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1,267 results for “data paper”
Kerosene freeze data for paper to be published:
<p>This dataset originates in an oil refinery producing, among other products, kerosene. The freeze point of the kerosene is an important specification. In the paper to be published the authors use data quality assessment methods to define periods of the data suitable for the derivation of an inferential model.</p>
Data for paper "Efficient Convex Zone Merging in Parametric Timed Automata"
<p>This is the experimental data for paper "Efficient Convex Zone Merging in Parametric Timed Automata"</p> <p>It comes in the form of two archives:</p> <ol> <li><strong>merging-artifact.zip</strong>: the whole set of benchmarks with the scripts to run</li> <li><strong>merging-artifact-FORMATS22-results.zip</strong>: all results executed 5 times</li> </ol>
Data used in ECLIPSER methods paper and GTEx snRNA-seq cross-tissue reference map analysis
<p>The tables were used in the papers: Rouhana*, Wang* <em>et al.,</em> ECLIPSER: identifying causal cell types and genes for complex traits through single cell enrichment of e/sQTL-mapped genes in GWAS loci, bioRxiv 2021, doi: https://doi.org/10.1101/2021.11.24.469720; and Eraslan <em>et al.,</em> Single-nucleus cross-tissue molecular reference maps to decipher disease gene function, bioRxiv 2021, doi: https://doi.org/10.1101/2021.07.19.452954. '<a href="https://zenodo.org/api/files/1f8d48d0-6bf7-4bec-b6ef-5c7a9ead8079/GTEx_v8_HG38_all_variants.tsv.gz">GTEx_v8_HG38_all_variants.tsv.gz</a>' is an input file for running GWASvar2gene on GTEx v8 eQTLs and sQTLs, and all other files are input files for ECLIPSER.</p>
Dataset linking to the paper "Exploring characteristics of national forest inventories for integration with global space-based forest biomass data"
<p>The dataset links to the study titled “Exploring characteristics of national forest inventories for integration with global space-based forest biomass data”. This study is published in the journal “Science of the Total Environment” and the publication can be found at <a href="https://doi.org/10.1016/j.scitotenv.2022.157788">https://doi.org/10.1016/j.scitotenv.2022.157788</a>. The dataset contains four csv files that were used to produce the results and other figures in the paper. The description of the individual data files contained in the dataset is given below.</p> <p><strong>NFI availability and characteristics data: </strong>The data file “NFI_availability_characteristics.csv” contains data on the total number of NFIs, the NFI extent, and the year of the most recent NFI in countries with NFI as reported in FRA 2020 country reports. The respective data variables in the data file are termed as Number_of_NFI, Latest_NFI_extent_FRA2020, and Latest_NFI_year_FRA2020 (NFI years generally refer to the years of data collection). In addition, the data file contains data on the region and tropical domain per country. The tropical and subtropical countries were considered tropical in the analysis and interpretation of the results. These data were used to produce Figure 2 of the study. ArcMap 10.7.1 was used for this purpose. </p> <p><strong>National biomass intercomparison data: </strong>The data file “national_biomass_intercomparison.csv” contains national forest AGB data for the year 2018 from FRA 2020 and CCI Biomass product that were used in the national biomass intercomparison analysis. The total (tons) and average space-based AGB (tons/ha) are extracted directly from the CCI Biomass Map 2018 for each country included in the study. The processing is done in Python and R environments. The spatial resolution of the map is 100 m. The average FRA AGB data in tons per ha was compiled from FRA 2020 country reports. The total FRA AGB data (tons) was estimated by multiplying each country's average FRA AGB data with FRA forest area data (in ha).