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.
1,079
datasets available to search
ShareScore release 0.9.0
Dataset results
1,079 results for “source data”
Data and source code from: Contingency and selection in mitochondrial genome dynamics
<p>Eukaryotic cells contain numerous copies of mitochondrial DNA (mtDNA), allowing for the coexistence of mutant and wild-type mtDNA in individual cells. The fate of mutant mtDNA depends on their relative replicative fitness within cells and the resulting cellular fitness within populations of cells. Yet the dynamics of the generation of mutant mtDNA and features that inform their fitness remain unaddressed. Here we utilize long read single-molecule sequencing to track mtDNA mutational trajectories in Saccharomyces cerevisiae. We show a previously unseen pattern that constrains subsequent excision events in mtDNA fragmentation. We also provide evidence for the generation of rare and contentious non-periodic mtDNA structures that lead to persistent diversity within individual cells. Finally, we show that measurements of relative fitness of mtDNA fit a phenomenological model that highlights important biophysical parameters governing mtDNA fitness. Altogether, our study provides techniques and insights into the dynamics of large structural changes in genomes that may be applicable in more complex organisms.</p>
Source Data files for: Primary cilia and SHH signaling impairments in human and mouse models of Parkinson's disease
<p>Parkinson’s disease (PD) as a progressive neurodegenerative disorder arises from multiple genetic and environmental factors. However, underlying pathological mechanisms remain poorly understood. Using multiplexed single-cell transcriptomics, we analyze human neural precursor cells (hNPCs) from sporadic PD (sPD) patients. Alterations in gene expression appear in pathways related to primary cilia (PC). Accordingly, in these hiPSC-derived hNPCs and neurons, we observe a shortening of PC. Additionally, we detect a shortening of PC in <em>PINK1</em>-deficient human cellular and mouse models of familial PD. Furthermore, in sPD models, the shortening of PC is accompanied by an increased SHH signal transduction. Inhibition of this pathway rescues the alterations in PC morphology and mitochondrial dysfunction. Thus, increased SHH activity due to ciliary dysfunction is needed for the development of pathoetiological phenotypes observed in sPD, like mitochondrial dysfunction. In sum, altered PC function is part of early PD pathoetiology and inhibiting the overactive SHH signaling is a potential neuroprotective therapy.</p>
Source data for: Nesting success of Red-winged Blackbirds (Agelaius phoeniceus) in marshes in an anthropogenic landscape
<p>Recent analyses show significant population declines in many abundant avian species, especially marsh-nesting species including the Red-winged Blackbird (RWBL). Hypothesized causes include reduced nesting success resulting from changing land use patterns and exposure to contaminants. Our goal was to test the hypothesis that landscape and nest characteristics as well as exposure to polychlorinated biphenyls (PCBs) correlate with nesting success. From 2008-2014, we measured clutch size, egg and nestling mass, hatching and fledging success, and daily survival of 1293 RWBL nests from 32 marshes in the Hudson River valley of New York. Using generalized linear effect and survival models, we found that: (1) Julian date was negatively related to hatching success and clutch size but positively related to egg mass; (2) nest height was negatively related to hatching success; (3) nestling mass decreased with increased nest density and distance to edges; (4) fledging success was significantly lower in nests closer to the ground that were far from water; and (5) clutch size and daily survival were higher in nests farther from water. Results showed that nesting success was correlated with variables associated with flooding, population density, and predation and provided no support for the predicted negative effects of PCB exposure.</p>
