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493 results for “example data”
AstroEBSD Superalloy Example Data
<p>This is an example dataset for use with AstroEBSD. It is a compressed version of the one found at 10.5281/zenodo.3617455, the data bundle for our paper 'Advancing characterisation with statistics from correlative electron diffraction and X-ray spectroscopy, in the scanning electron microscope' - https://doi.org/10.1016/j.ultramic.2020.112944.</p> <p>The .h5 file contains EBSD patterns and EDS spectra. The zip file contains an example analysis, including EDS spectra quantification in a .xls file, which is required for the EDS post-analysis. </p>
IPBES Data Management Tutorials - Session 4.4: Examples of active data management
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The <em>Data management of active research data </em>chapter provides an introduction for IPBES experts on how to manage data while actively being used, analyzed, and produced to fulfill the criteria of the IPBES data management policy.</p> <p>This session, <em>Examples of active data management</em>, gives concrete best-practice examples of how the recommendations could be integrated into the daily work of researchers</p>
oggmap/orthomap - example data
<p>Example data for python package oggmap/orthomap.</p> <p><strong>OrthoFinder: Ensembl release-105 (-S diamond_ultra_sens)</strong></p> <p>Includes OrthoFinder results (-S diamond_ultra_sens) for all translated coding sequences (CDS) from Ensembl release-105 (keeping only longest isoforms) and Xtropicalisv9.0.Named.primaryTrs.pep.fa from www.xenbase.org:</p> <ul> <li><ensembl_105_orthofinder_Orthogroups.GeneCount.tsv.zip></li> <li><ensembl_105_orthofinder_Orthogroups.tsv.zip></li> </ul> <p>Includes a table specifying the OrthoFinder species file names and its corresponding NCBI taxonomic IDs:</p> <ul> <li><ensembl_105_orthofinder_species_list.tsv></li> </ul> <p>Includes NCBI taxonomic tree for Ensembl release-105 species analysed:</p> <ul> <li><ensembl_105_species_tree_ncbi_topology_named.nw></li> <li><ensembl_105_species_tree_ncbi_topology_named.pdf></li> </ul> <p><strong>OrthoFinder: Ensembl release-110 (-S last)</strong></p> <p>Includes OrthoFinder results (-S last) for all translated coding sequences (CDS) from Ensembl release-110 (keeping only longest isoforms) and Xtropicalisv9.0.Named.primaryTrs.pep.fa from www.xenbase.org:</p> <ul> <li><ensembl_110_orthofinder_last_Orthogroups.GeneCount.tsv.zip></li> <li><ensembl_110_orthofinder_last_Orthogroups.tsv.zip></li> </ul> <p>Includes a table specifying the OrthoFinder species file names and its corresponding NCBI taxonomic IDs:</p> <ul> <li><ensembl_110_orthofinder_last_species_list.tsv></li> </ul> <p>Includes NCBI taxonomic tree for Ensembl release-110 species analysed:</p> <ul> <li><ensembl_110_species_tree_ncbi_topology_named.nw></li> <li><ensembl_110_species_tree_ncbi_topology_named.pdf></li> </ul> <p><strong>OrthoFinder: Ensembl release-111 (-S last)</strong></p> <p>Includes OrthoFinder results (-S last) for all translated coding sequences (CDS) from Ensembl release-111 (keeping only longest isoforms) and Xtropicalisv9.0.Named.primaryTrs.pep.fa from www.xenbase.org:</p> <ul> <li><ensembl_111_orthofinder_last_Orthogroups.GeneCount.tsv.zip></li> <li><ensembl_111_orthofinder_last_Orthogroups.tsv.zip></li> </ul> <p>Includes a table specifying the OrthoFinder species file names and its corresponding NCBI taxonomic IDs:</p> <ul> <li><ensembl_111_orthofinder_last_species_list.tsv></li> </ul> <p><strong>OrthoFinder: Ensembl release-112 (-S last)</strong></p> <p>Includes OrthoFinder results (-S last) for all translated coding sequences (CDS) from Ensembl release-112 (keeping only longest isoforms) and Xtropicalisv9.0.Named.primaryTrs.pep.fa from www.xenbase.org:</p> <ul> <li><ensembl_112_orthofinder_last_Orthogroups.GeneCount.tsv.zip></li> <li><ensembl_112_orthofinder_last_Orthogroups.tsv.zip></li> </ul> <p>Includes a table specifying the OrthoFinder species file