</p> <p>The data unit for total AGB was converted from tons to gigaton (Gt) in intercomparison analysis. The total CCI Map AGB estimates used in the analysis are termed as CCI_MAP_AGB_Gt in the data file and the average as CCI_Map_AGB_tons.ha. Similarly, the total FRA AGB data are termed as FRA_AGB_Gt and the average as FRA_AGB_ton.ha. The NFI availability and temporality were also used in intercomparison analysis and this data is termed as Latest_NFI_year_FRA2020 in the data file. The data were used to produce Figure 3 of the study in the R environment.</p> <p><strong>NFI plot design characteristics: </strong>The data file named “NFI_plot_design_characteristics.csv” contains data on variables that were used in the analysis of NFI plot designs in 46 tropical countries. This data file mainly contains the data that was used to produce Figure 4 and Figure 6 in the R environment. The value “uniform” in the sampling_stratification variable means no stratification was used in the sampling design. The variable name “psu” stands for primary sampling unit (both cluster and single plots), “psu_distance_km” for the distance between primary sampling units in km, “cluster_plotdis_m” for the distance between plots in meter in the cluster, “plotsize_ha” for plot (single and cluster plots ) size in ha, “plotshape” for plot shapes (single and cluster plots), “ILUA” for Integrated Land Use Assessment. The data were compiled from the latest NFI design manuals and NFI reports.</p> <p><strong>NFI years: </strong>The data file “NFI_years_tropical_countries_data.csv” contains data on NFI years of the latest NFI in 46 tropical countries that were used to produce Figure 1 using ArcMap 10.7.1. The years generally refer to the last years of data collection. Data were compiled from the latest country NFI design manual or NFI report. This included both ongoing and completed NFI.</p>
Data for paper entitled, "Discontinuous Precipitation in Mg-Al Alloy Studied in 3-Dimensions"
<p>Data for paper entitled, "Discontinuous Precipitation in Mg-Al Alloy Studied in 3-Dimensions" including:</p> <p>-Raw SEM imaging and EBSD data</p> <p>-Processed SEM images</p> <p>-3D slices</p> <p>-Supplementary summary figure</p> <p>-Supplementary video</p>
OpenFOAM cases of the paper "CFD modeling of pressure drop through an OCP server for data center applications"
<p>This dataset contains the<em> underling data</em> for the paper "<em>CFD modeling of pressure drop through an OCP server for data center applications</em>” in Energies Journal.</p> <p>Numerical simulations are performed using open-source CFD code OpenFOAM. Features of the numerical model described in the paper can be summarized as:</p> <ul> <li> A hexahedra (hex) and split-hex mesh was created using <em>snappyHexMesh</em> utility from the STL (Stereolithography) model of the server</li> <li><em>Allrun</em> script runs steady-state simulations for different inlet flow rates given in the <em>flowrates</em> file.</li> <li><em>k-omegaSST</em> turbulence closure model is used</li> <li>Convective and diffusive fluxes are computed using second-order accurate numerical schemes.</li> <li>Resultant matrices are solved using <em>GAMG</em> and <em>smoothSolver</em> methods.</li> </ul> <p>New libraries are developed for the detection of whether the solution reaches state-state and for the calculation of average pressures at the inlet and outlet of the server. These libraries need to be download via the following link and compiled before the cases run: </p> <p><a href="https://github.com/DSTECHNO/OCPFoam">https://github.com/DSTECHNO/OCPFoam</a></p> <p><strong>active_Server.tar.gz:</strong> OpenFOAM files and scripts for the steady-state simulation of turbulent flow flow over Leopard V3.1 model of OCP server neglecting fan blades.</p> <p><strong>inactive_Server.tar.gz:</strong> OpenFOAM files and scripts for the steady-state simulation of turbulent flow over Leopard V3.1 model of OCP server considering fan blades.</p> <p><strong>Results_active_Server.tar.gz:</strong> Simulation results for active server.</p> <p><strong>Results_inactiveServer.tar.gz: </strong>Simulation results for inactive server.</p>
