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16,872 results for “Differences”
Acclimation to water restriction implies different paces for behavioral and physiological responses in a lizard species
<p>Raw data of the article "Acclimation to Water Restriction Implies Different Paces for Behavioral and Physiological Responses in a Lizard Species" by Rozen-Rechels D. et al., published in Physiological and Biochemical Zoology 93(2):160-174 in 2020 (https://doi.org/10.1086/707409). These data are freely available in csv format. See the readme file for metadata explanation.</p> <p>Data were formatted by the first author David Rozen-Rechels and collected according to standards and procedures described in the companion journal article.</p> <p> </p>
Carbon emissions and economic assessment of farm operations under different tillage practices in organic rainfed almond orchards under semiarid Mediterranean conditions
<p>This dataset corresponds to yield, price and fuel consumption from organic rainfed almond orchards in SE Spain under different diversification and tillage practices. The objective is to carry out an integrated environmental (focused on the CO<sub>2</sub> emissions) and economic assessment of farm operations under different diversification and tillage practices through a cradle-to-farm gate life cycle assessment (LCA) based on these data.</p> <p>These data correspond to the open-access article " Carbon emissions and economic assessment of farm operations under different tillage practices in organic rainfed almond orchards under semiarid Mediterranean conditions" published in Scientia Horticulturae. (https://doi.org/10.1016/j.scienta.2019.108978), funded by the European Commission Horizon 2020 project Diverfarming [grant agreement 728003].</p>
UM experiments for "Continuous Structural Parameterization: A proposed method for representing different model parameterizations within one structure demonstrated for atmospheric convection"
<p>NetCDF4 files containing UM vn 11.1 data used in Lambert et al., 2020, Continuous Structural Parameterization: A proposed method for representing different model parameterizations within one structure demonstrated for atmospheric convection, submitted to Journal of Advances in Modeling Earth Systems.</p> <p>Key:</p> <p>"summaryday.nc" contain eleven months of data in each year, excluding either February or March.</p> <p>"summarydat2.nc" contain one month of data in each year, either February or March.</p> <p>"last5" indicates that for this simulation only the last five years of data are available.</p> <p>"llcs" are simulations with Lambert-Lewis.</p> <p>"gr" are simulations with Gregory-Rowntree.</p> <p>"llcsemu" are simulations with the Lambert-Lewis emulator.</p> <p>"gremu" are simulations with the Gregory-Rowntree emulator.</p> <p>"llcsemu_llcs" is the test simulation wherein the LLCS emulator is run equatorward of 30 degrees and the original LLCS convection scheme is run poleward of 30 degrees.</p> <p>"4xco2" have 4 x pre-industrial atmospheric carbon dioxide concentration. (Others have 1 x pre-industrial atmospheric carbon dioxide concentration.)</p> <p>"rh0.7" and "rh0.9" have LLCS RHCRIT set to 70% and 90% respectively.</p> <p>"30day" are one month simulations for July for which daily output are available. Other data are monthly mean only.</p> <p> </p>
Sex differences in visuomotor tracking
<p>This excel file contains individual data from two cohorts presented in our publication. </p> <p>Each excel sheet presents a separate portion of data (=>one sheet per figure)</p>
Data cleaning and analysis for the Master's thesis: DIFFERENCES IN CONSUMER PREFERENCES FOR UNWEATHERED AND WEATHERED WOOD
<p>The data and analytical support the Master's thesis submitted by Hana Remesova at the University of Primorska<br> Faculty of Mathematics, Natural Sciences, and Information Technologies. The .csv files are data files, the .Rmd file is an R markdown which can be run. The product of knitting the .Rmd file is the .html.</p>
JUMP - Data collection - Part II: Zonal jets using three different approaches, laboratory - Global Climate Models - observations.
