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.
1,421
datasets available to search
ShareScore release 0.9.0
Dataset results
1,421 results for “Behavioral data”
Replication Data for: "Copularity of French and Dutch (semi-)copular constructions: a behavioral profile analysis"
<p>This data package contains all the data relevant to reproduce the results presented in the publication "Copularity of French and Dutch (semi-)copular constructions: a behavioral profile analysis".</p>
Data for: Segmentation and Holocene Behavior of the Middle Strand of the North Anatolian Fault (NW Turkey)
<p>This dataset is associated to the article "Segmentation and Holocene Behavior of the Middle Strand of the North Anatolian Fault (NW Turkey)" published in Tectonics (<a href="https://doi.org/10.1029/2021TC006870">https://doi.org/10.1029/2021TC006870</a>).</p> <p>It includes the following:</p> <ul> <li>A description file, including a list of data files, and a description of how the marker quality score was determined in this study ("Supporting Information.docx")</li> <li>A table summarizing the historical earthquakes in the region of interest ("TableS1.xlsx")</li> <li>A table summarizing the paleoseismic investigations in the region of interest ("TableS2.xlsx")</li> <li>The full horizontal offset retrodeformations ("offsets_X.tif")</li> <li>A table of the offset values measured along the MNAF (TableS3.xlsx")</li> <li>The georeferenced fault map ("MNAF_2021.gml" and "MNAF_2021.xsd")</li> <li>A figure showing examples of vertical slip markers along the MNAF south of Iznik Lake ("FigS1.tif")</li> <li>A figure showing field examples of Late Quaternary faulting along the MNAF ("FigS2.png")</li> <li>A figure showing the results of the automatic fault discretization procedure ("FigS3.png")</li> </ul>
MoTiV: a Dataset of European User Mobility for Behavioral-Data
<p>Mobility is a system involving several stakeholders. Therefore, it is relevant to characterize mobility behavior and preferences in a detailed way, to enable nuanced decisions. Current paradigms rely mostly on time saving, proposing to users solutions that include the shortest path. Even though the value of travel time can be extended beyond travel duration, no dataset to characterize mobility and value of travel time from different perspectives exists. This creates a gap between novel mobility paradigms and the characterization of user mobility. To enable the mining of user mobility under these new paradigms, in this paper, we present the MoTiV (Mobility and Time Value) dataset, which contains data about travelers and their journeys, collected from a mobile application, called Woorti. Each trip contains multi-faceted information: from the transport mode, through its evaluation, to the positive/negative experience factors. We also present a use case, which compares corresponding legs with different transport modes, studying experience factors that negatively impact users. We conclude by discussing other application domains and research opportunities enabled by the dataset.</p>
What does it take to generate new growth - Survey data on company perceptions on innovative behavior
<p>This data includes raw survey data, a codebook and the survey form for the survey <em>what does it take to generate new growth? </em>The survey focused on comprehensively mapping the Finnish companies growth outlooks and their underlying management practices and principles. The study creates an overview of top managers’ views on Finnish companies’ growth, innovativeness, and the ability for renewal. It allows us to identify what sets high-growing companies apart from others. The Codebook is associated with an SPSS and CSV file including the data.</p>
An experimental data set for the analysis of the thermophysical behavior of a single-story naturally ventilated double-skin façade (DSF) under summer boundary conditions
<p>Double-skin facades (DSFs) are adaptive building envelope elements that offer the possibility to dynamically interact with the heat and mass flow between indoor and outdoor environments. Though designed to provide better performance compared to more conventional envelope solutions, these façade systems may, in some cases, underperform and lead to an increase in energy use or in thermal discomfort if not properly designed and operated. One of the known problems is the risk of overheating, in hot periods, in the ventilated cavity. In order to analyze this effect, we have systematically investigated the performance of a single-story, naturally ventilated DSF. The DSF is operated in the so-called outdoor air curtain mode and has venetian blinds installed in the 20 mm deep ventilated cavity. Tests were carried out under a steady-state regime corresponding to relevant summertime conditions. In an effort to enable the scientific community to access experimental data to analyze this problem further or for