cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5286/isis.e.rb1820424
title: Hardness anomaly in magnesium aluminosilicate (MAS) glass

description: Aluminosilicate glasses are highly important in the glass manufacturing industry where they are used to make products that include the touch screens for cell phones and tablets. Here, the glass properties are controlled by adding network modifiers such as Mg. However, little is known about the way in which Mg effects the glass structure, so little is known about the mechanisms of plastic deformation when cover glass is scratched. We will use neutron diffraction to elucidate the structural role of Mg in aluminosilicate glasses. The results will be combined with those obtained from other experimental techniques to build realistic structural models for understanding the mechanical response of these materials to sharp contact loading.
DOI: 10.5523/bris.932lqv3akioz2jrwngdxzshfo
title: Data for Publication: Experimental Path-Following of Equilibria Using Newton's Method

description: Models, output data and images for the two-part publication Experimental Path-Following of Equilibria Using Newton's Method.
DOI: 10.21415/t5vg6x
title: CABank English SCSAE Corpus
DOI: 10.3886/e119726v1
title: Impacts of Private Prison Contracting on Inmate Time Served and Recidivism

description: This is the replication kit for "Impacts of Private Prison Contracting on Inmate Time Served and Recidivism."<br><br>
DOI: 10.5291/ill-data.6-05-976
title: Structure of GeS2 Glass by Neutron Diffraction with Isotope Substitution
DOI: 10.34740/kaggle/dsv/3328630
title: Handwriting Same Text Image
DOI: 10.17632/36jj4nysgv.1
title: The influence of dermoscopy education over 14  Supplementary Table

description: Supplementary table 1 for "The influence of dermoscopy education over 14 years at a single institution: A retrospective cohort study”
DOI: 10.6084/m9.figshare.21781547
title: data (iNOS, COX2 bladder cancer and smoking.docx

description: biomarkers expression in bladder TCC and its association with smoking
DOI: 10.6084/m9.figshare.21781547.v1
title: data (iNOS, COX2 bladder cancer and smoking.docx

description: biomarkers expression in bladder TCC and its association with smoking
DOI: 10.6084/m9.figshare.21780356.v1
title: Young Hoon Soong & Eun Sung Chung Monthly potential evapotranspiration dataset based on CMIP6 GCM (MPI-ESM1-2-LR-Future period)

description: Regridding Global CMIP6 GCMs datasets(0.5 degree) case of MPI-ESM1-2-LR <br> Composition of datasets: Average temperature, Maximum temperature, Minimum temperature, Windspeed, Relative humidity, Solar radiation <br> SSP scenarios: SSP1-2.6; SSP2-4.5; SSP3-7.0; SSP5-8.5
DOI: 10.3886/e112673v1-131500
title: Replication data for: How the West "Invented" Fertility Restriction

description: We analyze the emergence of the first socioeconomic institution in history limiting fertility: west of a line from St. Petersburg to Trieste, the European Marriage Pattern (EMP) reduced childbirths by approximately one-third between the fourteenth and eighteenth century. To explain the rise of EMP we build a two-sector model of agricultural production—grain and livestock. Women have a comparative advantage in animal husbandry. After the Black Death in 1348–1350, land abundance triggered a shift toward the pastoral sector. This improved female employment prospects, leading to later marriages. Using detailed data from England, we provide strong evidence for our mechanism.
DOI: 10.34740/kaggle/dsv/4777072
title: Elon Musk Tweets
DOI: 10.6084/m9.figshare.20522778.v1
title: TraceParticles

description: data for publication
DOI: 10.5281/zenodo.7481048
title: MUSES Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) 8-Day Global 0.05º Geographic Grid Since 1981

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FAPAR product at 0.05º spatial resolution and 8-day temporal resolution. The MUSES FAPAR product is provided on Geographic grid and spans from 1981 to 2019 (continuously updated). It was generated from the MUSES LAI product at 0.05º resolution and other ancillary information using the complement to unity of the transmittance of PAR through the entire canopy (Xiao <em>et al</em>., 2015). The MUSES FAPAR values are the instantaneous values at 10:30 am local time, close approximation of daily average PAPAR values, and they are physically consistent with the corresponding MUSES LAI values. The MUSES FAPAR product is spatially complete and temporally continuous. <strong>Dataset Characteristics:</strong> Spatial Coverage: 180º W – 180º E, 90º S – 90º N; Temporal Coverage: 1981 – 2019; Spatial Resolution: 0.05º (approximately 5 km); Temporal Resolution: 8 days; Projection: Geographic; Data Format: HDF; Scale: 0.004; Valid Range: 0 – 250. <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>., Estimating the fraction of absorbed photosynthetically active radiation from the MODIS databased GLASS leaf area index product. <em>Remote Sensing of Environment</em>, 171,105-117, 2015. Xiao Zhiqiang, <em>et al</em>., Evaluation of Three Long Time Series for Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 56, 5509-5524, 2018. Zheng Y., Xiao Z., Li J., Yang H., Song J., Evaluation of Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products at 500 m Spatial Resolution. <em>Remote Sensing</em>, 14, 3304, 2022. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7481047
title: MUSES Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) 8-Day Global 0.05º Geographic Grid Since 1981