<p>The formation of large scale structures in three-dimensional (3D) turbulent flows. How small-scale dynamics organize in turbulent flows to grow large scale coherent circulation? is at the heart of fundamental studies in fluid dynamics. It appears to be equally important for our understanding of atmospheric dynamics, oceanography, meteorology and more generally geophysical fluid dynamics. Here, we deliver a data collection that <strong>(1)</strong> gathers measurements of 3D turbulent flows that emulate planetary atmospheres of the gas giants. Turbulent flows are explored using three different approaches, laboratory experiments, numerical simulations and direct planetary observations. All data set are computed in order to easily extract flow properties, i.e. high resolution maps of the different velocity components and flow vorticity (useful for further diagnostic). The data collected are fully discribed in Cabanes et al GRL (2020) "Revealing the intensity of turbulent energy transfer in planetary atmospheres" and can be used to compute <strong>(2)</strong> theoretical diagnostics with the numerical codes that allow to reveal the physical meaning of flow measurements. Numerical codes are available on https://github.com/scabanes</p> <p>We deliver (1) data collection and (2) numerical codes in the following files attached:</p> <p>(1) Data collection:</p> <ul> <li>A PDF file named <strong>JUMP-zonal-jets-data-collection-GRL.pdf</strong> that describes the following data files and nomenclature.</li> <li>A zip File of the velocity fields in the lab, interpolated on Polar and Cartesian grids <ul> <li><strong>JUMP-JetsInTheLab.zip</strong></li> </ul> </li> <li>A netcdf file of velocity fields of our Saturn reference simulation <ul> <li><strong>uvData-SRS-istep-312000-nstep-50-niz-12.nc</strong></li> </ul> </li> <li>Two netcdf files of velocity fields from Cassini observations of Jupiter<strong> </strong> <ul> <li><strong>uvData-JupObs-istep-0-nstep-4-niz-1.nc</strong></li> <li><strong>StatisticalData-JupObs.nc</strong></li> </ul> </li> <li>A zip file of potential vorticity profiles for Saturn and Jupiter observations <ul> <li><strong>IPV-QGPV-Jupiter-Saturn.zip</strong></li> </ul> </li> </ul> <p>(2) Numerical codes:</p> <ul> <li>Codes for statistical analysis in spherical geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FPOST&sa=D&sntz=1&usg=AFQjCNFuDU0eij4XGxQfReO92CHfJz6PBA">https://github.com/scabanes/POST</a></li> <li>Codes for statistical analysis in cylindrical geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FJUMP&sa=D&sntz=1&usg=AFQjCNGUQ1YIFhSxBAg4Hl_5gOLB_4LxLA">https://github.com/scabanes/JUMP</a></li> <li>Codes for statistical analysis in cartesian geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FJUMP&sa=D&sntz=1&usg=AFQjCNGUQ1YIFhSxBAg4Hl_5gOLB_4LxLA">https://github.com/scabanes/JUMP</a></li> </ul> <p> </p> <p>The purpose of this data collection is to reveal statistical properties of planetary flows. By computing the same analysis on different data sets the researcher allows direct confrontation of planetary observations with idealized laboratory and numerical models. Idealized models are specially designed to sweep on a large array of parameters in order to understand what parameters control planetary global circulation. The data collected and generated by the researcher deliver <strong>(1)</strong> velocity measurements of 3D turbulent flows using the different approaches (observations-laboratory-numerics) and <strong>(2)</strong> guidelines to compute the appropriate statistical analysis through the PTST. Here, the ground-breaking novelty is that the researcher deliver the possibility to compute statistical diagnostics adapted to the different geometries: the spherical geometry of planetary flows, i.e. 2D latitude-longitude maps, the cylindrical geometry of laboratory experiments, i.e. 2D flows in a rotating cylindrical tank, and the Cartesian geometry of idealized numerical simulations. Indeed, the math behind each statistical diagnostics must account for the different geometrical configurations in order to properly confront the different approaches. The PTST is also designed to be easily re-used by different communities such as experimentalists, numericists and atmosphericists that deal with 3D or 2D turbulent flows.</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement N° 797012.</p>
Gender Differences in Public Code Contributions: a 50-year Perspective - Replication Package
