Skip to main content
ConductScienceScienceDex
Powered by ShareScore

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.

33

datasets available to search

ShareScore release 0.9.0

Reset

Dataset results

33 results for “goal based”

Learn how ShareScore rates datasets ↗
zenodo44/100

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, Charri&egrave;re 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.&nbsp;&nbsp; &nbsp;&#39;2018_LeMerre_Neuron.pdf&#39; - this is a pdf version of the online publication.<br> 2.&nbsp;&nbsp; &nbsp;&#39;Chronic_LFP_data.mat&#39; - this is a Matlab data structure, which contains all the chronic LFP data for the publication.<br> 3.&nbsp;&nbsp; &nbsp;&#39;Silicon_Probe_data.mat&#39; - this is a Matlab data structure, which contains all the mPFC silicon probe recording data for the publication.<br> 4.&nbsp;&nbsp; &nbsp;&#39;Opto_Inactivation_data.mat&#39; - this is a Matlab data structure, which contains all the optogenetic inactivation data for the publication.<br> 5.&nbsp;&nbsp; &nbsp;&#39;Mus_Inactivation_data.mat&#39; - this is a Matlab data structure, which contains all the pharmacological (Muscimol) inactivation data for the publication.<br> 6.&nbsp;&nbsp; &nbsp;&#39;Learning_Days_Mtrx.mat&#39; - this is a Matlab data file, which contains the selected training days analyzed for the Trained condition in the Detection Task.<br> 7.&nbsp;&nbsp; &nbsp;&#39;Exposed_Days_Mtrx.mat&#39; - this is a Matlab data file, which contains the selected days analyzed for the Exposed condition in the Neutral Exposure.<br> 8.&nbsp;&nbsp; &nbsp;&#39;p_value_colormap.mat&#39; - this is a Matlab data file, which contains the color map used to display the p value in the Matlab codes &#39;plot_fig2A_SEP_D1_vs_Trained.m&#39;; &#39;plot_fig2B_Amplitude_D1_vs_Trained.m&#39;; &#39;plot_fig3A_SEP_D1_vs_Exposed.m&#39;; &#39;plot_fig4A_SEP_H_vs_M.m&rsquo;.<br> 9.&nbsp;&nbsp; &nbsp;&#39;p_value_colormap2.mat&#39; - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code &#39;plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m&rsquo;; &rsquo;plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m&rsquo;.<br> 10.&nbsp;&nbsp; &nbsp;&#39;scatterplot_colormap.mat&#39; - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code &#39;plot_fig2C_Scatterplot_Amplitude_vs_dprime.m&#39;.<br> 11.&nbsp;&nbsp; &nbsp;&#39;SEP_colormtrx.mat&#39; - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code &#39;plot_fig1B_Sensory_Evoked_Potentials.m&#39;; &#39;plot_figS3A_SEP_EMG_amplitude_ReactionTime.m&#39;.<br> 12.&nbsp;&nbsp; &nbsp;&#39;zscore_colormap.mat&#39; - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code &#39;plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m&#39;; &#39;plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m&rsquo;.<br> 13.&nbsp;&nbsp; &nbsp;&#39;Chronic_LFP_dataViewer.fig&#39; - this is a Matlab Figure file, which is the GUI layout for &#39;Chronic_LFP_dataViewer.m&#39;.<br> 14.&nbsp;&nbsp; &nbsp;&#39;Chronic_LFP_dataViewer.m&#39; - this is a Matlab code, which displays the data contained in &#39;Chronic_LFP_data.mat&#39;.<br> 15.&nbsp;&nbsp; &nbsp;&#39;Silicon_Probe_dataViewer.fig&#39; - this is a Matlab Figure file, which is the GUI layout for &#39;Silicon_Probe_dataViewer.m&#39;.<br> 16.&nbsp;&nbsp; &nbsp;&#39;Silicon_Probe_dataViewer.m&#39; - this is a Matlab code, which displays the data contained in &#39;Silicon_Probe_data.mat&#39;.<br> 17.&nbsp;&nbsp; &nbsp;&#39;plot_fig1B_Sensory_Evoked_Potentials.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results published in figure 1, panel B (Le Merre et al., 2018).<br> 18.&nbsp;&nbsp; &nbsp;&#39;plot_fig1C_Silicon_Probe_Hit_trials.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure 1, panel C (Le Merre et al., 2018).<br> 19.&nbsp;&nbsp; &nbsp;&#39;plot_fig2A_SEP_D1_vs_Trained.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 2, panel A (Le Merre et al., 2018).<br> 20.&nbsp;&nbsp; &nbsp;&#39;plot_fig2B_Amplitude_D1_vs_Trained.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 2, panel B (Le Merre et al., 2018).<br> 21.&nbsp;&nbsp; &nbsp;&#39;plot_fig2C_Scatterplot_Amplitude_vs_dprime.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 2, panel C (Le Merre et al., 2018).<br> 22.&nbsp;&nbsp; &nbsp;&#39;plot_fig3A_SEP_D1_vs_Exposed.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 3, panel A (Le Merre et al., 2018).<br> 23.&nbsp;&nbsp; &nbsp;&#39;plot_fig3B_Amplitude_D1_vs_Exposed.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 3, panel B (Le Merre et al., 2018).<br> 24.