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4,694 results for “Data Analysis”
Public Available Data Set of Process Flows from Internal Physical Inspections in the Failure Analysis Laboratory
<p>This data set was generated in accordance with the semiconductor industry and contains data of certain process flows in Failure Analysis (FA) laboratories focusing on the identification and analysis of anomalies or malfunctions in semiconductor devices. It comprises logistic data about the processing steps for the so-called Internal Physical Inspection (IPI).</p><p>A so-called IPI job is given as a sequence of tasks that must be performed to complete the job they belong to. It has an assigned unique ID and timestamps indicating the submission, the end, and the deadline to be met. A job also has an IPI classification assigned to it, providing general guidelines on the operations to be performed.</p><p>Every task within a job has its own type and working time, as well as the assigned resources. There are two main resources involved:</p><p> - the equipment; the machine used to perform the task,</p><p> - the operator; the person who performed the task.</p><p>In addition, general information about the type of the device to be analyzed is also available, such as the given (anonymized) package and basictype. Data also include the number of stressed samples within a device and the samples a task is performed on.</p><p>The dataset includes data from 4 years, specifically from January 2020 to December 2022.</p><p>Finally, the exact column structure is given as follows (python 3.9.5 datatype):</p><ul><li>JOB_ID [int64]: the unique ID of the job</li><li>JOB_SUBMISSION_DATE [object]: the date of the job submission</li><li>JOB_REQ_END_DATE [object]: the required end date (deadline)</li><li>JOB_FINISH_DATE [object]: the actual end date</li><li>JOB_BASICTYPE_H [object]: the given basictype denotation</li><li>JOB_PACKAGE_H [object]: the package denotation of the device</li><li>JSH_QTY_STRESSED [float64]: number of stressed samples</li><li>TASK_SUBMISSION_DATE [object]: the date of the task submission</li><li>TASK_WORKING_TIME [float64]: the amount of time (hours) the task needs to be completed</li><li>TASK_SAMPLE_NO [object]: the samples the task was performed on </li><li>TASK_CEQ_ID [float64]: the ID of the machine used to perform the task</li><li>TASK_CTKS_ID [int64]: the ID representing the task type</li><li>TASK_USR_ID [int64]: the ID of the operator performing the task</li><li>CIPI_LEVEL_0 [object]: a series of IPI classifications, indicating what is required to execute for a specific job</li></ul>
Data for: Thermal volume expansion as seen by Temperature-modulated optical refractometry, Oscillating dilatometry and Thermo-mechanical analysis
<p>The data is supplementary to the publication "Thermal volume expansion as seen by Temperature-modulated optical refractometry, Oscillating dilatometry and Thermo-mechanical analysis", DOI: <a href="https://doi.org/10.1016/j.polymertesting.2024.108340" target="_blank" rel="noopener">10.1016/j.polymertesting.2024.108340</a></p> <p>Key words: Thermal volume expansion, Temperature-modulated optical refractometry, Thermo-mechanical analysis, Dilatometry, Epoxy thermoset</p> <p>The data sets contain measured data on Thermo-mechanical analysis (TMA) and Temperature-modulated optical refractometry (TMOR) of a model epoxy polymer in the viscoelastic temperature range.</p> <p>Material details:</p> <ul> <li>Bisphenol A Diglycidyl ether (DGEBA, DER332) + Difunctional and trifunctional carbocylic acids (Pripol1040, Croda) +pyridine</li> <li>n-tetradecane, C14H30</li> </ul> <p>Funding received from:</p> <ul> <li>German Research Foundation (DFG), project number: 521902629.</li> <li>(Austrian) Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology and the Federal Ministry for Digital and Economic Affairs (COMET-Module project “Chemitecture”, project-no.: 21647048)</li> </ul>
Grapegenomics.com: a web portal with genomic data and analysis tools for wild and cultivated grapevines