model validation purposes, we released together with the open-access paper entitled "<strong>Characterization of a naturally ventilated double-skin façade through the design of experiments (DOE) methodology in a controlled environment</strong>," the entire set of experimental data collected during the tests. The data set contains the results of a series of experimental runs where different configurations of the DSF, as detailed below, have been subjected to various boundary conditions through a climate simulator facility equipped with a solar simulator device. The database is supported by a guide ("Guide.pdf"), where further explanations about how to read data and schematic drawings of the sensor layout are provided. Additional information about the original aims of the experiments, the detailed methods, and other data processing procedures can be found in the article mentioned above. The collection of experimental tests in this data set covers:</p> <ul> <li>49 steady-state measurements where the following factors were changed using different experimental designs: solar irradiance (0, 350, and 700 Wm<sup>-2</sup>), outdoor chamber temperature (15, 25, and 35 ℃), opening size (7, 21, and 42 dm<sup>2</sup>), and venetian blinds angle (closed blinds θ=0 º, θ=45 º, and open blinds θ=90 º) [file name: "Complete_data.csv"],</li> </ul> <p>Any inquiries about the experimental data can be sent to: <a href="mailto:aleksandar.jankovic@ntnu.no">aleksandar.jankovic@ntnu.no</a></p> <p>The activities presented in this paper were carried out within the research project "REsponsive, INtegrated, VENTilated - REINVENT – windows," supported by the Research Council of Norway through the research grant 262198, and the partners SINTEF, Hydro Extruded Solutions, Politecnico di Torino and Aalto University.</p>
Behavioral and fMRI Data: Nurturing the reading brain: Home literacy practices are associated with children's neural response to printed words through vocabulary skills
<p>This is the behavioral and fMRI dataset described in "Nurturing the reading brain: Home literacy practices are associated with children’s neural response to printed words through vocabulary skills". </p> <p>Because of anonymization concerns within the framework of EU privacy regulations (<a href="https://gdpr-info.eu">GDPR</a>), we cannot provide raw MRI data. Therefore, the fMRI data consists of individual pre-processed volumes, normalized into the MNI template (see paper for details about the preprocessing pipeline). Anonymized behavioral data and first level analyses are also provided for each participant (SPM.mat file as well as beta, con, spmT, RPV and ResMS files). Note that the dataset also include runs and GLM results for a third task (Dots) that was not analyzed in the paper. Finally, the <a href="https://www.psychopy.org">PsychoPy</a> implementation of the tasks is also provided. If you have any questions, please send an email to jerome.prado [at] univ-lyon1.fr. </p> <p><strong>IMPORTANT:</strong></p> <p>In accordance with EU privacy regulations, we ask that you sign and return a Data Use Agreement (DUA) before downloading the data. You can download the DUA <a href="https://zenodo.org/record/4965716/files/DUA.pdf?download=1">here</a>. Please, sign it and send it to jerome.prado [at] univ-lyon1.fr.</p>
Data and code from "No evidence of sex ratio manipulation by black-throated blue warblers in response to food availability" Kaiser et al. 2023 Behavioral Ecology and Sociobiology
This dataset is published in support of "No evidence of sex ratio manipulation by black-throated blue warblers in response to food availability" by Kaiser et al. 2023 in Behavioral Ecology and Sociobiology. Data and code to test the assumptions and key predictions of the Trivers-Willard hypothesis, which proposes that females produce more sons or daughters depending on food availability, in the black-throated blue warbler at the Hubbard Brook Experimental Forest, NH, 2007-2012. Datasets support analyses of sex ratio bias at both the nest and nestling levels. Data tables support the comparison of the ratio of variances in the scaled pre-fledging mass of male and female nestlings using an F test and reproduction of Figures 2a and 2b. Figures are those used in the published manuscript. Code supports the calculation of offspring sex ratio bias at the population level, and considering separately both low- and high-quality habitats, using the Neuhäuser test, statistical models testing the assumptions of the Trivers-Willard hypothesis, effects of food availability and parental provisioning on offspring sex ratio, and effects of food availability on pre-fledging nestling mass of sons and daughters, and a power analysis to determine the power to detect an effect of food supplementation on sex ratio. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the US Forest Service, Northern Research Station.
