curriculum for Data Scientists The FAIR Data Spaces Project as a Use Case", contains a brieff description of the FAIR Data Spaces project</p>
GDPR and the principle of purpose limitation in connecting Data Spaces demonstrators
<p>The presentation focuses on legal challenges arising from the GDPR when connecting Data Spaces using the Demonstrators. Special attention is given to the principle of purpose limitation of the GDPR. The Demonstrators serve a crucial role in connecting the data spaces of the NFDI and GAIA-X. The data in the two data spaces is gathered and used for usually completely different purposes. Thus, when connecting the spaces, the data may be processed for completely different purposes. The GDPR – when applicable – limits legitimate purposes of such further processing. Even if a purpose is legitimate, the GDPR imposes different obligations on controllers and processors. The presentation will dive into the applicability of the GDPR to the demonstrators, the roles of controller and processor regarding the demonstrators and will discuss the principle of purpose limitation and the obligations connected to it.</p>
Dataset for "Data-driven empirical conductance relations during auroral precipitation using incoherent scatter radar and all sky imagers" JGR-Space Physics
<p><strong>Dataset for "Data-driven empirical conductance relations during auroral precipitation using incoherent scatter radar and all sky imagers" JGR-Space Physics.</strong></p> <p>Processed ACF level data can be provided by contacting me.</p> <p><strong>DOI of the publication:</strong></p> <p><strong>README file:</strong></p> <p>PFISRInversions_ASI_v1.1_Fang_01042023_v08162023.h5</p> <p>HallConductance: Altitude integrated Hall Conductance from 85-150 km altitude, [ntime], mho<br> PedersenConductance: Altitude integrated Pedersen Conductance from 85-150 km, [ntime], mho<br> EnergyFlux: Energy flux after integrating the differential number flux, [ntime], W/m^2<br> AverageEnergy: Average Energy after integrating the differential number flux, [ntime], eV<br> Measured_ElectronDensity: measured electron density from PFISR, [ntime, naltitude], #/m^3<br> Modeled_ElectronDensity: modeled electron density produced by the MEM version, [ntime,naltitude], #/m^3<br> UnixTime: time in seconds since 1970-01-01 00:00:00 UT, [ntime], seconds<br> NumberFlux: differential number flux, [ntime, nenergy], #/m^2 s^-1 eV^-1<br> EnergyGrid: energy grid spanning 1 keV - 100 keV in 25 steps, [nenergy], eV<br> ASIStatus: auroral identification number, [ntime], no units<br> 1: Discrete aurora<br> 2: Diffuse aurora<br> 3: Pulsating aurora<br> 8: Unidentified aurora<br> -1: data that was flagged as unsuitable:<br> either TEC was too low (no auroral E-region possibly associated with red aurora),<br> the modeled electron density was not consistent at all with the observed electron density (bad fit)</p> <p>ASIYEAR-Final_date.xlsx<br> This is an excel file that contains the original auroral image identification near the zenith direction<br> We used all sky imager videos located: http://optics.gi.alaska.edu/realtime/data/MPEG/PKR_DASC_256/<br> These files were converted into python pickle files and used internally for the rest of the investigation.</p> <p>The columns corresponds to days, and the rows correspond to time in UT as decimal hours:<br> The ASI Key is the following:<br> # 1 Discrete<br> # 2 Diffuse<br> # 3 Pulsating<br> # 4 Cloudy/Clear<br> # 5 Moon<br> # 6 Possible faint Aurora with moon out<br> # 7 Substorm Breakup<br> # 8 Cloudy with aurora (can't make out type)</p>
Data repository accompanying "Many-Body Majorana Braiding without an Exponential Hilbert space"
<p>This repository includes the data and notebooks to generate the figures published in "Many-body Majorana braiding without an exponential Hilbert space".</p>
Data from: SNPs across time and space: population genomic signatures of founder events and epizootics in the House Finch (Haemorhous mexicanus)
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Data from: neglected puzzle pieces of urban green infrastructure: richness, cover, and composition of insect-pollinated plants in traffic-related green spaces
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Data from: Female American black bears do not alter space use or movements to reduce infanticide risk
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Data from: Distance-dependent seedling mortality and long-term spacing dynamics in a neotropical forest community
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Data from: Biomass production of tropical trees across space and time: The shifting roles of diameter growth and wood density
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Data from: North American Douglas-fir (P. menziesii) in Europe: establishment and reproduction within new geographic space without consequences for its genetic diversity
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A new Lower Permian ray-finned fish (Actinopterygii) from South Dakota and the use of tree space to find rogue taxa in phylogenetic analysis of morphological data
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Data from: N-mixture models estimate abundance reliably: a field test on Marsh Tit using time-for-space substitution
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Data from: A place-based participatory mapping approach for assessing cultural ecosystem services in urban green space
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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.