Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
5,805
datasets available to search
ShareScore release 0.9.0
Dataset results
5,805 results for “Data model”
Data for: Water system simulation modeling with hydropower optimization and environmental flows: An example with Pywr
<p>This dataset was used in the CenSierraPywr model created for the project "Optimizing Hydropower Operations While Sustaining Ecosystem Functions in a Changing Climate", for the California Energy Commission. Specifically, this data is to support reproducibility of the article describing the basic methods (Rheinheimer et al., in review). The model was built in Pywr, an open-source, linear programming-based Python package for modeling basin-scale water systems in the Central Sierra Nevada, California. Here, we focus on the Stanislaus and Upper San Joaquin River basins as they have high elevation hydropower typically operated to maximize revenue. CenSierraPywr consists of daily water allocations that include both hydroeconomic drivers for hydropower and more advanced environmental flows. Piecewise linear electricity prices from simulated hourly price data are used to drive discretionary hydropower, while environmental flows include the addition of ramping rates. Hydrological inputs include runoff data at the sub-basin level, based on the historical (1950 to 2011) daily gridded (1/16 degree) runoff data generated by the Variable Infiltration Capacity (VIC) hydrologic model developed by Livneh et al. (2013), forced with observed meteorological data and bias-corrected using local gauge data. All data inputs for reproducibility of CenSierraPywr for the Stanislaus and Upper San Joaquin Rivers are included, including original and preprocessed electricity and hydrological data and management-related data specific to certain hydropower projects or facilities.</p>
TOMCAT model data & IASI satellite data of O3, CO, H2O, CH4 and OH/derived OH for 2010 and 2017
<p>Monthly mean data of ozone (O3), carbon monoxide (CO), water vapour (H2O), methane (CH4) and the hydroxyl radical (OH) for 2010 and 2017.</p> <p>Model data is from the 3D chemical transport model TOMCAT (Chipperfield, 2006).</p> <p>Satellite observations are from the Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp-A satellite and retrieved using schemes developed by the Rutherford Appleton Laboratory (RAL). The Ch4 is from RAL's CH4 retrieval scheme (Siddans et al. 2020) and the O3, CO and H2O retrievals are from the extended version of RAL’s Infrared and Microwave Sounding (IMS-extended) scheme (Pope et al. 2021). </p> <p>Full description of the data can be found in Pimlott et al. (2022) (preprint: https://doi.org/10.5194/acp-2022-79) which has now been accepted for publication in ACP. </p>
Data for: "Dynamic species distribution modeling reveals the pivotal role of human-mediated long-distance dispersal in plant invasion"
<p>All the data needed to reproduce the results and Figures of our article:</p> <p>Botella, C., Bonnet, P., Hui, C., Joly, A., & Richardson, D. M. (2022). Dynamic Species Distribution Modeling Reveals the Pivotal Role of Human-Mediated Long-Distance Dispersal in Plant Invasion. <em>Biology</em>, <em>11</em>(9), 1293. <a href="https://doi.org/10.3390/biology11091293">https://doi.org/10.3390/biology11091293</a></p> <p>Please, find the R scripts and guidelines to reproduce our results on the article's Github repository :</p> <p><a href="https://github.com/ChrisBotella/plectranthus_barbatus/tree/main">https://github.com/ChrisBotella/plectranthus_barbatus/tree/main</a></p>
GENeSYS-MOD Transport Sensitivities: Data and model code for Hainsch (preprint): Identifying policy areas for the transition of the transportation sector
<p>This dataset contains all GENeSYS-MOD input data for Hainsch (preprint): Identifying policy areas for the transition of the transportation sector. doi: 10.5281/zenodo.6919452.</p> <p>With the input data files and the GAMS files, the model results presented in the preprint can be replicated.</p> <p>Furthermore, the output folder contains the result files for the base case and all sensitivities as well as the Tableau files which were used to generate the result figures.</p>
Data, scripts, and figures of the article: Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 3. Validation of prediction models
<p>This data set contains the data, JMP scripts, and figures of the article titled "Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 3. Validation of prediction models" to be published in the journal Animal - Open Space.</p>
Breeding tomato flavor: modeling consumer preferences of tomato landraces (raw data)
<p>Raw dataset associated with the publication:</p> <p><strong>Breeding tomato flavor: modeling consumer preferences of tomato landraces.</strong></p> <p>Villena, J.<sup>a</sup>, Moreno, C.<sup>a</sup>, Roselló, S.<sup>b</sup>, Beltran, J.<sup> c</sup>, Cebolla-Cornejo, J.<sup>d</sup>, Moreno, M.M.<sup>a*</sup></p> <p><em><sup>a</sup></em><em>University of Castilla-La Mancha, Higher Technical School of Agricultural Engineering in Ciudad Real, Ronda de Calatrava 7, 13071, Ciudad Real, Spain</em></p> <p><em><sup>b</sup></em><em>Joint Research Unit UJI-UPV ‐ Improvement of agri‐food quality. Agricultural Sciences and Natural Environment Department, Universitat Jaume I, Avda. Sos Baynat s/n, 12071 Castelló de la Plana, Spain</em></p> <p><em><sup>c </sup></em><em>Research Institute for Pesticides and Water (IUPA).</em> <em>Universitat Jaume I, Avda. </em><em>Sos Baynat s/n, 12071 Castelló de la Plana, Spain</em></p> <p><em><sup>d</sup></em><em>Joint Research Unit UJI-UPV ‐ Improvement of agri‐food quality. </em><em>COMAV. Universitat Politècnica de València, Cno. de Vera s/n, 46022 València, Spain</em></p> <p>*Corresponding author</p> <p> </p> <p> </p> <p>To be published in the journal Scientia horticulturae</p>
A simple model for daily basin-wide thermodynamic sea ice thickness growth retrieval: Data
<p>Data for:</p> <p>Anheuser, J., Liu, Y., and Key, J.: A daily basin-wide sea ice thickness retrieval methodology: Stefan's Law Integrated Conducted Energy (SLICE), The Cryosphere Discuss. [preprint], <a href="https://doi.org/10.5194/tc-2021-333">https://doi.org/10.5194/tc-2021-333</a>, in review, 2021.</p> <p> </p> <p>Scripts for producing data and figures can be found at:</p> <p>https://doi.org/10.5281/zenodo.6561431</p> <p> </p> <p> </p>
A convection-permitting hindcast based on the MOLOCH model and driven by ERA5: hourly precipitation data for years 1994 and 2011 (sample data)
<p>Hourly estimates of rainfall accumulations were produced within the framework of the SPITBRAN Special project, which received computational resources from ECMWF (https://www.ecmwf.int/en/research/special-projects/spitbran-2018).</p> <p>Numerical gridded data at 2.5 km grid spacing were obtained with the MOLOCH model set in a convection-permitting mode and fed by ERA5 data as initial and boundary conditions for the period 1979-2019 and over the Italian domain.</p> <p>Hourly rainfall accumulations of such long-term hindcast are provided for the years 1994 and 2011. File format is Grib2.</p>
