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More precise tracking of horizontal than vertical target motion with both the eyes and hand
<p>Those files contain individual data from a large cohort of participant (N=62). </p> <p>In the excel file (DATAmain), each sheet presents one set of variables (with individual value for each trial).</p> <p>This file contains information regarding eye and hand tracking performance (distance+lags), as well as smooth pursuit gains. </p> <p>The other files contain data that we used for the detailed analysis of saccades and lags, as well as the scripts that can be run with Perl. One script is for analysing the lag (Danion.pl) and the other one for analysing the saccades (saccades.pl). The other files (.txt and .dat) that were used for these analyses. Note that some library is needed (common_subroutines, draw_figure, and for the anova’s routines_that_use_R), meaning that you need to have R installed. </p> <pre>Regarding data acquisition we employed a program called Docometre that can be uploaded at the following address: http://139.124.68.1/buloup/index.php?selectedMenu=DOCoMETRe&lang=_fr When this program is installed, it needs to be run with BaselineTracking.dcm We also provide .BAS and .T91 files that correspond to the compiled version of each pattern Regarding visual stimuli, another program called ICE needs to be installed on a separate computer that receives information (target+cursor) from docometer, it can be uploaded at : https://trello.com/b/EtNCNrZH/icehttps://trello.com/b/EtNCNrZH/ice ICE needs to be run with Visuomotor.ice Visuomotor.icepro Visuomotor.txt and Visuomotor.icemat in the respective folder (icepro in Protocol folder, icemat and ice in Scenario Folder, and txt in Serie folder) Note that both Docometre and ICE need to be run with similar equipement as our (including Adwin Gold systems, Megatron joystick, video screen, graphic cards, and desktop eyelink providing analog signals to docometre). Adequate numbering of analogic channels needs also to be ensured. </pre>
Ionospheric Vertical Correlation Lengths Derived From IRI-2016 Model Errors
<p>Ionospheric vertical correlation lengths based on IRI-2016 model and Incoherent Scatter Radar (ISR) data.</p> <p><strong>Important! The analysis was performed in log space. </strong></p> <p>ISR used for this analysis:</p> <p>Jicamarca, Arecibo, Millstone Hill, Poker Flat ISR, and ResoluteBay North ISR.</p> <p>This metadata can be used for the construction of the covariance matrix for ionospheric data assimilation.</p> <p>Inside of the .nc file:</p> <p>lat=array of geomagnetic latitudes (degrees)<br> alt=arrays of altitudes (km)<br> vert_corr1=array of size (nalt, nlat), contains vertical correlation length above the reference point for different latitudes<br> vert_corr2=array of size (nalt, nlat), contains vertical correlation length below the reference point for different latitudes<br> </p>
Vertical Profiles of Convection-Permitting Simulations for Predicting Thunderstorm Occurrence
<p>This repository contains datasets for training and evaluation of the machine learning (ML) models in K. Vahid Yousefnia et al., <em>Inferring Thunderstorm Occurrence from Vertical Profiles of Convection-Permitting Simulations: Physical Insights from a Physical Deep Learning Model</em>, 2024 (submitted to <em>Artificial Intelligence for the Earth Systems,</em> preprint available at https://arxiv.org/abs/2409.20087).</p>
Asaia spp. accelerate development of the yellow fever mosquito, Aedes aegypti, via interactions with the vertically transmitted larval microbiome
<p><strong><span>Background:</span></strong><em> Aedes aegypti</em> mosquitoes are the primary vectors of yellow fever, dengue, chikungunya and Zika virus. Control programs primarily rely on insecticide application, which encounter challenges related to efficacy and resistance evolution. Alternative strategies, such as the sterile insect technique, highly depend on efficient mass-rearing of healthy insects prior to mass release. Based on effects seen in other mosquito species, we tested the hypothesis that acetic acid bacteria <span>of the </span><em>Asaia</em> <span>genus are</span> mutualist<span>s</span> for developing <em>Ae. aegypti</em> larvae. We tested for beneficial interactions across three <em>Asaia </em>species and whether <em>Asaia</em> inoculation benefited both axenic and conventionally reared larvae. To better understand the underlying mechanisms, we characterized the larval microbiome<span> </span>using culture-based methods and 16S rRNA gene amplicon sequencing.