MCR LTER: Coral Reefs: Stegastes behavior data in support of Kamath, et al., Oikos 2019
Stable between-group differences in collective behavior have been documented in a variety of social taxa. Here we evaluate the effects of such variation, often termed collective or colony-level personality, on coral recovery in a tropical marine farmerfish system. Groups of the farmerfish Stegastes nigricans cultivate and defend gardens of palatable algae on coral reefs in the Indo-Pacific. These gardens can promote the recruitment, growth, and survival of corals by providing a refuge from coral predation. Here we experimentally evaluate whether the collective defensive behavior of farmerfish colonies is correlated across intruder feeding guilds—herbivores, corallivores and egg-eating predators. Further, we evaluate if overall colony responsiveness or situation-specific responsiveness (i.e., towards herbivores, corallivores, or egg-eaters in particular) best predicts the growth of outplanted corals. Finally, we experimentally manipulated communities within S. nigricans gardens, adding either macroalgae or large colonies of coral, to assess if farmerfish behavior changes in response to the communities they occupy. Between-group differences in collective responsiveness were repeatable across intruder guilds. Despite this consistency, responsiveness towards corallivores (porcupinefish and ornate butterflyfish) was a better predictor of outplanted coral growth than responsiveness towards herbivores or egg-eaters. Adding large corals to farmerfish gardens increased farmerfish attacks towards intruders, pointing to possible positive feedback loops between their aggression towards intruders and the presence of corals whose growth they facilitate. These data provide evidence that among-group behavioral variation could strongly influence the ecological properties of whole communities. These data support the publication Kamath, A., J. N. Pruitt, A.J. Brooks, M.C Ladd, D.T. Cook, J.P. Gallagher, M.E. Vickers, S.J. Holbrook and R.J. Schmitt. 2019. Potential feedback between cor
Data for resonator behavior in the paper "Self-sensing, tunable monolayer MoS2 nanoelectromechanical resonators"
<p>Data for resonator behavior in the paper "Self-sensing, tunable monolayer MoS2<br> nanoelectromechanical resonators", <em>Nat. Commun.</em> 10, 4831 (2019); DOI:<a href="https://doi.org/10.1038/s41467-019-12795-1">10.1038/s41467-019-12795-1</a>.</p>
Reward perseveration is shaped by GABAA-mediated dopamine pauses: Behavior Data
<p>Behavior assay data used to generate the following figures in the paper "Reward perseveration is shaped by GABAA-mediated dopamine pauses":</p> <ul> <li>Figure 2, all panels</li> <li>Figure 4, all panels</li> <li>Ext. Fig 3, panels a-c, e, f</li> <li>Ext. Fig 5, all panels</li> </ul> <p>Specifics of the data:</p> <ul> <li>ddHTP.zip contains the control raw data in MATLAB files</li> <li>HTP.zip contains the experimental raw data in MATLAB files</li> <li>The various Behavior_summary_data.csv files contain the subject details and extracted analyses, as detailed in each title.</li> <li>ddHTP_other_DART_pilots.zip and the corresponding .csv file hold the raw and analyzed data for the ddHTP controls of ongoing experiments shown in Ext. Fig 5c. </li> </ul>
Reward perseveration is shaped by GABAA-mediated dopamine pauses: Histology from Behavior Data Mice
<p>Histology images for the experimental +HTP behavior mice in the dataset 10.5281/zenodo.10903566. This histology was used to generate the following figures in the paper "Reward perseveration is shaped by GABAA-mediated dopamine pauses":</p> <ul> <li>Ext. Fig 3, panel d</li> </ul> <p>Specifics of the data:</p> <ul> <li>HTP_Histo_Cohort_VX.zip contains the raw images (Olympus .vsi files) and drawn ROIs and fluorescence analysis (MATLAB files) for each mouse/tissue section in cohort VX</li> <li>HTP_Histo_Cohort_VZ.zip contains the raw images (Olympus .vsi files) and drawn ROIs and fluorescence analysis (MATLAB files) for each mouse/tissue section in cohort VZ</li> <li>HTP_Histo_Cohort_VAJ.zip contains the raw images (Olympus .vsi files) and drawn ROIs and fluorescence analysis (MATLAB files) for each mouse/tissue section in cohort VAJ</li> <li>HTP_Histo_Cohort_VAK.zip contains the raw images (Olympus .vsi files) and drawn ROIs and fluorescence analysis (MATLAB files) for each mouse/tissue section in cohort VAK</li> <li> Behavior_summary_data_HTP_Histology.csv contains the summarized fluorescence quantification per animal</li> </ul>
Open data repository, Knab et al., Prediction of stroke outcome in mice based on non-invasive MRI and behavioral testing
