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493 results for “example data”
Data set from example subject
<p>This is widefield calcium imaging data from an example subject. Data from two sessions is included: Septermber 20, 2018 and September 24, 2018. Both sessions are from subject mSM63, a triple transgenic Ai95 subject engaged in a detection task in two sensory modalities; GCaMP6 is expressed in all excitatory neurons. Data collection was by Simon Musall, Rirchard Sun and Steven Gluf. </p> <p>Each session contains several files that are used to train the encoding model. The files Vc.mat and rsVC.mat contain the original and downsampled widefield imaging data respectively. This data has also been reduced into spatial components U and temporal components V. The file [mousename]_SpatialDisc_[sessiondate]_Session*.mat contains most of the behavioral information (sensory modality and rate, choice, inter-trial interval, etc.) The Analog.dat files contain data for the piezo sensor (to track hindlimb movements) as well as stimulus onset time. There are also various opts.mat files that contain options used for the widefield imaging. </p> <p>The BehaviorVideo folders contain camera data for the sessions (the data has been SVD'ed for both video and video motion energy). This folder also has a file FilteredPupil.mat which contains data for pupil size, nose, and whisker pads. There is also a bhvOpts.mat file which contains some information on how the camera files were preprocessed. </p> <p>This file structure can be analyzed with rateDisc_RegressModel.mat to generate estimates of neural activity modulation by task variables, instructed, and uninstructed movements during decision-making. <br> </p>
SIMPA - Example data for realistic photoacoustic images
<p>To visually demonstrate the capabilities of the current version of SIMPA, an image of a human forearm was recorded from a volunteer using the MSOT Acuity Echo. The measurement was conducted within a healthy volunteer study that was approved by the ethics committee of the medical faculty of Heidelberg University under reference number S-451/2020, and the study is registered with the German Clinical Trials Register under reference number DRKS00023205.</p> <p>This dataset can be used to compare simulations based on the given segmentation with the real image.</p>
Python Time Normalized Superposed Epoch Analysis (SEAnorm) Example Data Set
<p>Solar Wind Omni and SAMPEX ( Solar Anomalous and Magnetospheric Particle Explorer) datasets used in examples for <a href="https://github.com/samwalton7645/SEA_Code">SEAnorm</a>, a time normalized superposed epoch analysis package in python.</p> <p>Both data sets are stored as either a HDF5 or a compressed csv file (csv.bz2) which contain a Pandas DataFrame of either the Solar Wind Omni and SAMPEX data sets. The data sets where written with pandas.DataFrame.to_hdf() and pandas.DataFrame.to_csv() using a compression level of 9. The DataFrames can be read using pandas.DataFrame.read_hdf( ) or pandas.DataFrame.read_csv( ) depending on the file format. </p> <p>The Solar Wind Omni data sets contains solar wind velocity (V) and dynamic pressure (P), the southward interplanetary magnetic field in Geocentric Solar Ecliptic System (GSE) coordinates (B_Z_GSE), the auroral electrojet index (AE), and the Sym-H index all at 1 minute cadence. </p> <p>The SAMPEX data set contains electron flux from the Proton/Electron Telescope (PET) at two energy channels 1.5-6.0 MeV (ELO) and 2.5-14 MeV (EHI) at an approximate 6 second cadence.</p> <p> </p>
Data Set for_Integrating torrefaction of pulp industry sludge with anaerobic digestion to produce biomethane and volatile fatty acids: An example of industrial symbiosis for circular bioeconomy
