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.
1,481
datasets available to search
ShareScore release 0.9.0
Dataset results
1,481 results for “data processing”
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</p>
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</p>
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</p>
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</p>
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</p>
Data Processing for a Small-Scale Long-Term Coastal Ocean Observing System Near Mobile Bay, Alabama: A Geoscience Papers of the Future (GPF) Data Set
<p>The Dauphin Island Sea Lab (DISL) has been operating a permanent moored oceanographic station at 30 05.410'N, 88 12.694'W, 25 km southwest of the entrance to Mobile Bay, Alabama, since 2004. It collects hydrographic and current velocity data.</p> <p>This set of data files was collected between 27 Jan and 18 May 2011. It includes the raw data at initial download from the instruments, several intermediate processing steps, and final processed files ready for initial scientific analysis.</p> <p>The files have been prepared as supplementary material for a Geoscience Paper of the Future (GPF) in prep for publication at Earth and Space Science, as part of the OntoSoft GPF Initiative.</p> <p> </p>
Data Processing for a Small-Scale Long-Term Coastal Ocean Observing System Near Mobile Bay, Alabama: A Geoscience Papers of the Future (GPF) Workflow Diagram
<p>The Dauphin Island Sea Lab (DISL) has been operating a permanent moored oceanographic station at 30 05.410'N, 88 12.694'W, 25 km southwest of the entrance to Mobile Bay, Alabama, since 2004. It collects hydrographic and current velocity data.</p> <p>This diagram shows the processing steps for data from the instruments at this mooring, from initial download to initial scientific analysis. The accompanying text explains how to apply the workflow to the example dataset (10.5281/zenodo.18943) using the provided software (10.5281/zenodo.32741). </p> <p>The files have been prepared as supplementary material for a Geoscience Paper of the Future (GPF) in prep for publication at Earth and Space Science, as part of the OntoSoft GPF Initiative.</p>
Data file for the paper "Using corrosion-like processes to remove poisons from electrocatalysts: a viable strategy to chemically regenerate irreversibly poisoned polymer electrolyte fuel cells", Electrochimica Acta 2016, DOI: 10.1016/j.electacta.2016.11.054
<p>Data used in producing the figures in the paper described below</p> <p>If you use this data then please specify as a reference</p> <p>B. K. Kakati, A. R. J. Kucernak, and K Fahy, "Using corrosion-like processes to remove poisons from electrocatalysts: a viable strategy to chemically regenerate irreversibly poisoned polymer electrolyte fuel cells "Electrochimica ActaDOI: 10.1016/j.electacta.2016.11.054</p> <p>Supported by funding from the Engineering and Physical Sciences Research Council under project EP/I037024/1 and the Technology Strategy board under the IDP11 framework for project 102283. </p>
Sarcoidosis Microbiome Post-Processed Sequence Data
<p>16S, ITS, virome and shotgun sequencing data after demultiplexing, quality control, OTU formation (if relevant) and taxonomic assignment for the sarcoidosis microbiome project.</p> <p>This is intended to be used in conjunction with the code at https://github.com/eclarke/sarcoid-microbiome-paper to reproduce the analysis performed for the associated paper (citation pending acceptance/publication).</p> <p> </p> <p> </p>
Data of 3D MHD Simulation for manuscript "Characteristics of Transpolar Arc Motion and its Corresponding Magnetospheric Dynamic Process"
<p>Data of 3D MHD Simulation for manuscript "Characteristics of Transpolar Arc Motion and its Corresponding Magnetospheric Dynamic Process"</p> <p>There are 6 types of data files:</p> <p>1) -3)MHD simulation results for FAC, plasma density, and temperature, projected at the x = -40RE position, with the viewpoint from the magnetotail towards the earth</p> <p>4) FAC mapping.rar. These data are the parametters in the plane of about Z=0 RE, which were mapped to the 7.2 Re, along the magnetic field lines.</p> <p>5) The simulation results of the model are plotted for FAC on Z=0RE.</p> <p>The results of the above data simulation plot are from 20171115 23:00 UT to 20171116 02:00 UT.</p> <p>6) XXBDd0142.rar, which is full 3D Simulation data at 2017.11.16 01:22 UT;</p> <p>All of these data include the following parameters:</p> <p>time, x, y, z, logrho, Vx, Vy, Vz, Bx, By, Bz, Pr, Jx, Jy, Jz, Edj</p> <p>7) SSUSI data at 2017.11.16.</p> <p> </p>
Supplementary Data for "Exploring the Integration of Large Language Models in Industrial Test Maintenance Processes"