<p><strong>Open data repository, Knab et al., Prediction of stroke outcome in mice based on non-invasive MRI and behavioral testing</strong></p> <p><strong>Latest version of files: repository_v2.0.zip, Behavior Data_v2.0.xlsx and MRI IDs Testing&Replication Cohort.xlsx (please ignore repository.zip)</strong></p> <p>Open data repository Knab et al. Prediction of stroke outcome in mice based on non-invasvive MRI and behavioral testing</p> <p>Open code and documentation of prediction models available via <a href="https://github.com/major-s/mouse-mcao-outcome-predictor">https://github.com/major-s/mouse-mcao-outcome-predictor</a></p> <p><strong>Content:</strong></p> <p>README.txt</p> <p>This information</p> <p><strong>dat</strong></p> <p>Contains MRI data in NIFTI format and secondary data from atlas registration. For documentation of atlas registration files see https://pubmed.ncbi.nlm.nih.gov/28829217/<br>Files used for the manuscript:<br>t2.nii: t2 weighted image acquired 24 h post stroke<br>masklesion.nii: manually delineated lesion<br>x_masklesion.nii: lesion in atlas space<br>ix_ANO.nii: Allen brain atlas in native space (i.e. matching t2.nii)<br>Lesion volume was calculated by volume of voxels unequal 0 in x_masklesion.nii<br>Overlap of regions defined by ix_ANO.nii with masklesion.nii were used for calculating percent damage in each atlas region</p> <p><strong>prediction_models</strong></p> <p>Contains separated training and test data as xlsx and csv files with lesion volumes in cubic mm of the Allen brain atlas space, percent damage per atlas region and behavioral data. The training data was used as input for training prediction models in MATLAB, the results were created using the test data.<br>The files have following sturcture:<br>Column 1: animal ID<br>Columns 2-537: MRI regions (column title corresponds to the region number as used in the Allen common coordinate framework)<br>Column 538: lesion volume<br>Column 539: initial performance (subacute deficit) = mean performance/deficit on days 2-6<br>Column 540: mean performance/deficit on days 2-6 = initial performance (subacute deficit) - this column equals column 539 but has different header which was used to train the residual from initial deficit<br>Column 541: residual performance/deficit<br>Column 542: test or training group<br>Consecutive rows contain data for each animal specified by the animal id</p> <p>The repository also contains all trained models, prediction results for the test data and tables with resulting median absolute error (MedAE) and 5th, 25th, 75th and 95 absolute error quantiles for each model.<br>The model files end with '_models.mat' and contain 50 independently trained models each. Each model version is specified by number 1-50.<br>The result files end with '_test_results.mat' or '_test_results.xlsx', files with MedAE and quantiles end with '_test_errors.xlsx' or '_test_errors.csv. The common part of filenames specifies the used paradigm<br>Folder 'subacute deficit prediction' contains:<br> - initial_performance_from_lesion_volume: prediction of subacute deficit using lesion volume<br> - initial_performance_from_segmented_mri: prediction of subacute deficit using segmented mri<br>Folder 'long-term outcome prediction' contains:<br> - lesion_volume: prediction of residual deficit using lesion volume<br> - segmented_mri: prediction of residual deficit using segmented_mri<br> - initial_performance: prediction of residual deficit using subacute deficit<br>Folder 'mri_inc_oob_imp' contains models trained using increasing number of mri segments sorted according to the out-of-bag importance. The number of used segments is given in the file name. The models, results and errors are separated in subfolders.</p> <p>Files with equal file name and different extension always contain the same data</p> <p><strong>templates</strong><br>Allen atlas, template, brain mask, hemisphere masks, tissue probability masks in NIFTI format including annotations of region IDs and parameter.m file for use in MATLAB toolbox ANTx2<br> </p>
Dataset of behavioral and neurophysiological data of a virtual sailing task published in: "Providing task instructions during motor training enhances performance and modulates attentional brain networks"
<p>Dataset belonging to the behavioral and neurophysiological data of the publication: "Providing task instructions during motor training enhances performance and modulates attentional brain networks". The two uploaded Zip files contain kinematic and electroencephalographic data of 36 participants for the Obstacle and HorizonTask.</p>
Raw data for: "CalDAG-GEFI mediates striatal cholinergic modulation of dendritic excitability, synaptic plasticity and psychomotor behaviors"