<p><a href="https://grapegenomics.com">Grapegenomics.com</a> is a web portal that provides public access to genome references for grapevine cultivars (<em>Vitis vinifera</em> ssp. <em>vinifera</em>), wild grapevines (<em>Vitis vinifera</em> ssp. <em>sylvestris</em>), various wild grape species (<em>Vitis</em> spp. and <em>Muscadinia</em> spp.), and major fungal pathogens affecting grapes.</p> <p>All genomes are accessible through dedicated genome browsers, and published genomes are available for complete <a href="https://www.grapegenomics.com/download.php">download</a>.</p> <p>The site hosts all genomes produced by the laboratory of Dario Cantù in the Department of Viticulture and Enology at the University of California, Davis, along with published genome references generated by others, such as PN40024 and Pinot noir ENTAV115. Instructions for genome submission are provided <a href="https://www.grapegenomics.com/submit.php">here</a>. The portal is maintained by Noé Cochetel (ndcochetel[at]ucdavis.edu). In this version 2.0, all genome browsers utilize <a href="https://jbrowse.org/jb2/">jbrowse 2</a>. <br><br>Link to the website: <a href="https://www.grapegenomics.com">https://www.grapegenomics.com</a> </p>
Topological Data Analysis of Monopoles in U(1) Lattice Gauge Theory — Data Release
<div>This release contains data used to prepare the publication <a href="https://arxiv.org/abs/2403.07739">X. Crean, J. Giansiracusa and B. Lucini, Topological Data Analysis of Monopoles in U(1) Lattice Gauge Theory (2024)</a>. There exists an <a href="https://doi.org/10.5281/zenodo.10806185">accompanying software release</a> that explains in detail how to extract and use the compressed data files on a Linux distribution (or compatible environment).</div>
Genome data from Effrenium voratum CCMP421, RCC1521, and rt-383 and their analysis
<p>This dataset represents secondary data generated from the genomic analysis of three isolates of <em>Effrenium voratum</em> (CCMP421, RCC1521, and rt-383), the early-diverging, free-living lineage of Symbiodiniaceae dinoflagellates. Theese data include <strong>(A)</strong> assembled genome sequences, predicted gene models and protein sequences, and <strong>(B)</strong> data and scripts associated with generation of graphs and figures presented in the key genome paper (Shah et al., 2024, Massive genome reduction predates the divergence of Symbiodiniaceae dinoflagellates, under review in <em>ISME Journal</em>). An earlier preprint of this manuscript is available at <em>bioRxiv</em>: <a href="https://doi.org/10.1101/2023.03.24.534093" target="_blank" rel="noopener">https://doi.org/10.1101/2023.03.24.534093</a>.</p> <p><strong>A. Genome assemblies, annotation and gene models. </strong>The dataset includes, for each taxon, (a) the <em>de novo</em> assembled genome sequences in FASTA format (<strong>*genome.fa.tgz</strong>), (b) structural annotation of the assembled genome in GFF3 format (<strong>*.genome.annotation.gff3.tgz</strong>), (c) the predicted protein-coding sequences of gene models in FASTA format (<strong>*genemodel.CDS.fa.tgz</strong>), (d) the predicted protein sequences of gene models in FASTA format (<strong>*genemodel.PROT.fa.tgz</strong>), and (c) the associated sequences and gene annotations of organellar genomic sequences (i.e. mitochondrial and plastid) (<strong>*organellar.tgz</strong>). Functional annotations of all gene models from the three genomes are available in the Excel spreadsheet (<strong>*GeneModels.xlsx</strong>).</p> <p><strong>B. Data and scripts associated with generation of graphs and figures in Shah et al. (2024). </strong>These files are organised based on key analyses specific to main figures and supplementary figures in the paper.</p> <p>See <strong>README.txt</strong> for a more-detailed description of the files.</p>
An Integrated Usability Framework for Evaluating Open Government Data Portals and Analysis of EU and GCC OGD Portals
<p><span>This dataset contains data collected during a study (<em><strong>"<a href="https://arxiv.org/ftp/arxiv/papers/2403/2403.08451.pdf">An Integrated Usability Framework for Evaluating Open Government Data Portals: Comparative Analysis of EU and GCC Countries</a>"</strong></em>) conducted by Fillip Molodtsov and Anastasija Nikiforova (University of Tartu).</span></p> <p><span> </span><span>It being made public both to act as supplementary data for the paper and in order for other researchers to use these data in their own work potentially contributing to the improvement of current data ecosystems and develop user-friendly, collaborative, robust, and sustainable open data portals.