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493 results for “example data”
Advanced open source data formats for geometrically and physically coupled systems - examples
<p>Some model files to describe a geometrically and physically coupled PDE and a ODE system, arising from discretization in space. The files correspond to:</p> <ul> <li>a simplified two component problem with coupling and an FMU in <strong>withFMU.json</strong></li> <li>the corresponding ODE in <strong>io_withFMU_FECoupled.json</strong></li> <li>a PDE model of a complete machine in <strong>ictimt_coupledModel.zip</strong>. This model belongs to https://doi.org/10.17973/MMSJ.2021_7_2021072</li> <li>the corresponding discrete model in <strong>ictimt_feCoupled.zip </strong>This model belongs to https://doi.org/10.17973/MMSJ.2021_7_2021072</li> </ul> <p> </p> <p> </p>
A framework for estimating global river discharge from the Surface Water and Ocean Topography satellite mission example data
<p>These files contain the Confluence pipeline outputs, prior information (SOS) and Simulated SWOT shape files from the example in the "A framework for estimating global river discharge from the Surface Water and Ocean Topography satellite mission" manuscript. </p>
The TDI data and PSD/sensitivity-related files for PyCBC LISA documentation example
<p>The TDI data and PSD/sensitivity-related files for PyCBC LISA documentation example, most of them are generated from LDC-Sangria<em> </em>dataset.</p>
steinbock results of IMC example data
<p>If you are working with these files, please cite them as follows:<br><br>Windhager, J., Zanotelli, V.R.T., Schulz, D. et al. An end-to-end workflow for multiplexed image processing and analysis. Nat Protoc (2023). <a href="https://doi.org/10.1038/s41596-023-00881-0">https://doi.org/10.1038/s41596-023-00881-0</a></p><p>This repository hosts the results of processing example imaging mass cytometry (IMC) data hosted at <a href="https://zenodo.org/record/5949116">zenodo.org/record/5949116</a> using the <i>steinbock </i>framework available at <a href="https://github.com/BodenmillerGroup/steinbock">github.com/BodenmillerGroup/steinbock</a>. Please refer to <strong>steinbock.sh </strong>for how these data were generated from the raw data.</p><p>The following files are part of this repository:</p><ul><li><strong>panel.csv</strong>: contains channel information regarding the used antibodies in <i>steinbock</i> format</li><li><strong>img.zip</strong>: contains hot pixel filtered multi-channel images derived from the IMC raw data. One file per acquisition is generated</li><li><strong>images.csv</strong>: contains metadata per acquisition</li><li><strong>pixel_classifier.ilp</strong>: ilastik pixel classifier (same as the one in <a href="https://zenodo.org/record/6043544">zenodo.org/record/6043544</a>)</li><li><strong>ilastik_crops.zip</strong>: image crops on which the ilastik classifier was trained (same as the ones in <a href="https://zenodo.org/record/6043544">zenodo.org/record/6043544</a>)</li><li><strong>ilastik_img.zip</strong>: contains multi-channel images (one per acquisition) in .h5 format for ilastik pixel classification</li><li><strong>ilastik_probabilities.zip</strong>: 3 channel images containing the pixel probabilities after pixel classification</li><li><strong>masks_ilastik.zip</strong>: segmentation masks derived from the ilastik pixel probabilities using the <strong>cell_segmentation.cppipe</strong> pipeline</li><li><strong>masks_deepcell.zip</strong>: segmentation masks derived by <i>deepcell</i> segmentation</li><li><strong>intensities.zip</strong>: Contains one .csv file per acquisition. Each file contains single-cell measures of the mean pixel intensity per cell and channel based on the files in <strong>img.zip </strong>and <strong>masks_deepcell.zip</strong>.</li><li><strong>regionprops.zip</strong>: Contains one .csv file per acquisition. Each file contains single-cell measures of the morphological features and location of cells based on <strong>masks_deepcell.zip</strong>.</li><li><strong>neighbors.zip</strong>: Contains one .csv file per acquisition. Each file contains an edge list of cell IDs indicating cells in close proximity based on <strong>masks_deepcell.zip</strong>.