<p>Figure 2. CDGI mediates the M1R modulation of dendritic excitability but not the M1R</p> <p>modulation of somatic excitability.</p> <p>(A and B) Sagittal sections through the brains of CDGI knockout mice in which the direct</p> <p>pathway was visualized (red) in D1-tdTomato mice (A) and the indirect pathway was visualized</p> <p>(green) in D2-GFP mice.</p> <p>(C) Sample somatic voltage changes evoked by 120pA current injections in iSPNs from WT</p> <p>(black) and CDGI-KO (red) before and after bath application of oxo-M (10 µM).</p> <p>(C-D) Current-response curves of iSPNs from WT (B, n=5 cells) and CDGI-KO mice (C, n=7</p> <p>cells). Somatic excitability of iSPNs was similarly enhanced by oxo-M in WT and CDGI-KO.</p> <p>(E) Sample somatic recordings in response to 140pA current injections in dSPNs from WT</p> <p>(black) and CDGI KO (red) before and after bath application of oxo-M (10 µM).</p> <p>(F-H) Current-response curves of dSPNs from WT (E) and CDGI-KO (F) mice (n=4-6).</p> <p>(I) Trains of five EPSPs were evoked by stimulation of glutamatergic afferent fibers at 40 Hz.</p> <p>Oxo-M (10 µM) increased EPSP summation in iSPNs of WT, but not in CDGI-KO or when</p> <p>M1Rs were blocked by M1R antagonist VU0255035 in WT (5 M).</p> <p>(J) Box plot showing the effect of oxoM on synaptic summation. The EPSP5/EPSP1 ratio was</p> <p>increased by oxoM in iSPNs of WT (p = 0.002, Wilcoxon test; n = 10), but not in iSPNs of 27</p> <p>CDGI-KO mice (p = 0.25, n = 9) or in iSPNs of WT mice in the presence of VU0255035 (p =</p> <p>0.69, n = 6).</p> <p>(K) Box plot showing the effect of oxoM on the kinetics of synaptic response. The decay time</p> <p>constant of EPSP5 was significantly increased by oxoM in iSPNs of WT (p = 0.002); but not</p> <p>when CDGI was genetically deleted (p = 0.65) or when M1R was pharmacologically blocked (p</p> <p>= 0.84).</p>
Learning to embed lifetime social behavior from interaction dynamics - Data
<p><strong>Interaction matrices and metadata used in "Learning to embed lifetime social behavior from interaction dynamics"</strong></p> <p>The following files are included:</p> <ul> <li>interactions_bn16_sparse.npz and interactions_bn19_sparse.npz: These are the interaction affinity matrices for the BN16 and BN19 datasets as described in the publication. The data is stored as compressed sparse tensors with time on the first, and the individuals on the second and third dimensions. The data was stored using the <a href="http://sparse.pydata.org">pydata/sparse</a> library 0.9.1</li> <li> <p>alive_bn16.csv and alive_bn19.csv: These files contain the dates of emergence (also corresponding to the dates they were introduced into the colonies) and heuristically determined number days alive for all individuals in the interaction matrices. Death dates were determined using a bayesian changepoint model and the number of daily detections of each individual</p> </li> <li> <p>rhythmicity_bn16.csv and rhythmicity_bn19.csv: These files contain the circadian rhythmicity values used in the evaluation of the method. The circadian rhythmicity is the <span class="math-tex">\(R^2\)</span> value of a sine with a 24 hour period fitted to the individuals' movement velocities over a three day window</p> </li> <li> <p>indices_bn16.csv and indices_bn19.csv: These files contain the mapping between the original marker IDs used during the recording of the data (which has gaps, because not all markers were used) and the sequential indices used in the interaction matrices. These files can therefore be used to look up the original ID of an individual based on it's index in the interaction matrix and vice versa</p> </li> <li> <p>time_spent_on_substrates.csv: This data was used for the mapping from factors to the proportion of time spent on various cell substrates (Figure 5). The positions of the individuals were accumulated by minute, and the column "location_descriptor_count" contains the total number of minutes on the respective day that the individual was detected</p> </li> </ul> <p>See <a href="https://doi.org/10.1101/2020.05.06.076943">10.1101/2020.05.06.076943</a> for more details about the bayesian changepoint model, circadian rhythmicity calculation, and location mapping.</p>
Data set for "Reward-based learning drives rapid sensory signals in medial prefrontal cortex and dorsal hippocampus necessary for goal-directed behavior"