</p> <p><strong>Results:</strong><span> <span>Even</span></span> though <em>Asaia </em>bacteria were transient members of the gut community in conventionally reared insects<span>, t</span>wo <em>Asaia </em>species accelerated larval development relative to controls.<span> Despite their transient nature, </span>the two mutualist <em>Asaia</em> species had lasting impacts on the larval microbiome, mostly by altering the relative abundance of the most dominant bacteria genera <em>Klebsiella</em> and <em>Pseudomonas</em> and other minor components<span>.</span> Axenic larvae that were inoculated with <em>Asaia </em>were dominated by this group, but always exhibited slower development than conventionally reared insects.</p> <p><strong>Conclusions:</strong> These results reveal <em>Asaia</em> as a poor mutualist for <em>Ae. aegypti</em>, with its<em> </em>positive effect on the host mediated by interactions with other bacteria. A practical application of <em>Asaia </em>for improving mass-rearing efficiency results from the acceleration of development time to pupation by a day.</p>
Air/Snow temperature vertical profiles at different nodes of the 'Limnopolar Lake' CALM site, in Byers Península Livingston Island, Antarctica (2013-2022)
<div> <div> </div> </div> <div> <p>Air or seasonal snow temperature data were collected at different heights above the ground between 2013 and 2022 using an array of temperature micro-loggers (iButton models by Maxim) mounted on vertical wooden masts. These measurements were conducted at various nodes within the 100x100 m 'Limnopolar Lake' CALM site (A25) grid of the PERMATHERMAL network, managed by the University of Alcalá, Madrid, Spain, to monitor active layer thickness on Byers Peninsula, Livingston Island, South Shetland Islands, Antarctica.</p> <p>In 2013, nine arrays were installed at nodes with relative coordinates (00,00), (00,05), (00,10), (05,00), (05,05), (05,10), (10,00), (10,05), and (10,10). Measurements were taken at heights of 2.5, 5, 10, 15, 20, 25, 30, and 40 cm above the ground surface using DS1921G iButton loggers, which recorded air/snow temperatures every 4 hours. This experiment, referred to as 'Mini', was active for only one year and is now discontinued.</p> <p>Between 2017 and 2022, three arrays were installed at nodes (00,00), (05,05), and (10,10). These arrays measured air/snow temperatures at heights of 2.5, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, and 160 cm above the ground surface using DS1922L iButton loggers, which recorded temperatures every 3 hours. This experiment, referred to as 'HR', has also been discontinued.</p> </div>
Air/Snow temperature vertical profiles at different sites in Livingston Island, Antarctica (2006-2023)
<p>Air or seasonal snow temperature data collected at different heights above the ground (2.5, 5, 10, 20, 40, 80, and 160 cm), generally recorded every 3 hours between 2006 and 2023, using an array of temperature micro-loggers (iButton models by Maxim) mounted along a vertical wooden mast. These measurements were taken at various stations of the PERMATHERMAL network, managed by the University of Alcalá, Madrid, Spain, to monitor the thermal dynamics of frozen soils on Livingston Island, South Shetland Islands, Antarctica.</p>
Air/Snow temperature vertical profiles at different sites in Deception Island, Antarctica (2008-2023)
<p>Air or seasonal snow temperature data collected at different heights above the ground (2.5, 5, 10, 20, 40, 80, and 160 cm), generally recorded every 3 hours between 2006 and 2023, using an array of temperature micro-loggers (iButton models by Maxim) mounted along a vertical wooden mast. These measurements were taken at various stations of the PERMATHERMAL network, managed by the University of Alcalá, Madrid, Spain, to monitor the thermal dynamics of frozen soils on Deception Island, South Shetland Islands, Antarctica.</p>
Air/Snow temperature vertical profiles at different nodes of the 'Crater Lake' CALM site in Deception Island, Antarctica (2012-2023)