</span></p> <p><span>***Purpose of the study***</span></p> <p><span>This paper develops an integrated framework for evaluating OGD portal effectiveness that accommodates user diversity (regardless of their data literacy and language), evaluates collaboration and participation, and the ability of users to explore and understand the data provided through them. </span></p> <p><span>The framework is validated by applying it to 33 national portals across European Union (EU) and Gulf Cooperation Council (GCC) countries, as a result of which we rank OGD portals, identify some good practices that lower-performing portals can learn from, and common shortcomings.</span></p> <p><span>***Methodology***</span></p> <p><span>(1) systematic literature review to establish a knowledge base and identify frameworks have been used to evaluate OGD portals, we conducted a systematic literature review - Dataset_ Usability_Framework_SLR;</span></p> <p><span>(2) development of the Integrated Usability Framework for Evaluating Open Government Data Portals, which content is based on the outputs of the first step, along with selected articles of experts in portal design, and an exploratory assessment of the French, Irish, Estonian and Spanish portals - Dataset_Integrated_Usability_Framework;</span></p> <p><span>(3) data collection, that is a completion of the protocol developed in the previous step by analysing 34 national OGD portals of the EU and GCC countries. When all individual protocols were collected, the total score are calculated using the weighting system. The average scores are calculated for the EU and GCC. The portals are ranked. The top portals (best performers) are determined for each dimension - Dataset_EU_GCC_OGDportal_Usability_results_clustering.</span></p> <p><span>(4) identification of relationships and patterns among different portals based on their performance metrics as a result of the cluster analysis. By calculating the average dimensional scores of portals from both types of clusters, their performance across multiple dimensions is evaluated - Dataset_EU_GCC_OGDportal_Usability_results_clustering.</span></p> <p> </p> <p><strong><em><span>For more details see Molodtsov, F., Nikiforova, A. (2024). “An Integrated Usability Framework for Evaluating Open Government Data Portals: Comparative Analysis of EU and GCC Countries”. In Proceedings of the 25th Annual International Conference on Digital Government Research (DGO 2024), June 11--14, 2024, Taipei, Taiwan, 10.1145/3657054.3657159</span></em></strong></p> <p><span>***Format of the file***</span></p> <p><span>.xls, .csv</span></p> <p><span>***Licenses or restrictions***</span></p> <p><span>CC-BY</span></p>
The portrayal of underlings in Eastern Cālukya copper plates: textual analysis data with revised codebook
<p>This is a textual analysis dataset derived from Eastern Cālukya copperplate grants. The second version of 24 April 2024 contains a slightly revised codebook (in DOC and PDF formats with identical contents) in addition to the earlier dataset. This revision is essentially identical to that reflected in the "Revised tag" column of the dataset, except that many definitions have been made clearer and tidier, and a small number of intermediate-level categories have been added for better hierarchisation. The codes in use have not been altered.</p> <p><br>The data accompany the following forthcoming publications (title and date of publication subject to change):</p> <p>Balogh, Dániel (forthcoming 2024), 'The portrayal of underlings in Eastern Cālukya copper plates'. In: Self-Representation and Presentation of Others in Indic Epigraphical Writing, edited by Annette Schmiedchen and Dániel Balogh. Wiesbaden: Harrassowitz.</p> <div> <div>Balogh, Dániel (forthcoming 2024). ‘Textual Analysis Methodology and Royal Representation in Copperplate Grants’. In <em>Bhūtārthakathane ... Sarasvatī: Reading Poetry as a History Book</em>, edited by Marco Franceschini, Chiara Livio, and Lidia Wojtczak. Studies on the History of Śaivism. Naples: UniorPress.</div> </div> <p>The former publication introduces the method sketched out on the introductory page of this dataset and studies a particular topic through this methodology, while the latter discusses the method in more detail. An account of the technical details is in preparation by Balogh.</p> <p><br>This dataset, the underlying research and the relevant publications are results of the project DHARMA ‘The Domestication of “Hindu” Asceticism and the Religious Making of South and Southeast Asia’. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no 809994).</p>