<p>Data set for: Le Merre P, Esmaeili V, Charrière E, Galan K, Salin P-A, Petersen CCH, Crochet S (2018) Reward-based learning drives rapid sensory signals in medial prefrontal cortex and dorsal hippocampus necessary for goal-directed behavior. Neuron, https://doi.org/10.1016/j.neuron.2017.11.031</p> <p>There are 44 files in this data upload:<br> 1. '2018_LeMerre_Neuron.pdf' - this is a pdf version of the online publication.<br> 2. 'Chronic_LFP_data.mat' - this is a Matlab data structure, which contains all the chronic LFP data for the publication.<br> 3. 'Silicon_Probe_data.mat' - this is a Matlab data structure, which contains all the mPFC silicon probe recording data for the publication.<br> 4. 'Opto_Inactivation_data.mat' - this is a Matlab data structure, which contains all the optogenetic inactivation data for the publication.<br> 5. 'Mus_Inactivation_data.mat' - this is a Matlab data structure, which contains all the pharmacological (Muscimol) inactivation data for the publication.<br> 6. 'Learning_Days_Mtrx.mat' - this is a Matlab data file, which contains the selected training days analyzed for the Trained condition in the Detection Task.<br> 7. 'Exposed_Days_Mtrx.mat' - this is a Matlab data file, which contains the selected days analyzed for the Exposed condition in the Neutral Exposure.<br> 8. 'p_value_colormap.mat' - this is a Matlab data file, which contains the color map used to display the p value in the Matlab codes 'plot_fig2A_SEP_D1_vs_Trained.m'; 'plot_fig2B_Amplitude_D1_vs_Trained.m'; 'plot_fig3A_SEP_D1_vs_Exposed.m'; 'plot_fig4A_SEP_H_vs_M.m’.<br> 9. 'p_value_colormap2.mat' - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code 'plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m’; ’plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m’.<br> 10. 'scatterplot_colormap.mat' - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code 'plot_fig2C_Scatterplot_Amplitude_vs_dprime.m'.<br> 11. 'SEP_colormtrx.mat' - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code 'plot_fig1B_Sensory_Evoked_Potentials.m'; 'plot_figS3A_SEP_EMG_amplitude_ReactionTime.m'.<br> 12. 'zscore_colormap.mat' - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code 'plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m'; 'plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m’.<br> 13. 'Chronic_LFP_dataViewer.fig' - this is a Matlab Figure file, which is the GUI layout for 'Chronic_LFP_dataViewer.m'.<br> 14. 'Chronic_LFP_dataViewer.m' - this is a Matlab code, which displays the data contained in 'Chronic_LFP_data.mat'.<br> 15. 'Silicon_Probe_dataViewer.fig' - this is a Matlab Figure file, which is the GUI layout for 'Silicon_Probe_dataViewer.m'.<br> 16. 'Silicon_Probe_dataViewer.m' - this is a Matlab code, which displays the data contained in 'Silicon_Probe_data.mat'.<br> 17. 'plot_fig1B_Sensory_Evoked_Potentials.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results published in figure 1, panel B (Le Merre et al., 2018).<br> 18. 'plot_fig1C_Silicon_Probe_Hit_trials.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure 1, panel C (Le Merre et al., 2018).<br> 19. 'plot_fig2A_SEP_D1_vs_Trained.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 2, panel A (Le Merre et al., 2018).<br> 20. 'plot_fig2B_Amplitude_D1_vs_Trained.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 2, panel B (Le Merre et al., 2018).<br> 21. 'plot_fig2C_Scatterplot_Amplitude_vs_dprime.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 2, panel C (Le Merre et al., 2018).<br> 22. 'plot_fig3A_SEP_D1_vs_Exposed.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 3, panel A (Le Merre et al., 2018).<br> 23. 'plot_fig3B_Amplitude_D1_vs_Exposed.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 3, panel B (Le Merre et al., 2018).<br> 24. 'plot_fig3C_ROC_Trained_vs_Exposed.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the ROCs in the same way as the published figure 3, panel C (Le Merre et al., 2018).<br> 25. 'plot_fig3C_ROC_Randomization.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the label shuffled ROCs in the same way as the published figure 3, panel C (Le Merre et al., 2018).<br> 26. 'plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure 3, panel D (Le Merre et al., 2018).<br> 27. 'plot_fig4A_SEP_H_vs_M.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 4, panel A (Le Merre et al., 2018).<br> 28. 'plot_fig4B_Amplitude_ H_vs_M.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure 4, panel B (Le Merre et al., 2018).<br> 29. 'plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure 4, panel C, left panel (Le Merre et al., 2018).<br> 30. 'plot_fig4C_Scatterplot_modulation_Hit_vs_Miss.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure 4, panel C, right panel (Le Merre et al., 2018).<br> 31. 'plot_fig4D_Photoinhibitions.m' - this is a Matlab code, which analyses the data in 'Opto_Inactivation_data.mat', and displays the results published in figure 4, panel D (Le Merre et al., 2018).<br> 32. 'plot_figS2D_Performance_DetectionTask_NeutralExposition.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure S2, panel D (Le Merre et al., 2018).<br> 33. 'plot_figS3A_SEP_EMG_amplitude_ReactionTime.m' - this is a Matlab code, which analyses the data in 'Chronic_LFP_data.mat', and displays the results in the same way as the published figure S3, panel A (Le Merre et al., 2018).<br> 34. 'plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure S3, panel B (Le Merre et al., 2018).