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.
4,753
datasets available to search
ShareScore release 0.7.1
Dataset results
4,753 results for “shape”
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>
Figures datasets for "Wave momentum shaping for moving objects in heterogeneous and dynamic media"
<p>Source data for Figures used in the manuscript "Wave momentum shaping for moving objects in heterogeneous and dynamic media".</p>
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>
Reward perseveration is shaped by GABAA-mediated dopamine pauses: Electrophysiology Data
<p>In vivo electrophysiological data, slice electrophysiological data, and HTP vs tyrosine hydroxylase immunohistochemistry data. These data were used to generate the following figures in the paper "Reward perseveration is shaped by GABAA-mediated dopamine pauses":</p> <ul> <li>Figure 1, panels c-f</li> <li>Ext. Fig 1, all panels</li> <li>Ext. Fig 2, all panels</li> </ul> <p>Specifics of the data:</p> <ul> <li>Slice_Ephys .csv files contain the summarized in vitro electrophysiology data.</li> <li>HTP_Mouse_*.7z and ddHTP_Mouse_*.7z are zipped folders with the raw data, sorted data, and extracted cells for each recording, grouped by mouse the recordings were obtained from.</li> <li>HTP final_grouped spiking analysis_n39.mat is the MATLAB data file with all the extracted firing metrics for all 39 +HTP cells.</li> <li>ddHTP final_grouped spiking analysis_n18.mat is the MATLAB data file with all the extracted firing metrics for all 18 ddHTP cells.</li> <li> Ephys_Summary_Data .csv files contain the summarized in vivo electrophysiology data.</li> <li>VHist4_HTP and TH.7z contains the raw histological images, drawn ROIs, and ilastik cell counting performed to compare tyrosine hydroxylase and HTP expression.</li> </ul>
Reward perseveration is shaped by GABAA-mediated dopamine pauses: Fiber Photometry Data
<p>Fiber photometry data. These data were used to generate the following figures in the paper "Reward perseveration is shaped by GABAA-mediated dopamine pauses":</p> <ul> <li>Figure 3, all panels</li> <li>Ext. Fig 4, all panels</li> </ul> <p>Specifics of the data:</p> <ul> <li>FiberPho_Cohort_*.zip files contain all of the raw fiber photometry and behavior data for each mouse, grouped by cohort.</li> <li>FiberPho_Histo_Cohort_*.zip files contain the histological images and ROIs for each mouse, grouped by cohort. </li> <li>HTP_grouped_data.zip contains the grouped analysis fiber photometry and behavior MATLAB files for the experimental group.</li> <li>ddHTP_grouped_data.zip contains the grouped analysis fiber photometry and behavior MATLAB files for the control group.</li> <li>The various .csv files contain the summarized mouse, cohort, and analysis details and data.</li> </ul> <p> </p>
A Thermogelling Organic-Inorganic Hybrid Hydrogel with Excellent Printability, Shape Fidelity and Cytocompatibility for 3D Bioprintingg
<p>Dataset for manuscript submitted for peer review</p>
Supporting data set for: Simulations of the Electrochemical Oxidation of Shape-Selected Nanoparticle Catalysts
<p>This dataset contains input and output files for simulations of the oxidation of a set of shape-selected, 3 nm platinum nanoparticles associated with the manuscript found at https://arxiv.org/abs/2201.07605.</p> <p>The simulations are performed using a grand-canonical Monte-Carlo algorithm[1,2] in combination with the ReaxFF reactive force field method as implemented in the Amsterdam Density Functional (ADF) software package version 2017.106 by Software for Chemistry and Materials (SCM). The Pt/O ReaxFF force field parameterized by Fantauzzi et al. was used for the simulations.