</li><li><strong>ome.zip</strong>: contains .ome.tiff files derived from img.zip; one file per acquisition</li><li><strong>histocat.zip</strong>: contains single-channel .tiff files with segmentation masks derived from <strong>masks_deepcell.zip</strong> for upload to histoCAT (<a href="https://bodenmillergroup.github.io/histoCAT/">bodenmillergroup.github.io/histoCAT</a>)</li><li><strong>cells.csv</strong>: contains intensity and regionprop measurements of all cells</li><li><strong>cells_csv.zip</strong>: contains intensity and regionprop measurements of all cells per acquisition</li><li><strong>cells.fcs</strong>: contains intensity and regionprop measurements of all cells in fcs format</li><li><strong>cells_fcs.zip</strong>: contains intensity and regionprop measurements of all cells per acquisition in fcs format</li><li><strong>cells.h5ad</strong>: contains intensity, regionprop and neighbor measurements of all cells in <i>anndata </i>format</li><li><strong>cells_h5ad</strong>: contains intensity regionprop and neighbor measurements of all cells per acquisition in <i>anndata </i>format</li><li><strong>graphs.zip</strong>: contains spatial object graphs in .graphml format; one file per acquisition</li></ul>
Example data for SOPiNet
<p>This is the example data for use the SOPiNet at https://github.com/RegiusQuant/ESIDLM.</p> <p>The details of SOPiNet can be foud at <a href="https://doi.org/10.1016/j.envpol.2023.121509">https://doi.org/10.1016/j.envpol.2023.121509</a></p>
Example data set for NG-QTAIM eigenvector-following trajectories: rotary f-NAIBP motor
<p>Example dataset for NG-QTAIM eigenvector following trajectories: the F-NAIBP molecular rotary motor.</p>
Concentration-, Temperature- and Solvent-Dependent Self-Assembly: Merocyanine Dimerization as a Showcase Example for Obtaining Reliable Thermodynamic Data
<p><strong>Abstract:</strong> Mathematical models for the concentration-, temperature- and solvent-dependent analysis of self-assembly equilibria are derived for the most simple case of dimer formation, to highlight the assumptions these models and the thus determined thermodynamic parameters are based on. The three models were applied to UV/Vis absorption data for the dimerization of a highly dipolar merocyanine dye in 1,4-dioxane. Isothermal titration calorimetry (ITC) dilution experiments were performed as an independent reference technique. While the concentration-dependent analysis is according to our studies the most reliable method, also the less time-consuming temperature-dependent evaluation can give accurate results in the present example, despite small thermochromic effects. In contrast, the strong negative solvatochromism of the merocyanine tampers with the results from the solvent-dependent evaluation. Even though the studies presented in this work are limited to the monomer-dimer equilibrium of a dipolar dye, the basic principles can be transferred to other chromophores and different self-assembly models, including those for supramolecular polymerization.</p>
Keras video classification example with a subset of UCF101 - Action Recognition Data Set (top 10 videos)
<p>Classify video clips with natural scenes of actions performed by people visible in the videos.</p> <p>See the UCF101 Dataset web page: <a href="https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101">https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101</a></p> <p>This example datasets consists of the 10 most numerous video from the UCF101 dataset. For the top 5 version, see: <a href="https://doi.org/10.5281/zenodo.7924745">https://doi.org/10.5281/zenodo.7924745</a> .</p> <p>Based on this code: <a href="https://keras.io/examples/vision/video_classification/">https://keras.io/examples/vision/video_classification/</a> (needs to be updated, if has not yet been already; see the issue: <a href="https://github.com/keras-team/keras-io/issues/1342">https://github.com/keras-team/keras-io/issues/1342</a>).