names and its corresponding NCBI taxonomic IDs:</p> <ul> <li><ensembl_112_orthofinder_last_species_list.tsv></li> </ul> <p><strong>OrthoFinder: Ensembl release-113 (-S last)</strong></p> <p>Includes OrthoFinder results (-S last) for all translated coding sequences (CDS) from Ensembl release-113 (keeping only longest isoforms) and Xtropicalisv9.0.Named.primaryTrs.pep.fa from www.xenbase.org:</p> <ul> <li><ensembl_113_orthofinder_last_Orthogroups.GeneCount.tsv.zip></li> <li><ensembl_113_orthofinder_last_Orthogroups.tsv.zip></li> </ul> <p>Includes a table specifying the OrthoFinder species file names and its corresponding NCBI taxonomic IDs:</p> <ul> <li><ensembl_113_orthofinder_last_species_list.tsv></li> </ul> <p><strong>OrthoFinder: Ensembl release-114 (-S last)</strong></p> <p>Includes OrthoFinder results (-S last) for all translated coding sequences (CDS) from Ensembl release-114 (keeping only longest isoforms) and Xtropicalisv9.0.Named.primaryTrs.pep.fa from www.xenbase.org:</p> <ul> <li><ensembl_114_orthofinder_last_Orthogroups.GeneCount.tsv.zip></li> <li><ensembl_114_orthofinder_last_Orthogroups.tsv.zip></li> </ul> <p>Includes a table specifying the OrthoFinder species file names and its corresponding NCBI taxonomic IDs:</p> <ul> <li><ensembl_114_orthofinder_last_species_list.tsv></li> </ul> <p><strong>OrthoFinder: Ensembl release-115 (-S last)</strong></p> <p>Includes OrthoFinder results (-S last) for all translated coding sequences (CDS) from Ensembl release-115 (keeping only longest isoforms) and Xtropicalisv9.0.Named.primaryTrs.pep.fa from www.xenbase.org:</p> <ul> <li><ensembl_115_orthofinder_last_Orthogroups.GeneCount.tsv.zip></li> <li><ensembl_115_orthofinder_last_Orthogroups.tsv.zip></li> </ul> <p>Includes a table specifying the OrthoFinder species file names and its corresponding NCBI taxonomic IDs:</p> <ul> <li><ensembl_115_orthofinder_last_species_list.tsv></li> </ul> <p><strong>OrthoFinder: WormBase release-WS288 + WormBase ParaSite release-WBPS18 (-S last)</strong></p> <p>Includes OrthoFinder results (-S last) for all translated coding sequences (CDS) from WormBase release-WS288, WormBase ParaSite release-WBPS18 (keeping only longest isoforms) and dd_Smed_v6.pcf.contigs.fasta (transdecoder and miniprothint peptides) from https://planmine.mpibpc.mpg.de:</p> <ul> <li><WS288_WBPS18_orthofinder_last_Orthogroups.GeneCount.tsv.zip></li> <li><WS288_WBPS18_orthofinder_last_Orthogroups.tsv.zip></li> </ul> <p>Includes a table specifying the OrthoFinder species file names and its corresponding NCBI taxonomic IDs:</p> <ul> <li><WS288_WBPS18_orthofinder_last_species_list.tsv></li> </ul> <p>Includes NCBI taxonomic tree for WormBase release-WS288 and WormBase ParaSite release-WBPS18 species analysed:</p> <ul> <li><WS288_WBPS18_species_tree_ncbi_topology_named.nw></li> <li><WS288_WBPS18_species_tree_ncbi_topology_named.pdf></li> </ul> <p><strong>OrthoFinder: WormBase release-WS293 + WormBase ParaSite release-WBPS19 (-S last)</strong></p> <p>Includes OrthoFinder results (-S last) for all translated coding sequences (CDS) from WormBase release-WS293, WormBase ParaSite release-WBPS19 (keeping only longest isoforms):</p> <ul> <li><WS293_WBPS19_orthofinder_last_Orthogroups.GeneCount.tsv.zip></li> <li><WS293_WBPS19_orthofinder_last_Orthogroups.tsv.zip></li> </ul> <p>Includes a table specifying the OrthoFinder species file names and its corresponding NCBI taxonomic IDs:</p> <ul> <li><WS293_WBPS19_orthofinder_last_species_list.tsv></li> </ul> <p>Includes NCBI taxonomic tree for WormBase release-WS293 and WormBase ParaSite release-WBPS19 species analysed:</p> <ul> <li><WS293_WBPS19_species_tree_ncbi_topology_named.nw></li> <li><WS293_WBPS19_species_tree_ncbi_topology_named.pdf></li> </ul> <p><strong>Broccoli: Example (default settings)</strong></p> <p>Includes Broccoli results (default settings) for translated coding sequences (CDS) from four plant sepcies (keeping only longest isoforms) namely A. lyrata, A. thaliana, C. hirsuta and