<p>Air or seasonal snow temperature data were collected at different heights above the ground between 2012 and 2023 using an array of temperature micro-loggers (iButton models by Maxim) mounted on vertical wooden masts. These measurements were conducted at various nodes within the 100x100 m 'Crater Lake' CALM site (A16) grid of the PERMATHERMAL network, managed by the University of Alcalá, Madrid, Spain, to monitor active layer thickness in Deception Island, South Shetland Islands, Antarctica.</p> <p>In 2012, nine arrays were installed at nodes with relative coordinates (00,00), (00,05), (00,10), (05,00), (05,05), (05,10), (10,00), (10,05), and (10,10). Measurements were taken at heights of 2.5, 5, 10, 15, 20, 25, 30, and 40 cm above the ground surface using DS1921G iButton loggers, which recorded air/snow temperatures every 4 hours. This experiment, referred to as 'Mini', was active until early 2021.</p> <p>Between 2017 and 2023, four arrays were installed at nodes (00,010), (05,05), (06,00), and (10,00). These arrays measured air/snow temperatures at heights of 2.5, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, and 160 cm above the ground surface using DS1922L iButton loggers, which recorded temperatures every 3 hours. Three of the arrays of this experiment, referred to as 'HR', has also been discontinued in early 2021, althought one of them was active until early 2024.</p>
Attribution of 2022 August Heavy Precipitation Event in South Korea Using High-resolution Pseudo Global Warming Simulations: Sensitivity to Vertical Temperature Changes
<p>Post-processed CPM simulation datasets used for the paper "Attribution of 2022 August Heavy Precipitation Event in South Korea Using High-resolution Pseudo Global Warming Simulations: Sensitivity to Vertical Temperature Changes".</p>
BALTRAD_VPTS - Vertical profiles of biological targets derived from European weather radars
<p><em>BALTRAD_VPTS - Vertical profiles of biological targets derived from European weather radars</em> is a vertical profile time series dataset published by the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a>. It contains animal movement data derived from 151 European weather radars in 18 countries, with varying coverage from 2012 to 2023. These data were created by processing weather radar data - provided by the Operational Programme for the Exchange of Weather Radar Information (<a href="https://www.eumetnet.eu/activities/observations-programme/current-activities/opera/">OPERA</a>) - with methods optimized for extracting bird targets. The resulting data are vertical profile time series (VPTS), containing the density, speed and direction of biological targets within a weather radar (<code>radar</code>) volume, grouped into altitude bins (<code>height</code>) and measured over time (<code>datetime</code>). The data are also available in the <a href="https://aloftdata.eu/browse/?prefix=baltrad/">Aloft bucket</a>.</p> <div> <div>See Desmet et al. (2025, <a href="https://doi.org/10.1038/s41597-025-04641-5">https://doi.org/10.1038/s41597-025-04641-5</a>) for a more detailed description of this dataset.</div> </div> <h2>Files</h2> <p>VPTS data in this deposit are organized per country (.tgz file), radar (directory), year (directory) and month (.csv.gz file). Fields in the data follow the <a href="https://aloftdata.eu/vpts-csv/">VPTS CSV</a> format and are described in <code>vpts-csv-table-schema.json</code>. An overview of what data are available is provided in <code>coverage.csv</code>. Radar metadata can be found at <a href="https://aloftdata.eu/radars/">https://aloftdata.eu/radars/</a>.</p> <ul> <li><strong>coverage.csv</strong>: coverage of the VPTS data, representing the number of unique hours, heights, source files and records for each radar and date combination.</li> <li><strong>vpts-csv-table-schema.json</strong>: technical description of the fields in the VPTS data.</li> <li><strong>be.tgz</strong>: VPTS data from 2 radars in Belgium.</li> <li><strong>ch.tgz</strong>: VPTS data from 5 radars in Switzerland.</li> <li><strong>cz.tgz</strong>: VPTS data from 2 radars in Czechia.</li> <li><strong>de.tgz</strong>: VPTS data from 20 radars in Germany.</li> <li><strong>dk.tgz</strong>: VPTS data from 5 radars in Denmark.</li> <li><strong>ee.tgz</strong>: VPTS data from 2 radars in Estonia.</li> <li><strong>es.tgz</strong>: VPTS data from 15 radars in Spain.