cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc25bmv0
title: CCDC 2006222: Experimental Crystal Structure Determination

description: Related Article: José A.F. de Oliveira, Geovana G. Terra, Thiago G. Costa, Bruno Szpoganicz, Priscila P. Silva-Caldeira, Ívina P. de Souza, Elene C. Pereira-Maia, Adailton J. Bortoluzzi|2023|J.Inorg.Biochem.|239|112087|doi:10.1016/j.jinorgbio.2022.112087
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.cc2cx64n
title: CCDC 2201841: Experimental Crystal Structure Determination

description: Related Article: Jonathan Bould, Oleg Tok, William Clegg, Michael G.S. Londesborough, Miroslava Litecká, Marcel Ehn|2023|Inorg.Chim.Acta|547|121341|doi:10.1016/j.ica.2022.121341
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.cc2cz74r
title: CCDC 2203794: Experimental Crystal Structure Determination

description: Related Article: Jonathan Bould, Oleg Tok, William Clegg, Michael G.S. Londesborough, Miroslava Litecká, Marcel Ehn|2023|Inorg.Chim.Acta|547|121341|doi:10.1016/j.ica.2022.121341
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.cc2db2tn
title: CCDC 2214231: Experimental Crystal Structure Determination

description: Related Article: Pheello Nkoe, Amanda-Lee E. Manicum, Hitler Louis, Frederick P. Malan, Wakopo J. Nzondomyo, Kelechi Chukwuemeka, Sibusiso A. Sithole, Ann Imojara, Chioma M. Chima, Ernest C. Agwamba, Tomsmith O. Unimuke|2023|J.Mol.Liq.|370|121045|doi:10.1016/j.molliq.2022.121045
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: 10.6084/m9.figshare.21782270
title: Training Dataset

description: The generated dataset used to train and evaluate the models.
DOI: 10.6084/m9.figshare.21782270.v1
title: Training Dataset

description: The generated dataset used to train and evaluate the models.
DOI: 10.5281/zenodo.5109403
title: Data for GRL - NJU-CPOL dataset

description: NJU-CPOL dataset including variables of reflectivity (dBZ), differential reflectivity (ZDR) and specific differential phase (KDP), at height of 3 km CAPPI.
DOI: 10.5281/zenodo.5109402
title: Data for GRL - NJU-CPOL dataset

description: NJU-CPOL dataset including variables of reflectivity (dBZ), differential reflectivity (ZDR) and specific differential phase (KDP), at height of 3 km CAPPI.
DOI: 10.5281/zenodo.7486397
title: Data for GRL - NJU-CPOL dataset

description: NJU-CPOL dataset including variables of reflectivity (dBZ), differential reflectivity (ZDR) and specific differential phase (KDP), at height of 3 km CAPPI.
DOI: 10.5281/zenodo.7483388
title: Large animal model of chronic thromboembolic pulmonary hypertension

description: Echocardiographic, histological and mRNA expression data.
DOI: 10.5281/zenodo.7483387
title: Large animal model of chronic thromboembolic pulmonary hypertension

description: Echocardiographic, histological and mRNA expression data.
DOI: 10.6084/m9.figshare.21782285.v1
title: Properties of sustainable eco-friendly concrete made with pulverized pond ash: A feasibility study

description: Authors declares all necessary data and materials are available and mentioned in the manuscript.
DOI: 10.6084/m9.figshare.21782285
title: Properties of sustainable eco-friendly concrete made with pulverized pond ash: A feasibility study

description: Authors declares all necessary data and materials are available and mentioned in the manuscript.
DOI: 10.5066/p9li9xy7
title: Supplemental Data: Estimated effects of pumping on groundwater storage and Walker River stream efficiencies in Smith and Mason Valleys, west-central Nevada