<p>Industrial symbiosis, which allows the sharing of resources between different industries, could help to improve the overall feasibility of bio-based chemicals production. In that regard, this study focused on integrating the torrefaction of pulp industry sludge with anaerobic digestion. More specifically, anaerobic digestion (AD) of pulp sludge-derived torrefaction condensate (TC) was studied to evaluate the biomethane and volatile fatty acid (VFA) potential. The torrefaction condensate produced at 275 and 300 °C was used in AD. The volatile solid content (VS) was 6.69 and 9.01% for the condensate produced at 275 and 300 °C, respectively. The organic fraction of TC mainly contained acetic acid, 2-furanmethanol, and syringol. The methane yield was in the range of 481–772 mL/g VS for the mesophilic and 401–746 mL/g VS for the thermophilic process, respectively. The VFA yield was in the range of 1.1 to 3.4 g/g VS for mesophilic and from 1.5 to 4.7 g/g VS in thermophilic conditions, when methanogenesis was inhibited. Finally, pulp sludge TC is a feasible feedstock to produce platform chemicals like VFA. However, at higher substrate loading, signs of process inhibition were observed because of the relatively increasing concentration of microbial inhibitors</p>
Data bases for Measuring impacts of oral health promotion interventions on health inequities: the example of New Caledonia
<p>Extract from the New Caledonian (NC) epidemiological survey database for identifying the determinants and risk factors explaining the presence of untreated dental caries and to compare the prevalence and severity of dental caries between 2012 and 2019, in order to identify potential changes that occurred in NC.</p>
Example subjects for Mobilise-D data standardization
<p>Standardized data from Mobilise-D participants (YAR dataset) and pre-existing datasets (ICICLE, MSIPC2, Gait in Lab and real-life settings, MS project, UNISS-UNIGE) are provided in the shared folder, as an example of the procedures proposed in the publication "Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization" that is currently under review in Scientific data. Please refer to that publication for further information. Please cite that publication if using these data. </p> <p>The code to standardize an example subject (for the ICICLE dataset) and to open the standardized Matlab files in other languages (Python, R) is available in github (https://github.com/luca-palmerini/Procedure-wearable-data-standardization-Mobilise-D).</p>
Example input files and output data for 1D hydrodynamic simulations of shock compressed iron
<p>Example input files and output data for 1D hydrodynamic simulations of shock compressed iron. Input files consists of 3 examples from the SIMEX github wiki page for a 50 micron CH ablator with 5 micro Fe foil (laser pulse is a 6 ns flat top pulse, 1064 nm with 0.3 TW/cm<sup>2</sup>). Output data are from Esther hydrocode in .txt format and the SIMEX opmd.h5 format.</p>
Example data set from Diamond Light Source VMXi beamline (Eiger 4M data, NeXus format)
<p>Data set recorded from Thermolysin crystal record <em>in situ</em> with Eiger 4M detector, to demonstrate file format used for this instrument at Diamond Light Source. Processing results using xia2 / DIALS:</p> <p> </p> <pre>For AUTOMATIC/DEFAULT/SAD Overall Low High High resolution limit 1.97 5.35 1.97 Low resolution limit 46.86 46.87 2.01 Completeness 59.8 73.2 6.0 Multiplicity 5.8 8.0 1.1 I/sigma 13.6 24.4 1.8 Rmerge(I) 0.072 0.050 0.308 Rmerge(I+/-) 0.067 0.048 0.000 Rmeas(I) 0.078 0.054 0.436 Rmeas(I+/-) 0.076 0.054 0.000 Rpim(I) 0.028 0.018 0.308 Rpim(I+/-) 0.035 0.023 0.000 CC half 0.997 0.998 0.450 Wilson B factor 13.401 Anomalous completeness 51.3 78.8 0.8 Anomalous multiplicity 3.3 4.8 1.0 Anomalous correlation 0.039 -0.006 0.000 Anomalous slope 0.987 dF/F 0.103 dI/s(dI) 1.086 Total observations 85069 8229 82 Total unique 14747 1031 73 Assuming spacegroup: P 6 2 2 Other likely alternatives are: P 61 2 2 P 65 2 2 P 62 2 2 P 64 2 2 P 63 2 2 Unit cell (with estimated std devs): 93.7184(3) 93.7184(3) 130.864(2) 90.0 90.0 120.0 </pre> <p> </p>
Example dataset for openPMD-conform molecular dynamics data (MD domain extension)