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FAPAR product at 0.05º spatial resolution and 8-day temporal resolution. The MUSES FAPAR product is provided on Geographic grid and spans from 1981 to 2019 (continuously updated). It was generated from the MUSES LAI product at 0.05º resolution and other ancillary information using the complement to unity of the transmittance of PAR through the entire canopy (Xiao <em>et al</em>., 2015). The MUSES FAPAR values are the instantaneous values at 10:30 am local time, close approximation of daily average PAPAR values, and they are physically consistent with the corresponding MUSES LAI values. The MUSES FAPAR product is spatially complete and temporally continuous. <strong>Dataset Characteristics:</strong> Spatial Coverage: 180º W – 180º E, 90º S – 90º N; Temporal Coverage: 1981 – 2019; Spatial Resolution: 0.05º (approximately 5 km); Temporal Resolution: 8 days; Projection: Geographic; Data Format: HDF; Scale: 0.004; Valid Range: 0 – 250. <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>., Estimating the fraction of absorbed photosynthetically active radiation from the MODIS databased GLASS leaf area index product. <em>Remote Sensing of Environment</em>, 171,105-117, 2015. Xiao Zhiqiang, <em>et al</em>., Evaluation of Three Long Time Series for Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 56, 5509-5524, 2018. Zheng Y., Xiao Z., Li J., Yang H., Song J., Evaluation of Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products at 500 m Spatial Resolution. <em>Remote Sensing</em>, 14, 3304, 2022. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7481537
title: MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 0.05º Geographic Grid Since 1981

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 0.05º spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on Geographic grid and spans from 1981 to 2018 (continuously updated). It was generated from the MUSES LAI product at 0.05º resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. <strong>Dataset Characteristics:</strong> Spatial Coverage: 180º W – 180º E, 90º S – 90º N; Temporal Coverage: 1981 – 2018; Spatial Resolution: 0.05º (approximately 5 km); Temporal Resolution: 8 days; Projection: Geographic; Data Format: HDF; Scale: 0.004; Valid Range: 0 – 250. <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7481536
title: MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 0.05º Geographic Grid Since 1981

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 0.05º spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on Geographic grid and spans from 1981 to 2018 (continuously updated). It was generated from the MUSES LAI product at 0.05º resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. <strong>Dataset Characteristics:</strong> Spatial Coverage: 180º W – 180º E, 90º S – 90º N; Temporal Coverage: 1981 – 2018; Spatial Resolution: 0.05º (approximately 5 km); Temporal Resolution: 8 days; Projection: Geographic; Data Format: HDF; Scale: 0.004; Valid Range: 0 – 250. <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.6084/m9.figshare.21780356
title: Young Hoon Soong & Eun Sung Chung Monthly potential evapotranspiration dataset based on CMIP6 GCM (MPI-ESM1-2-LR-Future period)

description: Regridding Global CMIP6 GCMs datasets(0.5 degree) case of MPI-ESM1-2-LR <br> Composition of datasets: Average temperature, Maximum temperature, Minimum temperature, Windspeed, Relative humidity, Solar radiation <br> SSP scenarios: SSP1-2.6; SSP2-4.5; SSP3-7.0; SSP5-8.5
DOI: 10.6084/m9.figshare.21780356.v3
title: Young Hoon Soong & Eun Sung Chung Monthly potential evapotranspiration dataset based on CMIP6 GCM (MPI-ESM1-2-LR-Future period)

description: Regridding Global CMIP6 GCMs datasets(0.5 degree) case of MPI-ESM1-2-LR <br> Composition of datasets: Average temperature, Maximum temperature, Minimum temperature, Windspeed, Relative humidity, Solar radiation <br> SSP scenarios: SSP1-2.6; SSP2-4.5; SSP3-7.0; SSP5-8.5
DOI: 10.6084/m9.figshare.21780356.v2
title: Young Hoon Soong & Eun Sung Chung Monthly potential evapotranspiration dataset based on CMIP6 GCM (MPI-ESM1-2-LR-Future period)

description: Regridding Global CMIP6 GCMs datasets(0.5 degree) case of MPI-ESM1-2-LR <br> Composition of datasets: Average temperature, Maximum temperature, Minimum temperature, Windspeed, Relative humidity, Solar radiation <br> SSP scenarios: SSP1-2.6; SSP2-4.5; SSP3-7.0; SSP5-8.5
DOI: 10.5445/ir/1000152294
title: Result Data of the Publication "State or Market: Investments in New Nuclear Power Plants in France and Their Domestic and Cross-border Effects"
DOI: 10.5281/zenodo.7486136
title: Set of CFT files from EBSD maps