Survey Data and Analysis on Open Access Strategies (2022)
<p><strong>Description:</strong><br>This dataset includes the analysis, codebook, and raw survey data from a 2022 survey titled <em>"Which Open Access Strategies Are Relevant?"</em>. The survey targeted professionals in library and information sciences specializing in open access and scholarly publishing.</p> <p>The dataset is based on 100 adjusted responses (<em>N=100</em>) and aims to provide insights into the strategies and challenges associated with open access implementation in academic and professional environments.</p> <p><strong>Contents:</strong></p> <ol> <li><strong>analysis_oa-strategies-2024-01-14.xlsx</strong>: Processed data and key analyses, including summary tables and graphs.</li> <li><strong>codebook_oa-strategies_2024-01-14.xlsx</strong>: Comprehensive documentation of variables, codes, and their definitions for interpretation of the raw data.</li> <li><strong>survey_results_oa-strategies_2024-01-14.xlsx</strong>: Anonymized raw data from the survey, suitable for further analysis.</li> </ol> <p><strong>Methodology:</strong><br>The survey employed a structured questionnaire distributed in 2022 to professionals in library and information sciences. It focused on identifying key strategies, institutional policies, and perceived barriers to open access. The collected data were cleaned and anonymized to ensure privacy and compliance with ethical standards.</p> <p><strong>Purpose and Use:</strong><br>This dataset is designed for researchers, policymakers, and information science professionals. It is particularly valuable for studying open access adoption strategies, evaluating institutional policies, and conducting comparative research.</p>
Galápagos Archipelago Refined Analysis Validation data
<p>This dataset includes (near) surface data variables from the <a href="https://data.klima.tu-berlin.de/GAR/">GAR</a> dataset for the model validation period from 2022-04-01 to 2023-03-31.</p> <p>As this data is part of the GAR dataset, please find additional data at <a href="https://data.klima.tu-berlin.de/GAR/">https://data.klima.tu-berlin.de/GAR/</a></p> <p>The <a href="https://www.unidata.ucar.edu/software/netcdf/">netCDF</a> format is self-describing, so that all needed metadata are included within the files.</p> <p>The file names are composed with the following structure:</p> <p><model-setup>_<horizontal-resolution>_<time-resolution>_<variable-name>.nc</p> <p>The shorthands in the file names represent the following:</p> <p><strong>MM</strong> = Name of the model setup, described in Schmidt et al. (unpublished)</p> <p><strong>d02km</strong> = domain with a grid spacing of 2 km</p> <p><strong>2d</strong> = spatial dimensions (2d data, single level)</p> <p><strong>3d_press</strong> = spatial dimensions (3d data, pressure level)</p> <p><strong>d</strong> = time frequency of the data (daily)</p> <p><strong>m</strong> = time frequency of the data (monthly)</p> <p><strong>y</strong> = time frequency of the data (yearly)</p> <p><strong>psfc</strong> = surface (sfc) pressure</p> <p><strong>q2</strong> = water vapor mixing ratio (qv) st 2 m</p> <p><strong>q</strong> = mixing ratio</p> <p><strong>prcp</strong> = total precipitation (step-wise)</p> <p><strong>et</strong> = actual evapotranspiration (step-wise)</p> <p><strong>t2</strong> = temperature (temp) at 2 m</p> <p><strong>theta</strong> = potential temperature</p> <p><strong>sh2 </strong>= specific humidity at 2 m</p> <p><strong>rh2 </strong>= relative humidity at 2 m</p> <p><strong>u10</strong> = 10 m u-wind component</p> <p><strong>v10</strong> = 10 m v-wind component</p> <p><strong>ws10</strong> = 10 m wind speed</p> <p><strong>w</strong> = w-wind component</p> <p><strong>wd10</strong> = 10 m wind direction</p> <p><strong>hgt </strong>= surface height</p> <p><strong>landmask </strong>= landmask</p> <p> </p> <p>The data is in accordance with the <a href="https://cfconventions.org/">CF Conventions</a> CF-1.8</p>
MALDI MS data and metadata from "A biocodicological analysis of the medieval library and archive from Orval Abbey, Belgium"
<p>See <a href="https://doi.org/10.1098/rsos.210210">Ruffini-Ronzani et al</a>.</p>
Data to support the publication "Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe", https://doi.org/10.3390/land10121362