[3] Simulations were performed at oxygen chemical potential conditions corresponding to 200-1000 K at ultra-high vacuum (UHV, <em>p</em><sub>O2</sub> = 10<sup>-10</sup> mbar) and 400-1200 K at near-ambient pressure (NAP, <em>p</em><sub>O2</sub> = 1 mbar) conditions. The following nanoparticle shapes were used as input structures for the simulations: (111)-indexed octahedron, (100)-indexed cube, (110)-indexed dodecahedron, (111)- and (100)-indexed cuboctahedron, mixed-indexed sphere, and (730)-indexed tetrahexahedron.</p> <p>The folder structure is as follows:<br> Particle shape -> pressure condition -> temperature condition -> simulation input and output files</p> <p>The simulation input and output files are of the following filetypes:<br> control: Input parameters for the ReaxFF software.<br> control_MC: Input parameters for the GCMC subroutine that interacts with the ReaxFF software.<br> geo: Atomic input coordinates in BGF file format.<br> geo_MCXXXXXX: Atomic output coordinates in BGF file format and ReaxFF total energy result for GCMC step XXXXXX.</p> <p>Simulations were performed for a total of 25,000 iterations. Only accepted GCMC steps result in the creation of a geo_XXXXXX output file. Therefore, the index XXXXXX is not continuous since output files are not written at every iteration. Other ReaxFF-specific output has been filtered in order to declutter the dataset.</p> <p>[1] T. P. Senftle, R. J. Meyer, M. J. Janik, A. C. T. van Duin, J. Chem. Phys. 2013, 139, 044109.<br> [2] T. P. Senftle, M. J. Janik, A. C. T. van Duin, J. Phys. Chem. C 2014, 118, 4967–4981.<br> [3] D. Fantauzzi, J. Bandlow, L. Sabo, J. E. Mueller, A. C. T. van Duin, T. Jacob, Phys. Chem. Chem. Phys. 2014, 16, 23118–23133.</p>
Data Set related to Synaptic inhibition in the lateral habenula shapes reward anticipation.
<p>The lateral habenula (LHb) supports learning processes enabling the prediction of upcoming rewards. While reward-related stimuli decrease the activity of LHb neurons, whether this anchors on synaptic inhibition to guide reward-driven behaviors remains poorly understood. Here, we combine in vivo two-photon calcium imaging with Pavlovian conditioning in mice and report that anticipatory licking emerges along with decreases in cue-evoked calcium signals in individual LHb neurons. In vivo multiunit recordings and pharmacology reveal that the cue-evoked reduction in LHb neuronal firing relies on GABA<sub>A</sub>-receptor activation. In parallel, we observe a postsynaptic potentiation of GABA<sub>A</sub>-receptor-mediated inhibition, but not excitation, onto LHb neurons together with the establishment of anticipatory licking. Finally, strengthening or weakening postsynaptic inhibition with optogenetics and GABA<sub>A</sub>-receptor manipulations enhances or reduces anticipatory licking, respectively. Hence, synaptic inhibition in the LHb shapes reward anticipation.</p>
Shape Optimization of Thermoacoustic Systems Using a Two-Dimensional Adjoint Helmholtz Solver
<p>rijke stands for Rijke tube and tswc stands for turbulent swirl combustor.</p> <p>mesh_*.xml contains the mesh in xml format.</p> <p>p_dir_*.xml and p_adj_*.xml contain the direct and adjoint fields in xml format.</p> <p>omega_*.txt contains the eigenvalue.</p> <p>my_dict_*.pickle contains the control points and the shape derivatives as byte streams.</p> <p>The dataset also contains the pvd (ParaView Data) files for the amplitude and phase of the direct and adjoint fields.</p>
Results of the 3D detection of cracks in tested disc-shaped specimens
<p> </p> <p>3D images of fatigue crack obtained by laboratory tomography and synchrotron tomography within bi-disc specimens.</p>
Supplemental Material to "Consistent quantification of precipitate shapes and sizes in two and three dimensions using central moments"
<p>Supplemental material to manuscript "Consistent quantification of precipitate shapes and sizes in two and three dimensions using central moments" published in IMMJ "Integrating Materials and Manufacturing Innovation" 2022</p>
Public Dataset for "Did State-sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter"