C. rubella:</p> <ul> <li><broccoli_example_table_OGs_protein_counts.txt></li> <li><broccoli_example_table_OGs_protein_names.txt></li> </ul> <p>Includes a table specifying the Broccoli species file names and its corresponding NCBI taxonomic IDs:</p> <ul> <li><broccoli_example_species_list.tsv></li> </ul> <p><strong>Pre-calculated orthomaps:</strong></p> <p>Includes pre-calculated gene age assignments for <em>C. elegans</em> (Sun et al. 2021), <em>H. vulgaris</em> (Cazet et al. 2022) and <em>D. rerio</em> (Ensembl-105; Ensembl-110; Ensembl-113):</p> <ul> <li><Sun2021_Orthomap.tsv></li> <li><Cazet2022_Orthomap.tsv></li> <li><zebrafish_ensembl_105_orthomap.tsv></li> <li><zebrafish_ensembl_110_orthomap.tsv></li> <li><zebrafish_ensembl_113_orthomap.tsv></li> </ul> <p><strong>Pre-calculated Ensembl release-113 orthomaps (OrthoFinder -S last):</strong></p> <p>Includes extracted orthomaps for all species from Ensembl release-113 based on OrthoFinder (-S last):</p> <ul> <li><ensembl_113_orthofinder_last_orthomaps.tsv.zip></li> </ul> <p><strong>Pre-calculated Ensembl release-114 orthomaps (OrthoFinder -S last):</strong></p> <p>Includes extracted orthomaps for all species from Ensembl release-114 based on OrthoFinder (-S last):</p> <ul> <li><ensembl_114_orthofinder_last_orthomaps.tsv.zip></li> </ul> <p><strong>Pre-calculated Ensembl release-115 orthomaps (OrthoFinder -S last):</strong></p> <p>Includes extracted orthomaps for all species from Ensembl release-115 based on OrthoFinder (-S last):</p> <ul> <li><ensembl_115_orthofinder_last_orthomaps.tsv.zip></li> </ul> <p><strong>Pre-calculated evolutionary indices:</strong></p> <p>Includes pre-calculated TajimaD, NormalizedPi, FayWu, Fst for C. elegans (Ma et al. 2021):</p> <ul> <li><Ma2021_Fst.tsv></li> </ul> <p><strong>eggNOG database version 6.0 orthomaps:</strong></p> <p>Includes extracted orthomaps for all Eukaryota from eggNOG database version 6.0 (Hernández-Plaza et al. 2022):</p> <ul> <li><eggnog6_eukaryota_orthomaps.tsv.zip></li> </ul> <p><strong>myTAI example data:</strong></p> <p>Includes example data from the myTAI R package (Drost et al. 2018)</p> <ul> <li><PhyloExpressionSetExample.h5ad></li> </ul> <p><strong>PLAZA database version 5.0 orthomaps:</strong></p> <p>Includes extracted orthomaps for either HOMFAM or ORTHOFAM groups of plants from PLAZA database version 5.0 (Van Bel et al. 2022):</p> <ul> <li><plaza_v5_dicots_HOMFAM_orthomaps.tsv.zip></li> <li><plaza_v5_dicots_ORTHOFAM_orthomaps.tsv.zip></li> <li><plaza_v5_monocots_ORTHOFAM_orthomaps.tsv.zip></li> <li><plaza_v5_monocots_ORTHOFAM_orthomaps.tsv.zip></li> </ul> <p><strong>Mouse synonyms:</strong></p> <p>Table of <em>Mus musculus</em> gene synonyms obtained from here https://github.com/mustafapir/geneName/blob/master/data/mouse_synonyms1.rda and converted into a table.</p> <ul> <li><mouse_synonyms.tsv></li> </ul>
Example of datasets processed to demonstrate a multisource data integration methodology
<p>This dataset contains the data processed to demonstrate the multi-source spatial data integration methodology proposed in the paper "Multisource spatial data integration for use cases applications".</p> <p>It contains:</p> <p>- the building footprint extracted from the IFC model of a newly designed building in WKT format, by using the GeoBIM_Tool (<a href="https://github.com/twut/GEOBIM_Tool">https://github.com/twut/GEOBIM_Tool</a>);</p> <p>- the extrusion of the footprint until the measured height measured with the same GeoBIM_Tool;</p> <p>- a portion of the Rotterdam 3D city model generated with 3dfier and available at https://3d.bk.tudelft.nl/opendata/3dfier/, converted in CityJSON with the citygml-tools (https://www.cityjson.org/tutorials/conversion/), developed to convert data between CityGML and CityJSON.</p>
Phase Object Reconstruction for 4D-STEM using Deep Learning, (4D-STEM Example Data)
