cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5562985
title: Reconstruction of Mediterranean sea-level changes and contributions for 1960-2018

description: This data set contains gridded estimates of relative sea-level changes and their instantaneous rates for 1960-2018 in the Mediterranean Sea, separated into the individual contributions of: Sterodynamic changes (i.e., ocean dynamics and thermal expansion). Contemporary GRD (i.e., changes in Earth gravity, Earth rotation, and solid-earth deformation due to land-mass changes). GIA (i.e., glacial isostatic adjustment). Short-term variability (interannual to decadal). These estimates have been obtained by combining tide gauge observations and satellite altimetry data with fingerprints of land-mass changes (from Frederikse et al., Nature 2020) using a spatiotemporal Bayesian hierarchical model. This work has been carried out within the framework of the EuroSea project funded by European Union’s Horizon 2020 research and innovation programme under grant agreement No 862626.
DOI: 10.5281/zenodo.6797928
title: Reconstruction of Mediterranean sea-level changes and contributions for 1960-2018

description: <strong>Data supporting the paper:</strong> <strong>Calafat, F. M., Frederikse, T., and Horsburgh, K. (2022). The Sources of Sea-Level Changes in the Mediterranean Sea since 1960, Journal of Geophysical Research Oceans, under review.</strong> Please cite the paper above when using this data set. This new version of the data set has been published to support the paper above and includes more data than the previous version as well as several improvements and refinements. <em>Data description:</em> <strong>Bayesian_estimates_Mediterranean_sea_level.nc:</strong> this file contains gridded estimates of relative sea-level changes and their instantaneous rates for 1960-2018 in the Mediterranean Sea, separated into the individual contributions of: Sterodynamic changes (i.e., ocean dynamics and thermal expansion). Contemporary GRD (i.e., changes in Earth gravity, Earth rotation, and solid-earth deformation due to land-mass changes). GIA (i.e., glacial isostatic adjustment). Short-term variability (interannual to decadal). <strong>data_input.mat:</strong> this file contains all of the data needed to run the Bayesian hierarchical model. This includes the observational data from tide gauges and satellite altimetry as well as the ensemble-mean and ensemble covariance matrices for the sea-level fingerprints associated with contemporary GRD effects and GIA. The Bayesian estimates have been obtained using a spatiotemporal Bayesian hierarchical model (see paper). This work has been carried out within the framework of the EuroSea project funded by European Union’s Horizon 2020 research and innovation programme under grant agreement No 862626.
DOI: 10.5281/zenodo.5562984
title: Reconstruction of Mediterranean sea-level changes and contributions for 1960-2018

description: <strong>Data supporting the paper:</strong> <strong>Calafat, F. M., Frederikse, T., and Horsburgh, K. (2022). The Sources of Sea-Level Changes in the Mediterranean Sea since 1960, Journal of Geophysical Research Oceans, under review.</strong> Please cite the paper above when using this data set. This new version of the data set has been published to support the paper above and includes more data than the previous version as well as several improvements and refinements. Version 2.3 has been created to include regional estimates of rates due to the inverse barometer effect. <em>Data description:</em> <strong>Bayesian_estimates_Mediterranean_sea_level.nc:</strong> this file contains gridded estimates of relative sea-level changes and their instantaneous rates for 1960-2018 in the Mediterranean Sea, separated into the individual contributions of: Sterodynamic changes (i.e., ocean dynamics and thermal expansion). Contemporary GRD (i.e., changes in Earth gravity, Earth rotation, and solid-earth deformation due to land-mass changes). GIA (i.e., glacial isostatic adjustment). Short-term variability (interannual to decadal). Inverse barometer effect. <strong>data_input.mat:</strong> this file contains all of the data needed to run the Bayesian hierarchical model. This includes the observational data from tide gauges and satellite altimetry as well as the ensemble-mean and ensemble covariance matrices for the sea-level fingerprints associated with contemporary GRD effects and GIA. The Bayesian estimates have been obtained using a spatiotemporal Bayesian hierarchical model (see paper). This work has been carried out within the framework of the EuroSea project funded by European Union’s Horizon 2020 research and innovation programme under grant agreement No 862626.
DOI: 10.5281/zenodo.7015246
title: Reconstruction of Mediterranean sea-level changes and contributions for 1960-2018