Data set for the paper: Intercomparison of ocean colour algorithms for picophytoplankton carbon in the ocean
<p>This dataset contains the phytoplankton carbon,Cphy, obtained from in situ counts of phytoplankton cells using ow cytometry presented in the paper [13]. The location and time of the samples have been matched with the satelllite data in the Ocean Colour Climate Change Initiative (OCCCI) dataset. This dataset is the match between the in situ Cphy and the products from using the OCCCI inputs (i.e. chlorophyll concentration, backscattering coecient, phytoplankton absorption) with 6 different algorithms. This document describes the dataset details: data sources, computation of Cphy, selected data.</p>
Supplementary data for the paper: "Resilient crystalline admixture in ultra-high performance self-healing concrete under cyclic freeze-thaw with de-icing salts"
<p>Supplementary data for the paper: "Resilient crystalline admixture in ultra-high performance self-healing concrete under cyclic freeze-thaw with de-icing salts"<br><br>Open data concerning experimental work. <span>This study investigates the influence of a crystalline admixture (CA) in Ultra-high performance (fibre-reinforced) concrete under freeze-thaw (FT) cycles with de-icing salts with focus on single cracks with a width of around 120 µm, specifically focusing on the ability of the healing products of CA to survive and the ability to re-heal after a healing regime following FT exposure. </span></p>
Dataset supporting the paper: Deciphering Oxygen Distribution and Hypoxia Profiles in the Tumor Microenvironment: A Data-Driven Mechanistic Modeling Approach
<p>The necessary image files for the paper titled "Deciphering Oxygen Distribution and Hypoxia Profiles in the Tumor Microenvironment: A Data-Driven Mechanistic Modeling Approach"</p>
Data for the paper "Particle method for the numerical simulation of the path-dependent McKean-Vlasov equation"
<p>This deposit contains the data obtained by the method described in [A. Bernou, Y. Liu, Particle method for the numerical simulation of the path-dependent McKean-Vlasov equation, 2024]. The notebooks used to generate them through a suitable Euler scheme can be find at https://github.com/ArmdBrn/McKean_PathDep, along with files containing the estimated errors. <br>The two models considered are:<br>- a modified Ornstein-Uhlenbeck model with path-dependency;<br>- a model of neural masses with intrinsic potentiation leading to path-dependent dynamics. </p>
Data and Code for the paper 'Indication of long-range correlations governing city size'
<div> <h1>Summary</h1> <p>This repository contains the preprocessed data necessary for constructing the city network as described in the related paper, as well as the code to do the Shortest-path Fluctuation Analysis (SFA) on those networks.</p> <div> <h2>Under the <code>data</code> folder</h2> <ul> <li> <p>In the <code>Node_list</code> folder, each <em><code>CSV</code></em> file has the name convention like <code>AL_1000_node_list.csv</code>, for example, this means the table contains the list of <strong>nodes</strong> that make up the network for <strong>Austria</strong> (as country code, <code>AL</code>) at the spatial clustering distance threshold of <strong>1000</strong>m. The table has 3 columns with column names, and without row names.</p> <ul> <li> <p>Each row in the table is a record of one node, the <strong><em>node ID</em></strong> is the <code>row_id</code> of the record in the table, the first record has a row_id of <strong>0</strong>.</p> </li> <li> <p>The <strong>first</strong> column in the table is the <em><code>X</code></em> coordinate of the mass center of the node.</p> </li> <li> <p>The <strong>second</strong> column in the table is the <em><code>Y</code></em> coordinate of the mass center of the node.</p> </li> <li> <p>The <strong>third</strong> column in the table is the <strong>Size</strong> of the node, i.e., the number of pixels of this city.