<br> 35. 'plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m' - this is a Matlab code, which analyses the data in 'Silicon_Probe_data.mat', and displays the results in the same way as the published figure S4, panel A (Le Merre et al., 2018).<br> 36. 'plot_figS4B_Pharmacological_Inactivations.m' - this is a Matlab code, which analyses the data in 'Mus_Inactivation_data.mat', and displays the results published in figure S4 (Le Merre et al., 2018).<br> 37. 'Load_LFP_Multisite_database.m' - this is a Matlab code, which is called in the Matlab codes that analyze the data in 'Chronic_LFP_data.mat'.<br> 38. 'Load_Silicon_Probe_database.m' - this is a Matlab code, which is called in the Matlab codes that analyze the data in 'Silicon_Probe_data.mat'.<br> 39. 'Load_Optogenetic_Inactivation_database.m' - this is a Matlab code, which is called in the Matlab code that analyzes the data in 'Opto_Inactivation_data.mat'.<br> 40. 'Load_Pharmacological_Inactivation_database.m' - this is a Matlab code, which is called in the Matlab code that analyzes the data in 'Mus_Inactivation_data.mat'.<br> 41. 'bonf_holm.m' - this is a Matlab code developed by D. M. Groppe, which is called in the Matlab code 'plot_figS4B_Pharmacological_Inactivations.m':<br> https://ch.mathworks.com/matlabcentral/fileexchange/28303-bonferroni-holm-correction-for-multiple-comparisons<br> 42. 'boundedline.m' - this is a Matlab code developed by K. Kearney, which is called in the Matlab codes 'plot_fig1C_Silicon_Probe_Hit_trials.m'; 'plot_fig2A_SEP_D1_vs_Trained.m'; 'plot_fig3A_SEP_D1_vs_Exposed.m’; 'plot_fig3C_ROC_Trained_vs_Exposed.m'; 'plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m'; 'plot_fig4A_SEP_H_vs_M.m'; 'plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m'; 'plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m'; 'plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m':<br> https://ch.mathworks.com/matlabcentral/fileexchange/27485-boundedline-m<br> 43. 'inpaint_nans.m' - this is a Matlab code, which is called in the Matlab code 'boundedline.m'.<br> 44. 'PSTH_Simple.m' - this is a Matlab code developed by V. Esmaeili, which is called in the Matlab codes 'plot_fig1C_Silicon_Probe_Hit_trials.m'; 'plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m'; 'plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m'; 'plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m'; 'plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m’.</p>
Data Set for "Alteration's control on frictional behavior and the depth of the ductile shear zone in geothermal reservoirs in volcanic arcs" I: Lesser Antilles Volcanic Arc
<p>Data set for the 48 friction experiments performed for gouge samples (altered andesitic rocks) from the Lesser Antilles used in the manuscript, "Alteration's control on frictional behavior and the depth of the ductile shear zone in geothermal reservoirs in volcanic arcs". This data set can be used in combination with the data set for the Cascades used in the same manuscript (doi:10.5281/zenodo.10964936). This large combined data set (of 108 frictional experiments) represents a unique opportunity to systematically study frictional behaviour in the framework of rate and state. All samples are tested in wet and dry conditions at 10, 30, and 50 MPa with velocity steps and slide-hold-slides. These two data sets have the further advantage of being performed with exactly the same protocol (same run in, same initial gouge thickness, same velocity steps, same hold periods), in the same machine, by the same operator (or by an operator who was trained and supervised by the original operator). </p>
Data associated with "Microbiota-derived metabolites inhibit Salmonella virulent subpopulation development by acting on single-cell behaviors"
<p>Data used for the publication Microbiota-derived metabolites inihibit Salmonella virulent subpopulation development by acting on single-cell behaviors. </p> <p> </p> <p>all_hi_2307202.csv Single-cell quantifications of Salmonella SPI-1 reporter cells grown in the presence of SCFAs.</p> <p>all_no_2307202.csv Single-cell quantifications of Salmonella SPI-1 reporter cells grown in the absence of SCFAs.</p> <p>odmeasurements.csv OD measurements of plate-reader assays of Salmonella SPI-1 reporter cells and controls grown in a range of SCFA conditions. </p> <p>gfpmeasurements.csv GFP measurements of plate-reader assays of Salmonella SPI-1 reporter cells and controls grown in a range of SCFA conditions. </p>
Supplemental data from: Nature or nurture: A genetic basis for the behavioral selection of depth in siscowet and lean lake charr (Salvelinus namaycush) ecomorphs