Code and Velocity Data for Sustained indentation in 2D models of continental collision involving whole mantle subduction
<p>Contains the Python code for all models and resolution tests using the <a href="https://www.underworldcode.org/intro-to-underworld">underworld geodynamics code</a> in "Sustained indentation in 2D models of continental collision involving whole mantle subduction" submitted to GJI</p>
Data to accompany the publication "Combined biophysical and genetic modelling approaches reveal complementary information about population connectivity of New Zealand green-lipped mussels"
<p>Data to accompany the publication "Combined biophysical and genetic modelling approaches reveal complementary information about population connectivity of New Zealand green-lipped mussels". </p> <p>migrationmatrix14.txt contains the particle tracking matrix, with the total number of particles that migrated from row i to column j (out of a total of 2217864 particles released per population).</p> <p>mussel_microsat_Genepop.txt contains the microsatellite data for each population in Genepop format.</p>
Data depository - "Quantifying the effect of wind on volcanic plumes: implications for plume modelling"
<p>This depository contains all data to understand, evaluate, and build upon the research reported in the manuscript: "Quantifying the effect of wind on volcanic plumes: implications for plume modelling", submitted to Journal of Geophysical Research.</p> <p><strong>Abstract</strong></p> <p>The considerable effects that wind can have on estimates of mass eruption rates (MERs) in explosive eruptions based on volcanic plume height are well known but difficult to quantify rigorously. Many explicitly wind-affected plume models have the additional difficulty that they require the use of centerline heights of bent-over plumes, a parameter not easily obtained directly from observational data. We tested two such models by using the time series of varying plume heights and wind speeds of the 2010 Eyjafjallajökull eruption. The mapped fallout and photos taken during this eruption allow us to estimate the plume geometry and to empirically constrain input parameters for the two models tested. Two strategies are presented to correct the difference in maximum plume height and centerline height: (i) based on plume radius, and (ii) by using the plume type parameter ∏, which quantifies the relative influence of buoyancy and cross-wind on the plume dynamics, to discriminate weak, intermediate and strong plumes. The results indicate that it may be more appropriate to classify plumes as either wind-dominated, intermediate or buoyancy-dominated, where the relative effects of both wind and MER define the type. The analysis of the Eyjafjallajökull data shows that the MER estimates from both models are considerably improved when a plume-type dependent centreline-correction is applied and the wind entrainment coefficient <em>β</em> is refined. For this particular eruption, we find that the best value for <em>β</em> lies between 0.22 and 0.34, unlike previous suggestions that set this parameter to 0.50.</p>
Common guillemots in the Baltic Sea studied with video surveillance and object detection: raw data, annotations, model, and model outputs
<p>The data comes from common guillemots studied at Stora Karlsö, Sweden between 2019 and 2021. The common guillemots breed at an artificial cliff, and has been filmed continusly from above over three breeding seasons. Using the video material, a YOLOv5 model has been trained to detect adult birds, chicks and eggs. The dataset contains annotations (bounding boxes) used for training the model, the model itself, and outputs from the model (object detections).</p> <p>The data can be used and shared freely.</p>
Model output data for Smith et al., "Effects of increasing the category resolution of the sea ice thickness distribution in a coupled climate model on Arctic and Antarctic sea ice"
<p>Model output data for Smith et al., "Effects of increasing the category resolution of the sea ice thickness distribution in a coupled climate model on Arctic and Antarctic sea ice", in review in Journal of Geophysical Research-Oceans, 2022. Details on CESM model settings and run setups can be found within the manuscript. </p>
An SEM Approach to Validating the Psychological Model of Musical Groove (Data Set)
<p>Data set for the study "An SEM Approach to Validating the Psychological Model of Musical Groove"</p>
Data and model code for article "Wildflower phenological escape differs by continent and spring temperature"
<p>This dataset contains seven spreadsheets and one R script. The spreadsheet "NC_Lee_etal_data.xls" contains the master data file associated with the article "Wildflower phenological escape differs by continent and spring temperature", along with a metadata sheet that describes the column names. The other six spreadsheets contain the continent (EA = East Asia, ENA = Eastern North America, and EU = Europe) x lifeform (forbs or trees) specific datasets used in the supplementary models containing spatial autocorrelation terms.</p> <p>The script contains annotated example code to run the models used in the final analysis.</p> <p>Questions about the code or analysis should be directed to the lead author (Benjamin R Lee).</p>
Bayesian Samples and Data Behind Figures: Comprehensive Bayesian Modeling of Tidal Circularization in Open Cluster Binaries part I
<p>Auxiliary data associated with the article <a href="https://ui.adsabs.harvard.edu/abs/2022MNRAS.516.6145P/abstract">"Comprehensive Bayesian Modeling of Tidal Circularization in Open Cluster Binaries part I: M 35, NGC 6819, NGC 188" by Penev, K & Schussler, J</a></p> <p>The type of data corresponds to a particular filename format. Bayesian samples are in HDF5 format, directly as saved by the <a href="https://emcee.readthedocs.io/en/stable/index.html">emcee</a> sampler (see <a href="https://emcee.readthedocs.io/en/stable/user/backends/">https://emcee.readthedocs.io/en/stable/user/backends/</a>). All other files are in AAS-journal style machine readable tables format generated by <a href="https://github.com/cds-astro/cds.pyreadme">cdspyreadme</a> python library.</p> <p>Description of contents by filename format:</p> <pre><code><CLUSTER>_<BINARY ID>_.*.h5</code></pre> <p>Bayesian analysis samples constraining the tidal dissipation efficiency of the given binary. The values of the sampled system and tidal dissipation parameters are stored as blobs (<a href="https://emcee.readthedocs.io/en/stable/user/blobs/">https://emcee.readthedocs.io/en/stable/user/blobs/)</a></p> <pre><code><CLUSTER>_<BINARY ID>_lgQ_period.mrt</code></pre> <p>The 2.3%, 15.9%, 84.1%, and 97.7% quantiles of <span class="math-tex">\(\log_{10}Q_\star'\)</span> for the given binary as a function of tidal period</p> <pre><code><CLUSTER>_<BINARY ID>_burnin_period.mrt</code></pre> <p>The MCMC burn-in period before the 2.3%, 15.9%, 84.1%, and 97.7% quantiles of <span class="math-tex">\(\log_{10}Q_\star'\)</span> for the given binary are considered converged (see article text).</p> <pre><code><CLUSTER>_<BINARY ID>_cdfstd_period.mrt</code></pre> <p>The standard deviation of the <span class="math-tex">\(CDF(\log_{10}Q_\star')\)</span> for the given binary as a function of tidal period for each of the quantiles. The maximum likelihood value is the target percentile, i.e. one of: 2.3%, 15.9%, 84.1%, and 97.7%</p>