description: <strong>Data supporting the paper:</strong> <strong>Calafat, F. M., Frederikse, T., and Horsburgh, K. (2022). The Sources of Sea-Level Changes in the Mediterranean Sea since 1960, Journal of Geophysical Research Oceans, under review.</strong> Please cite the paper above when using this data set. This new version of the data set has been published to support the paper above and includes more data than the previous version as well as several improvements and refinements. Version 2.1 has been created to include estimates of rates due to small-scale processes. <em>Data description:</em> <strong>Bayesian_estimates_Mediterranean_sea_level.nc:</strong> this file contains gridded estimates of relative sea-level changes and their instantaneous rates for 1960-2018 in the Mediterranean Sea, separated into the individual contributions of: Sterodynamic changes (i.e., ocean dynamics and thermal expansion). Contemporary GRD (i.e., changes in Earth gravity, Earth rotation, and solid-earth deformation due to land-mass changes). GIA (i.e., glacial isostatic adjustment). Short-term variability (interannual to decadal). <strong>data_input.mat:</strong> this file contains all of the data needed to run the Bayesian hierarchical model. This includes the observational data from tide gauges and satellite altimetry as well as the ensemble-mean and ensemble covariance matrices for the sea-level fingerprints associated with contemporary GRD effects and GIA. The Bayesian estimates have been obtained using a spatiotemporal Bayesian hierarchical model (see paper). This work has been carried out within the framework of the EuroSea project funded by European Union’s Horizon 2020 research and innovation programme under grant agreement No 862626.
DOI: 10.5281/zenodo.7046682
title: Reconstruction of Mediterranean sea-level changes and contributions for 1960-2018

description: <strong>Data supporting the paper:</strong> <strong>Calafat, F. M., Frederikse, T., and Horsburgh, K. (2022). The Sources of Sea-Level Changes in the Mediterranean Sea since 1960, Journal of Geophysical Research Oceans, under review.</strong> Please cite the paper above when using this data set. This new version of the data set has been published to support the paper above and includes more data than the previous version as well as several improvements and refinements. Version 2.2 has been created to include estimates of rates due to the inverse barometer effect. <em>Data description:</em> <strong>Bayesian_estimates_Mediterranean_sea_level.nc:</strong> this file contains gridded estimates of relative sea-level changes and their instantaneous rates for 1960-2018 in the Mediterranean Sea, separated into the individual contributions of: Sterodynamic changes (i.e., ocean dynamics and thermal expansion). Contemporary GRD (i.e., changes in Earth gravity, Earth rotation, and solid-earth deformation due to land-mass changes). GIA (i.e., glacial isostatic adjustment). Short-term variability (interannual to decadal). Inverse barometer effect. <strong>data_input.mat:</strong> this file contains all of the data needed to run the Bayesian hierarchical model. This includes the observational data from tide gauges and satellite altimetry as well as the ensemble-mean and ensemble covariance matrices for the sea-level fingerprints associated with contemporary GRD effects and GIA. The Bayesian estimates have been obtained using a spatiotemporal Bayesian hierarchical model (see paper). This work has been carried out within the framework of the EuroSea project funded by European Union’s Horizon 2020 research and innovation programme under grant agreement No 862626.
DOI: 10.6084/m9.figshare.20173208
title: ELISA Plate IDs.xlsx

description: ELISA plate results from the 15 participants included in the study for salivary cortisol vs. occupational stress.
DOI: 10.34740/kaggle/dsv/4156720
title: Tales from the Pizza Guy
DOI: 10.15151/esrf-es-897133091
title: The Netherlands BAG

description: The Netherlands BAG includes groups situated at five Dutch universities (Utrecht, Groningen, Amsterdam, Wageningen and Eindhoven) and at the Netherlands Cancer Institute in Amsterdam, with a total of twelve PIs. The structural biology research carried out in these groups is highly diverse, including prokaryotic and eukaryotic proteins with a strong focus on targets and processes of biomedical relevance. Central to many projects are protein receptors and their interactions with other proteins and with small molecule modulators. An additional major research area concerns structure-based protein engineering and design aiming at the characterization and engineering of new enzymes for green chemistry and new-to-nature reactions. Projects deal with membrane and soluble proteins, cell surface receptors, and protein-protein, protein-ribonucleotide, protein-drug and enzyme-ligand complexes.
DOI: 10.3886/e179201
title: Supporting young children with disabilities during the COVID19 pandemic: Evidence from caregivers in Virginia