description: This USGS data release consists of 15 geospatial datasets for Smith Valley and Mason Valley, west-central Nevada, including contours representing groundwater levels based on depth-to-water measurements, groundwater-level change rasters calculated from groundwater-level contours, groundwater-level change rasters calculated from depth-to-water point values, and groundwater monitoring well locations with groundwater-level data used to develop groundwater-level contours. Groundwater-level contours representing conditions in 1970 (Huxel and Harris, 1969; Rush and Schroer, 1976) and 2006 (Lopes and Allander, 2009) were compiled from previous studies and revised with minor adjustments. Two additional groundwater-level contour datasets representing conditions in 1995 and 2020 were generated for this study. Additionally, this data release includes 2 tabular datasets of compiled surface-water diversions, USGS streamflow discharge, and calculated stream efficiency values for the East Walker, West Walker, and Walker Rivers in Smith and Mason Valleys, Nevada. Geospatial datasets include: 1) Revised groundwater-level contours for Smith Valley and Mason Valley, Nevada, 1970 2) Groundwater-level contours for Smith Valley and Mason Valley, Nevada, 1995 3) Revised groundwater-level contours for Smith Valley and Mason Valley, Nevada, 2006 4) Groundwater-level contours for Smith Valley and Mason Valley, Nevada, 2020 5) Groundwater-level change for Smith Valley and Mason Valley, Nevada, 1970-1995, approach 1 6) Groundwater-level change for Smith Valley and Mason Valley, Nevada, 1996-2006, approach 1 7) Groundwater-level change for Smith Valley and Mason Valley, Nevada, 2007-2020, approach 1 8) Groundwater-level change for Smith Valley and Mason Valley, Nevada, 1970-2020, approach 1 9) Groundwater-level change for Smith Valley and Mason Valley, Nevada, 1970-1995, approach 2 10) Groundwater-level change for Smith Valley and Mason Valley, Nevada, 1996-2006, approach 2 11) Groundwater-level change for Smith Valley and Mason Valley, Nevada, 1996-2006, approach 2 12) Groundwater-level change for Smith Valley and Mason Valley, Nevada, 2007-2020, approach 2 13) Common extent for estimating groundwater-level and groundwater storage-volume change, Smith Valley and Mason Valley, Nevada 14) Groundwater monitoring well locations and median water-level measurements used to develop groundwater-level contours for Smith Valley and Mason Valley, Nevada, 1995 15) Groundwater monitoring well locations and median water-level measurements used to develop groundwater-level contours for Smith Valley and Mason Valley, Nevada, 2020 Tabular datasets include: 16) Surface-water diversions from the East Walker, West Walker, and Walker Rivers in Smith and Mason Valleys, Nevada, 1931-2020 17) Streamflow discharge and stream efficiency for the East Walker, West Walker, and Walker Rivers in Smith and Mason Valleys, Nevada, 1948-2020
DOI: 10.5066/p9vhwtyc
title: Laboratory and Field Data for a Performance Evaluation of the PhytoFind, an In-Place Phytoplankton Classification Tool

description: This data release presents the laboratory and field results of a performance evaluation conducted on the Turner Designs, Inc., PhytoFind, an in-place phytoplankton classification tool. The laboratory evaluation included tests that were designed to characterize PhytoFind performance under a range of conditions that may affect phytoplankton fluorescence and sensor response. Laboratory tests included a range of phytoplankton types, morphologies, and biomass; water temperatures; and matrix challenges. Laboratory evaluations on the PhytoFind were performed at U.S. Geological Survey offices in Orlando, Florida and Troy, New York on various dates from July to November 2019. Data from a moving-boat survey was designed to assess the effect of dissolved organic matter (DOM) on instrument function and the efficacy of the integrated DOM compensation feature. The PhytoFind was exposed to both freshwater and estuarine environments during the moving-boat survey, which was conducted from July 15 - 19, 2019 in south Florida along the Okeechobee Waterway. Laboratory data included in this data release: PhytoFind measurements, other ancillary sensor measurements, laboratory-extract chlorophyll-a concentrations, phytoplankton enumeration and classification data, and other ancillary laboratory data for each experiment. Moving-boat survey data included in this data release: PhytoFind measurements and other ancillary information.�
DOI: 10.5281/zenodo.7197572
title: Code and data from "Mother cells control daughter cell proliferation in intestinal organoids to minimize proliferation fluctuations"

description: Includes the microscopy images, cell tracking data and scripts used in the publication Huelsz-Prince, Guizela, et al. "Mother cells control daughter cell proliferation in intestinal organoids to minimize proliferation fluctuations." <em>eLife </em> 11:e80682 (2022). https://doi.org/10.7554/eLife.80682 .
description: {"references": ["Huelsz-Prince, Guizela, et al. \"Mother cells control daughter cell proliferation in intestinal organoids to minimize proliferation fluctuations.\" eLife 11:e80682 (2022). https://doi.org/10.7554/eLife.80682 ."]}
DOI: 10.5281/zenodo.7197573
title: Code and data from "Mother cells control daughter cell proliferation in intestinal organoids to minimize proliferation fluctuations"

description: Includes the microscopy images, cell tracking data and scripts used in the publication Huelsz-Prince, Guizela, et al. "Mother cells control daughter cell proliferation in intestinal organoids to minimize proliferation fluctuations." <em>eLife </em> 11:e80682 (2022). https://doi.org/10.7554/eLife.80682 .
description: {"references": ["Huelsz-Prince, Guizela, et al. \"Mother cells control daughter cell proliferation in intestinal organoids to minimize proliferation fluctuations.\" eLife 11:e80682 (2022). https://doi.org/10.7554/eLife.80682 ."]}
DOI: 10.6084/m9.figshare.21782327
title: screening for bio-orthogonal click reactions