Born Digital, Born Slippery - Data Modelling for Born Digital/Hybrid objects (March 2022)
<p>The report Preserving and sharing born-digital and hybrid objects from and across the National Collection discusses the challenges for cultural heritage institutions in cataloguing, preserving, and providing access to four born digital and hybrid objects/projects. This response looks at the issues brought up by those discussions, in particular for the purpose of cataloguing, and the extent, within existing cataloguing standards, they can be supported. Where there does not appear to be support within current standards, model development activity is proposed to allow the expansion of the current `born physical` focused cataloguing standards to also handle born digital and hybrid object cataloguing.</p>
Data and models for "Modelling human behaviour in cognitive tasks with latent dynamical systems"
<p>Ebb and Flow gameplay data and trained model parameters for:</p> <p>Jaffe, P.I., Poldrack, R.A., Schafer, R.J. & Bissett, P.G.<em> </em>Modelling human behaviour in cognitive tasks with latent dynamical systems. <em>Nat Hum Behav</em> (2023). https://doi.org/10.1038/s41562-022-01510-8 </p> <p>Ebb and Flow is a task-switching game offered as a part of the Lumosity cognitive training platform (Lumos Labs, Inc.). The data and model parameters are organized by participant/model in individual archived directories (140 participants; 245 models). Within each model directory, “data_pre_split.pickle” contains the raw Ebb and Flow data. The processed model inputs for the training, validation, and holdout/test splits are contained in the files "train_model_inputs.pt", "val_model_inputs.pt", and "test_model_inputs.pt", respectively. Other metadata associated with each split is contained in "train_other_data.pkl", "val_other_data.pkl", and "test_other_data.pkl". The parameters from the trained model are stored in “model_params.pth”. Some intermediate analysis products are contained in the subfolder “model_analysis”.</p> <p>Metadata for all models can be found in “model_metadata.csv”. The metadata field “switch_cost_type” identifies models that were trained on data with (sc+) or without (sc-) a switch cost (note that models marked “NA”, except for the optimal models, were also trained on data with a switch cost but were not included in the paired comparison of the sc+ and sc- models; see manuscript for details). The "exgauss" field identifies models that were trained with an exGaussian response template (coded as "exgauss+"); models identified as "exgauss-" were trained with a Gaussian kernel and were used in paired comparisons with the exgauss+ models. The "early" field identifies models that were trained with early-stage practice data if set to TRUE. The "optimal" field identifies models that were trained to perform the task optimally if set to TRUE. The other metadata fields are self-explanatory.</p> <h2><strong>Fast command line download instructions (macOS/linux) </strong></h2> <p>For help downloading on Windows, see <a href="https://github.com/dvolgyes/zenodo_get">https://github.com/dvolgyes/zenodo_get</a>.<strong><br></strong></p> <p>1) Copy and save the complete list of files below to a text file, e.g. "files.txt". Save it to the same directory you would like to save the data to. </p> <p>2) Install parallel if it's not already installed:</p> <pre><code>sudo apt-get install parallel</code></pre> <p>3) Run the following from the directory with files.txt (all data will be saved here). The flag -jN will create N parallel wget instances to download the files, e.g.:</p> <pre><code>cat files_test.txt | parallel -j8 wget {}</code></pre> <p>4) Unzip the files and cleanup:</p> <pre><code>unzip "*.zip" rm *.zip files.txt</code></pre> <h2><strong>List of files</strong></h2> <p>https://zenodo.org/records/7102065/files/ages80to89_u4120_exgauss.zip<br>https://zenodo.org/records/7102065/files/optimal_square9.zip<br>https://zenodo.org/records/7102065/files/optimal_square8.zip<br>https://zenodo.org/records/7102065/files/optimal_square7.zip<br>https://zenodo.org/records/7102065/files/optimal_square6.zip<br>https://zenodo.org/records/7102065/files/optimal_square5.zip<br>https://zenodo.org/records/7102065/files/optimal_square4.zip<br>https://zenodo.org/records/7102065/files/optimal_square3.zip<br>https://zenodo.org/records/7102065/files/optimal_square2.zip<br>https://zenodo.org/records/7102065/files/optimal_square1.zip<br>https://zenodo.org/records/7102065/files/optimal_square10.zip<br>https://zenodo.org/records/7102065/files/model_metadata.csv<br>https://zenodo.org/records/7102065/files/ages80to89_u5484_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5484_early.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5441_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5441_early.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5150_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5121_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5121_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5121_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5121_early.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4928_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4928_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4760_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4760_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4760_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4532_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4532_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4278_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4278_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4239_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4122_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4122_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4120_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u3701_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u3667_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u3667_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u2831_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u2831_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1887_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1459_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1459_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1447_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1441_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1441_early.