<p>These files contain the raw depth and temperature sensor data from siscowet and lean lake charr (<em>Salvelinus namaycush</em>) ecomorphs tagged with pop-up satellite archival tags (PSATs). These fish were produced from wild gametes taken from Lake Superior and reared in a common garden study for nine years and then tagged with PSATs and released in southern Lake Superior. The dataset is supplemental to:</p> <p>Goetz, F., Sitar, S., Seider, M., and Jasonowicz, A. 2022. Nature or nurture: A genetic basis for the behavioral selection of depth in siscowet and lean lake charr (<em>Salvelinus namaycush</em>) ecomorphs. Canadian Journal of Fisheries and Aquatic Sciences. (in press).</p> <p><strong>Data description for metadata.csv:</strong></p> <p>This file contains the metadata associated with each tag deployment. This includes biological data as well as key mission paramters.</p> <table> <thead> <tr> <td>Column</td> <td>Type</td> <td>Description</td> </tr> </thead> <tbody> <tr> <td>mission_id</td> <td>integer</td> <td>mission identifier</td> </tr> <tr> <td>tag_sn</td> <td>integer</td> <td>tag serial number</td> </tr> <tr> <td>ecotype</td> <td>string</td> <td>lake trout ecotype</td> </tr> <tr> <td>release_date</td> <td>string</td> <td>date of tag release</td> </tr> <tr> <td>length_mm</td> <td>float</td> <td>total length in mm</td> </tr> <tr> <td>weight_g</td> <td>float</td> <td>weight in g</td> </tr> <tr> <td>lipid</td> <td>float</td> <td>lipid level meadured by Distell fatmeter set in research mode</td> </tr> <tr> <td>release_site</td> <td>string</td> <td>release site (deep or shallow site)</td> </tr> <tr> <td>sampling_rate</td> <td>string</td> <td>sampling interval of tag (format=HH:MM:SS)</td> </tr> <tr> <td>mission_end_utc</td> <td>datetime</td> <td>programmed tag pop off date and time in UTC time (format=YYYY-MM-DD HH:MM:SS)</td> </tr> <tr> <td>notes</td> <td>string</td> <td>notes and comments</td> </tr> </tbody> </table> <p> </p> <p><strong>Data description for the raw sensor data files:</strong></p> <p>The raw sensor data is found in the files that are prefixed with "raw-sensor-data". The data for each tag is in contained in a seperate file and the files are named as follows "raw-sensor-data-{<em><strong>mission_identifier</strong></em>}-{<em><strong>tag_serial_number</strong></em>}.csv".</p> <table> <thead> <tr> <td>Column</td> <td>Type</td> <td>Description</td> </tr> </thead> <tbody> <tr> <td>mission_id</td> <td>integer</td> <td>mission identifier</td> </tr> <tr> <td>tag_sn</td> <td>integer</td> <td>tag serial number</td> </tr> <tr> <td>timestamp_utc</td> <td>datetime</td> <td>timestamp of sensor reading (format=YYYY-MM-DD HH:MM:SS)</td> </tr> <tr> <td>depth_m</td> <td>string</td> <td>depth in meters</td> </tr> <tr> <td>temperature_c</td> <td>string</td> <td>temperature in degrees celcius</td> </tr> </tbody> </table>
Data to support Whitney JL, Coleman RR, Deakos MH "Genomic evidence indicates small island-resident populations and sex-biased behaviors of Hawaiian Reef Manta Rays"
<p>Datasets supporting the manuscript: Whitney JL, Coleman RR, Deakos MH "Genomic evidence indicates small island-resident populations and sex-biased behaviors of Hawaiian Reef Manta Rays". <em>BMC Ecology and Evolution </em><strong>23</strong>, 31 (2023). https://doi.org/10.1186/s12862-023-02130-0</p> <p>Nuclear data:</p> <p>"Mobula-alfredi_nuclear_reference_RAD_contigs.fasta" is a fasta of 359,751 contigs that serve as the reference for nuclear alignment of genotypes to RAD loci. Contigs begin and end with GATC cut site.</p> <p>Mobula-alfredi_nuclear_all_2048snps_38genotypes.vcf is a VCF file with all 2048 nuclear SNPs in final filtered SNP dataset. 38 genotypes are included from Maui Nui and Hawaii Island. This 2048 SNPs includes both 2038 neutral and 10 outlier SNPs. </p> <p>Mobula-alfredi_nuclear_neutral_2038snps_38genotypes.vcf is a VCF file with 2038 neutral nuclear SNPs genotyped in 38 individuals from Maui Nui and Hawaii Island. </p> <p>Mobula-alfredi_nuclear_outliers_10snps_38genotypes.vcf is a VCF file with 10 outlier SNPs genotyped in 38 individuals from Maui Nui and Hawaii Island. </p> <p>Structure (.str) files are also provided in addition to VCFs. In all files Population prefixes M=Maui Nui and K=Hawaii Island. </p> <p>Mitochondrial data:</p> <p>Mobula-alfredi_mitogenome_34haplotypes_9sites_min4x.vcf is a VCF file with 9 variant sites across the mitogenome haplotyped in 34 individuals from Maui Nui and Hawaii Island. </p> <p>Mobula-alfredi_mitogenome_34haplotypes_allsites_min4x.fasta is a FASTA file with whole mitogenomes aligned to OP562409 [https://www.ncbi.nlm.nih.gov/nuccore/OP562409]. Sites with less than 4x coverage were masked with Ns. </p> <p>Mobula-alfredi_mitogenome_reference_OP562409.fasta is a FASTA file containing the <em>Mobula alfredi</em> reference mitogenome OP562409 [https://www.ncbi.nlm.nih.gov/nuccore/OP562409].</p> <p> </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.