</li> <li><strong>fi.tgz</strong>: VPTS data from 13 radars in Finland.</li> <li><strong>fr.tgz</strong>: VPTS data from 26 radars in France.</li> <li><strong>hr.tgz</strong>: VPTS data from 7 radars in Croatia.</li> <li><strong>il.tgz</strong>: VPTS data from 1 radar in Israel.</li> <li><strong>nl.tgz</strong>: VPTS data from 3 radars in the Netherlands.</li> <li><strong>no.tgz</strong>: VPTS data from 11 radars in Norway.</li> <li><strong>pl.tgz</strong>: VPTS data from 8 radars in Poland.</li> <li><strong>pt.tgz</strong>: VPTS data from 3 radars in Portugal.</li> <li><strong>se.tgz</strong>: VPTS data from 22 radars in Sweden.</li> <li><strong>si.tgz</strong>: VPTS data from 2 radars in Slovenia.</li> <li><strong>sk.tgz</strong>: VPTS data from 4 radars in Slovakia.</li> </ul> <h2>Acknowledgements</h2> <p>This dataset was processed using infrastructure provided by the University of Amsterdam, SURF Cooperative, Ghent University and the Research Institute for Nature and Forest (INBO). It was mainly supported by the <a href="https://globam.science/">GloBAM project</a>, funded through the 2017-18 Belmont Forum and BiodivERsA joint call for research proposals under the BiodivScen ERA-Net COFUND programme.</p>
UVA_VPTS - Vertical profiles of biological targets derived from weather radars in Belgium, Germany and the Netherlands
<p><em>UVA_VPTS - Vertical profiles of biological targets derived from weather radars in Belgium, Germany and the Netherlands</em> is a vertical profile time series dataset published by the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a>. It contains animal movement data derived from 24 weather radars in Belgium, Germany and the Netherlands, with varying coverage from 2008 to 2023. These data were created by processing weather radar data - provided by the Royal Meteorological Institute of Belgium (<a href="https://www.meteo.be/">RMI</a>), German Meteorological Service (<a href="https://www.dwd.de/">DWD</a>) and Royal Netherlands Meteorological Institute (<a href="https://www.knmi.nl/">KMNI</a>) - with methods optimized for extracting bird targets. The resulting data are vertical profile time series (VPTS), containing the density, speed and direction of biological targets within a weather radar (<code>radar</code>) volume, grouped into altitude bins (<code>height</code>) and measured over time (<code>datetime</code>). The data are also available in the <a href="https://aloftdata.eu/browse/?prefix=uva/">Aloft bucket</a>.</p> <p>See Desmet et al. (2025, <a href="https://doi.org/10.1038/s41597-025-04641-5">https://doi.org/10.1038/s41597-025-04641-5</a>) for a more detailed description of this dataset.</p> <h2>Files</h2> <p>VPTS data in this deposit are organized per country (.tgz file), radar (directory), year (directory) and month (.csv.gz file). Fields in the data follow the <a href="https://aloftdata.eu/vpts-csv/">VPTS CSV</a> format and are described in <code>vpts-csv-table-schema.json</code>. An overview of what data are available is provided in <code>coverage.csv</code>. Radar metadata can be found at <a href="https://aloftdata.eu/radars/">https://aloftdata.eu/radars/</a>.</p> <ul> <li><strong>coverage.csv</strong>: coverage of the VPTS data, representing the number of unique hours, heights, source files and records for each radar and date combination.</li> <li><strong>vpts-csv-table-schema.json</strong>: technical description of the fields in the VPTS data.</li> <li><strong>be.tgz</strong>: VPTS data from 3 radars in Belgium.</li> <li><strong>de.gz</strong>: VPTS data from 18 radars in Germany.</li> <li><strong>nl.gz</strong>: VPTS data from 3 radars in the Netherlands.</li> </ul> <h2>Acknowledgements</h2> <p>This dataset was processed using infrastructure provided by the University of Amsterdam, SURF Cooperative, Ghent University and the Research Institute for Nature and Forest (INBO). It was mainly supported by the <a href="https://globam.science/">GloBAM project</a>, funded through the 2017-18 Belmont Forum and BiodivERsA joint call for research proposals under the BiodivScen ERA-Net COFUND programme.</p>
Diel vertical migration promotes prokaryotic diversity in the Red Sea mesopelagic