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1427_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1427_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1427_early.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1172_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1172_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1172_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u975_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u96_expt2.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u96_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u910_expt2.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u910_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u910_early.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4913_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4913_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4913_early.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4876_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4876_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4864_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4172_expt2.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4172_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3898_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u384_expt2.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u384_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3694_expt2.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3694_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3694_early.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3538_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3509_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3509_early.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3457_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3457_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3218_expt2.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3218_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3143_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3143_early.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u2266_expt2.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u2266_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u21_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u2141_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u2141_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u2022_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u1276_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u1276_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u627_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u5459_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u5459_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u5069_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u5069_early.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u4993_expt2.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u4993_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u4964_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u4852_expt2.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u4852_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u478_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u438_expt2.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u438_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u438_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u388_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u388_early.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u3724_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u3724_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u3469_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u3461_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u3461_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u3461_early.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u3328_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u3328_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u3066_expt2.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u3066_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u268_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u2490_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u2186_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u2186_early.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u1597_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u1597_early.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u3969_expt1.zip<br>https://zenodo.org/records/7102065/files/ages60to69_u1194_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u697_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u697_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u697_early.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u5128_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u5128_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u4609_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u452_expt2.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u452_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u4220_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u4150_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u4107_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u3536_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u3316_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u3316_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u3199_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u3195_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u2584_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u2584_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u2347_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u2347_early.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u2247_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u195_expt2.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u195_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u195_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u1917_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u1917_early.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u1590_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u1434_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u1434_early.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u1325_expt1.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u1325_early.zip<br>https://zenodo.org/records/7102065/files/ages50to59_u1027_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u970_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u734_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u734_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u734_early.