TrainRuns.jl: an Open-Source Tool for Running Time Estimation - Supplement Data
<p>This additional data contains the initial data and the calculated results for comparing FBS and TrainRuns.jl.</p> <p><strong>File description</strong></p> <ul> <li><em>local.yaml</em>: input parameters for the local train</li> <li><em>freight.yaml</em>: input parameters for the freight train</li> <li><em>running_path.yaml</em>: input parameters for the path</li> <li><em>freight_FBS.csv</em>: export of calculation from FBS for the freight train</li> <li><em>freight_TrainRuns.csv</em>: export of calculation from TrainRun.jl converted in FBS units for the freight train</li> <li><em>freight_diff.csv</em>: the calculated difference between FBS.csv and TrainRuns.csv for the freight train</li> <li><em>local_FBS.csv</em>: export of calculation from FBS for the local train</li> <li><em>local_TrainRuns.csv</em>: export of calculation from TrainRun.jl converted in FBS units for the local train</li> <li><em>local_diff.csv</em>: the calculated difference between FBS.csv and TrainRuns.csv for the local train</li> <li><em>running_path.csv</em>: converted running_path.yaml for displaying</li> <li><em>comparison.tex</em>: LaTeX code for the graph in comparison.pdf</li> </ul> <p><strong>Sources</strong></p> <p>The calculations in FBS were done with the file 'Ostsachsen_V220.railml'. FBS needs a commercial license, which can be purchased. License for 'Ostsachsen_V220.railml' is Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0).<br> The file 'Ostsachsen_V220.railml' can be found at:<br> https://www.railml.org/en/user/exampledata.html (last accessed 2022-06-06 with login) -> "Real world railway examples from professional tools" -> "East Saxony railway network by FBS" -> "Ostsachsen_V220.railml"</p> <p>Other sources are mentioned in the files.</p>
Semi-empirical error ellipsoid clustering for identifying the second-order structural features from a laboratory AE source location cloud—method, validation, and application to a hydraulic fracturing test [DATA]
<p>Data and metadata for the publication "Semi-empirical error ellipsoid clustering for identifying the second-order structural features from a laboratory AE source location cloud—method, validation, and application to a hydraulic fracturing test", published in Earth and Space Science.</p>
Source data for "Ca2+ channels couple spiking to mitochondrial metabolism in substantia nigra dopaminergic neurons"
<p><strong>Fig.1Aa-c.tif</strong></p> <p>2PLSM images (Fura-2 filled neuron) used for the reconstruction in Fig.1A</p> <p> </p> <p><strong>Fig1CDJ.xlsx</strong></p> <p>Numerical data for the charts in Fig. 1C, Fig.1D, Fig.1J</p> <p> </p> <p><strong>Fig.1F_GCEPIA1er.tif</strong></p> <p>Confocal image (green channel, G-CEPIA1er) for Fig. 1F</p> <p> </p> <p><strong>Fig.1F_TH.tif</strong></p> <p>Confocal image (blue channel, anti-TH immunostaining) for Fig. 1F</p> <p> </p> <p><strong>Fig.1G_GCEPIA1er.tif</strong></p> <p>Confocal image (green channel, G-CEPIA1er) for Fig. 1G</p> <p> </p> <p><strong>Fig.1G_CRT.tif</strong></p> <p>Confocal image (magenta channel, anti-CRT immunostaining) for Fig. 1G</p> <p> </p> <p><strong>Fig.1H_GCEPIA1er.tif</strong></p> <p>Confocal image (green channel, G-CEPIA1er) for Fig. 1H</p> <p> </p> <p><strong>Fig.1H_TH.tif</strong></p> <p>Confocal image (blue channel, anti-TH immunostaining) for Fig. 1H</p> <p> </p> <p><strong>Fig. 2B_mitoGCaMP6.tif</strong></p> <p>Confocal image (green channel, mito-GCaMP6) for Fig. 2B</p> <p> </p> <p><strong>Fig.2B_TH.tif</strong></p> <p>Confocal image (blue channel, anti-TH immunostaining) for Fig. 2B</p> <p> </p> <p><strong>Fig. 