<p>Soil organic carbon content and water content at the different pressure points, as measured by Ioanna Panagea for the publication "Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe", https://doi.org/10.3390/land10121362 from the the long term experiments belonging in some of the SoilCare project partners. </p>
XCT data of metallic feedstock powder with pore size analysis
<p><strong>X-Ray computed tomography (XCT) scan of 11 individual metallic powder particles, made of (Mn,Fe)<sub>2</sub>(P,Si) alloy</strong></p> <p>The data set consists of 4 single XCT scans which have been stitched together [3] after reconstruction.<br> The powder material is an (Mn,Fe)<sub>2</sub>(P,Si) alloy with an average density of 6.4 g/cm³. The particle size range is about 100 - 150 µm with equivalent pore diameters up to 75 µm. The powder and the metallic alloy are described in detail in [1, 2].</p> <p><strong>Data acquisition</strong></p> <p>The data was acquired using a Zeiss Xradia 620 Versa X-ray microscope which provides the opportunity of optical magnification.</p> <table> <caption><strong>Tomographic imaging parameters</strong></caption> <tbody> <tr> <td>XCT system</td> <td>Zeiss Xradia 620 Versa</td> </tr> <tr> <td>Voltage</td> <td>80</td> <td>kV</td> </tr> <tr> <td>Power</td> <td>10</td> <td>W</td> </tr> <tr> <td>Source filtering</td> <td>"<em>LE2</em>" (system specific)</td> <td>-</td> </tr> <tr> <td>Source-object distance</td> <td>10</td> <td>mm</td> </tr> <tr> <td>Object-detector distance</td> <td>10</td> <td>mm</td> </tr> <tr> <td>Geom. magnification</td> <td>2</td> <td>-</td> </tr> <tr> <td>Optical magnification</td> <td>20</td> <td>-</td> </tr> <tr> <td>Native pixel size</td> <td>13.5</td> <td>µm</td> </tr> <tr> <td>Binning</td> <td>2x2</td> <td>px</td> </tr> <tr> <td>Voxel size</td> <td>0.68</td> <td>µm</td> </tr> <tr> <td>No. of projections per scan</td> <td>801</td> <td>1</td> </tr> <tr> <td>No. of scans</td> <td>4</td> <td>-</td> </tr> <tr> <td>Exposure time per projection</td> <td>5</td> <td>s</td> </tr> </tbody> </table> <p> </p> <p><strong>Projection data</strong> (801 single TIFF-files each):</p> <ul> <li>proj_00</li> <li>proj_01</li> <li>proj_02</li> <li>proj_03</li> </ul> <p><strong>Reconstructed data</strong>:</p> <ul> <li>raw-volume (MnFePSi-Powder_80kV_10W_LE2_20x_5s_801_0p68_BHC=2_Stitch_U16_966x1020x2916.raw + header.txt)</li> <li>analyzed data as Volume Graphics Studio MAX 3.4.5 project</li> </ul> <p><strong>Stitched 2D data</strong> (images stitched with ImageJ-Plugin described in [3]<strong>:</strong></p> <ul> <li>Stitched_0deg_Projections.tif</li> <li>Pores+Particles_Analysis.tif</li> </ul> <p> </p> <p>[1] G.-R. Jaenisch, U. Ewert, A. Waske, and A. Funk, “Radiographic Visibility Limit of Pores in Metal Powder for Additive Manufacturing,” Metals, vol. 10, no. 12, p. 1634, Dec. 2020. https://doi.org/10.3390/met10121634</p> <p>[2] X. Miao et al., “Printing (Mn,Fe)2(P,Si) magnetocaloric alloys for magnetic refrigeration applications,” J. Mater. Sci., vol. 55, no. 15, pp. 6660–6668, May 2020. https://doi.org/10.1007/s10853-020-04488-8</p> <p>[3] S. Preibisch, S. Saalfeld, and P. Tomancak, “Globally optimal stitching of tiled 3D microscopic image acquisitions,” Bioinformatics, vol. 25, no. 11, pp. 1463–1465, Jun. 2009.</p>
An experimental data set for the analysis of the thermophysical behavior of a single-story naturally ventilated double-skin façade (DSF) under summer boundary conditions
<p>Double-skin facades (DSFs) are adaptive building envelope elements that offer the possibility to dynamically interact with the heat and mass flow between indoor and outdoor environments. Though designed to provide better performance compared to more conventional envelope solutions, these façade systems may, in some cases, underperform and lead to an increase in energy use or in thermal discomfort if not properly designed and operated. One of the known problems is the risk of overheating, in hot periods, in the ventilated cavity. In order to analyze this effect, we have systematically investigated the performance of a single-story, naturally ventilated DSF. The DSF is operated in the so-called outdoor air curtain mode and has venetian blinds installed in the 20 mm deep ventilated cavity. Tests were carried out under a steady-state regime corresponding to relevant summertime conditions. In an effort to