</p> <p>Testing if data can be downloaded from figshare with `wget`, see: <a href="https://github.com/mojaveazure/angsd-wrapper/issues/10">https://github.com/mojaveazure/angsd-wrapper/issues/10</a></p> <p>For generating the subset, see this notebook: <a href="https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb">https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb</a> -- however, it also needs to be adjusted (if has not yet been already - then I will post a link to the notebook here or elsewhere, e.g., in the corrected notebook with Keras example).</p> <p>I would like to thank Sayak Paul for contacting me about his example at Keras documentation being out of date. </p> <p>Cite this dataset as:</p> <p>Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. <em>arXiv preprint arXiv:1212.0402</em>. <a href="https://doi.org/10.48550/arXiv.1212.0402">https://doi.org/10.48550/arXiv.1212.0402</a></p> <p>To download the dataset via the command line, please use:</p> <pre><code class="language-bash">wget -q https://zenodo.org/record/7882861/files/ucf101_top10.tar.gz -O ucf101_top10.tar.gz tar xf ucf101_top10.tar.gz</code></pre> <p> </p>
Output data for the PyCoxMunk example notebooks
<p>The PyCoxMunk python library allows users to calculate the expected sea surface reflectance under a given set of geometric and meteorological conditions. It is designed for use in satellite remote sensing and supports the majority of common satellite platforms.</p> <p> </p> <p>This archive contains data related to the example notebooks distributed with PyCoxMunk. Here you can find:</p> <p>1) An input dataset for the SLSTR example notebook.</p> <p>2) Output files for the SLSTR, SEVIRI and GOES example notebooks.</p>
Keras video classification example with a subset of UCF101 - Action Recognition Data Set (top 5 videos)
<p>Classify video clips with natural scenes of actions performed by people visible in the videos.</p> <p>See the UCF101 Dataset web page: <a href="https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101">https://www.crcv.ucf.edu/data/UCF101.php#Results_on_UCF101</a></p> <p>This example datasets consists of the 5 most numerous video from the UCF101 dataset. For the top 10 version see: <a href="https://doi.org/10.5281/zenodo.7882861">https://doi.org/10.5281/zenodo.7882861</a> .</p> <p>Based on this code: <a href="https://keras.io/examples/vision/video_classification/">https://keras.io/examples/vision/video_classification/</a> (needs to be updated, if has not yet been already; see the issue: <a href="https://github.com/keras-team/keras-io/issues/1342">https://github.com/keras-team/keras-io/issues/1342</a>).</p> <p>Testing if data can be downloaded from figshare with `wget`, see: <a href="https://github.com/mojaveazure/angsd-wrapper/issues/10">https://github.com/mojaveazure/angsd-wrapper/issues/10</a></p> <p>For generating the subset, see this notebook: <a href="https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb">https://colab.research.google.com/github/sayakpaul/Action-Recognition-in-TensorFlow/blob/main/Data_Preparation_UCF101.ipynb</a> -- however, it also needs to be adjusted (if has not yet been already - then I will post a link to the notebook here or elsewhere, e.g., in the corrected notebook with Keras example).</p> <p>I would like to thank Sayak Paul for contacting me about his example at Keras documentation being out of date. </p> <p>Cite this dataset as:</p> <p>Soomro, K., Zamir, A. R., & Shah, M. (2012). UCF101: A dataset of 101 human actions classes from videos in the wild. <em>arXiv preprint arXiv:1212.0402</em>. <a href="https://doi.org/10.48550/arXiv.1212.0402">https://doi.org/10.48550/arXiv.1212.0402</a></p> <p>To download the dataset via the command line, please use:</p> <pre><code class="language-bash">wget -q https://zenodo.org/record/7924745/files/ucf101_top5.tar.gz -O ucf101_top5.tar.gz tar xf ucf101_top5.tar.gz</code></pre>
Single-cell information extracted from IMC example data