description: This item consists of the auxiliary data for https://arxiv.org/abs/2212.07621. It contains the .xyz and .log files for all the reaction profiles computed as part of the active learning procedure, as well as the computed QM descriptors for all the species featured in the search space. For more context, see the original paper.
DOI: 10.6084/m9.figshare.21782327.v1
title: screening for bio-orthogonal click reactions

description: This item consists of the auxiliary data for https://arxiv.org/abs/2212.07621. It contains the .xyz and .log files for all the reaction profiles computed as part of the active learning procedure, as well as the computed QM descriptors for all the species featured in the search space. For more context, see the original paper.
DOI: 10.6084/m9.figshare.20627052
title: Variability in training unlocks generalization in visual perceptual learning through invariant representations

description: Human behavioral data from "<strong>Variability in training unlocks generalization in visual perceptual learning through invariant representations</strong>" by Giorgio L. Manenti, Aslan Satary Dizaji and Caspar M. Schwiedrzik <br> <br> <strong>longformatHumData.csv</strong> contains: 112x[subjID dayID averagePerf groupID fractionCCatReference]. dayID is 1= baseline, 2=last training, 3=transfer 1, 4= transfer 2. GroupID is 1=high precision low variability, 2=high precision high variability, 3=low precision low variability, 4=low precision high variability.<br> <strong>longformatSFpilot.csv</strong> contains: 56x[subjID, SF_ID averagePerf]. SF_ID is SF in cpd. <br> <strong>longformat_FigureS2.csv</strong> contains: 112x[subjID dayID averagePerf groupID]. dayID ist 1= baseline, 2=last training, 3=trainsfer SF only, 4= transfer SF and Loc. GroupID 1=high precision low variability, 2=high precision high variability, 3=low precision low variability, 4=low precision high variability. <br> <strong>location_preTest.csv</strong> contains: 980x[subjID trialID (correct/wrong)_location1 (correct/wrong)_location2 groupID]. GroupID 1=high precision low variability, 2=high precision high variability, 3=low precision low variability, 4=low precision high variability. Location1 = training location of main experiment; Location 2 is transfer location of main experiment. N.B. 7 trails per subject are eliminated as reference stimulus was shown. <br> <strong>learningcurveThresholds.csv</strong> contains: 112x[subjID dayID threshold groupID]. dayID ist 1= baseline, 2=last training, 3=trainsfer SF only, 4= transfer SF and Loc. GroupID 1=high precision low variability, 2=high precision high variability, 3=low precision low variability, 4=low precision high variability. <br> <strong>adaptation.csv</strong> contains: 112x[subjID dayID adaptation groupID]. dayID ist 1= baseline day , 2=last training day. Adaptation= Accuracy( 3rd third – 2nd third) GroupID 1=high precision low variability, 2=high precision high variability, 3=low precision low variability, 4=low precision high variability. <br> Please contact C. M. Schwiedrzik (c.schwiedrzik@eni-g.de) in case you have any questions.
DOI: 10.6084/m9.figshare.20627052.v1
title: Variability in training unlocks generalization in visual perceptual learning through invariant representations

description: Human behavioral data from "<strong>Variability in training unlocks generalization in visual perceptual learning through invariant representations</strong>" by Giorgio L. Manenti, Aslan Satary Dizaji and Caspar M. Schwiedrzik <br> <br> <strong>longformatHumData.csv</strong> contains: 112x[subjID dayID averagePerf groupID fractionCCatReference]. dayID is 1= baseline, 2=last training, 3=transfer 1, 4= transfer 2. GroupID is 1=high precision low variability, 2=high precision high variability, 3=low precision low variability, 4=low precision high variability.<br> <strong>longformatSFpilot.csv</strong> contains: 56x[subjID, SF_ID averagePerf]. SF_ID is SF in cpd. <br> <strong>longformat_FigureS2.csv</strong> contains: 112x[subjID dayID averagePerf groupID]. dayID ist 1= baseline, 2=last training, 3=trainsfer SF only, 4= transfer SF and Loc. GroupID 1=high precision low variability, 2=high precision high variability, 3=low precision low variability, 4=low precision high variability. <br> <strong>location_preTest.csv</strong> contains: 980x[subjID trialID (correct/wrong)_location1 (correct/wrong)_location2 groupID]. GroupID 1=high precision low variability, 2=high precision high variability, 3=low precision low variability, 4=low precision high variability. Location1 = training location of main experiment; Location 2 is transfer location of main experiment. N.B. 7 trails per subject are eliminated as reference stimulus was shown. <br> <strong>learningcurveThresholds.csv</strong> contains: 112x[subjID dayID threshold groupID]. dayID ist 1= baseline, 2=last training, 3=trainsfer SF only, 4= transfer SF and Loc. GroupID 1=high precision low variability, 2=high precision high variability, 3=low precision low variability, 4=low precision high variability. <br> <strong>adaptation.csv</strong> contains: 112x[subjID dayID adaptation groupID]. dayID ist 1= baseline day , 2=last training day. Adaptation= Accuracy( 3rd third – 2nd third) GroupID 1=high precision low variability, 2=high precision high variability, 3=low precision low variability, 4=low precision high variability. <br> Please contact C. M. Schwiedrzik (c.schwiedrzik@eni-g.de) in case you have any questions.
DOI: 10.5066/p95wyhhe
title: Near-Surface Spatial Water-Quality Surveys along the Caloosahatchee River, St. Lucie River and Lake Okeechobee in July and August 2019, south Florida