2C_mitoGCaMP6.tif</strong></p> <p>Confocal image (green channel, mito-GCaMP6) for Fig. 2C</p> <p> </p> <p><strong>Fig.2C_COXIV.tif</strong></p> <p>Confocal image (magenta channel, anti-COXIV immunostaining) for Fig. 2C</p> <p> </p> <p><strong>Fig. 2D_mitoGCaMP6.tif</strong></p> <p>Confocal image (green channel, mito-GCaMP6) for Fig. 2D</p> <p> </p> <p><strong>Fig.2D_TH.tif</strong></p> <p>Confocal image (blue channel, anti-TH immunostaining) for Fig. 2D</p> <p> </p> <p><strong>Fig.2FGJL.xlsx</strong></p> <p>Numerical data for the charts in Fig. 2F, Fig.2G, Fig.2J, Fig.2L</p> <p> </p> <p><strong>Fig.3BDE.xlsx</strong></p> <p>Numerical data for the charts in Fig. 3B, Fig.3D, Fig.3E</p> <p> </p> <p><strong>Fig.3C_Alexa.tif</strong></p> <p>MAX Projection of z-stack of 2PLSM images (magenta channel, Alexa 594 dye) used to generate Fig.3C top panel</p> <p> </p> <p><strong>Fig.3C_mitoGCaMP6.tif</strong></p> <p>MAX Projection of z-stack of 2PLSM images (green channel, mito-GCaMP6) used to generate Fig.3C top panel</p> <p> </p> <p><strong>Fig.3C_inset_dendritic_mitoGCaMP6.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.3C bottom left panel</p> <p> </p> <p><strong>Fig.3C_inset_soma_mitoGCaMP6.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.3C bottom right panel</p> <p> </p> <p><strong>Fig. 4A_PercevalHR.tif</strong></p> <p>Confocal image (green channel, PercevalHR) for Fig.4A</p> <p> </p> <p><strong>Fig.4A_TH.tif</strong></p> <p>Confocal image (blue channel, anti-TH immunostaining) for Fig.4A</p> <p> </p> <p><strong>Fig. 4B_PercevalHR.tif</strong></p> <p>Confocal image (green channel, PercevalHR) for Fig.4B</p> <p> </p> <p><strong>Fig.4B_TH.tif</strong></p> <p>Confocal image (blue channel, anti-TH immunostaining) for Fig.4B</p> <p> </p> <p><strong>Fig.4G.xlsx</strong></p> <p>Numerical data for the charts in Fig.4G</p> <p> </p> <p><strong>Fig.5BDFHI.xlsx</strong></p> <p>Numerical data for the charts in Fig.5B, Fig.5D, Fig.5F, Fig.5H, Fig.5I</p> <p> </p> <p><strong>Fig.6ADEFJKLN.xlsx</strong></p> <p>Numerical data for the charts in Fig.6A, Fig.6D, Fig.6E, Fig.6F, Fig.6J, Fig.6K, Fig.6L, Fig.6N</p> <p> </p> <p><strong>Fig.6H_mitoroGFP.tif</strong></p> <p>2PLSM image (green channel, mito-roGFP) for Fig.6H</p> <p> </p> <p><strong>Fig.6M_MCU-KO.tif</strong></p> <p>Combined EM micrographs used to generate Fig. 6M right side</p> <p> </p> <p><strong>Fig.6M_wildtype.tif</strong></p> <p>Combined EM micrographs used to generate Fig. 6M left side</p> <p> </p> <p><strong>Fig.7CEFG.xlsx</strong></p> <p>Numerical data for the charts in Fig.7C, Fig.7E, Fig.7F, Fig.7G</p> <p> </p> <p><strong>Fig.8CEHJK.xlsx</strong></p> <p>Numerical data for the charts in Fig.8C, Fig.8E, Fig.8H, Fig.8J, Fig.8K</p> <p> </p> <p><strong>Fig.S1A_bottom.tif</strong></p> <p>2PLSM image (green channel, G-CEPIA1er) for Fig.S1A bottom panel (low Ca2+)</p> <p> </p> <p><strong>Fig.S1A_top.tif</strong></p> <p>2PLSM image (green channel, G-CEPIA1er) for Fig.S1A top panel (high Ca2+)</p> <p> </p> <p><strong>Fig.S1B.xlsx</strong></p> <p>Numerical data for the chart in Fig.S1B</p> <p> </p> <p><strong>Fig.S2A_baseline.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.S2A middle panel (baseline)</p> <p> </p> <p><strong>Fig.S2A_MAX.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.S2A top panel (high Ca2+)</p> <p> </p> <p><strong>Fig.S2A_min.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.S2A bottom panel (low Ca2+)</p> <p> </p> <p><strong>Fig.S2C_baseline.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.S2C middle panel (baseline)</p> <p> </p> <p><strong>Fig.S2C_MAX.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.S2C top panel (high Ca2+)</p> <p> </p> <p><strong>Fig.S2C_min.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.S2C bottom panel (low Ca2+)</p> <p> </p> <p><strong>Fig.S2E_baseline.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.S2E bottom left panel (baseline)</p> <p> </p> <p><strong>Fig.S2E_end.