description: Set of .ctf files from EBSD maps (treated data) used in the articles Demouchy et al., submitted at European Journal Mineralogy. Demouchy, S., Thieme, M., Barou F., Beausir B., Taupin V., Cordier P., (2022-2023). Dislocation and disclination densities in experimentally deformed polycrystalline olivine. Submitted to <em>Eur. J. Min</em>., in revision (minor revision), dec 2022.
description: {"references": ["Demouchy, S.,\u00a0Thieme, M.,\u00a0Barou F., Beausir B., Taupin V., Cordier P.,\u00a0(2022-2023).\u00a0Dislocation and disclination densities\u00a0in experimentally deformed polycrystalline olivine. Submitted to\u00a0Eur. J. Min., in revision (minor revision), dec 2022..Xarchiv: https://doi.org/10.31223/X5706V"]}
DOI: 10.5281/zenodo.7486135
title: Set of CFT files from EBSD maps

description: Set of .ctf files from EBSD maps (treated data) used in the articles Demouchy et al., submitted at European Journal Mineralogy. Demouchy, S., Thieme, M., Barou F., Beausir B., Taupin V., Cordier P., (2022-2023). Dislocation and disclination densities in experimentally deformed polycrystalline olivine. Submitted to <em>Eur. J. Min</em>., in revision (minor revision), dec 2022.
description: {"references": ["Demouchy, S.,\u00a0Thieme, M.,\u00a0Barou F., Beausir B., Taupin V., Cordier P.,\u00a0(2022-2023).\u00a0Dislocation and disclination densities\u00a0in experimentally deformed polycrystalline olivine. Submitted to\u00a0Eur. J. Min., in revision (minor revision), dec 2022..Xarchiv: https://doi.org/10.31223/X5706V"]}
DOI: 10.6084/m9.figshare.20522778.v2
title: TraceParticles

description: data for publication
DOI: 10.6084/m9.figshare.20522778.v3
title: TraceParticles

description: data for publication
DOI: 10.6084/m9.figshare.20522778
title: TraceParticles

description: data for publication
DOI: 10.6084/m9.figshare.20522778.v5
title: TraceParticles

description: data for publication
DOI: 10.6084/m9.figshare.20522778.v4
title: TraceParticles

description: data for publication
DOI: 10.5445/ir/1000153725
title: 3D µCT images of specimens of carbon fiber reinforced polyamide 6 plaque, fiber orientation tensor data of these images, and three Python code files for two different algebraic and one machine learning based tensor interpolation algorithms

description: This dataset includes 3D µCT images of nine different specimen of 10 mm \times 10 mm of a carbon fiber reinforced polyamide 6 plaque produced in the long fiber reinforced thermoplastic direct (LFT-D) process. The position of the specimen in the plaque can be learned from the referenced publication (Blarr et al., Implementation and comparison of algebraic and machine learning based tensor interpolation methods applied to fiber orientation tensor fields obtained from CT images, Computational Materials Science, 2022). After small pre-processing steps, the fiber orientation tensor of each of the image stacks is determined with the help of the structure tensor based implementation of Pinter et al. The code can be found here: https://sourceforge.net/p/composight/code/HEAD/tree/trunk/SiOTo/StructureTensorOrientation/FibreOrientation/StructureTensorOrientationFilter.cxx#l186. Hence, nine .dat-files containing the fiber orientation tensor of second order are also included in this dataset. Most importantly, this dataset contains three different Python codes. The author implemented a different interpolation method in each of those codes; two algebraic and one machine learning based one. The component averaging method is the simplest; the decomposition method is mathematically more difficult. It works with the decomposition of the tensor into shape and orientation and subsequent separate invariant and quaternion weighting, before reassembling the then interpolated tensor. The deep learning based method is the only Jupyter notebook in this dataset, where an ANN is implemented for the same interpolation task. Please consider the reference paper mentioned before for details. For the visualization of the tensor glyphs, a Matlab function by Barmpoutis is used, which can be found here: https://de.mathworks.com/matlabcentral/fileexchange/27462-diffusion-tensor-field-dti-visualization.
DOI: 10.6084/m9.figshare.8248826
title: Data_Fletcher_EcologyLetters_2019