<p>This page details the steps needed to replicate the findings of the paper: <a href="https://upsilon.cc/~zack/">Stefano Zacchiroli</a>, <em>Gender Differences in Public Code Contributions: a 50-year Perspective</em>, <a href="https://www.computer.org/csdl/magazine/so">IEEE Software</a>, 2021.</p> <p>After retrieving the replication package, follow the instruction described in the README.html file.</p>
Systematic Data Analysis and Diagnostic Machine Learning Reveal Differences between Compounds with Single- and Multitarget Activity
<p>The deposited files contain balanced data sets of multi-target (MT) and single-target (ST) compounds (CPDs) used for machine learning studies (https://dx.doi.org/10.1021/acs.molpharmaceut.0c00901). The first file (st_mt_data.tsv) contains 15,142 MT- and 15,081 ST-CPDs and the second (st_dt_data.tsv) 1828 DT- and 1776 ST-CPDs. For each CPD, a nonstereo_aromatic_SMILES representation, the original ChEMBL_cid, UniProt (target) IDs, and CPD category (CPD_CAT) (i.e. DT/MT/ST) is provided. DT stands for 'diverse-target' and denotes a subset of MT-CPDs (as detailed in the publication). In addition, a CPD is tagged “Y” if it continued to be present in the data set after removal of 50% randomly selected CPDs or 50% CPD nearest neighbors (NN), respectively.</p>
FESOM2 simulations with increasing sea-ice model complexity under different atmospheric forcings
<p><strong>Introduction</strong></p> <p>This dataset has been compiled in support of the paper "Impact of sea-ice model complexity on the performance of an unstructured sea-ice/ocean model under different atmospheric forcings" by Zampieri et al., submitted to the Journal of Advances in Modeling Earth Systems (JAMES) published by the American Geophysical Union (AGU).</p> <p><strong>Scientific description of the dataset</strong></p> <p>The dataset contains the results of sea-ice simulations performed with the Finite-volumE Sea ice-Ocean Model version 2 (FESOM2), based on six model configurations: C1-E, C1-N, C2-E, C2-N, C3-E, and C3-N. As described in the paper, the complexity of the sea-ice model increases from the setup C1 to C3. The suffix -E and -N indicate respectively the ERA5 and NCEP atmospheric forcings used as boundary conditions for the FESOM2 model. As two iterations of the Green's function approach for the optimization of the parameter space have been performed, each configuration features three separate simulations: a control run (cnt), a first-round of optimization (opt_1), and a second and final round of optimization (opt_2). The parameter optimization is based on various sea-ice observations retrieved over the period 2002–2015. In total, 18 simulations compose the dataset (6 configurations x 3 realizations). The following 2D monthly-averaged variables are provided: the sea-ice concentration, the sea-ice thickness, the meridional and zonal components of the sea-ice velocity, and the snow thickness on top of the sea ice. The fields are defined on a global unstructured mesh denominated "CORE2", which is also included in the database.</p> <p><strong>Technical description of the dataset</strong></p> <p>As an unstructured model output is not widely diffused in the sea-ice community, we include here some suggestions for handling and analyzing the simulation results.</p> <p>The files can be interpolated to a regular grid using the following <strong><a href="https://code.mpimet.mpg.de/projects/cdo">CDO</a></strong> commands:</p> <ol> <li>Add grid description to model file: <strong><em>cdo setgrid,CORE2_mesh.nc var.fesom.yyyy.nc temp.nc</em></strong></li> <li>Interpolate to regular grid: <strong>cdo remapycon,r360x180 temp.nc var.fesom.interpolated.yyyy.nc</strong></li> </ol> <p>Furthermore, the python package<strong> <a href="https://code.mpimet.mpg.de/projects/cdo">pyfesom2</a></strong> can be used for plotting the unstructured model data and for interpolating it to a regular grid. The R package <strong><a href="https://github.com/FESOM/spheRlab">spheRlab</a></strong> can be used for plotting the model data directly on its unstructured grid and for performing further analysis. More information can be found on the <strong><a href="https://fesom.de/cmip6/work-with-awi-cm-unstructured-data/">FESOM website</a></strong>.