&nbsp;&nbsp; &nbsp;&#39;plot_fig3C_ROC_Trained_vs_Exposed.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the ROCs in the same way as the published figure 3, panel C (Le Merre et al., 2018).<br> 25.&nbsp;&nbsp; &nbsp;&#39;plot_fig3C_ROC_Randomization.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the label shuffled ROCs in the same way as the published figure 3, panel C (Le Merre et al., 2018).<br> 26.&nbsp;&nbsp; &nbsp;&#39;plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure 3, panel D (Le Merre et al., 2018).<br> 27.&nbsp;&nbsp; &nbsp;&#39;plot_fig4A_SEP_H_vs_M.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 4, panel A (Le Merre et al., 2018).<br> 28.&nbsp;&nbsp; &nbsp;&#39;plot_fig4B_Amplitude_ H_vs_M.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 4, panel B (Le Merre et al., 2018).<br> 29.&nbsp;&nbsp; &nbsp;&#39;plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure 4, panel C, left panel (Le Merre et al., 2018).<br> 30.&nbsp;&nbsp; &nbsp;&#39;plot_fig4C_Scatterplot_modulation_Hit_vs_Miss.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure 4, panel C, right panel (Le Merre et al., 2018).<br> 31.&nbsp;&nbsp; &nbsp;&#39;plot_fig4D_Photoinhibitions.m&#39; - this is a Matlab code, which analyses the data in &#39;Opto_Inactivation_data.mat&#39;, and displays the results published in figure 4, panel D (Le Merre et al., 2018).<br> 32.&nbsp;&nbsp; &nbsp;&#39;plot_figS2D_Performance_DetectionTask_NeutralExposition.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure S2, panel D (Le Merre et al., 2018).<br> 33.&nbsp;&nbsp; &nbsp;&#39;plot_figS3A_SEP_EMG_amplitude_ReactionTime.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure S3, panel A (Le Merre et al., 2018).<br> 34.&nbsp;&nbsp; &nbsp;&#39;plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure S3, panel B (Le Merre et al., 2018).<br> 35.&nbsp;&nbsp; &nbsp;&#39;plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure S4, panel A (Le Merre et al., 2018).<br> 36.&nbsp;&nbsp; &nbsp;&#39;plot_figS4B_Pharmacological_Inactivations.m&#39; - this is a Matlab code, which analyses the data in &#39;Mus_Inactivation_data.mat&#39;, and displays the results published in figure S4 (Le Merre et al., 2018).<br> 37.&nbsp;&nbsp; &nbsp;&#39;Load_LFP_Multisite_database.m&#39; - this is a Matlab code, which is called in the Matlab codes that analyze the data in &#39;Chronic_LFP_data.mat&#39;.<br> 38.&nbsp;&nbsp; &nbsp;&#39;Load_Silicon_Probe_database.m&#39; - this is a Matlab code, which is called in the Matlab codes that analyze the data in &#39;Silicon_Probe_data.mat&#39;.<br> 39.&nbsp;&nbsp; &nbsp;&#39;Load_Optogenetic_Inactivation_database.m&#39; - this is a Matlab code, which is called in the Matlab code that analyzes the data in &#39;Opto_Inactivation_data.mat&#39;.<br> 40.&nbsp;&nbsp; &nbsp;&#39;Load_Pharmacological_Inactivation_database.m&#39; - this is a Matlab code, which is called in the Matlab code that analyzes the data in &#39;Mus_Inactivation_data.mat&#39;.<br> 41.&nbsp;&nbsp; &nbsp;&#39;bonf_holm.m&#39; - this is a Matlab code developed by D. M. Groppe, which is called in the Matlab code &#39;plot_figS4B_Pharmacological_Inactivations.m&#39;:<br> https://ch.mathworks.com/matlabcentral/fileexchange/28303-bonferroni-holm-correction-for-multiple-comparisons<br> 42.&nbsp;&nbsp; &nbsp;&#39;boundedline.m&#39; - this is a Matlab code developed by K. Kearney, which is called in the Matlab codes &#39;plot_fig1C_Silicon_Probe_Hit_trials.m&#39;; &#39;plot_fig2A_SEP_D1_vs_Trained.m&#39;; &#39;plot_fig3A_SEP_D1_vs_Exposed.m&rsquo;; &#39;plot_fig3C_ROC_Trained_vs_Exposed.m&#39;; &#39;plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m&#39;; &#39;plot_fig4A_SEP_H_vs_M.m&#39;; &#39;plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m&#39;; &#39;plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m&#39;; &#39;plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m&#39;:<br> https://ch.mathworks.com/matlabcentral/fileexchange/27485-boundedline-m<br> 43.&nbsp;&nbsp; &nbsp;&#39;inpaint_nans.m&#39; - this is a Matlab code, which is called in the Matlab code &#39;boundedline.m&#39;.<br> 44.&nbsp;&nbsp; &nbsp;&#39;PSTH_Simple.m&#39; - this is a Matlab code developed by V. Esmaeili, which is called in the Matlab codes &#39;plot_fig1C_Silicon_Probe_Hit_trials.m&#39;; &#39;plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m&#39;; &#39;plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m&#39;; &#39;plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m&#39;; &#39;plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m&rsquo;.</p>