<p>ABSTRACT: The diel vertical migration (DVM) of fish provides an active transport of labile dissolved organic matter (DOM) to the deep ocean, fueling the metabolism of heterotrophic bacteria and archaea. We studied the impact of DVM on the mesopelagic prokaryotic diversity of the Red Sea focusing on the mesopelagic deep scattering layer (DSL) between 450-600 m. Despite the general consensus of homogeneous conditions in the twilight zone, we observed variability in physico-chemical variables and distinct seasonal indicator prokaryotes inhabiting the DSL, representing between 2% (summer) to over 10% (winter) of total sequences. The DSL samples diverged from the surrounding mesopelagic waters in multidimensional scaling analysis and were distributed according to depth (47% of variance explained). We identified the sources of diversity that contribute to the DSL using spring depth profiles. On average, 7% was related to probable sinking from the epipelagic, 34% was common among the other mesopelagic waters and 38% was attributable to the DVM, with 21% of species being unique to the DSL. We conclude that the mesopelagic physico-chemical properties shape a rather uniform prokaryotic community, but that the 200 m wide DSL contributes uniquely and in a high proportion to the diversity of the Red Sea mesopelagic.</p> <p>The raw 16S sequences used in this research article are available at <a href="https://www.ebi.ac.uk/ena/browser/view/PRJEB49545">https://www.ebi.ac.uk/ena/browser/view/PRJEB49545</a> as 67 paired fastq sequences with consecutive accession numbers: ERX7411972 – ERX7412038.</p> <p>The 2 files stored in this repository represent: a) the clean 16S sequences count and taxonomic affiliation (SILVA132 Database) and b) the metadata associated to each of the 67 samples (lat, long, temperature, salinity, nutrient concentrations, bacterial abundance, bacterial size, etc)</p>
Cubic vertex-transitive graphs on up to 1280 vertices.
<p>The <strong>Census of cubic vertex-transitive graphs</strong> contains the list of all cubic vertex-transitive graphs on at most 1280 vertices, together with a number of graph-theoretic properties (listed below). </p> <p>The list of graphs was originally compiled by Pablo Spiga, Gabriel Verret, and Primož Potočnik. The authors described the theoretical results and computations that were needed to compile the list in the paper <a href="https://doi.org/10.1016/j.jsc.2012.09.002">Cubic vertex-transitive graphs on up to 1280 vertices</a>. The original dataset files are available on <a href="https://www.fmf.uni-lj.si/~potocnik/work.htm">Potočnik's website</a>.</p> <p>The graph-theoretic properties were either computed or verified with the SageMath system, with a few exceptions. Some of the properties were not supported by SageMath at the time of computation, and were contributed: is_cayley, odd_girth, is_partial_cube (based on some previous code). The methods <a href="https://github.com/DiscreteZOO/DiscreteZOO-sage/blob/df8c8368a4912bd5396001139a13a9f3b9b2863f/discretezoo/entities/cvt/cvtgraph.py#L177-L249">is_moebius_ladder, is_prism, and is_spx</a> (together with <a href="https://github.com/DiscreteZOO/DiscreteZOO-sage/blob/df8c8368a4912bd5396001139a13a9f3b9b2863f/discretezoo/entities/spx/spxgraph.py#L182-L300">SPX constructions</a>) were computed in DiscreteZOO.</p> <p>A searchable version of this dataset is available on an instance of the <a href="https://data.mathhub.info/">MathDataHub</a> platform hosted at <a href="http://mdh.graphsym.net/collection/CVT">mdh.graphsym.net/collection/CVT</a>.</p> <p>You can download this collection either as an SQLite database or as CSV files. Contents:</p> <ul> <li>master_db_zenodo.sqlite3: the SQLite database,</li> <li>create_database.sql: the SQL script that creates empty database schema used in the SQLite database (not necessary for opening the database),</li> <li>main_Graph.txt: the main table containing graphs as a CSV file,</li> <li>main_Graph.sample.txt: a sample of the main table,</li> <li>main_CLTime.txt: the table containing canonical labelling computation times as a CSV file.</li> <li>main_CLTime.sample.txt: a sample of the canonical labeling table</li> </ul> <p><strong>Structure and contents</strong></p> <p>Each graph is given a census-specific identifier <em>CVT[n,i]</em> (the <em>i</em>-th graph of order <em>n</em> in the census). Some graphs have a list of more easily recognized names (such as the Petersen graph). The dataset contains graphs in two formats: the <a href="https://users.cecs.anu.edu.au/~bdm/data/formats.html">sparse6 format</a> and a format readable by the computer algebra system Magma. For compatibility with SQLite, boolean values are represented with ones and zeroes (true and false, respectively).