<p>Dataset for the "Did State-sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter" paper. </p> <p>The full text of the paper can be found <a href="https://zenodo.org/record/4699959#.YngKatNBy3K">here</a>.</p> <p>The folder "Tweet_IDs" contains the complete list of the 152,514,929 tweet IDs (together with their timestamps) which we used for the analysis in the study: "Did State-sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter"</p> <p>by Nikos Salamanos, Michael J. Jensen, Costas Iordanou and Michael Sirivianos</p> <p>We have split the tweets into separate .zip files based on the date listed in their timestamps.</p> <p>The crawling took place from September 21 to November 7, 2016 (47 days; we did not collect data on 02/10/2016).</p> <p>Each "tweet_day_X.zip" file contains the file "tweet_day_X.csv", where X in [1,2,...,47]. For instance, the file "tweets_day_1.zip" contains the tweets of the 1st day: 09/21/2016.</p> <p>Please cite the paper in any published work that uses any of these resources. </p> <p>@misc{nikos_salamanos_2021_4699959,<br> author = {Nikos Salamanos and<br> Michael J. Jensen and<br> Costas Iordanou and<br> Michael Sirivianos},<br> title = {{Did State-sponsored Trolls Shape the 2016 US <br> Presidential Election Discourse? Quantifying<br> Influence on Twitter}},<br> month = apr,<br> year = 2021,<br> publisher = {Zenodo},<br> version = 3,<br> doi = {10.5281/zenodo.4699959},<br> url = {https://doi.org/10.5281/zenodo.4699959}<br> }</p>
Tabular datasets for "In situ structural analysis reveals membrane shape transitions during autophagosome formation"
<p>Tabular source data for all plots in the manuscript "In situ structural analysis reveals membrane shape transitions during autophagosome formation". The article is available at https://doi.org/10.1101/2022.05.02.490291. The naming of the sheets in the .xlsx files corresponds to the figure number and panel.</p>
Xanthene[n]arenes: Exceptionally Large, Bowl-Shaped Macrocyclic Building Blocks Suitable for Self-Assembly
<p>Data underlying the figures in the publication “Xanthene[<em>n</em>]arenes: Exceptionally Large, Bowl-Shaped Macrocyclic Building Blocks Suitable for Self-Assembly”, published in <em>J</em><em>ACS Au</em> 2021, 1, 11, 1885–1891. <a href="https://doi.org/10.1021/jacsau.1c00343">https://doi.org/10.1021/jacsau.1c00343</a></p>
Supplementary material for "Patterns of high-flying insect abundance are shaped by landscape type and abiotic conditions"
<p><strong>Abstract</strong></p> <p>Insects are of increasing conservation concern as a severe decline of both biomass and biodiversity have been reported. At the same time, data on where and when they occur in the airspace is still sparse, and we currently do not know whether their density is linked to the type of landscape above which they occur. Here, we combine data of high-flying insect abundance from six locations across Switzerland representing rural, urban and mountainous landscapes, which was recorded using vertical-looking radar devices. We analysed the abundance of high-flying insects in relation to meteorological factors, daytime, and type of landscape. Air pressure was positively related to insect abundance, wind speed showed an optimum, and temperature and wind direction did not show a clear relationship. Mountainous landscapes showed a higher insect abundance than the other two landscape types. Insect abundance increased in the morning, decreased in the afternoon, had a peak after sunset, and then declined again, though the extent of this general pattern slightly differed between landscape types. We conclude that the abundance of high-flying insects is not only related to abiotic parameters, but also to the type of landscapes. Thus, conservation measures implemented on the ground should start to also account for the needs of high-flying insects.</p>
Shape from Shading Digital Elevation Model for Oxia Planum Candidate Landing Site