opencc-by-4.0Dec 2017View details →
zenodo40/100

Figure 8. Potential at every point; it is highest in the obstacles and lowest at the goal-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

<p>The numerical potential field path planner is guaranteed to produce a<br> path even if the start or goal is placed in an obstacle. If there is no possible way to get from the start<br> to the goal without passing through an obstacle then the path planner will generate a path through<br> the obstacle, although if there is any alternative then the path will do that instead. For this reason, it<br> is important to make sure that there is some possible path, although there are ways around this<br> restriction such as returning an error if the potential at the start point is too high. The path is found<br> by moving to the neighboring square with the lowest potential, starting at any point in the space and<br> stopping when the goal is reached.</p>

opencc-by-4.0Nov 2011View details →
zenodo40/100

Figure 7. Obstacle force (repulsive potential) and goal force obstacle force-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

<p>Since the motion trajectory of UAV is divided into several median points that the UAV<br> should reach them one by one in a sequence the output obtained after the execution of AI will be a<br> set of position and velocity vectors. So the task of the trajectory will be to guide the UAV through<br> the obstacles to reach the destination. The routine used for this purpose is the potential field method<br> (also an alternative new method is in progress which models the UAV motion through opponents<br> same as the owing of a bulk of water through obstacles) [5]. In this method, different electrical<br> charges are assigned to UAV, obstacles, and the destination. Then by calculating the potential field<br> of this system of charges a path will be suggested for the UAV.</p>