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.S2E bottom right panel</p> <p> </p> <p><strong>Fig.S2E_peak.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.S2E bottom center panel</p> <p> </p> <p><strong>Fig.S2F.xlsx</strong></p> <p>Numerical data for the chart in Fig.S2F</p> <p> </p> <p><strong>Fig.S3ABC.xlsx</strong></p> <p>Numerical data for the charts in Fig.S3A, Fig.S3B, Fig.S3C</p> <p> </p> <p><strong>Fig.S4CD.xlsx</strong></p> <p>Numerical data for the charts in Fig.S4C, Fig.S4D</p> <p> </p> <p><strong>Fig.S5ABC.xlsx</strong></p> <p>Numerical data for the charts in Fig.S5A, Fig.S5B, Fig.S5C</p> <p> </p> <p><strong>Fig.S7A.tif</strong></p> <p>2PLSM image (green channel, GCaMP6) for Fig.S7A</p> <p> </p> <p><strong>Fig.S7CEFGH.xlsx</strong></p> <p>Numerical data for the charts in Fig.S7C, Fig.S7E, Fig.S7F, Fig.S7G, Fig.S7H</p> <p> </p> <p><strong>Fig.S8ABCDEF.xlsx</strong></p> <p>Numerical data for the charts in Fig.S8A, Fig.S8B, Fig.S8C, Fig.S8D, Fig.S8E, Fig.S8F</p> <p> </p> <p><strong>Fig.S9AHIJK.xlsx</strong></p> <p>Numerical data for the charts in Fig.S9A, Fig.S9H, Fig.S9I, Fig.S9J, Fig.S9K</p> <p> </p> <p><strong>Fig.S9B_wildtype_DLStr.tif</strong></p> <p>Confocal image of dorso-laterateral striatum in wildtype mouse (red channel, anti-TH immunostaining) for Fig.S9B</p> <p> </p> <p><strong>Fig.S9C_MCU-KO_DLStr.tif</strong></p> <p>Confocal image of dorso-laterateral striatum in MCU-KO mouse (red channel, anti-TH immunostaining) for Fig.S9C</p> <p> </p> <p><strong>Fig.S9D_wildtype_SN.tif</strong></p> <p>Confocal image of midbrain in wildtype mouse (red channel, anti-TH immunostaining) for Fig.S9D</p> <p> </p> <p><strong>Fig.S9E_MCU-KO_SN.tif</strong></p> <p>Confocal image of midbrain in MCU-KO mouse (red channel, anti-TH immunostaining) for Fig.S9E</p> <p> </p> <p><strong>Fig.S9F_wildtype_openfield.png</strong></p> <p>Open field path tracked for wildtype mouse for Fig.S9F</p> <p> </p> <p><strong>Fig.S9G_MCU-KO_openfield.png</strong></p> <p>Open field path tracked for MCU-KO mouse for Fig.S9G</p> <p> </p> <p><strong>Fig.S10A.xlsx</strong></p> <p>Numerical data for the charts in Fig.S10A</p>
Data from "Asymmetric visual capture of virtual sound sources in the distance dimension"
<p>This repository will contain raw and processed data used and described in:</p> <p><strong>Zahorik P (2022) Asymmetric visual capture of virtual sound sources in the distance dimension. Front. Neurosci. 16:958577. doi: 10.3389/fnins.2022.958577</strong></p>
Source Data for 'The imprint of star formation on stellar pulsations'
<p>This repository holds the source data and plotting routines for all Figures and Tables of the artice 'The imprint of star formation on stellar pulsations'.</p> <p><a href="https://zenodo.org/api/files/2ff2ea54-edc1-4fdc-9af8-4f839fbb089e/source_data.zip">source_data.zip </a>holds the source data. Each file provides a data set. Different data sets used in this publication are i.e. one parameter of a evolutionary track (i.e. 2Msun_classic_history_star_age.txt), one parameter of a structure model (i.e. 2Msun_classic_different_ov_profile_zams_mass.txt), or one parameter of a theoretical frequency set (i.e. GYRE_summary_classic_pre_ms_stage_n_pg.txt). All files have one column and are directly loaded in <a href="https://zenodo.org/api/files/2ff2ea54-edc1-4fdc-9af8-4f839fbb089e/plot_utils.py">plot_utils.py </a>.</p> <p><a href="https://zenodo.org/api/files/2ff2ea54-edc1-4fdc-9af8-4f839fbb089e/plot_utils.py">plot_utils.py </a>holds the plotting Routines for Figures 1-7 and Figures A1-A43.</p> <p><a href="https://zenodo.org/api/files/2ff2ea54-edc1-4fdc-9af8-4f839fbb089e/figures.py">figures.py </a>executes the plotting routines from <a href="https://zenodo.org/api/files/2ff2ea54-edc1-4fdc-9af8-4f839fbb089e/plot_utils.py">plot_utils.py</a>.</p> <p><a href="https://zenodo.org/api/files/2ff2ea54-edc1-4fdc-9af8-4f839fbb089e/Figures.zip">Figures.zip </a>holds the resulting Figures.<br> </p> <p>Requirements to execute the plotting routines are:</p> <pre>Python 3.7.5 numpy 1.19.5 matplotlib 3.3.3 pandas 1.2.2 cmcrameri 1.4 scipy 1.2.3 seaborn 0.11.1</pre>