<p><strong>Overview </strong></p> <p>This repository contains 2 example 4D-STEM datasets format from the paper <a href="https://arxiv.org/abs/2202.12611">"Phase Object Reconstruction for 4D-STEM using Deep Learning"</a>. The data was written to hdf5 for compatibility with the python programming language. When reading from these files consider possibly different storage conventions (Row major vs. column major format). Data may need to be transposed accordingly.</p> <p> </p> <p><strong>Parameters</strong></p> <p>The twisted bilayer graphene dataset is simulated. The smaller file is an experimental SrTiO<sub>3</sub> dataset.</p> <table> <thead> <tr> <th scope="row"> </th> <th scope="col">Graphene</th> <th scope="col">STO</th> </tr> </thead> <tbody> <tr> <th scope="row">E0</th> <td>200kV</td> <td>300kV</td> </tr> <tr> <th scope="row">Apeture</th> <td>25 mrad</td> <td>20 mrad</td> </tr> <tr> <th scope="row">Detector Size</th> <td>2.5 Å<sup>-1</sup></td> <td>1.6671 Å<sup>-1</sup></td> </tr> <tr> <th scope="row">Dimensions</th> <td>101x101x128x128</td> <td>60x60x64x64</td> </tr> <tr> <th scope="row">Step Size</th> <td>0.2</td> <td>0.1818</td> </tr> </tbody> </table> <p><br> </p>
PEPerMINT Example Data
<p>Example data for the PEPerMINT peptide abundance imputation method.</p> <p> </p> <p>The data is derived from several publically available datasets (see <a href="https://www.biorxiv.org/content/biorxiv/early/2024/03/25/2024.03.23.586248.full.pdf">the PEPerMINT paper</a> for detailed information).</p>
NIfTI-MRS Example Data
<p>Example data accompanying the <a href="https://doi.org/10.5281/zenodo.5084789">data format specification for the NIfTI-MRS file format</a>. NIfTI-MRS is a NIfTI derived format for storing magnetic resonance spectroscopy (MRS) and spectroscopic imaging (MRSI) data.</p> <p>Example data is generated from code at the <a href="https://github.com/wexeee/mrs_nifti_standard">NIfTI-MRS GitHub repository</a>.</p> <p>Each file is named example_{n}.nii.gz and corresponds to the following list:</p> <ol> <li>Manually converted SVS - Water suppressed STEAM</li> <li>spec2nii converted SVS - Water suppressed STEAM</li> <li>spec2nii converted edited SVS - MEGA-PRESS</li> <li>Manually converted 31P FID-MRSI (CSI sequence)</li> <li>spec2nii converted sLASER-localised 1H MRSI<strong>s</strong></li> <li>j-difference editing example (MEGA-PRESS)</li> <li>Variable echo time: full representation</li> <li>Variable echo time: short representation</li> <li>Multiple dynamic parameters "Fingerprinting"</li> <li>FSL-MRS processed MEGA-PRESS spectrum</li> </ol>
Example data set for the R package riversCentralAsia
<p>This data set contains example data for demonstrating the functionality of the R package riversCentralAsia. riversCentralAsia (https://github.com/hydrosolutions/riversCentralAsia) includes several functions for pre-processing hydrological data to facilitate hydrological modelling with RS MINERVE (https://crealp.github.io/rsminerve-releases/). The package is used extensively in the open-source teaching course Modeling of Hydrological Systems in Semi-Arid Central Asia (https://hydrosolutions.github.io/caham_book/). </p>
Example imaging mass cytometry raw 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 imaging mass cytometry (IMC) dataset serves as an example to demonstrate raw data processing and downstream analysis tools. The data was generated as part of the <strong>I</strong>ntegrated i<strong>MMU</strong>noprofiling of large adaptive <strong>CAN</strong>cer patient cohorts (IMMUcan) project (<a href="https://immucan.eu">immucan.eu</a>) using the Hyperion imaging system (<a href="https://www.fluidigm.com/products-services/instruments/hyperion">www.fluidigm.com/products-services/instruments/hyperion</a>). To get an overview on the technology and available analysis strategies, please visit <a href="https://bodenmillergroup.github.io/IMCWorkflow/">bodenmillergroup.github.io/IMCWorkflow</a>. The individual data files are described below:</p><ul><li><strong>Patient1.zip, Patient2.zip, Patient3.zip, Patient4.zip</strong>: raw data files of 4 patient samples. Each .zip archive contains a folder in which one .mcd file (IMC raw data) and multiple .txt files (one per acquisition) can be found.