enable the scientific community to access experimental data to analyze this problem further or for model validation purposes, we released together with the open-access paper entitled "<strong>Characterization of a naturally ventilated double-skin façade through the design of experiments (DOE) methodology in a controlled environment</strong>," the entire set of experimental data collected during the tests. The data set contains the results of a series of experimental runs where different configurations of the DSF, as detailed below, have been subjected to various boundary conditions through a climate simulator facility equipped with a solar simulator device. The database is supported by a guide ("Guide.pdf"), where further explanations about how to read data and schematic drawings of the sensor layout are provided. Additional information about the original aims of the experiments, the detailed methods, and other data processing procedures can be found in the article mentioned above. The collection of experimental tests in this data set covers:</p> <ul> <li>49 steady-state measurements where the following factors were changed using different experimental designs: solar irradiance (0, 350, and 700 Wm<sup>-2</sup>), outdoor chamber temperature (15, 25, and 35 ℃), opening size (7, 21, and 42 dm<sup>2</sup>), and venetian blinds angle (closed blinds θ=0 º, θ=45 º, and open blinds θ=90 º) [file name: "Complete_data.csv"],</li> </ul> <p>Any inquiries about the experimental data can be sent to: <a href="mailto:aleksandar.jankovic@ntnu.no">aleksandar.jankovic@ntnu.no</a></p> <p>The activities presented in this paper were carried out within the research project "REsponsive, INtegrated, VENTilated - REINVENT – windows," supported by the Research Council of Norway through the research grant 262198, and the partners SINTEF, Hydro Extruded Solutions, Politecnico di Torino and Aalto University.</p>
Data for "Traceable X-ray focal spot reconstruction by circular edge analysis: From sub-microfocus to mesofocus"
<p>Raw data used to create figures for the paper "Traceable X-ray focal spot reconstruction by circular edge analysis: From sub-microfocus to mesofocus" <a href="https://doi.org/10.1088/1361-6501/ac6225">https://doi.org/10.1088/1361-6501/ac6225</a></p>
10-day backward trajectories from ECMWF analysis data along the ship track of the Antarctic Circumnavigation Expedition in austral summer 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>This dataset contains 10-day backward trajectories along the ship track of the Antarctic Circumnavigation Expedition from Nov 2016 – April 2017 calculated with the Lagrangian analysis tool LAGRANTO using the 3D-wind fields from the European Centre for Medium Range Weather Forecasts (ECMWF) operational analysis data. The trajectories were started from up to 56 vertical levels between 0 and 500 hPa a.s.l. and various variables were interpolated along the trajectories.</p> <p><strong>Dataset contents</strong></p> <ul> <li>trajs_ACE.zip: lsl_${year}${month}${day}_${hour}, trajectory files (containing all trajectories starting at ${year}${month}${day} ${hour}UTC at the ACE track from different vertical levels), comma-separated values</li> <li>fig_map.zip: map_long10_${year}${month}${day}_${hour}.png, map plots of all trajectories starting at ${year}${month}${day} ${hour}UTC coloured by pressure, portable network graphics</li> <li>fig_cross.zip: cross10_q_${year}${month}${day}_${hour}.png, cross-section plots of all trajectories starting at ${year}${month}${day} ${hour}UTC coloured by specific humidity, portable network graphics</li> <li>data_file_header.txt, metadata for lsl-files, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This 10-day backward trajectory dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Data for "Temperate Regenerative Agriculture practices increase soil carbon but not crop yield – a meta-analysis"
<p>Supplementary Files for systematic review and meta-analysis: Temperate Regenerative Agriculture practices increase soil carbon but not crop yield – a meta-analysis</p> <p> </p>
Data and analysis for Association of meeting 24-hour movement guidelines with low back pain among adults
