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.
404
datasets available to search
ShareScore release 0.9.0
Dataset results
404 results for “striatum”
Differential contributions of ventral striatum subregions in the motivational and hedonic components of the affective response to reward
Open the record for dataset details and reuse information.
DTI data from 'Fiber architecture in the ventromedial striatum and its relation with the bed nucleus of the stria terminalis'
Open the record for dataset details and reuse information.
Chronic Ethanol Exposure Produces Sex-Dependent Impairments in Value Computations in the Striatum
<div> <div>These datasets and scripts are organized by figures. All data are stored as .mat format and can be open and manipulated using MATLAB. Scripts are all written in MATLAB and can be ran in MATLAB.</div> <div>There are two ways to run the code to reproduce each figures and statistics.</div> <div>1. Run RUN_ME.m. In this case, the file will automatically excute scripts to load corresponding data and figures.</div> <div>2. Open individual script to load corresponding data and generate statistics and figures.</div> <br> <div>All scripts here have been validated and tested. The system and coding environment is:</div> <div>- Windows 11 24H2</div> <div>- MATLAB 2023a</div> <br> <div>Matlab dependent package (not all are required but those are installed in my environment):</div> <div>- Bioinformatics Toolbox v4.17</div> <div>- Communications Toolbox v8.0</div> <div>- Computer Vision Toolbox v10.4</div> <div>- Curve Fitting Toolbox v3.9</div> <div>- Data Acquisition Toolbox v4.7</div> <div>- Database Toolbox v11.0</div> <div>- Deep Learning HDL Toolbox v1.5</div> <div>- Deep Learning Toolbox v14.6</div> <div>- DSP HDL Toolbox v1.2</div> <div>- Econometrics Toolbox v6.2</div> <div>- Financial Toolbox v6.5</div> <div>- Fixed-point Designer v7.6</div> <div>- Image Processing Toolbox v11.7</div> <div>- MATLAB Coder v5.6</div> <div>- MATLAB Compiler v8.6</div> <div>- MATLAB Compiler SDK v7.2</div> <div>- MATLAB Report Generator v5.14</div> <div>- MATLAB Support for MinGW-w64 C/C++ Compiler v23.1.0</div> <div>- Optimization Toolbox v9.5</div> <div>- Parallel Computing Toolbox v9.5</div> <div>- FR Toolbox v4.5</div> <div>- Signal Integrity Toolbox v1.3</div> <div>- Simulink v10.7</div> <div>- Statistics and Machine Learning Toolbox v12.5</div> <div>- Symbolic Math Toolbox v9.3</div> <div>- Text Analytics Toolbox v1.10</div> <div>- Wavelet Toolbox v6.3</div> </div>
Patterns of reduced cortical thickness and striatum pathological morphology in cocaine addiction
<p>This dataset includes all the data and scripts needed to reproduce the analysis and results on the manuscript "Patterns of reduced cortical thickness and striatum pathological morphology in cocaine addiction" (<a href="https://www.biorxiv.org/content/early/2018/04/22/306068">link</a>). The brain data is not raw, as T1w were not defaced. We will do so in the near future for version 2.0. Instead we include only the "output/thickness" files used in the final analysis. For the use of raw T1w images, please contact the main author EAGV.</p> <p> </p> <p>Note: Paths will differ in the script.</p>
Herbarium specimen image of Trifolium striatum L., part of the collection of Royal Botanic Gardens, Kew
Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published.<br><br>Content of this deposition:<br><br>- A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values.<br>- A JPEG image file of the scanned herbarium sheet.<br>- A lossless TIFF image from which the JPEG image has been derived.