description: The READ ME file describes the data source and analysis file for producing the tables and figures in Supporting young children with disabilities during the COVID19 pandemic: Evidence from caregivers in Virginia<br><br>Abstract:<br>Early care and education (ECE) experiences shape children’s developmental trajectories, particularly for children who have or may have disabilities. However, caregivers of children with disabilities have faced considerable challenges finding care for their children, which have increased during the COVID19 pandemic. Using survey data from nearly 3,000 caregivers of preschool age children in Virginia collected in December 2020-January 2021, we find that caregivers of children with disabilities were less likely to find ECE programs that met their needs, more likely to experience high-levels of stress, and more likely to be concerned about their child’s development than were caregivers of children without disabilities. Concerns about child development were particularly pronounced among caregivers of children with disabilities in remote/hybrid instructional settings. Our findings suggest a disproportionate impact of ECE disruption on caregivers of children with disabilities and the need for targeted supports for these families moving forward.
DOI: 10.6084/m9.figshare.20870488
title: chlorophytum_comosum_zanfona_1.fasta.gz

description: .......
DOI: 10.6084/m9.figshare.20870488.v1
title: chlorophytum_comosum_zanfona_1.fasta.gz

description: .......
DOI: 10.5281/zenodo.7045870
title: Mapping of new style MassBank Accessions to wikidata

description: The MassBank Accession schema changed with MassBank Record Format 2.6.0. This dataset maps the existing wikidata entries by 220831-16:13 to the new accession format.
DOI: 10.5281/zenodo.7045869
title: Mapping of new style MassBank Accessions to wikidata

description: The MassBank Accession schema changed with MassBank Record Format 2.6.0. This dataset maps the existing wikidata entries by 220831-16:13 to the new accession format.
DOI: 10.6084/m9.figshare.3761562.v2330
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.5281/zenodo.7556060
title: Relocated earthquake catalog using DiTing

description: Relocated earthquake catalog from 2021/08/01 to 2022/09/20 using the DiTingPicker method
DOI: 10.5281/zenodo.7556059
title: Relocated earthquake catalog using DiTing

description: Relocated earthquake catalog from 2021/08/01 to 2022/09/20 using the DiTingPicker method
DOI: 10.6084/m9.figshare.21936372
title: Misinformation and Trust

description: Data from pooled cross-national surveys conducted in 2019 and 2021
DOI: 10.5517/ccdc.csd.cc2czyq1
title: CCDC 2204494: Experimental Crystal Structure Determination

description: Related Article: Jun Fan, Ming-Chung Yang, Ming-Der Su, Cheuk-Wai So|2023|Inorg.Chem.|62|863|doi:10.1021/acs.inorgchem.2c03660
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2czyr2
title: CCDC 2204495: Experimental Crystal Structure Determination

description: Related Article: Jun Fan, Ming-Chung Yang, Ming-Der Su, Cheuk-Wai So|2023|Inorg.Chem.|62|863|doi:10.1021/acs.inorgchem.2c03660
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2czys3
title: CCDC 2204496: Experimental Crystal Structure Determination

description: Related Article: Jun Fan, Ming-Chung Yang, Ming-Der Su, Cheuk-Wai So|2023|Inorg.Chem.|62|863|doi:10.1021/acs.inorgchem.2c03660
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2czyt4
title: CCDC 2204497: Experimental Crystal Structure Determination

description: Related Article: Jun Fan, Ming-Chung Yang, Ming-Der Su, Cheuk-Wai So|2023|Inorg.Chem.|62|863|doi:10.1021/acs.inorgchem.2c03660
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.21227/gg9z-0802
title: Change Detection Dataset

description:   As a common dataset for change detection, its image can be divided into three parts: the image before the change, the image after the change, and the label image showing the changed area. This dataset is characterized by significant seasonal differences between bi-temporal image pairs, which makes up for some of the deficiencies in existing datasets. The labels for this dataset include some irregular changes, such as the appearance and disappearance of cars; but do not include seasonal changes, such as changes in the ground surface caused by snowfall.
DOI: 10.6084/m9.figshare.21913752.v1
title: PBW_SNP_DArTseq.csv