<p>Introduction</p> <p>This data and code forms the analytical process of a study examining associations between meeting different combinations of 24-h movement guidelines (that integrates a recommendations on physical activity, sedentary behaviour, and sleep) with prevalence, frequency and intensity of low back pain in a sample of adults aged 18 years and over. </p> <p>Notes: </p> <p>* the raw data is provided alongside this upload, but the processing is not addressed here. <br> * the authors of this document are a subset of the authors of the related paper.<br> * this document and the related data files were uploaded at the time of submission for review. An update providing the doi of the related paper will be provided when it is available.</p>
Data and analysis script for "The (non)effect of personalization in climate texts on credibility of climate scientists: A case study on sustainable travel"
<p>Dataset and analysis script for the article "<strong>The (non)effect of personalization in climate texts on credibility of climate scientists</strong><strong>: A case study on sustainable travel</strong>", under review at Geoscience Communication (https://doi.org/10.5194/egusphere-2024-543)</p>
Data of the article Analysis of the self-archiving policies of journals in the highest rank category of the Finnish journal classification system within computer science, physics and electronic engineering
<p>The publication forum level three journals representing the three fields of science of computer science, computer science and electrical engineering were identified by utilizing the MinEdu field search filter while searching for the top-ranked journals from the publication channel search (https://www.tsv.fi/julkaisufoorumi/haku.php?lang=en), which is based on Field of Science, Statistics Finland classification (https://www.stat.fi/meta/luokitukset/tieteenala/001-2010/index_en.html). The data were extracted during august 2017 consists of total of 127 individual journals. It is worth noting that circa 30 journals were classified into more than one fields of sciences under scrutiny. First, the journals were divided into representing gold and hybrid model journals. Second, green open access policies of the identified hybrid journals were analyzed using Laakso’s (2014) publisher policy coding framework. Also publishers of the individual journals were identified and subsequently added to the data.</p> <p>NOTE! The data includes the shortest embargo to either institutional or subject repositories. For example, Elsevier had no embargo to opening accepted manuscripts from arXiv subject repository and thus no embargoes to Elsevier's journals are included within this datasheet.</p> <p>Data is in CSV. format</p> <p> </p> <p> </p>
Data from Automated plankton image analysis using convolutional neural networks
<p>Datasets and code from Luo et al., "Automated plankton image analysis using convolutional neural networks." Limnology and Oceanography Methods.</p> <p>Data include:</p> <p>1) 42,564 item training library, sorted in 108 classes,</p> <p>2) 42,548 item test set for filtering thresholds, sorted into 38 groups. These images are independent from the training library, and are used for setting the thresholds for post-classification filtering.<br> CSV file: Luo_etal_FT_images_pred.csv contains the image name, predicted class, predicted probability, and validated group. Note that the file class_to_group.csv is needed to match up the class names to the group names.</p> <p>3) 75,000 item fully random, validated set for confusion matrix calculations, sorted into 38 groups. This set is a representation of the full dataset, selected at random after classification. <br> CSV file: Luo_etal_confusionmatrix_images.csv contains the image name, predicted class, predicted probability, and validated group. Note that the file class_to_group.csv is needed to match up the class names to the group names.</p> <p> </p> <p>Scripts and programs:</p> <p>1) Segmentation.zip contains the scripts and executables for the segmentation program.</p> <p>2) Plankton_template.zip contains the archived version of the SparseConvNet program used in manuscript (current version available at: https://github.com/btgraham/SparseConvNet or https://github.com/facebookresearch/SparseConvNet)<br> Note that google-sparsehash is necessary for running SparseConvNet.<br> Also, plankton_epoch-150.cnn are the weights from the training used in the manuscript, and should be placed in the /weights folder if you want to replicate the classifications.</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.