</li><li><strong>compensation.zip</strong>: This .zip archive holds a folder which contains one .mcd file and multiple .txt files. Multiple spots of a "spillover slide" were acquired and each .txt file is named based on the spotted metal. This data is used for channel spillover correction. For more information, please refer to the original publication: <a href="https://doi.org/10.1016/j.cels.2018.02.010">Compensation of Signal Spillover in Suspension and Imaging Mass Cytometry</a></li><li><strong>panel.csv</strong>: This file contains metadata for each antibody/channel used in the experiment. The <i>full</i> column indicates which channel should be analysed. The <i>ilastik</i> column specifies which channels were used for ilastik pixel classification and the <i>deepcell</i> column indicates the channels used for deepcell segmentation.</li><li><strong>sample_metadata.csv</strong>: This file links each patient to their cancer type (SCCHN - head and neck cancer; BCC - breast cancer; NSCLC - lung cancer; CRC - colorectal cancer).</li></ul>
Environmental data associated to particular health events example dataset
<p>The data represents and example output for environmental data (i.e. climate and pollution) linked with individual events through <strong>location</strong> and <strong>time</strong>. The linkage is the result of a semantic query that integrates environmental data <strong>within an area relevant to the event</strong> and selects a <strong>period of data before the event</strong>.</p> <p>The resulting event-environmental linked data contains:</p> <ul> <li>The data for analysis as a data table (.csv) and graph (.ttl)</li> <li>The metadata describing the linkage process and the data (.csv and .ttl)</li> <li>The interactive report to explore the (meta)data (.html)</li> </ul> <p>The graph files are ready to be shared and published as Findable, Accessible, Interoperable and Reusable (FAIR) data, including the necessary information to be reused by other researchers in different contexts.</p>
IMC Segmentation Pipeline results of example IMC 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="http://10.5281/zenodo.5949116">10.5281/zenodo.5949116</a> using the IMC Segmentation Pipeline available at <a href="https://github.com/BodenmillerGroup/ImcSegmentationPipeline">https://github.com/BodenmillerGroup/ImcSegmentationPipeline</a> (DOI: <a href="http://10.5281/zenodo.6402666">10.5281/zenodo.6402666</a>) v3.6. Please refer to <a href="https://github.com/BodenmillerGroup/steinbock">https://github.com/BodenmillerGroup/steinbock</a> as alternative processing framework and <a href="http://10.5281/zenodo.6043600">10.5281/zenodo.6043600</a> for the data generated by <i>steinbock</i>.</p><p>The following files are part of the <strong>analysis.zip</strong> folder when running the IMC Segmentation Pipeline:</p><ul><li><strong>cpinp</strong>: contains input files for the segmentation pipeline</li><li><strong>cpout</strong>: contains all final output files of the pipeline: <i>cell.csv</i> containing the single-cell features; <i>Experiment.csv</i> containing CellProfiler metadata; <i>Image.csv</i> containing acquisition metadata; <i>Object relationships.csv</i> containing an edge list indicating interacting cells; <i>panel.csv</i> containing channel information; <i>var_cell.csv</i> containing cell feature information; <i>var_Image.csv</i> containing acquisition feature information; <i>images </i>containing the hot pixel filtered multi-channel images and the channel order; <i>masks</i> containing the segmentation masks; <i>probabilities </i>containing the pixel probabilities.