source data: ion regulation precedes gas exchange at gills
<p>These are source data for a submitted manuscript entitled "Ion regulation at gills precedes gas exchange and the origin of vertebrates". All associated animal care and experimentation conformed to the guidelines set by the Canadian Council on Animal Care (CCAC) and was approved by the University of British Columbia's Animal Care Council (ACC) under the animal use protocol #A19-0284.</p> <p> </p>
Third-order momentum advection on the quasi-hexagonal C-grid on the sphere: Data and source code
<p>This upload contains data and source code accompanying the paper submitted to JAMES (Journal of advances in modeling Earth systems) under the title 'Third-order momentum advection on the quasi-hexagonal C-grid on the sphere'</p> <p>See README files for further details.</p>
LaMEM source code and input files corresponding to Present‐day upper‐mantle architecture of the Alps: Insights from data‐driven dynamic modelling
<p>This repository contains LaMEM source code and input files for the models presented in Kumar, A., Cacace, M., Scheck-Wenderoth, M., Götze, H.-J., & Kaus, B. J. P. (2022). Present-day upper-mantle architecture of the Alps: Insights from data-driven dynamic modeling. Geophysical Research Letters, 49, e2022GL099476. https://doi. org/10.1029/2022GL099476</p>
Data to reproduce the results presented in Lake et al. 2022. Hydrological processes, https://doi.org/10.1002/hyp.14726 ("Using particle size distributions to fingerprint suspended sediment sources – evaluation at laboratory and catchment scales")
<p>This repository contains data on particle size distribution data obtained from the laboratory and field experiments as described in Lake et al., 2022. </p> <p>The data contains the input files as needed for the modelling:</p> <p>- In the excel files the particle size distribution data for the target SS</p> <p>- In the text file the particle size distribution data from the sources.</p> <p> </p> <p>Furthermore, the data contains the resulting output files.</p> <p> </p>
Source data for "Lonely individuals process the world in idiosyncratic ways"
<p>The following includes the source data for the manuscript titled "Lonely individuals process the world in idiosyncratic ways".</p>
Data accompanying "Petabit per second data transmission using a chip-scale microcomb ring resonator source"
<p>Data for reproducing the figures from the article: <strong>Petabit per second data transmission using a chip-scale microcomb ring resonator source. </strong></p>
0.48 Angstrom 3,5-dinitrobenzoic acid (3,5-DNBA) C2/c polymorph single crystal X-ray diffraction data set recorded at Diamond Light Source I19-1
<p>Data set from 3,5-dinitrobenzoic acid (3,5-DNBA) C2/c polymorph recorded during in house research (DLS proposal: NR18193). Each run is saved as a single compressed tar file for convenience, runs 1, 2, 3 correspond to 3 x 170 degree omega scans at phi 0, 120, 240 degrees with 2-theta of 30 degrees, run 4 phi scan at 2-theta 0, runs 5-8 omega scans at 2-theta 55 degrees, giving data to 0.48A.</p> <p> </p> <p>Now including scale factor graph & report from processing</p>
Data from: Orchid trade at the source: Epiphytic species with conspicuous flowers in low-elevation forests are more locally collected in a Philippine key biodiversity area