<p>This package contains supplementary data not directly included in the paper, including per-commit results for each prototype and the prompts used in the proof-of-concept implementations.</p>
Data bundle for egon-data: A transparent and reproducible data processing pipeline for energy system modeling
<p><strong>egon-data</strong> provides a transparent and reproducible open data based data processing pipeline for generating data models suitable for energy system modeling. The data is customized for the requirements of the research project <strong>eGon</strong>. The research project aims to develop tools for an open and cross-sectoral planning of transmission and distribution grids. For further information please visit the eGon <a href="https://ego-n.org/">project website</a> or its <a href="https://github.com/openego/eGon-data">Github repository.</a></p> <p>egon-data retrieves and processes data from several different external input sources. As not all data dependencies can be downloaded automatically from external sources we provide a data bundle to be downloaded by egon-data.</p> <p>The following data sets are part of the available data bundle:</p> <ol> <li> <p><strong>climate_zones_germany</strong></p> <ul> <li> <p>Climate zones in Germany</p> </li> <li> <p>source: Own representation based on DWD TRY climate zones</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>cutouts</strong></p> <ul> <li> <p>Weather data from Europe in 2011. Source: ERA5</p> </li> </ul> </li> <li> <p><strong>demand_regio_backup</strong></p> <ul> <li> <p>Electricity and heat demands</p> </li> </ul> </li> <li> <p><strong>emobility</strong></p> <ul> <li> <p>Data on eMobility mit_trip_data:<br>motorized individual travel - individual trips of electric vehicles (EV) generated with a modified version of simBEV v0.1.3 (https://github.com/rl-institut/simbev/tree/1f87c716d14ccc4a658b8d2b01fd12b88a4334d5). simBEV generates driving profiles for BEVs and PHEVs based upon MID data (BMVI) per RegioStaR7 region type (BBSR).</p> </li> <li> <p>Reiner Lemoine Institut, June 2022</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>entsoe</strong></p> <ul> <li> <p> </p> </li> </ul> </li> <li> <p><strong>gas_data</strong></p> <ul> <li> <p>CH4 infrastructure</p> </li> <li> <p>Biogas demand</p> </li> <li> <p>CH4 demand</p> </li> <li> <p>Source: SciGRID_gas</p> </li> </ul> </li> <li> <p><strong>geothermal_potential</strong></p> <ul> <li> <p>Spatial distribution of deep geothermal potentials in Germany</p> </li> <li> <p>source: <a href="https://doi.org/10.3390/en11020332">Assessment and Public Reporting of Geothermal Resources in Germany: Review and Outlook</a></p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>household_electricity_demand_profiles</strong></p> <ul> <li> <p>Annual profiles in hourly resolution of electricity demand of private households for different household types (singles, couples, other) with varying number of elderly and children.<br>The profiles were created using a bottom-up load profile generator by Fraunhofer IEE developed in the Bachelor's thesis "Auswirkungen verschiedener Haushaltslastprofile auf PV-Batterie-Systeme" by Jonas Haack, Fachhochschule Flensburg, December 2012.<br>The columns are named as follows: "<HH_TYPE_PREFIX>a<PROFILE_ID>", e.g. P2a0000 is the first profile of a couple's household with 2 children. See publication below for the list of prefixes. Values are given in Wh.<br>A related conference paper can be obtained here: http://publica.fraunhofer.de/documents/N-374761.html</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>household_heat_demand_profiles</strong></p> <ul> <li> <p>Sample heat time series including hot water and space heating for single- and multi-familiy houses. The profiles were created using the loadprofile generator by Fraunhofer IEE developed in the Master's thesis "Synthesis of a heat and electrical load profile for single and multi-family houses used for subsequent performance tests of a multi-component energy system", Simon Ruben Drauz, RWTH Aachen University, March 2016</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>hydrogen_network</strong></p> <ul> <li> <p>Planned H2 infrastructure</p> </li> <li> <p>Forecast H2 demand</p> </li> <li> <p>Source: fnb-gas</p> </li> </ul> </li> <li> <p><strong>hydrogen_storage_potential_saltstructures</strong></p> <ul> <li> <p>The data are taken from figure 7.1 in Donadei, S., et al., (2020), p. 7-5..</p> </li> <li> <p>Source: Flach lagernde Salze, (c) BGR Hannover, 2021.<br>Datenquelle: InSpEE-Salzstrukturen, (c) BGR, Hannover, 2015. &<br>Donadei, S., Horváth, B., Horváth, P.-L., Keppliner, J., Schneider, G.