opencc-by-4.0Nov 2011View details →
zenodo40/100

Figure 6. Goal force-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

<p>Since the motion trajectory of UAV is divided into several median points that the UAV<br> should reach them one by one in a sequence the output obtained after the execution of AI will be a<br> set of position and velocity vectors. So the task of the trajectory will be to guide the UAV through<br> the obstacles to reach the destination. The routine used for this purpose is the potential field method<br> (also an alternative new method is in progress which models the UAV motion through opponents<br> same as the owing of a bulk of water through obstacles) [5]. In this method, different electrical<br> charges are assigned to UAV, obstacles, and the destination. Then by calculating the potential field<br> of this system of charges a path will be suggested for the UAV. At a higher level, predictions can be<br> used to anticipate the position of the obstacles and make better decisions in order to reach the<br> desired vector. In our path- planning algorithm, an articial potential field is set up in the space; that<br> is, each point in the space is assigned a scalar value. The value at the goal point is set to be 0 and the<br> value of the potential at all other points is positive.</p>

opencc-by-4.0Nov 2011View details →
zenodo40/100

Data set for "Dopamine dynamics in nucleus accumbens across reward-based learning of goal-directed whisker-to-lick sensorimotor transformations in mice"

<p>Data set for: Huang J, Crochet S, Sandi C, Petersen CCH (2024) Dopamine dynamics in nucleus accumbens across reward-based learning of goal-directed whisker-to-lick sensorimotor transformations in mice. Heliyon 10: e37831. https://doi.org/10.1016/j.heliyon.2024.e37831<br><br></p> <p>There are 2 files in this upload:</p> <p>1. The file named "2024_Huang_Heliyon.pdf" is the Open Access pdf of the online publication in Heliyon.</p> <p>2. The file named "Huang_data_code.zip" (~6 GB) is a zipped version of a folder "Huang_data_code" (~6 GB), which contains the data analysed in the study along with the Matlab codes used to generate the published figures. To access the data and codes, first unzip the file. You need to install the Matlab 'Signal Processing' and 'Curve Fitting' Toolboxes. In Matlab, add the path of the folder "Huang_data_code" and all subfolders. The main folder unzips into three subfolders: i) "Huang_dLight_data_code", which contains the dLight data; ii) "Huang_muscimol_data_code", which contains the behavioral data for muscimol inactivation experiments; and iii) "Huang_singletrial_example", which contains the data for the single trial example data shown in Figure 1C (note for this to run you first need to load the data file "JH056_190308_WD.mat"). In the folder "Huang_dLight_data_code", you can also find a "DataViewer" to visualise the data trial-by-trial, which you can run by executing "DataViewer.mlapp" directly from the subfolder "Huang_dLight_data_code" after loading the data "Huang_database.mat".</p>

opencc-by-4.0Sep 2024View details →
zenodo40/100

A goal-based FAIRification planning method

<p>This image illustrates the first draft of the goal-based FAIRification planning method, which builds on experience gained from recent FAIRification projects and feedback from experts on FAIR.</p> <p><strong>An up-to-date version of this method is described at</strong> <a href="https://doi.org/10.21203/rs.3.rs-3092538/v1">https://doi.org/10.21203/rs.3.rs-3092538/v1</a></p>

opencc-by-4.0Mar 2023View details →
zenodo40/100

Data for "Feeding climate and biodiversity goals with novel plant-based meat and milk alternatives"

<p>Research data supporting the study&nbsp; &quot;Feeding climate and biodiversity goals with novel plant-based meat and milk alternatives&quot;.</p> <p>It contains:<br> 1) merged.gdx - data derived from the original scenario database<br> 2) map.csv - mapping of food commodities to food groups used for analysis<br> 3) manure.csv -&nbsp; results on nitrogen input to cropland and N crop fertilization from manure<br> 4) AgMIP_regions.shp - shape file used to make maps<br> 5) Paper_visuals_NCOM.R - R script to analyze and visualize the data. It reproduces the main figures in the paper and the appendix<br> &nbsp;&nbsp; last tested for R Studio 2022.12.0 Build 353, Release (7d165dcf, 2022-12-03) for Windows 10 Pro, 64-bit operating system</p> <p>Instructions:<br> The R code, file 4, reads in files 1, 2 and 3 and generates figures, tables and maps.<br> The directories (line 50 and 58) need to be updated to the location of the data (the current folder).&nbsp;&nbsp;&nbsp;</p> <p>&nbsp;</p>

opencc-by-4.0Jul 2023View details →
zenodo36/100

Datos de la investigación: La voz del rehén: adapting creative sustainable development goal-based physical-artístico project during COVID pandemic situation