</li><li><strong>histocat</strong>: contains single channel .tiff files per acquisition for upload to histoCAT (<a href="https://bodenmillergroup.github.io/histoCAT/">https://bodenmillergroup.github.io/histoCAT/</a>)</li><li><strong>crops</strong>: contains upscaled image crops in .h5 format for ilastik (<a href="https://www.ilastik.org/">https://www.ilastik.org/</a>) training</li><li><strong>ometiff</strong>: contains .ome.tiff files per acquisition, .png files per panorama and additional metadata files per slide</li><li><strong>ilastik</strong>: multi channel images for ilastik pixel classification (<i>_ilastik.full</i>) and their channel order (<i>_ilastik.csv</i>); upscaled multi channel images for ilastik pixel prediction (<i>_ilastik_s2.h5</i>); upscaled 3 channel images containing ilastik pixel probabilities (<i>_ilastik_s2_Probabilities.tiff</i>).</li></ul><p>The remaining files are part of the root directory:</p><ul><li><strong>docs.zip: </strong>Documentation of the pipeline in markdown format</li><li>I<strong>MCWorkflow.ilp: </strong>Ilastik pixel classifier pre-trained on the example data</li><li><strong>resources.zip: </strong>The CellProfiler pipelines and CellProfiler plugins used for the analysis</li><li><strong>scripts.zip: </strong>Python notebooks used for pre-processing and downloading the example data</li><li><strong>src.zip: </strong>Scripts for the imcsegpipe python package</li></ul>
Missing data in the analysis of multilevel and dependent data (Examples)
<p>Example data sets and computer code for the book chapter titled "Missing Data in the Analysis of Multilevel and Dependent Data" submitted for publication in the second edition of "Dependent Data in Social Science Research" (Stemmler et al., 2015). This repository includes the computer code (".R") and the data sets from both example analyses (Examples 1 and 2). The data sets are available in two file formats (binary ".rda" for use in R; plain-text ".dat").</p> <p>The data sets contain simulated data from 23,376 (Example 1) and 23,072 (Example 2) individuals from 2,000 groups on four variables:</p> <p><code>ID</code> = group identifier (1-2000)<br> <code>x</code> = numeric (Level 1)<br> <code>y</code> = numeric (Level 1)<br> <code>w</code> = binary (Level 2)</p> <p>In all data sets, missing values are coded as "NA".</p>
D^2EPC BIM-based Digital Twin data model example and real-time building measurements
<p>An example building digital twin data model, developed within the H2020 project D^2EPC, corresponding to the first out of six Case Studies (CERTH nZEB Smart House DIH). The following files are provided:</p><p>i) The BIM-based data model of the building parameters (.json file)</p><p>ii) Building real-time collected measurements within the project (in separate .json files):</p><ul><li>Living room: CO2, temperature, humidity, luminance, presence, PM2.5, TVOCs, loudness, smoke</li><li>Office: temperature, humidity, luminance, presence</li><li>Entire ground floor: HVAC system electrical energy consumption</li><li>Entire first floor: HVAC system electrical energy consumption</li><li>Entire building: electrical energy consumption (lighting & appliances)</li><li>Building PV installation: electrical energy production</li></ul><p> </p>
Gross methane production and consumption estimated for intact soil cores from agricultural plots including environmental covariates and example raw isotope pool dilution data
This study was performed to determine how different soil moistures, soil sources, and agricultural practices affected the gross CH4 fluxes (i.e., rates of methanogenesis) of soils. We extracted intact soil cores from two agricultural sites in the USA in row crop plots under conventional, no-till, and organic management. We then took them to the lab, manipulated their moisture levels, incubated them at room temperature for 22 weeks, and measured gas fluxes at weeks 6 and 21. We developed and utilized a new form of CH4 isotope pool dilution (IPD) to estimate gross CH4 production and consumption fluxes. This new method can measure IPD in a bag headspace that loses volume over time due to sampling. We fit the IPD model to the data and extracted gross CH4 production (P) and consumption (K) constants. These along with calculated fluxes and covariates measured (e.g., moisture, inorganic N) are reported in the main data table.