-S., &<br>Zander-Schiebenhöfer, D. (2020). Teilprojekt Bewertungskriterien und<br>Potenzialabschätzung. BGR. Informationssystem Salz: Planungsgrundlagen,<br>Auswahlkriterien und Potenzialabschätzung für die Errichtung von Salzkavernen<br>zur Speicherung von Erneuerbaren Energien (Wasserstoff und Druckluft) –<br>Doppelsalinare und flach lagernde Salzschichten: InSpEE-DS. Sachbericht.<br>Hannover: BGR.</p> </li> <li> <p>License: The original data are licensed under the GeoNutzV, see <a href="https://sg.geodatenzentrum.de/web_public/gdz/lizenz/geonutzv.pdf">https://sg.geodatenzentrum.de/web_public/gdz/lizenz/geonutzv.pdf</a></p> </li> </ul> </li> <li> <p><strong>industrial_gas_demand</strong></p> </li> <li> <p><strong>industrial_sites</strong></p> <ul> <li> <p>Information about industrial sites with DSM-potential in Germany from a Master's thesis by Danielle Schmidt. The data set includes own information on the coordinates of every industrial site.</p> </li> <li> <p>source: Schmidt, Danielle. (2019). Supplementary material to the masters thesis: NUTS-3 Regionalization of Industrial Load Shifting Potential in Germany using a Time-Resolved Model [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3613767</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>mastr_geocoding</strong></p> </li> <li> <p><strong>nep2035_version2021</strong></p> <ul> <li> <p>Data extracted from the German grid development plan - power</p> </li> <li> <p>source: Netzentwicklungsplan Strom 2035 (2021), erster Entwurf | Übertragungsnetzbetreiber (M) CC-BY-4.0</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>pipeline_classification_gas</strong></p> <ul> <li> <p>Parameters for the classification of gas pipelines</p> </li> <li> <p>source: Single parameters extracted from <a href="https://www.econstor.eu/bitstream/10419/173388/1/1011162628.pdf">Electricity, Heat and Gas Sector Data for Modelling the German System</a></p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>pypsa_eur</strong></p> </li> <li> <p><strong>regions_dynamic_line_rating</strong></p> <ul> <li> <p>German regions suitable to model dynamic line rating</p> </li> <li> <p>source: Own representation based on <a href="https://www.transnetbw.de/files/pdf/netzentwicklung/netzplanungsgrundsaetze/UENB_PlGrS_Juli2020.pdf">Grundsätze für die Ausbauplanung des Deutschen Übertragungsnetze (2020)</a></p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>re_potential_areas</strong></p> <ul> <li> <p>Eligible areas for wind turbines and ground-mounted PV systems.</p> </li> <li> <p>Reiner Lemoine Institut, January 2022</p> </li> <li> <p>License: Attribution 4.0 International (CC BY 4.0)</p> </li> </ul> </li> <li> <p><strong>wind_offshore_status2019</strong></p> <ul> <li> <p> </p> </li> </ul> </li> <li> <p><strong>WZ_definition</strong></p> <ul> <li> <p>Definitions of industrial and commercial branches</p> </li> <li> <p>source: <a href="https://www.destatis.de/static/DE/dokumente/klassifikation-wz-2008-3100100089004.pdf">Klassifikation der Wirtschaftszweige (WZ 2008)</a></p> </li> <li> <p>Extract from Terms of Use: © Statistisches Bundesamt, Wiesbaden 2008 Vervielfältigung und Verbreitung, auch auszugsweise, mit Quellenangabe gestattet.</p> </li> </ul> </li> <li> <p><strong>zensus_households</strong><strong> </strong></p> <ul> <li> <p>Dataset describing the amount of people living by a certain types of family-types, age-classes,sex and size of household in Germany in state-resolution.</p> </li> <li> <p>source: Data retrieved from <a href="https://ergebnisse2011.zensus2022.de/datenbank/online">Zensus Datenbank</a> by performing these steps:</p> <ul> <li> <p>Search for: "1000A-2029"</p> </li> <li> <p>or choose topic: "Bevölkerung kompakt"</p> </li> <li> <p>Choose table code: "1000A-2029" with title "Personen: Alter (11 Altersklassen)/Geschlecht/Größe desprivaten Haushalts - Typ des privaten Haushalts (nach Familien/Lebensform)"</p> </li> <li> <p>Change setting "GEOLK1" to "Bundesländer (16)" higher resolution "Landkreise und kreisfreie Städte (412)" only accessible after registration.</p> </li> </ul> </li> <li> <p>Extract from Terms of Use: © Statistische Ämter des Bundes und der Länder 2021, Vervielfältigung und Verbreitung, auch auszugsweise, mit Quellennachweis gestattet.</p> </li> </ul> </li> <li> <p><strong>zensus_population</strong></p> </li> <li> <p><strong>district_heating_shares_egon.csv</strong></p> </li> </ol>
Simulation data in "Intense magnetic reconnection process embedded in three-dimensional turbulent current sheet"
<p>Simulation data and program used for the research "Intense magnetic reconnection process embedded in three-dimensional turbulent current sheet".</p>
Pre-Processed Data Sets for Marine Spatial Planning of a Wave-Powered Aquaculture Farm in the Northeast U.S.