</p> </li> </ul> </li> <li> <p>In the <code>Edge_list</code> folder, each <em><code>TXT</code></em> file has the name convention like <code>AL_1000_edges.txt</code>, for example, this means the table contains the list of <strong>edges</strong> that make up the network for <strong>Austria</strong> (as country code, <code>AL</code>) at the spatial clustering distance threshold of <strong>1000</strong>m. The table has 2 columns without column name, without row name.</p> <ul> <li> <p>Each row in the table is a record of one edge that is composed of a pair of nodes, the <strong><em>node ID</em></strong> is the <code>row_id</code> of that node in the <strong>node_list</strong> table.</p> </li> <li> <p>The <strong>first</strong> column in the table is the <strong><em>ID</em></strong> of the node on one side of an edge.</p> </li> <li> <p>The <strong>second</strong> column in the table is the <strong><em>ID</em></strong> of the node on the other side of an edge.</p> </li> </ul> </li> </ul> </div> <div> <h2>Under the <code>code</code> folder</h2> <ul> <li> <p>The <code>SFA_args_LSPT_out.cpp</code> file contains the <strong>C++</strong> code to do the SFA calculation, detailed information can be found in the documentation of the code file.</p> </li> <li> <p>The <code>SFA_config.ini</code> file contains some configuration parameters for running the compiled program, details can also be found in the file. This file has to be put in the same folder as the compiled executable file.</p> </li> </ul> </div> </div>
Precomputed data accompanying the paper "Reduced Data-Driven Turbulence Closure for Capturing Long-Term Statistics"
<p>Together with the python code in <a href="https://github.com/rik-stra/tau_orthogonal_method_for_2D_turbulence" target="_blank" rel="noopener">https://github.com/rik-stra/tau_orthogonal_method_for_2D_turbulence</a>, this dataset lets the user interact with and reproduce the results in the paper "Reduced Data-Driven Turbulence Closure for Capturing Long-Term Statistics".</p> <p> All data in this dataset is computed with the provided code.</p>
Raw metabolomics data of the paper: New findings in the metabolism of the saffron apocarotenoids, crocins and crocetin, by the human gut microbiota
<p>Raw dataset of the metabolomicas data of the study : New findings in the metabolism of the saffron apocarotenoids, crocins and crocetin, by the human gut microbiota.</p> <p>The csv file contain the raw data matrix exported from MS-DIAL software after total ions aligment across all study samples.</p>
Panel data used in the paper ""The shadow price of irrigation water in major groundwater depleting countries"
<p>The panel data are used in an econometric analysis estimating Cobb-Douglas production functions that are subsequently used to calculate the shadow price (current marginal value) or irrigation water in 11 major groundwater depleting countries.</p>
AOSLO Single Cell Blood Flow - Raw Data (eLife paper: Joseph et al. 2019)
<p>Raw AOSLO videos of eLife paper Joseph et al. 2019 - 'Imaging single-cell blood flow in the smallest to largest vessels in the living retina'. <a href="https://urldefense.proofpoint.com/v2/url?u=https-3A__doi.org_10.7554_eLife.45077&d=DwMFaQ&c=kbmfwr1Yojg42sGEpaQh5ofMHBeTl9EI2eaqQZhHbOU&r=EdsTL7DuEvOHun7eVBmBd9sxUuPhDEmdFDf0tlkKUO4&m=56MyNrE_d-v6PSU7Go9NePVOrIpAvWHBaC8wVvgs3_k&s=G7SkWT-fSV9d3SJmpWEUUGp2a6mGotbG-uAwNZftpIo&e=">https://doi.org/10.7554/eLife.45077</a> . For additional data or questions, please contact author Aby Joseph (aby.joseph@rochester.edu, dreamworks1991@gmail.com)</p>
Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms"
<p>Data used in paper "A comparative study of calibration methods for low-cost ozone sensors in IoT platforms", submitted for publication. The data consists of: (i) raw data from three nodes with four MICS 2614 metal-oxide ozone sensors deployed in Spain, summer 2017, and (ii) raw data of five alphasense OX-B431 and NO2-B43F electro-chemical sensors, four deployed in Italy and one in Austria, summers 2017 and 2018. Moreover, we have added the calibrated data using four machine learning methods: Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR).</p>