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u660_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u4878_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u4487_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u3887_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u3574_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u3233_expt2.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u3233_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u3055_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u2772_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u2771_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u2771_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u2492_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u2492_early.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u2482_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u2482_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u2166_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u1892_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u1739_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u1739_early.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u1614_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u1614_early.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u1468_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u1468_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u1468_early.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u1462_expt1.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u1462_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages40to49_u1397_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u98_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u878_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u878_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u5155_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u5155_early.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u4851_expt2.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u4851_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u4496_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u4367_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u4367_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u418_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u418_early.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u4039_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u4024_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u3873_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u3859_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u3859_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u3859_early.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u3850_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u3850_early.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u3531_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u3365_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u2856_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u2280_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u2106_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u2106_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u1495_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u1387_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u1248_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u1248_early.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u953_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u953_early.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u890_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u5396_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u5326_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u4587_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u4587_early.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u4504_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u4504_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u4394_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u4394_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u3975_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u3975_early.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u3750_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u367_expt1.zip<br>https://zenodo.org/records/7102065/files/ages30to39_u3365_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u3257_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u3257_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u3139_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u2809_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u2360_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u224_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u1559_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u1474_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u1444_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u1259_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u1259_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u1259_early.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u1076_expt1.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u1076_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages20to29_u1076_early.zip</p> <p> </p>
Python scripts / Jupyter Notebooks and data for training segmentation models on slide scans of diatom preparations from river Menne
<p>This archive contains the Jupyter Notebooks and data used for the deep learning experiments published in Kloster et al. 2022: Improving deep learning-based segmentation of diatoms in gigapixel-sized virtual slides by object-based tile positioning and object integrity constraint.</p> <p>The notebooks are numbered according to the order in which they are to execute. Please refer to the comments and documentation within the notebooks as well as to the manuscript for details. The data (image data, mask data & segmentation ground truth in COCO format for several different tiling strategies) is stored in separate subfolders corresponding with data usage (model training, validation, test) and tiling strategy. Please refer to the "readme" files for detailed information.</p> <p> </p> <p> </p>
Experimental data for Distributed parametric model checking timed automata under non-Zenoness assumption
<p>This is the experimental data mentioned in the paper "Distributed parametric model checking timed automata under non-Zenoness assumption", published in FMSD (Formal Methods in System Design), 2022.</p> <p>Experiments were conducted using the distributed version of IMITATOR 2.9.2 (Butter Incaberry) on two Intel Xeon Silver 4114 at 2.2GHz (20 cores in total), and 96GiB of memory, running under Ubuntu 16.04 LTS 64-bit.</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.