</p> <p>Data columns with types:</p> <ul> <li>canonical_label (string): the graph in sparse6 format, canonically labelled with nauty (call with no additional arguments), version: nauty-27r3,</li> <li>cvt_index (string): the census-specific identifier <em>CVT[n,i]</em> (the <em>i</em>-th graph of order <em>n</em> in the census),</li> <li>data (string): the graph in sparse6 format,</li> <li>raw_magma_code (string): the list of vertex neighbourhoods, readable by the computer algebra system Magma,</li> <li>name (string): a list of names of the graph,</li> <li>number_of_vertices (integer): number of vertices in the graph,</li> <li>clique_number (integer): the number of vertices in the largest clique subgraph,</li> <li>diameter (integer): the greatest distance between any pair of points,</li> <li>girth (integer): the length of the shortest cycle in the graph,</li> <li>is_arc_transitive (boolean): for every two ordered pairs of adjacent vertices, does there exist an automorphism, mapping one to the other,</li> <li>is_bipartite (boolean): can the vertices of the graph be partitioned into two sets, such that every edge connects a vertex in one set to a vertex in the other set,</li> <li>is_cayley (boolean): can the graph be constructed as a Cayley graph of some group for some generating set,</li> <li>is_distance_regular (boolean): for any two vertices <em>v</em> and <em>w</em>, does the number of vertices at distance <em>j</em> from <em>v</em> and at distance <em>k</em> from <em>w</em> depend only upon <em>j</em>, <em>k</em>, and <em>i = d(v, w)</em>,</li> <li>is_distance_transitive (boolean): for any two vertices <em>v</em> and <em>w</em> at any distance <em>i</em>, and any other two vertices <em>x</em> and <em>y</em> at the same distance, is there an automorphism of the graph that carries <em>v</em> to <em>x</em> and <em>w</em> to <em>y</em>,</li> <li>is_edge_transitive (boolean): for every two edges, does there exist an automorphism, mapping one to the other,</li> <li>is_hamiltonian (boolean): does the graph have a cycle that visits each vertex exactly once,</li> <li>is_partial_cube (boolean): is the graph isometric to a subgraph of a hypercube,</li> <li>is_split (boolean): can the vertices of the graph be partitioned into a clique and an independent set,</li> <li>is_strongly_regular (boolean): do there exists <span class="math-tex">\(\lambda\)</span> and <span class="math-tex">\(\mu\)</span> such that every two adjacent vertices have <span class="math-tex">\(\lambda\)</span> common neighbours and every two non-adjacent vertices have <span class="math-tex">\(\mu\)</span> common neighbours,</li> <li>odd_girth (integer): length of the shortest odd cycle,</li> <li>triangles_count (integer): the number of cycles of length <em>3</em> in the graph,</li> <li>is_moebius_ladder (boolean): is the graph a Möbius ladder,</li> <li>is_prism (boolean): is the graph a skeleton of a prism,</li> <li>is_spx (boolean): does the graph belong to the Split Praeger-Xu family of graphs,</li> <li>vertex_stabilizer (list of integer pairs): the order of the vertex stabilizer of the graph's automorphism group, given as a prime factorization; a list of pairs <span class="math-tex">\((p_i, e_i)\)</span> of primes and exponents such that <span class="math-tex">\(\prod_i p_i^{e_i}\)</span> is the order of the vertex stabilizer.</li> </ul> <p>This work was partially supported by ARRS research project no. J1-1691.</p>
Data from the parametric analysis of masonry buttressed arches with limit analysis subjected to vertical self-weight plus a proportional horizontal live load
<p>For each one of the simulations performed from the parametric analysis of masonry buttressed arches with limit analysis subjected to vertical self-weight plus a proportional horizontal live load, this database contains a .txt, a .vtk and a .png file. In the .txt file the elapsed time and the collapse multiplier of each simulation can be found. The .vtk file contains all the geometry and displacement values of every masonry buttressed arch. Finally, the .png file presents the collapse mechanism obtained. </p>