<p>This dataset results from the molecular dynamics (MD) simulation of the photon-sample interaction. The photons are propagated through the SASE1 beamline and the SPB-SFX instrument at European XFEL, with an initial energy of 5 keV. The sample is the two-nitrogenase iron protein (2nip) with 4348 atoms. The simulation is performed with a demo version of XMDYN. The datasets were rewritten from the original XMDYN output into an hdf5 format that complies with the openPMD metadata standard for particle and mesh data and the proposed domain extension of this standard for MD data. The dataset "pure_2nip_pmi_out.opmd.h5" conforms the openPMD metadata MD domain extension strictly, while the dataset "pure_2nip_pmi_out.opmd.ff.h5" stores form factor results additionally for SingFEL diffraction simulation.</p> <p>This dataset is part of the Deliverable D5.1 in Workpackage 5 (Virtual Neutron and X-ray Laboratory) of the Photon and Neutron Open Science Cloud (PaNOSC).</p> <p>This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 823852.<br> </p>
Example Photon raytracing openPMD data
<p>This is an example datafile in hdf (h5) format, complying to the openPMD domain extension for photon raytracing simulations, as defined in: https://github.com/PaNOSC-ViNYL/openPMD-standard/blob/latest/EXT_PRAYTRACE.md . The dataset was created with the provided script generate_data.py and presents an example output file.</p> <p>This dataset is part of the Deliverable D5.1 in Workpackage 5 (Virtual Neutron and X-ray Laboratory) of the Photon and Neutron Open Science Cloud (PaNOSC).</p> <p>This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 823852.</p>
GBIF map data latest examples: species Gadus morhua
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GBIF map data latest examples: genus Gadus
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GBIF map data latest examples: order Gadiformes
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GBIF map data latest examples: family Gadidae
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Example Dataset for npstat: Population genetics from Pooled NGS data NPStat v1: User guide
<p>Example Dataset for npstat to test the program and the different options.</p> <p>The example dataset contains a pileup file with sequences of of the 2L chromosome from fifteen pooled inbreed individuals of <em>Drosophila melanogaster </em>(<span>doi: 10.1038/nature10811</span>). The dataset also contains the sequence reference of the 2L chromosome in fasta format, an outgroup sequence in fasta format of <em>D. yakuba</em> (SRR26246471), a GFF3 annotation file and a file with a brief list of selected SNPs to be analyzed.</p>
Cows, Pigs and People: Example data of cubic insulin from three different species recorded on Diamond Light Source I24
<p>Data collected at 100K on 10th May 2024 at I24 (Diamond Light Source) to investigate automatic grouping of datasets containing very subtle differences. Crystals grown by Cicely Tam following standard techniques with coordination from Felicity Bertram. For each of bovine, porcine, and human insulin, 10 degree wedges are included. Insulin from these three sources differ by 1-3 amino acids, but are otherwise structurally isomorphous. </p> <p>The purpose of the data upload is to make data available for tutorials using the DIALS toolchain (see e.g. examples at https://github.com/graeme-winter/dials_tutorials) however data are available for all purposes without limitation. </p> <p>Key:</p> <p>CIX - bovine insulin</p> <p>PIX - porcine insulin</p> <p>X - human insulin</p>
Integrated DInSAR + GNSS example data sets
<p>This data repository contains sample datasets of raw DInSAR time series (NSBAS_PARAMS.h5), raw, interpolated GNSS time series maps (GPS_East/North/Up.h5) , errors associated with the GNSS data (GPS_East/North/Up_sigma.h5), and integrated DInSAR + GNSS time series (fused.h5). Details about the data can be read about in the following publication: [Corsa, B. "Integration of DInSAR Time Series and GNSS data for Continuous Volcanic Deformation Monitoring and Eruption Early Warning Applications" <em>Remote Sens.</em> <strong>2022</strong>, <em>14</em>(3), 784; <a href="https://doi.org/10.3390/rs14030784">https://doi.org/10.3390/rs14030784</a>]. The raw DInSAR time series spans 245 dates between 2015-11-11 to 2021-04-13 over the Big Island of Hawaii. The current raw GPS data and fused time series used 22 data points between those same dates. </p>