<p>This article assesses the effects on creativity of an interdisciplinary physicall-artistic programme and their effects during pandemic context. The sample comprised 97 individuals &ndash;54 men and 43 women&ndash; with an average age of 21.37&plusmn;1.56, all of whom were studying a Primary Education Teacher Training Degree at Zaragoza University &ndash;Spain&ndash;. It was a pre-experimental study with measures pre-post programme &ndash;adapted to pandemic situation, replacing the acrosport content to skipping rope, juggling and aerial dance&ndash;. The creative skills assessment instrument was the PIC-A. A Student&rsquo;s T-test and ANOVA test were performed to discover significant differences in pre-post in both academic years. Results showed that: i) participants obtained significant improvements in total score of creativity in both academic years; ii) the adaptations made to the intervention programme during the pandemic year were effective in developing creativity. These studies confirm the importance of incorporating interdisciplinary creativity programmes into the university system.</p>

opencc-by-4.0Mar 2024View details →
zenodo36/100

Diverse Hits in de Novo Molecule Design: A Diversity-based Comparison of Goal-directed Generators

<p>Results for the paper "<strong>Diverse Hits in de Novo Molecule Design: A Diversity-based Comparison of Goal-directed Generators"</strong> in the form of the generated molecules and their associated scores. The relevant code to reproduce and visualize the results can be found at https://github.com/ml-jku/diverse-hits.&nbsp;</p> <p>&nbsp;</p>

opencc-by-4.0Apr 2024View details →
ClinicalTrials.gov36/100

App-based Education and GOal-setting in Rheumatoid Arthritis

ClinicalTrials.gov study NCT05888181. IPD Sharing: UNDECIDED. Countries: 1. Publications: 9.

restrictedIPD-UNDECIDEDFeb 2026View details →
zenodo32/100

Goal-based h-adaptivity of the 1-D diamond difference discrete ordinate method

<p>In accordance with EPSRC funding requirements this folder contains the raw data relevant to the named paper.</p>

opencc-by-4.0Jan 2017View details →
ClinicalTrials.gov32/100

COPM-Based Goal Setting Strategies in the PICU

ClinicalTrials.gov study NCT07128446. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov32/100

Goal Directed Fluid Management Based on Non-invasive Monitoring

ClinicalTrials.gov study NCT01883752. IPD Sharing: Not stated. Countries: 1. Publications: 10.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov32/100

Arterial Pressure Based Cardiac Output for Goal-Directed Therapy in Abdominal Surgery

ClinicalTrials.gov study NCT00549419. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov32/100

Feasibility of a Goal-based Agenda Setting Intervention

ClinicalTrials.gov study NCT04696484. IPD Sharing: YES. Countries: 1. Publications: 4.

controlledIPD-YESFeb 2026View details →
ClinicalTrials.gov32/100

Goal Directed Hemodynamic Therapy Based on Noninvasive Monitoring in Patients With Hip Fracture

ClinicalTrials.gov study NCT02479321. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov32/100

Conventional Fluid Management vs Plethysmographic Variability Index -Based Goal Directed Fluid Management

ClinicalTrials.gov study NCT05239286. IPD Sharing: NO. Countries: 1. Publications: 4.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov32/100

Video-based Teach-to-goal Intervention on Inhaler Technique on Jordanian Adults With Asthma and COPD

ClinicalTrials.gov study NCT05664347. IPD Sharing: NO. Countries: 1. Publications: 57.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov32/100

Effect of Goal-Oriented GMFM-88 Based Program in Cerebral Palsy.

ClinicalTrials.gov study NCT06709742. IPD Sharing: NO. Countries: 1. Publications: 5.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov32/100

Goal Setting and Mobile-based Self-management Tool to Improve Diet Quality of People With High Blood Pressure

ClinicalTrials.gov study NCT06988501. IPD Sharing: UNDECIDED. Countries: 1. Publications: 12.

restrictedIPD-UNDECIDEDFeb 2026View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record