<p>This data set contains the calibrated and map-projected HiRISE image ESP_037558_1985 and the matching Shape from Shading DEM using the method described in Hess et al., (2019a), and in more detail in Hess et al. (2022). The SfS DTM was part of the EPSC abstract Hess et al., (2019b). When using the data please reference Hess et al. (2022) for the method.</p> <p>ESP_037558_1985_30cm_o.cub: Image data in radiances, ISIS cube file, Equirectangular map projection at 0.25 m/pixel resolution.</p> <p>ESP_037558_1985_30cm_DEM.cub: Digital Elevation Model (DEM) with heights in meter, ISIS cube file, Equirectangular map projection at 0.25 m/pixel resolution.</p> <p>Cube files can be converted to other data formats using gdal (https://gdal.org/) or directly loaded in, e.g., ArcGIS or QGIS.</p> <p> </p> <p>Hess, M., Wohlfarth, K., Grumpe, A., Wöhler, C., Ruesch, O., and Wu, B.: ATMOSPHERICALLY COMPENSATED SHAPE FROM SHADING ON THE MARTIAN SURFACE: TOWARDS THE PERFECT DIGITAL TERRAIN MODEL OF MARS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1405–1411, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1405-2019, 2019a.</p> <p>Hess, Marcel. "High Resolution Digital Terrain Model for the Landing Site of the Rosalind Franklin (ExoMars) Rover." Proc. European Planetary Science Congress, EPSC-DPS2019-1533-4, Geneva, Switzerland, 2019b.</p> <p>Hess, M.; Tenthoff, M.; Wohlfarth, K.; Wöhler, C. Atmospheric Correction for High-Resolution Shape from Shading on Mars. <em>J. Imaging</em> <strong>2022</strong>, <em>8</em>, 158. https://doi.org/10.3390/jimaging8060158</p>
Multiscale analysis of triglycerides with X-ray scattering: Implementing a shape-dependent model for CNP characterization - Supporting Dataset
<p>This dataset contains the files used to substantiate the outcomes of the publication "<em>Multiscale analysis of triglycerides with X-ray scattering: Implementing a shape-dependent model for CNP characterization</em> <em>" </em></p> <p>The dataset includes:</p> <ul> <li>X-ray scattering profiles - in absolute units</li> <li>Images used to measure CNP distributions</li> </ul> <p>Relevant abbreviations: </p> <ul> <li>SSS - Tristearin</li> <li>OOO - Triolein</li> <li>FHRO - Fully Hydrogenated Rapeseed Oil</li> <li>HOSO - High Oleic Sunflower Oil</li> </ul>
Data and code for: Habitat preference of an herbivore shapes the habitat distribution of its host plant
<p>Initial release of analysis and code for:</p> <p>Alexandre, N. M., P. T. Humphrey, A. D. Gloss, J. Lee, J. Frazier, H. A. Affeldt III, and N. K. Whiteman. 2018. Habitat preference of an herbivore shapes the habitat distribution of its host plant. Ecosphere 00(00):e02372. (full citation pending)</p> <p>Release published to accompany corrected proofs on 2018-Jul-26.</p>
Data to "Retinal Blur from Natural Scenes and Eye Shape"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p><strong>Maiello, G</strong>., Harrison, W. J., Vera-Diaz, F. A., & Bex, P. J. (in preparation). Retinal Blur from Natural Scenes and Eye Shape.</p>
A Semi-Automatic Classification Approach for River Shape Extraction from Sentinel-2 Imagery
<p>To extract the river from satellite imagery, at first we have to classify the waterbody from satellite imagery. Then we will differentiate the river from waterbody. We have used three different techniques to classify the waterbody from satellite imagery. At first, pixel based iso-cluster unsupervised classification was used to classify the waterbody in Sentinel 2 imagery. We excluded the supervised classification in decided methodology as we are interested about automatic process of river extraction. Then we have used Segment mean shift classification tool as image segmentation techniques. Indices based NDWI (Normalize difference water indices) classification was also used in this research.</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.