description: This dataset contains genome-wide SNPs for pink bollworm (<em>Pectinophora gossypiella</em>) samples from Australia, India, Pakistan, and U.S.. SNP data was generated by Diversity Arrays Technology, Canberra.
DOI: 10.6084/m9.figshare.21913752
title: Pink bollworm (Pectinophora gossypiella) SNP data (DArTseq)

description: This dataset contains genome-wide SNPs for pink bollworm (<em>Pectinophora gossypiella</em>) samples from Australia, India, the U.S., and Pakistan. SNP data was generated by Diversity Arrays Technology, Canberra.
DOI: 10.6084/m9.figshare.21913752.v3
title: Pink bollworm (Pectinophora gossypiella) SNP data (DArTseq)

description: This dataset contains genome-wide SNPs for pink bollworm (<em>Pectinophora gossypiella</em>) samples from Australia, India, the U.S., and Pakistan. SNP data was generated by Diversity Arrays Technology, Canberra.
DOI: 10.6084/m9.figshare.21913752.v2
title: Pink bollworm (Pectinophora gossypiella) SNP data (DArTseq)Pink bollworm

description: This dataset contains genome-wide SNPs for pink bollworm (<em>Pectinophora gossypiella</em>) samples from Australia, India, the U.S., and Pakistan. SNP data was generated by Diversity Arrays Technology, Canberra.
DOI: 10.5281/zenodo.7552930
title: MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2004 (185–361)

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 250m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 250m 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. This dataset is the MUSES FVC product in 2004 (185–361). <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2004 (001–177)</strong> and <em><strong>click here</strong> to download the MUSES FVC produ</em>ct <strong>in 2005 (001–177)</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2004 (185–361) Spatial Resolution: 250m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 100 <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.7552929
title: MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2004 (185–361)

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 250m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 250m 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. This dataset is the MUSES FVC product in 2004 (185–361). <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2004 (001–177)</strong> and <em><strong>click here</strong> to download the MUSES FVC produ</em>ct <strong>in 2005 (001–177)</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2004 (185–361) Spatial Resolution: 250m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 100 <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: 10.13011/m3-3agy-7y28
title: Optimization of Laser-Patterned Electrode Architectures for Fast Charging of Li-ion Batteries Using Simulations Parameterized by Machine Learning
DOI: 10.5517/ccdc.csd.cc2d5761
title: CCDC 2209562: Experimental Crystal Structure Determination

description: Related Article: Kingshuk Debsharma, Sunanda Dey, Soumya Jyoti Ghosh, Suvendu Maity, Chittaranjan Sinha|2023|J.Indian Chem.Soc.|100|100878|doi:10.1016/j.jics.2023.100878
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d5772
title: CCDC 2209563: Experimental Crystal Structure Determination

description: Related Article: Kingshuk Debsharma, Sunanda Dey, Soumya Jyoti Ghosh, Suvendu Maity, Chittaranjan Sinha|2023|J.Indian Chem.Soc.|100|100878|doi:10.1016/j.jics.2023.100878
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2b9dbd
title: CCDC 2153983: Experimental Crystal Structure Determination

description: Related Article: Jia-Wei Wang, Fan Ma, Tao Jin, Piao He, Zhi-Mei Luo, Stephan Kupfer, Michael Karnahl, Fengyi Zhao, Zihao Xu, Tao Jin, Tianquan Lian, Yong-Liang Huang, Long Jiang, Li-Zhi Fu, Gangfeng Ouyang, Xiao-Yi Yi|2022|J.Am.Chem.Soc.|145|676|doi:10.1021/jacs.2c11740
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2df703
title: CCDC 2217244: Experimental Crystal Structure Determination

description: Related Article: Jia-Wei Wang, Fan Ma, Tao Jin, Piao He, Zhi-Mei Luo, Stephan Kupfer, Michael Karnahl, Fengyi Zhao, Zihao Xu, Tao Jin, Tianquan Lian, Yong-Liang Huang, Long Jiang, Li-Zhi Fu, Gangfeng Ouyang, Xiao-Yi Yi|2022|J.Am.Chem.Soc.|145|676|doi:10.1021/jacs.2c11740
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.