Data from the parametric analysis of masonry buttressed arches with limit analysis subjected to vertical self-weight plus a proportional concentrated vertical live load applied at mid-span
<p>For each one of the simulations performed from the parametric analysis of masonry buttressed arches with limit analysis subjected to vertical self-weight plus a proportional concentrated vertical live load applied at mid-span, this database contains a .txt, a .vtk and a .png file. In the .txt file the elapsed time and the collapse multiplier of each simulation can be found. The .vtk file contains all the geometry and displacement values of every masonry buttressed arch. Finally, the .png file presents the collapse mechanism obtained. </p>
Data used in the article: "Climate change impacts the vertical structure of marine ecosystem thermal ranges"
<p>This dataset is used in the manuscript "Climate change impacts the vertical structure of marine ecosystem thermal ranges" accepted in Nature Climate Change 2022.</p>
A data set of monthly global ocean vertical velocity from 1950-2014
<p>This data set provides monthly global ocean vertical velocity from 1950-2014. It was constructed from 41 CMIP6 models (historical experiment). It may be used for investigating the large-scale upwelling and downwelling.</p> <p>Note that this data set has not been widely tested. Please feel free to contact the author if you had any questions or concerns.</p> <p>It will be greatly appreciated if you could send the author an email when you used this data set, so that the author can better improve this data set, and more importantly, provide you with updated data sets or any modifications.</p> <p> </p> <p> </p>
CMIP6 model vertically-integrated net primary production data
<p>Vertically-integrated net primary production (NPP) data from 12 models that participated in phase six of the Coupled Model Intercomparison Project (CMIP6). All data pulled from the Earth System Grid Federation.</p> <p>All model output was regridded onto a common, regular horizontal grid of 1x1 degrees (360 x 180) in longitude by latitude.</p> <p>Units are mol C per metre squared per second.</p> <p>Models are:</p> <ol> <li>ACCESS-ESM1-5</li> <li>CanESM5</li> <li>CESM2</li> <li>CNRM-ESM2-1</li> <li>GFDL-CM4</li> <li>GFDL-ESM4</li> <li>IPSL-CM6A-LR</li> <li>MIROC-ES2L</li> <li>MPI-ESM1-2-HR</li> <li>MRI-ESM2-0</li> <li>NorESM2</li> <li>UKESM1-0-LL</li> </ol>
DATASET Invertebrate sounds from photic to mesophotic coral reefs reveal vertical stratification and diel diversity
<p>This dataset contains 17 wave folders. The original files were used for the study published by Raick et al. (2024) in Oecologia (10.1007/s00442-024-05572-5), while subsampled versions of these files were used for the studies published by Raick et al. (2023) in Coral Reefs (10.1007/s00338-022-02343-7) and Raick et al. (2023) in Scientia Marina (10.3989/scimar.05395.078).</p>
Viewing behavior and vertical eye-level light for non-image-forming effects
<p>When considering non-image-forming (NIF) light effects on people, knowing the light vertically at eye-level is necessary. However, people are dynamic in their behavior and constantly change their viewing direction. This means that light measured vertically towards a constant direction might differ from the actual light that reaches people’s eyes. If the difference is large, viewing behavior might need to be included in lighting design measurements and simulations predicting the potential of the light to induce NIF light effects. This dataset was collected during an experiment on the difference between the actual dynamic eye-level light of office workers while seated at a desk (dynamic condition) and light measured statically towards a computer screen (static condition). The dataset was collected to test the hypothesis: "There is a significant and relevant difference between simultaneously measured static and dynamic light conditions in an office environment occupied by one user." It includes measured and simulated light quantities (illuminance, alpha-opic quantities according to CIE S026 and light-driven alertness according to the non-visual direct response model) together with participants' measured face orientation (horizontal and vertical) in an office environment with a single user.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.