Data set for "Cell type-specific membrane potential changes in dorsolateral striatum accompanying reward-based sensorimotor learning"
<p>Data set for: Sippy T, Chaimowitz C, Crochet S, Petersen CCH (2021) Cell type-specific membrane potential changes in dorsolateral striatum accompanying reward-based sensorimotor learning. FUNCTION 2: zqab049. https://doi.org/10.1093/function/zqab049</p> <p>There are 2 files in this upload:</p> <p>1. The file named "<strong>2021_Sippy_FUNCTION.pdf</strong>" is the Open Access pdf of the online publication in FUNCTION.</p> <p>2. The file named "<strong>Sippy_data_code.zip</strong>" (~5 GB) is a zipped version of a folder ‘<em>Sippy_data_code</em>’, which contains the data analyzed in the study along with the Matlab codes used to generate the published figures. To access the data and the codes, first unzip the file, add the folder with subfolders to the Matlab path and run the different codes. The current folder must be the main folder (‘<em>Sippy_data_code</em>’). You first need to run ‘AnalyzeDataStructure.m’ and afterwards you can run the other codes. Each code computes and plots the results used in the corresponding figure. Figures are saved in the subfolder ‘Figures’.</p> <p>The subfolder ‘<em>Data</em>’ contains the data structure ‘<em>Data.mat</em>’ to be analyzed, as well as a Matlab file called ‘<em>p_value_colormap.mat</em>’ used to plot the p value color bars in some figures.</p> <p>The subfolder ‘<em>Functions</em>’ contains functions called by the main codes.</p> <p>The subfolder ‘<em>Codes</em>’ contains the following codes:</p> <p><em>‘AnalyzeDataStructure.m’: </em>computes the results and saves them as a new data structure called ‘<em>Analyzed_Data</em>’, in the subfolder ‘<em>Results</em>’.</p> <p><em>‘Figure_1.m’: </em>computes and plots the results for the panels D, E and F of Figure 1.</p> <p><em>‘Figure_2.m’: </em>computes and plots the results for the panels D-G and I-K of Figure 2.</p> <p><em>‘Figure_3.m’: </em>computes and plots the results for the panels A-F of Figure 3.</p> <p><em>‘SuppFigure_2.m’: </em>computes and plots the results for the panels B, D and F of Supplementary Figure 2.</p> <p><em>‘SuppFigure_3.m’: </em>computes and plots the results for the panels A-D of Supplementary Figure 3.</p> <p><em>‘SuppFigure_4.m': </em>computes and plots the results for the panels A-C of Supplementary Figure 4.</p> <p> </p> <p>The data structures contain the following fields:</p> <p><em>‘Mouse_Name’</em>: name of the mouse.</p> <p><em>‘Mouse_RecordingDate’</em>: date of recording (YMD).</p> <p><em>‘Mouse_DateOfBirth’</em>: date of birth of the mouse (YMD).</p> <p><em>‘Mouse_Sex’</em>: sex of the mouse (F or M).</p> <p><em>‘Mouse_Genotype’</em>: genotype of the mouse (strain of the two parents): A2A-Cre = Adora2a-Cre mice; D1-Cre = Drd1a-Cre mice; TdTomato = Lox-Stop-Lox-tdTomato mice; D1TdTomato = Drd1a-tdTomato mice; D2GFP = Drd2-GFP mice.</p> <p><em>‘Mouse_Level’</em>: Training level (NAÏVE or EXPERT).</p> <p><em>‘Cell_Counter’</em>: cell recorded in a given mouse.</p> <p><em>‘Cell_Type’</em>: type of the recorded cell (dSPN, iSPN or TAN).</p> <p><em>‘Cell_TargetedBrainArea’</em>: Brain area targeted (DLS).</p> <p><em>‘Cell_Recovered’</em>: Indicate cells that have been labelled and anatomically recovered (TRUE).</p> <p><em>‘Cell_Coordinates’</em>: Cell coordinates (in mm) relative to bregma (Lateral, AP, Ventro-dorsal)</p> <p><em>‘Cell_Fluorescence’</em>: expression of the genetically encoded fluorophore (FALSE or TRUE) and fluorophore (TdTomato or GFP). A neuron recorded in a Drd1a-tdTomato x Drd2-GFP (cf <em>Mouse_Genotype</em>) with <em>Cell_Fluorescence= {TRUE, TdTomato} is considered as a dSPN </em>(cf <em>Cell_Type</em>).</p> <p><em>‘Sweep_Counter’</em>: number of the sweep recorded for a given neuron (data were acquired across successive continuous sweeps of 30-300 s).</p> <p><em>‘Sweep_Type’</em>: experimental condition during that sweep (characterization = electrophysiological identification of the neurons; behavior = behavioral task).</p> <p><em>‘Sweep_MembranePotential’</em>: membrane potential recording (mV) after cutting of the APs.</p> <p><em>‘Sweep_CurrentInjected’</em>: current injected into the cell (pA).</p> <p><em>‘Sweep_PiezoLick’</em>: voltage signal from the piezo sensor attached to the water spout used to detect licking in behavior sweeps.