</p> <p>The following naming convention is adopted for the model variables:</p> <ul> <li><strong><em>a_ice</em></strong> → sea-ice concentration</li> <li><strong><em>m_ice</em></strong> → sea-ice volume per unit area of ice</li> <li><strong><em>m_snow </em></strong>→ snow-volume per unit area of ice</li> <li><strong><em>vice</em></strong> → meridional component of the sea-ice velocity</li> <li><strong><em>uice</em></strong> → zonal component of the sea-ice velocity</li> </ul> <p>Three types of simulation are included:</p> <ul> <li><strong>cnt </strong>→ control run before the parameters optimization (2000–2019)</li> <li><strong>opt_1 </strong>→ after the first iteration of the parameter optimization method (2000–2015)</li> <li><strong>opt_2</strong> → after the second iteration of the parameter optimization method (2000–2019)</li> </ul> <p>Do not hesitate to contact the corresponding author (lorenzo.zampieri@awi.de) for additional information about the data processing and for any other issue with this dataset.</p> <p> </p> <p> </p>
Kanban tasks opening and close dates differences between git and Jira
<p>Dataset contains about three years of records from a software development company.</p> <p>Dataset contains the task start and end date registered in Jira software and the open and merged dates for those same tasks extracted from git records.</p> <p>This dataset compares those opening and close dates and shows the differences</p>
Supplementary Data – Indian Covid19 Cases at Different Temperature
<p>Supplimentary Datasets of A STATEWISE STATISTICAL ANALYSIS ON COVID19 CASES IN INDIA AT DIFFERENT TEMPERATURE derived from Curve Expert 1.4 and Excell Spreadsheet . </p>
RDF version of the data from Choi, JS., Ha, M.K., Trinh, T.X. et al. Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources. Sci Rep 8, 6110 (2018)
<p>RDF version of the data from Choi, JS., Ha, M.K., Trinh, T.X. et al. Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources. Sci Rep 8, 6110 (2018)</p>
Supplementary files for Molecular Differences Between Squamous Cell Carcinoma and Adenocarcinoma Cervical Cancer Subtypes: Potential Prognostic Biomarkers
<p>Supplementary files for Molecular Differences Between Squamous Cell Carcinoma and Adenocarcinoma Cervical Cancer Subtypes: Potential Prognostic Biomarkers</p>
Plotting sex differences in global causes of morbidity
<p>Data, code and graphics for causes of disability-adjusted life-years globally, separated by sex and by age-group. Data was downloaded from the World Health Organisation, 2012 records. This was used in a commissioned review article for eLS http://www.els.net/WileyCDA/ , Gilks, William P (October 2016) Sex Differences in Disease Genetics. In: eLS. John Wiley & Sons, Ltd: Chichester. DOI: 10.1002/9780470015902.a0026936 and is also available as an unreviewed, un-edited pre-print on bioarxiv http://dx.doi.org/10.1101/063651 http://biorxiv.org/content/early/2016/07/13/063651</p>
Plotting sex differences in genetics of waist-hip ratio
<p>For plotting results of genome-wide association study results on waist-hip ratio, originally by Shungin et al 2015 doi: 10.1038/nature14132, to be used in a commissioned review article for eLS http://www.els.net/WileyCDA/ , to be published soon, and currently available as an unreviewed, un-edited pre-print on bioarxiv http://dx.doi.org/10.1101/063651</p>
A Corpus of Biblical Names in the Greek New Testament to Study the Additions, Omissions, and Variations across Different Manuscripts
<p>The analysis of textual variants of verses in the Ancient Greek New Testament across different manuscripts has mainly been done by close reading with manual effort. With the increasing number of transcriptions of the different manuscripts, quantitative analyses (so-called distant reading) can be used to search for patterns of omission, addition, or other variations, to formulate novel hypotheses to be investigated by close reading. In this work, we present a corpus of biblical names including spelling variation and inflections and their mentions in the transcriptions of the Ancient Greek New Testament.</p>
Simulated TSCH dataset using different slotframe matrix configurations
<p>The current dataset was obtained by using a specifically developed simulator, to analyze the behavior of a time slotted channel hopping (TSCH) wireless sensor network (WSN), under different operating conditions.</p> <p>The configuration of the simulator, the characteristics of the network, and the generated traffic patterns are reported in [1].<br>Different configurations of the slotframe matrix, which allows slots to be reserved for specific pairs of nodes, are analyzed.</p> <p>File "<code>network_topology.pdf</code>" reports the topology of the simulated wireless network.</p> <p>The four analyzed configurations, which are deeply described in [1], are:</p> <ul> <li><strong>Star</strong>, whose slotframe matrix configuration is reported in the "<code>Star.conf</code>" file.