Downscaled 1 km RACMO2 data used in CESM2 Greenland SMB evaluation paper
<p>Post-processed RACMO2.3p2 data used to carry out part of the analysis in the paper titled “Present day Greenland ice sheet climate and surface mass balance in CESM2”, in review with JGR Earth Surface.</p> <p>This data stems from a RACMO2.3p2 regional climate simulation over Greenland at 11 km which was averaged over the period 1961-1990 and statistically downscaled to 1 km. The original resolution of this dataset is 11 km and further described in Noël et al., 2018, <a href="https://doi.org/10.5194/tc-12-811-2018">https://doi.org/10.5194/tc-12-811-2018</a></p> <p>This data is published for archiving purposes only. Data requests for the latest version of RACMO2 output can be made free of charge to Brice Noël (B.P.Y.Noel@uu.nl) and Michiel van den Broeke (M.R.vandenBroeke@uu.nl). In your request, please specify the variables of interest, time period and time frequency, and area of interest.</p> <p> </p>
Data and Code open availability for the FRETsael paper
<p>This dataset includes the raw files of the measurements of Actin-Myosin interactions in SH-SY5Y cells, as well as the Matlab code for the simulations and analyses used in the paper about FRETsael: "<strong>FRET-sensitized acceptor emission localization (FRETsael) – nanometer localization of biomolecular interactions using fluorescence lifetime imaging</strong>"</p>
Data for the paper "Construction of an invertible mapping to boundary conforming coordinates for arbitrarily shaped toroidal domains."
<p>Data and scripts for the paper "Construction of an invertible mapping to boundary conforming coordinates for arbitrarily shaped toroidal domains."</p> <p>Presented at the "JOINT VARENNA - LAUSANNE INTERNATIONAL WORKSHOP: THEORY OF FUSION PLASMAS, 2024" and published in PPCF.</p>
Stacks of microCT Scans, Cell size, weight, volume and thallus size data supporting the paper 'Mechanical regulation of tissue flatness in Marchantia'
<div> <div> <div> <p>These data are the supporting elements to the following paper: 'Mechanical regulation of tissue flatness in Marchantia'</p> </div> </div> </div> <p> .tif files contain MicroCT (MCT) scans of 16-day-old <em>Marchantia polymorpha</em> thalli. Three genotypes were analysed here: <strong><em>fer-2</em></strong> mutant (from Mecchia et al., 2022), <strong>FER-OE #9</strong> (proMpEF1::MpFERONIA-mCitrine trangenic line 9)<strong> </strong>from Mecchia et al., 2022), and Tak-1 (WT line). These plants were grown in 3 different media: Gamborgh B5 + vitamins and 0.6, 1.2 and 2.5% agar, and one stress condition consisting of the adjunction of a thin PDMS film at 4, to mimich external mechanical stimulus (only performed on thalli grown on 1.2% agar).</p> <p>MicroCT scans were performed at the faculity of odontology of Université Paris-Cité (Plateform imagerie du vivant) with the technical support of Lotfi Slimani and Baptiste Casel. https://piv.u-paris.fr/micro-ct-haute-resolution/ </p> <p>All files already have embeded scales.</p> <p>Each file name consists of a unique ID number in the following form:</p> <p>P+<LETTER>+<NUMBER>-<CONDITION></p> <p>-LETTER: One letter = one imaging session</p> <p>-NUMBER: Individual and Genotype: 33-40 -> Tak1; 200-207-><em>fer-2</em>; 41-49 -> FER-OE</p> <p>-CONDITION : AGAR0.6/AGAR2.5/PDMS. Absence of condition indicates growth on standard medium (1.2% agar). PDMS indicated growth on standard medium and supplementation of a topping PDMS film at day 4)</p> <p> </p> <p>-Volume data were calculated from MicroCT scans</p> <p>-thallus projected surfaces were calculated from MicroCT scans</p> <p><a href="https://zenodo.org/api/records/13981438/draft/files/Lambda%20curvature%20calculation.ipynb/content" target="_blank" rel="noopener noreferrer">-Lambda curvature calculation.ipynb</a> is suited for MorphographX mesh exported .txt files.</p> <p> </p> <p> </p> <p> </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.