<p>If you are working with these files, please cite them as follows:<br><br>Windhager, J., Zanotelli, V.R.T., Schulz, D. et al. An end-to-end workflow for multiplexed image processing and analysis. Nat Protoc (2023). <a href="https://doi.org/10.1038/s41596-023-00881-0">https://doi.org/10.1038/s41596-023-00881-0</a></p><p>This repository contains additional information related to IMC example data available at <a href="https://zenodo.org/record/5949116">zenodo.org/record/5949116</a>. The following files are available and are part of the <a href="https://bodenmillergroup.github.io/IMCDataAnalysis/">IMC Data Analysis workflow</a></p><ul><li><strong>gated_cells.zip:</strong> contains SpatialExperiment objects storing cells that were manually gated based on their expression values to derive ground truth cell phenotype labels.</li><li><strong>spe.rds:</strong> SpatialExperiment object containing the single-cell information (mean intensity per cell and per channel; cellular metadata; channel metadata) of the processed data.</li><li><strong>images.rds:</strong> CytoImageList object containing the spillover-corrected images.</li><li><strong>masks.rds:</strong> CytoImageList object containing the segmentation masks.</li></ul>
Example data for working with the ASpecD framework
<p>ASpecD is a Python framework for handling spectroscopic data focussing on reproducibility. In short: Each and every processing step applied to your data will be recorded and can be traced back. Additionally, for each representation of your data (e.g., figures, tables) you can easily follow how the data shown have been processed and where they originate from.</p> <p>To provide readers of the publication describing the ASpecD framework with a concrete example of data analysis making use of recipe-driven data analysis, this repository contains both, a recipe as well as the data that are analysed, as shown in the publication describing the ASpecD framework:</p> <ul> <li>Jara Popp, Till Biskup: ASpecD: A Modular Framework for the Analysis of Spectroscopic Data Focussing on Reproducibility and Good Scientific Practice. Chemistry--Methods 2:e202100097, 2022. doi:10.1002/cmtd.202100097</li> </ul>
Example Eiger data with 6 virtual data sets dereferencing images
<p>Standard Eiger data set from Diamond Light Source I04, consisting of 180° of rotation data from a cubic insulin crystal, with 6 virtual data sets each corresponding to 30° of data, to illustrate how multi-trigger Eiger data sets could be structured efficiently. </p> <p> </p> <p>Virtual data sets made with </p> <p> </p> <p><a href="https://github.com/graeme-winter/NXmxtools/blob/master/vdsmaker.py">https://github.com/graeme-winter/NXmxtools/blob/master/vdsmaker.py</a></p> <p> </p> <p>Key feature is that the underlying data type (UINT16) corresponds to the virtual data set type. </p> <p> </p> <p>This will require HDF5 1.10 series to read. </p> <p> </p> <p>Files:</p> <p>insu_d200_1.nxs - NXmx formatted data with internal VDS</p> <p>insu_d200_1_000001.h5 - real data 1/2</p> <p>insu_d200_1_000002.h5 - real data 2/2</p> <p>insu_d200_1_1.nxs - VDS subset of data 1/6</p> <p>...</p> <p>insu_d200_1_6.nxs - VDS subset of data 1/6</p> <p>insu_d200_1_master.h5 - DECTRIS style master file</p> <p>insu_d200_1_meta.h5 - metadata</p> <p>insu_d200_1_meta_pack.h5 - repacked metadata (not used)</p>
Implementation of a 4Pi-SMS super-resolution microscope - Example data II
<p>4Pi-SMS image of Nup96-SNAP labelled with BG-Alexa 647 in the lower nuclear envelope of a U2OS cell in TDE-based index-matching imaging buffer</p>
Example data set for 2D adaptive slice-specific z-shimming
<p>Input data and corresponding results for the scripts provided on github (https://github.com/neuroimaging-mug/R2s-mapping) for adpative slice-specific z-shimming in presence of macroscopic field variations.</p> <p>Please unzip all file in the repository path of ‘R2s-mapping’.</p>
Code and data required to run examples in MBHdesign description paper
<p>Code and data required to run examples in:</p> <p>Foster (2020) MBHdesign: an R-package for efficient spatial survey designs. Methods In Ecology and Evolution</p> <p>The Hippolyte data are originally sourced from </p> <p>Spinoccia, M. (2018). AusSeabed bathymetry holdings. Retrieved July 14, 2020, from http://pid.geoscience.gov.au/dataset/ga/116321</p> <p>The code is produced using the knitr::purl() function from the manuscript's Rmarkdown file.</p>
Example data map compressed with ISO29500-2 with 3 entry point for ArcGIS, MiraMon and OWS context file.