</li> <li><strong>Load</strong>, whose slotframe matrix configuration is reported in the "<code>Load.conf</code>" file.</li> <li><strong>Parallel</strong>, whose slotframe matrix configuration is reported in the "<code>Parallel.conf</code>" file.</li> <li><strong>LPR</strong>, whose slotframe matrix configuration is reported in the "<code>LPR.conf</code>" file.</li> </ul> <p>A typical "<code>.conf</code>" file has the following format:<br><code># Offset src dest</code><br><code>0 4 1</code><br><code>0 6 2</code><br><code>0 8 3</code><br><code>0 10 9</code><br><code>1 1 0</code><br>where the first column represents the slot offset, i.e., the time slot in the slotframe matrix (which repeats periodically over time), in which a transmission opportunity is scheduled. Since many concurrent transmissions between different couple of nodes and different channels are possible simultaneously, more than one transmission could be scheduled at the same time. In the example, four transmission opportunities are scheduled in slot offset number 0.<br>The second column of each row represents the source node, while the third column represents the destination node. For instance, the schedule "<code>0 10 9</code>" represents the scheduled transmission at slot offset 0 from the source node 10 to the destination node 9.</p> <p> </p> <p>For each configuration, a corresponding file with the extension "<code>.dat</code>" contains the log generated in the simulation. An example is the following: <br><code>00083 72204 FLOW: 6 11 -> 0 LOST: 0 TRIES: 3 LATENCY: 540</code><br><code>00082 72156 FLOW: 5 10 -> 0 LOST: 0 TRIES: 4 LATENCY: 3460</code><br><code>00084 78013 FLOW: 0 4 -> 0 LOST: 0 TRIES: 2 LATENCY: 1240</code><br>where the transmission in a path from the source node (e.g., 11) to a destination node (e.g., the root node 0) is summarized with a single line in the log.</p> <p>Each line is composed of the following fields:</p> <ul> <li><em><packet number></em>: an integer number (e.g., <code>00083</code>) that uniquely identifies a packet transmitted in a multi-hop fashion from the source node to the destination node.</li> <li><em><queuing_time></em>: the queuing time expressed in terms of number of slots. In the simulation, slots have a length of 20 ms.</li> <li><em><flow_index></em>: the word "<code>FLOW:</code>" followed by an integer number identifying the flow. The simulation contains seven periodic flows with periods 6001, 6003, 6005, 6007, 6011, 6013, and 6017 expressed in terms of number of slots, for flows with index 0, 1, 2, 3, 4, 5, 6, respectively. For instance, "FLOW: 6" has a period of 6017 slots, which corresponds to 120.34 s (i.e., about 2 minutes).</li> <li><em><path></em>: an integer value representing the source node of the path, followed by the characters "<code>-></code>", followed by another integer value representing the destination node. For instance, "<code>11 -> 0</code>" represents the transmission in the path between node 11 and node 0.</li> <li><em><lost></em>: is an indication if the packet was lost in the path ("<code>LOST: 1</code>") or the packet arrived correctly at the destination ("<code>LOST: 0</code>"). A packet is lost if on a given link reached the maximum number of retransmissions.</li> <li><em><tries></em>: is the sum of the transmissions performed in each link. For instance, the link "<code>10 -> 0</code>" is composed of 3 hops. The value "<code>TRIES: 4</code>" means that a retransmission was performed for one of the links in the path.</li> <li><em><latency></em>: the transmission latency of the packet from when it was queued to when it reached its destination. The latency is expressed in ms.</li> </ul> <p>For each condition, the number of logged packets (i.e., lines) is 36,742,162, corresponding to 20 years of simulation.</p> <p> </p> <p>In addition, the code of the simulator is provided in the file "<code>TSCHmodeler.zip</code>".