Benchmark and training data for replicating financial and insurance examples
<p>This dataset contains training, validation and out-of-sample test data for two European calls and two examples of portfolio of variable annuity guarantees.</p>
African Swine Fever Worldwide Epidemiology Data - OIE Webscrape example - Geocoded using Google API and Manual
<p>Example African Swine Fever dataset generated by programs described in following publication </p> <p>Title: Web-scraping programmatic techniques in aggregating difficult to access OIE WAHIS animal disease outbreak information; using African Swine Fever in Europe as an example.</p> <p>Short running title: Methods for web-scraping OIE WAHIS data.</p> <p>Abstract: This study describes and makes available new methods for acquiring difficult to access, publicly available, disease surveillance data. It uses World Organisation for Animal Heath (OIE) data on African Swine Fever (ASF) outbreaks in Belarus and its neighbouring European countries to showcase the importance of adequate disease surveillance data to inform decision-making. The data acquired from these methods allow for large-scale, geospatial outbreak mapping and summary statistics of any terrestrial disease listed on the OIE World Animal Health Information System (WAHIS) database. These techniques will make important epidemiological data more accessible to the scientific community and aid in gaining further insight into the occurrence and spread of OIE listed diseases in a timely manner, fulfilling an important function of disease surveillance.</p>
Data Management Plan (DMP) Process Example
<p>This diagram is an example of a funding program solicitation mapped to the select key components of a data management plan, major data lifecycle process, infrastructure and resources, and proposed elements for sustainability of a funded research project. This diagram was developed out of a need to illustrate introductory DMP processes workflows for education, teaching, and training purposes. The DCC Checklist for a Data Management Plan (2013) and the USGS Data Lifecycle Model (2013) were adapted in this diagram.</p>
Data and results of the example used in the SI-Hg D1 protocol for the SI-traceable calibration of elemental mercury (Hg0) gas generators used in the field
<p>During the SI-Hg project a metrological traceable protocol for the calibration of mercury gas generators used in the field was developed and validated. The SI-Hg calibration protocol specifies the procedures for establishing traceability to the SI units for the quantitative output of elemental mercury generators that are employed in regulatory applications for emission monitoring or testing. This protocol provides methods for</p><ul><li>the experimental procedures to compare the output of elemental mercury gas generators</li><li>the data processing for determination of mercury concentration and the expanded uncertainty of the mercury concentration obtained from the elemental mercury gas generator.</li></ul><p>In the protocol examples are given to explain the data processing, determining the mercury concentration and corresponding uncertainty. In this repository the raw data and results used for the example calculated with the data processing script can be found.</p>
Example data from a virtual mass comparison using the Digital Calibration Certificate (DCC) scheme
<p><strong>The example data is an outcome of a project on a fully automated evaluation of a virtual mass comparison using the Digital Calibration </strong><strong>Certificate (DCC) schema.</strong></p> <p>We present the proof of concept for the first fully automated evaluation of a virtual mass comparison. The focus of this comparison is to demonstrate a possibility of digital transformation in metrology using an automated evaluation chain of different tools as well as machine-interpretable files for data transfer and final report. This publication provides the example data being used as input and output of the automated comparison evaluation tool.</p>
Example code and data for ubms: An R package for fitting hierarchical occupancy and N-mixture abundance models in a Bayesian framework
<p>This repository contains an R script (grouse_example.R) and data (grouse_data.csv) used to reproduce the grouse abundance analysis described in Kellner, K. F., et al. (2021) ubms: An R package for fitting hierarchical occupancy and N-mixture abundance models in a Bayesian framework. Methods in Ecology and Evolution. The R script requires installation of the ubms R package, which can be obtained from CRAN (https://cran.r-project.org/package=ubms).</p> <p>The repository also contains an additional example occupancy analysis (occupancy_example.R) using the crossbill dataset included with the unmarked R package.</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.