description: Data provided of Fletcher et al. Toward a unified framework for connectivity that disentangles movement and mortality in space and time. Ecology Letters.<br>Data used in Table S1 and Figure 4c, d. <br>Data files for comparing connectivity metrics to explain observed movements (move) of cactus bugs (Chelinidea vittiger) in experimental landscapes (landscape; n = 15) undergoing habitat loss and fragmentation. Movement data come from mark-resight surveys. <br>Connectivity metrics for least-cost path (lcp, lcp.mort), commute distance (commute, commute.mort), and Euclidean distance (distance) were calculated with the gdistance package in R. The spatial Absorbing Markov chain metrics (samc.d, samc.z) calculated with the samc package in R.<br>For movement analysis and comparison among metrics, movements summarized across post-treatment surveys between pairs of patches (patchi, patchj) within each landscape. <br>For abundance / life expectancy (z) correlations, population estimates for adult C. vittiger (AdultN; AdultN.SE for standard error of estimate) calculated for entire landscape for each post-treatment survey (SurveyNumbers 6:24), using methods described in Fletcher et al. (2018, Ecology). <br>
DOI: 10.25934/pr00000141.0
title: Available IPD datapackage for study 'A Randomized, Double-Blind, Parallel-Group, Placebo-Controlled Fixed Dose Study Evaluating the Efficacy and Safety of Paroxetine CR in Elderly Outpatients Diagnosed With Major Depressive Disorder'

description: Major Depressive Disorder (MDD) Study in Elderly Outpatients
DOI: 10.5281/zenodo.7486154
title: Persian Speech to Test dataset

description: The Persian Speech to Text dataset is an open source dataset for training machine learning models for the task of transcribing audio files in the Persian language into text. It is the largest open source dataset of its kind, with a size of approximately 60GB of data. The dataset consists of audio files in the WAV format and their transcripts in CSV file format. This dataset is a valuable resource for researchers and developers working on natural language processing tasks involving the Persian language, and it provides a large and diverse set of data to train and evaluate machine learning models on. The open source nature of the dataset means that it is freely available to be used and modified by anyone, making it an important resource for advancing research and development in the field.
DOI: 10.6084/m9.figshare.21781727
title: citydata.xlsx

description: this is the data of Persian version of cityBiTs <br>
DOI: 10.6084/m9.figshare.21781727.v1
title: citydata.xlsx

description: this is the data of Persian version of cityBiTs <br>
DOI: 10.6084/m9.figshare.21776351
title: POCI CSV dataset of all the citation data

description: This dataset contains all the citation data (in CSV format) included in POCI, released on 27 December 2022. In particular, each line of the CSV file defines a citation, and includes the following information: <strong>[field "oci"]</strong> the Open Citation Identifier (OCI) for the citation; <strong>[field "citing"]</strong> the PMID of the citing entity; <strong>[field "cited"]</strong> the PMID of the cited entity; <strong>[field "creation"]</strong> the creation date of the citation (i.e. the publication date of the citing entity); <strong>[field "timespan"]</strong> the time span of the citation (i.e. the interval between the publication date of the cited entity and the publication date of the citing entity); <strong>[field "journal_sc"]</strong> it records whether the citation is a journal self-citations (i.e. the citing and the cited entities are published in the same journal); <strong>[field "author_sc"]</strong> it records whether the citation is an author self-citation (i.e. the citing and the cited entities have at least one author in common).<br> This version of the dataset contains: 717,654,703 citations; 26,024,862 bibliographic resources. The size of the zipped archive is 9.6 GB, while the size of the unzipped CSV file is 50 GB. Additional information about POCI at official webpage.
DOI: 10.6084/m9.figshare.21776366
title: POCI N-Triples dataset of all the citation data

description: This dataset contains all the citation data (in N-Triples format) included in POCI, released on 27 December 2022. In particular, any citation in the dataset, defined as an individual of the class <em>cito:Citation</em>, includes the following information: <strong>[citation IRI]</strong> the Open Citation Identifier (OCI) for the citation, defined in the final part of the URL identifying the citation (https://w3id.org/oc/index/coci/ci/[OCI]); <strong>[property "cito:hasCitingEntity"]</strong> the citing entity identified by its PMID URL (https://pubmed.ncbi.nlm.nih.gov/[PMID]); <strong>[property "cito:hasCitedEntity"]</strong> the cited entity identified by its PMID URL (https://pubmed.ncbi.nlm.nih.gov/[PMID]); <strong>[property "cito:hasCitationCreationDate"]</strong> the creation date of the citation (i.e. the publication date of the citing entity); <strong>[property "cito:hasCitationTimeSpan"]</strong> the time span of the citation (i.e. the interval between the publication date of the cited entity and the publication date of the citing entity); <strong>[type "cito:JournalSelfCitation"]</strong> it records whether the citation is a journal self-citations (i.e. the citing and the cited entities are published in the same journal); <strong>[type "cito:AuthorSelfCitation"]</strong> it records whether the citation is an author self-citation (i.e. the citing and the cited entities have at least one author in common). This version of the dataset contains: 717,654,703 citations; 26,024,862 bibliographic resources. The size of the zipped archive is 25 GB, while the size of the unzipped N-Triples file is 610 GB. Additional information about POCI can be retrieved in the official webpage.