</p> <p>To run the simulations reported in [1], you have to execute the command:</p> <ul> <li>For experiment in Section IV.A <ul> <li><code>python3 -m TSCHmodeler conf/simple.conf</code></li> <li><code>python3 -m TSCHmodeler conf/simple_1week.conf</code></li> </ul> </li> <li>For experiment in Section IV.B <ul> <li><code>python3 -m TSCHmodeler conf/star_minimal.conf</code> for the <strong>star</strong> minimal configuration</li> <li><code>python3 -m TSCHmodeler conf/star_load.conf</code> for the <strong>load</strong> minimal configuration</li> <li><code>python3 -m TSCHmodeler conf/star_parallel.conf</code> for the <strong>parallel</strong> minimal configuration</li> <li><code>python3 -m TSCHmodeler conf/star_LPR.conf</code>for the <strong>LPR</strong> minimal configuration</li> </ul> </li> <li>For experiment in Section IV.C <ul> <li><code>python3 -m TSCHmodeler conf/large_40_nodes.conf</code></li> <li><code>python3 -m TSCHmodeler conf/large_121_nodes.conf</code></li> </ul> </li> </ul> <p> </p> <p>References:<br>[1] S. Scanzio, P. Chiavassa, G. Formis, G. Paolini and G. Cena, “A Lightweight Simulation Environment for TSCH-Based Wireless Sensor Networks,” in IEEE Transactions on Industrial Cyber-Physical Systems, 2025. doi: <a title="https://doi.org/10.1109/TICPS.2025.3620370" href="https://doi.org/10.1109/TICPS.2025.3620370" target="_blank" rel="noopener">10.1109/TICPS.2025.3620370</a></p>
Dataset of publication "Investigation of the discharge coefficient in the laminar boundary layer regime of critical flow Venturi nozzles calibrated with different gases including hydrogen"
<p>The attached files contain experimental raw data and fluid properties for the nozzles 1 and 2 mentioned in the paper. The data in the attached files can be used to calculate the Cd values published in the paper.</p>
BST/NOAA PSL Level 2 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH
<p>This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). These data were collected near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 18th, 2023. Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies LLC. </p> <p> </p> <p>Each zip file contains a set of four Level 2 NetCDF files which provides the highest spatial resolution available for each of four products for a given flight location. With the Level 2 data, each flight location and variable can have different spatial resolutions depending on the sensor type, retrieval algorithm, and flight altitude. The file name convention for the zip files is as follows.</p> <p> </p> <p>uas_L2_yyyymmdd_hhmmss_vX.X.zip </p> <p>where</p> <p>L2 = Level 2 data </p> <p>yyyymmdd = year,month,day</p> <p>hhmmss = hour,minute,second</p> <p>vX.X = version number</p> <p>Time is the flight start time in UTC.</p> <p> </p> <p>The NetCDF file format contained in the zip files has a similar format to the zip files with convention</p> <p> </p> <p>uas_<var>_L2_yyyymmdd_hhmmss.nc </p> <p>where</p> <p><var> = vsm, dem, ndvi, or stmp</p> <p>vsm = volumetric soil moisture</p> <p>dem = digital elevation</p> <p>ndvi = normalized difference vegetation index</p> <p>stmp = surface temperature</p> <p> </p> <p>Note that each flight location using the E2 aerial platform required two flights so starting flight times for the soil moisture NetCDF files are different from the other three products.</p> <p><strong>November 2023 update</strong>: Version 2.0 added flight data from 2023. Version 2.0 includes an updated calibration of the soil moisture retrieval that has been applied to 2023 data, and a mask was applied to the soil moisture retrieval over water surfaces for both 2022 and 2023 data.</p> <p><strong>December 2023 update</strong>: Version 2.1 updated soil moisture data with a wet bias in v2.0 for flights #2 (17:40:35 UTC) and #3 (19:24:45 UTC) on July 27, 2022.</p>
Full Body Motion Capture of Single Individuals Following External Perturbations from Different Directions
<p>This dataset is composed of C3D files corresponding to full body motion of participants undergoing external perturbation at shoulder height with different sensory conditions. The temporal force profiles of the perturbations are also available.</p> <p>The following experiment received ethical approval from an ethics committee and all participants signed an informed consent form relative to the processing of their data. <br>The experiments were carried on 21 healthy young adults (10 females, 11 males). All were between 20 and 38 yo with a mean age of 27.2 (std: 4.2). Mean mass was 70.2 (std: 12.1) kg and height was 1.74 (std: 0.08) m. </p> <p>Participants motion was recorded using 45 reflective markers and a 23 Qualisys camera system (200Hz). <br>The markers were placed on participants following standardised anatomical landmarks. <br>The output signal of the force sensor was processed using a Butterworth low pass filter with a 5Hz cutoff frequency without phase shift. <br>The force sensor was synchronised with the motion capture software.<br>Tree reflective markers were also placed along the pole in order to retrieve the exact direction of the perturbations. </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.