</p> <p><em>‘Sweep_Trial’</em>: voltage command triggering the onset of each trial (both Catch and Stimulus trials) in behavior sweeps.</p> <p><em>‘Sweep_WhiskerStim’</em>: voltage command triggering the onset of each whisker stimulus in behavior sweeps.</p> <p><em>‘Sweep_Valve’</em>: voltage command triggering the opening of the valve delivering the reward in Hit trials.</p> <p><em>‘Sweep_SamplingRate’</em>: sampling rate (sample.s<sup>-1</sup>) of the recorded signals for each sweep.</p> <p><em>‘Sweep_TimeStamp’</em>: time at the beginning of the recorded sweep (H/min/s).</p> <p><em>‘Sweep_Reward’</em>: voltage command indicating reward availability during the response window following whisker stimulus in behavior sweeps.</p> <p><em>‘Sweep_APThresh’</em>: Threshold (V) used to detect action potentials (AP) during current injection.</p> <p> </p>
Dorsal striatum coding for the timely execution of action sequences
<p>The automatic initiation of actions can be highly functional. But occasionally these actions cannot be withheld and are released at inappropriate times, impulsively. Striatal activity has been shown to participate in the timing of action sequence initiation and it has been linked to impulsivity. Using a self-initiated task, we trained adult male rats to withhold a rewarded action sequence until a waiting time interval has elapsed. By analyzing neuronal activity we show that the striatal response preceding the initiation of the learned sequence is strongly modulated by the time subjects wait before eliciting the sequence. Interestingly, the modulation is steeper in adolescent rats, which show a strong prevalence of impulsive responses compared to adults. We hypothesize this anticipatory striatal activity reflects the animals' subjective reward expectation, based on the elapsed waiting time, while the steeper waiting modulation in adolescence reflects age-related differences in temporal discounting, internal urgency states, or explore-exploit balance. </p>
Chenopodium album L. subsp. striatum (Krasan) Murr (BR0000014450419)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Fig. 3 in Molecular Phylogeny of the Sand-dwelling Dinoflagellate Planodinium striatum and Chrysodinium gen. nov. for Plagiodinium ballux (Dinophyceae)
Fig. 3. Line drawings of the plate arrangement of Planodinium striatum (A–C), Plagiodinium belizeanum (D–F) and Chrysodinium ballux gen. nov. & comb. nov. (=Plagiodinium ballux) (G–I). Left lateral (A), right lateral (B), apical (C) views of Planodinium striatum redrawn from Hoppenrath et al. (2014). Left lateral (D), right lateral (E) and apical (F) views of Plagiodinium belizeanum redrawn from Wakeman et al. (2018). Left lateral (G), ventral (H) and apical (I) views of Chrysodinium ballux gen. & comb. nov. redrawn from Yamada et al. (2018) with a re-interpreted tabulation.
Fig. 1 in Molecular Phylogeny of the Sand-dwelling Dinoflagellate Planodinium striatum and Chrysodinium gen. nov. for Plagiodinium ballux (Dinophyceae)
Fig. 1. Light (A–M) and scanning electron microscopy (N–O) images of Planodinium striatum isolated in June 2012 at Wimereux, France. (A–B) A cell in left lateral and dorsal views. Asterisk (*) indicates the pusule. (C–M) Different views of another cell. The arrows indicate hypothecal plates. (N) Cell in ventro-left lateral view. (O) Another cell in left lateral view. The arrowheads indicate the trichocysts. The inset shows a large pore surrounded by several small pores. Scale bar = 10 μm.
Sisyrinchium striatum Sm. (BR0000015270344V)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Trifolium striatum L. (BR0000010411254)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Trifolium striatum L. (BR0000010412152)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Trifolium striatum L. (BR0000010411865)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Trifolium striatum L. (BR0000010411278)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Trifolium striatum L. (BR0000005161942)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Trifolium striatum L. (BR0000010411032)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Trifolium striatum L. (BR0000010411605)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Trifolium striatum L. (BR0000010411599)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Trifolium striatum L. (BR0000010411483)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
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.