<p>Orchids are the most heavily traded plant group globally, putting pressure on wild populations in many source countries like the Philippines. Despite its rich orchid diversity, there remains a notable gap in understanding the factors driving orchid trade within the country. To address this knowledge gap and support orchid conservation efforts, we utilized a five-year orchid diversity dataset extensively collected through floristic field and village garden surveys in one of the largest key biodiversity areas in the southern Philippines. We employed a trait-based approach to investigate ecological drivers of local orchid collection within this source area. Our results show that around 36% of local orchid diversity have predicted collection risks of ≥50%. Notably, locally collected orchid species exhibited multiple, large, and conspicuously colored flowers that are found in low-elevation forests and higher up in forest stratum. Elevational distribution and flower size emerged as the strongest predictors, potentially influencing collection preferences. Our analysis of predicted collection risks underscores the vulnerability of both threatened and non-threatened orchid species to local collection pressures. Moreover, we highlight the practical utility of our trait-based approach in predicting risks and informing management strategies for local orchid conservation. This research marks a significant step towards identifying ecological drivers influencing orchid trade at its source, providing insights that can inform targeted conservation strategies across many key biodiversity areas for this highly diverse, charismatic, and threatened plant family.</p>
Enhancing Full Waveform Inversion of Field GPR Data: A Source-Independent Approach with Dynamic Reference Selection via SE-Wave-U-Net
<p>Data presented in the manuscript titled 'Enhancing Full Waveform Inversion of Field GPR Data: A Source-Independent Approach with Dynamic Reference Selection via SE-Wave-U-Net'</p>
Pilot 1 Model-based decision support for testing drought-related adaptation strategies in the Aa of Weerijs river basin, the Netherlands: Hydrological model description, input data sources and model results
<p>This dataset contains: the report with the description of the model structure, the input data sources and the spatial locations within the catchment for which surface and groundwater results data are provided.</p>
Changes in holopelagic Sargassum spp. biomass composition across an unusual year - supporting particle data and analysis source code
<p>As specified in 'Data, Materials, and Software Availability' of Tonon et al. (2024), PNAS, Vol. 121, e2312173121, https://doi.org/10.1073/pnas.2312173121, the following data and software are provided:</p> <p>Particle forward tracking (statistical data – for Fig. 2)</p> <p>Particle backward tracking (primary data – for Fig. 3)</p> <p>Analysis of primary data for gridded particle fractional coverage and mean age (Fortran source code)</p>
Polyconvex inelastic Constitutive Artificial Neural Networks: Source code and data
<p>This dataset contains the source code of the polyconvex extension of the inelastic Constitutive Artificial Neural Network (iCANN) as well as the data for the examples from the publication:</p> <p>Holthusen, H., Lamm, L., Brepols, T., Reese, S., & E. Kuhl.<em> Polyconvex inelastic Constitutive Artificial Neural Networks.</em></p> <p> </p> <p><strong>Results:</strong> Discovering a model for the polymer VHB 4910 subjected to cyclic loading</p> <p>Here, we investigate the ability of the polyconvex iCANN to discover and learn a model for the material response of VHB 4910 polymer subjected to cyclic loading at different stretch rates.</p> <p>The experimental data are taken from the literature:</p> <p>Hossain, M., Vu, D. K., & Steinmann, P. (2012). Experimental study and numerical modelling of VHB 4910 polymer. <em>Computational Materials Science</em>, <em>59</em>, 65-74.</p> <p><a href="https://doi.org/10.1016/j.commatsci.2012.02.027">https://doi.org/10.1016/j.commatsci.2012.02.027</a></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.