<p>A simple map consisting of a 1:1 000 000 country boundaries vector file, produced by the FAO (United Nations – FAOStat; geodata.grid.unep.ch/options.php?selectedID=2135) on top of a 5’ digital elevation model raster file (produced by the NOAA and NGDC; geodata.grid.unep.ch/options.php?selectedID=1414). Data has been obtained from the UNEP EDE Data Portal (UNEP 2013). Vector file is a Shapefile (a de facto standard) (ESRI 1998), while the raster file consists of either a raw signed 16-bit data or a long known TIFF file (Adobe 1992, Perkins 1995). Metadata and symbolization files are included. OPC specifies how to explicitly relate different parts using .rels files. These files are XML files with the same name as that of its respective source part, adding “.rels” and placed in a “rels” folder. Each of these files lists the target parts related to its source and the semantics of this relation.</p> <p>OPC can define entry points to the data by listing them in a “.rels” part in the root “rels” folder. In this file, three map files: for the ESRI software a world.mxd map, for the MiraMon software a world.mmm map, and a world.xml map in the form of an atom file following the new Web Service common standard (OGC OWS) context document. A geospatial application reading the package will determine which entry part it better supports to start recovering the data.</p>
Eddy covariance data processing workflow example utilizing openeddy and REddyProc R packages
<p>The example dataset is provided within the folder structure required by the workflow files (version 2025-04-27; amended on 2025-07-31) related to the R package openeddy version 0.0.0.9009. Only files needed for successful processing are included. It is shared here as part of a data processing example at <a href="https://github.com/lsigut/EC_workflow">https://github.com/lsigut/EC_workflow</a> to overcome the file size limitation of GitHub.</p>
Spreadsheet with reverse analysis routine for STC assessment in electrolyzers with CO2RR example data
<p>Supplementary material to manuscript "Unfolding electrolyzer characteristics to reveal solar-to-chemical efficiency potential: rapid analysis method bridging electrochemistry and photovoltaics " accepted for publication to ChemSusChem journal in November 2024<br>Authors: Oleksandr Astakhov, Therese Cibaka, Lars Wieprecht, Uwe Rau, Tsvetelina Merdzhanova IMD-3 Photovoltaics Forschungszentrum Jülich GmbH, Jülich, Germany<br>Version 5 14.11.2024<br>Contact: Oleksandr Astakhov email: o.astakhov@fz-juelich.de<br>Calculation of maximum STC (solar to chemical efficiency, same as STF - solar to fuel efficiency) in photovoltaic-driven electrolyzers (PV-EC) <br>The analysis addresses any type of PV-EC systems with direct and indirect coupling<br>Coupling efficiency ηC is treated as a constant explicitly. Set ηC to be 100% to evaluate the limit of STC the electrolyzer can achieve<br>The analysis is performed towards individual products. If EC produces several products each of them must be analysed in a separate table<br>STC dependencies for three major parameters PV-efficiency, Irradiance G and PV-to-EC area ratio AR are calculated<br>Example dataset is for EC cell for CO2 reduction with data presented for CO as a product</p>
Example data used in tutorial of HEIG (v1.2.0)
<p>The repository contains example data used in tutorial of HEIG v1.2.0. Note, this data is not compatible with v1.0.0, and some options are supported in v1.1.0. HEIG is a statistical framework for efficiently conducting joint analysis for large-scale imaging and genetic data.</p> <p>The dataset has the following file structures:</p> <ul> <li>input <ul> <li>images1: 500 simulated images</li> <li>images2: 500 simulated images</li> <li>genotype: genotype data in PLINK bfile</li> <li>misc: miscellaneous files</li> <li>ldr_sumstats: 19 LDR GWAS summary statistic files of the superior fronto-occipital fasciculus</li> <li>ld_regu8580: LD matrices with regularization {85%,80%}, including ~460,000 genotype array SNPs</li> <li>ld_regu7570: LD matrices with regularization {75%,70%}, including ~460,000 genotype array SNPs</li> <li>visualization: example files for visualization</li> </ul> </li> <li>output <ul> <li>fpca: results of functional PCA</li> <li>genotype: hail.MatrixTable of genotype data</li> <li>gwas: LDR GWAS results generated by HEIG</li> <li>herigc: results of heritability and (cross-trait) genetic correlation analysis</li> <li>images: preprocessed 1000 images in H5DF file</li> <li>ldr: LDRs constructed from 1000 images</li> <li>sumstats: preprocessed 19 LDR GWAS summary statistic files of the superior fronto-occipital fasciculus</li> <li>visualization: visualization results</li> <li>voxelgwas: voxel-level GWAS results</li> </ul> </li> </ul> <p>The results in ouput are produced by data in input, which can verify if the user runs the code correctly.</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.