High-resolution sea ice drift and deformation example data derived from Sentinel-1 in the Arctic Ocean during MOSAiC
<p>This data set contains two high-resolution sea ice drift and deformation fields from 30/31 December 2019 and 20/21 June 2021. They were acquired in the Transpolar Drift along the drift track of the research campaign "Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC). Drift fields were calculated from Sentinel-1, HH polarization SAR images acquired in enhanced wide mode. These had a pixel resolution of 50 m in Polar Stereographic North projection (latitude of true scale: 70 N, center longitude: 45 W). We used an ice tracking algorithm introduced by Thomas et al. (2008, 2011) and modified by Hollands and Dierking (2011) to derive drift from sequential pairs. The time step between two sequential images is approximately one day. The resulting drift data set was defined on a regular grid with a spatial resolution of 700 m. Outliers in the velocity data were reduced by a 3x3 point running median filter covering an area of 2.1x2.1 km. For the deformation estimates, we calculated deformation using a linear approximation based on Green's Theorem that relates the double integral over a plane to the line integral along a simple curve surrounding the plane. We discretized the curve applying the trapezoid method that linearly interpolates velocity between the vertices of the grid cells. This work contains modified Copernicus Sentinel data (2020)</p> <p>Related publications:</p> <p><strong>von Albedyll, L., Haas, C., and Dierking, W.</strong>: Linking sea ice deformation to ice thickness redistribution using high-resolution satellite and airborne observations, The Cryosphere, 15, 2167–2186, <a href="https://doi.org/10.5194/tc-15-2167-2021">https://doi.org/10.5194/tc-15-2167-2021</a>, 2021.</p> <p><strong>Hollands, Thomas; Dierking, Wolfgang (2011):</strong> Performance of a multiscale correlation algorithm for the estimation of sea-ice drift from SAR images: initial results. <em>Annals of Glaciology</em>, <strong>52(57)</strong>, 311-317, <a href="https://doi.org/10.3189/172756411795931462">https://doi.org/10.3189/172756411795931462</a></p> <p><strong>Thomas, Mani; Geiger, Cathleen A; Kambhamettu, Chandra (2008):</strong> High resolution (400 m) motion characterization of sea ice using ERS-1 SAR imagery. <em>Cold Regions Science and Technology</em>, <strong>52(2)</strong>, 207-223, <a href="https://doi.org/10.1016/j.coldregions.2007.06.006">https://doi.org/10.1016/j.coldregions.2007.06.006</a></p> <p><strong>Thomas, Mani; Kambhamettu, Chandra; Geiger, Cathleen A (2011):</strong> Motion Tracking of Discontinuous Sea Ice. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, <strong>49(12)</strong>, 5064-5079, <a href="https://doi.org/10.1109/TGRS.2011.2158005">https://doi.org/10.1109/TGRS.2011.2158005</a></p>
INCA Example Data - Precipitation
<p>This <a href="https://www.unidata.ucar.edu/software/netcdf/">netcdf</a> includes the output of a single run of the <a href="https://www.meteoswiss.admin.ch/home/services-and-publications/produkte.subpage.html/en/data/products/2021/nowcasting-inca-ch.html">INCA nowcasting system</a> for variable precipitation (mm/h).</p>
Supporting Data -- Evaluating Mask R-CNN Models to Extract Terracing across Oceanic High Islands: an example from Sāmoa.
<p>This dataset provides supplemental information for the manuscript, "Diverse terracing practices revealed by automated lidar analysis across the Sāmoan islands", submitted to Archaeological Prospection. The dataset contains a trained Mask R-CNN deep learning model designed for detecting archaeological terracing features on the islands of American Samoa, associated training data, and the raw and cleaned output of detected terraces.</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.