<p>These data sets are intended for marine spatial planning applications including the modeling of wave-powered aquaculture farms. They work in tandem with the Python model developed by the SEA Lab to evaluate potential sites for this development in the Northeastern U.S. The code for this model is available on GitHub at <a href=" https://github.com/symbiotic-engineering/aquaculture">https://github.com/symbiotic-engineering/aquaculture</a>, and details about the data and model are discussed in several related publications.</p>
Processed data for Jowhar et al, "A ubiquitous GC content signature underlies multimodal mRNA regulation by DDX3X"
<p>Processed data for Jowhar et al, "A ubiquitous GC content signature underlies multimodal mRNA regulation by DDX3X".</p> <p>It contains the data objects to reproduce all figures of the paper.</p> <p>More info here: <strong><a href="https://github.com/calviellolab/DDX3X_GC_paper">https://github.com/calviellolab/DDX3X_GC_paper</a></strong></p>
Climate Security on social media: raw and processed data from Twitter
<p>This dataset reflects climate security dialogues on Twitter, from January 2014 to May 2023.</p>
Code and processed data - Control points for design of taxonomic composition in synthetic human gut consortia
<p>Code, optical density data, and sequencing tally files (relative abundance from 16S sequencing of defined communities) for <strong>Control points for design of taxonomic composition in synthetic human gut consortia. </strong>Files are organized by experiment with a readme file contained within the .zip file. "Setup" folders have information about expeirmental design, "rawData" folders have optical density and tallyfiles, "analysis" folders have scripts used to analyze data and generate models. </p>
Guanabara Bay sea level data and processing routines
<p>Sea level data from Guanabara Bay and processing routines used in the article entitled "Mean sea level, tidal components and surges in Guanabara Bay Guanabara Bay (Rio de Janeiro) from 1990 to 2021, submitted to the International Journal of Climatology".</p>
Experimental signals and processed data used in the research work "On-chip phonon-magnon reservoir for neuromorphic computing"
<p>The data set includes the raw experimental data, processed experimental data, and numerically modeled dependencies in the respective folders:</p><p>The raw experimental data (magnon readout, as measured) are given for all processed signals presented in the respective figures (folders 'Figure2', 'Figure3' and 'Sup Figure1') and used for the ANN training (folder '3x3Sets & AugmentedVisualShapes'). </p><p>The signals presented in the folders '\3x3Sets & AugmentedVisualShapes\3x3SET##' are named following the scheme shown in Fig. 3a. The signals in the folder \3x3Sets & AugmentedVisualShapes\RandomizedShapes' are simulated using the procedure described in the Methods section as "Drawing of randomized visual shapes". The sets of calculated statistical parameters used for the shapes' recognition are in the folder ''\3x3Sets & AugmentedVisualShapes\Parameters'</p><p>The waveforms for the trajectories formed by randomly selected 4, 5, 6, and 7 discrete positions and their statistical parameters are presented in the corresponding folder. </p>
Processed data files for conplastic mice mitochondrial DNA profiling
<p>This dataset contains all tables used and generated in analyses for the conplastic mice mitochondrial DNA profiling project. Please refer to serrano2023_directory<em>_</em>of_files.pdf for a description of the files. The serrano2023_directory_of_files.pdf also contains field descriptions for: </p> <ul> <li>somatic_mutations.vcf</li> <li>haplotype_mutations.vcf</li> <li>supertable.txt</li> <li>cleaned_read_depth_per_pos.txt</li> <li>adjusted_mut_freq_for_haplotypes.csv</li> <li>mut_freq_per_type.csv</li> </ul> <p>All scripts that generate the files in this zenodo repo can be found at <a href="https://github.com/sudmantlab/conplastic_mt_profiling">https://github.com/sudmantlab/conplastic_mt_profiling </a></p> <p> </p> <p>Single stranded and duplex consensus fastq files can be found at the following zenodo repositories:</p> <p><a href="https://doi.org/10.5281/zenodo.10403218">(Wildtype) B6 fastq files</a></p> <p><a href="https://doi.org/10.5281/zenodo.10403087">B6-mtAKR fastq files</a></p> <p><a href="https://doi.org/10.5281/zenodo.10397996">B6-mtALR fastq files</a></p> <p><a href="https://doi.org/10.5281/zenodo.10294450">B6-mtFVB fastq files</a></p> <p><a href="https://doi.org/10.5281/zenodo.10211698">B6-mtNZB fastq files</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.