description: This U.S. Geological Survey (USGS) Data Release provides spatial water-quality data collected from the Caloosahatchee River, St. Lucie River and Lake Okeechobee July 15-18 and August 19-21 of 2019, south Florida. Geo-referenced measurements of near surface water temperature, specific conductance, dissolved oxygen, pH, turbidity, chlorophyll fluorescence, phycocyanin fluorescence, and fluorescence of dissolved organic matter were recorded at 5 second intervals and nitrate+nitrite was recorded at 1 minute intervals during water-quality surveys in order to create high resolution water-quality maps of the study area.
DOI: 10.5066/p9m9joaf
title: Phytoplankton Community Composition and Abundance in Lake Okeechobee and the Okeechobee Waterway, Florida, USA, July and August 2019

description: This data release contains phytoplankton classification and enumeration results from two moving boat surveys conducted on July 15 - 18, 2019 and August 19 - 22, 2019. These data were collected from the Caloosahatchee River and estuary, the St. Lucie River and estuary, and Lake Okeechobee with funding provided by the USGS Priority Ecosystems Greater Everglades Program.
DOI: 10.6084/m9.figshare.21695432.v1
title: Data about perceived stress, PTSD symptoms and defense mechanisms during the COVID-19 pandemic

description: Dataset linked to a Data in Brief article.
DOI: 10.34740/kaggle/dsv/4778433
title: Mahanagar Yatayat Dataset
DOI: 10.6084/m9.figshare.21782516
title: e.vcf

description: <strong>Population structure and selective signature of sheep around the Tarim Basin</strong>
DOI: 10.6084/m9.figshare.21782516.v1
title: e.vcf

description: <strong>Population structure and selective signature of sheep around the Tarim Basin</strong>
DOI: 10.6084/m9.figshare.18586136.v2
title: Chinese hydropower database, HP model and outputs

description: You are welcome to use and find errors in the database. I hope you can inform me of any errors you find by sending me an email titled "Hydropower database errors" to meviolet@126.com. <br> 1. Chinese Hydropower Database: Sheet "CHD" is the hydropower database in mainland China, which comprises 3591 (3646 in the updated version 2022.06.14) hydropower plant records spread across 29 provincial-level divisions; Sheet "Attribute description" describes attributes provided in the CHD database. <br> 2. HP model: R script for simulating mothly hydroelectricity production based on monthly gridded streamflow series and CHD <br> 3. CSV files: The outputs of HP model with streamflow from different model combinations, monthly HP from 1971-2099 for each conventional plant record.
DOI: 10.3886/e183323
title: It’s Complicated: Everyday Discrimination Across the Transition into Adulthood

description: Experiences during emerging adulthood hold particular importance for future health and economic well-being. This study uses Panel Study of Income Dynamics Transition into Adulthood Supplement data to explore growth trajectories of perceived discrimination across the transition into adulthood and to examine how gendered racialized group status might shape perceived discrimination among U.S. emerging adult men and women racialized as white, non-Hispanic, Black, non-Hispanic, or Hispanic (n=2,532) using multilevel models. Results showed that as emerging adults age there is a decreasing pattern of perceived everyday discrimination, though there were individual differences in the rate and direction of change. Women racialized as Black, non-Hispanic had significantly lower perceived discrimination than all other groups except Hispanic women. Each of the six gendered racialized subgroups had similar patterns of perceived discrimination over time. More research is needed to better understand differences in the rate and direction of change in perceived discrimination over time.<br><br><br><br><br>
description: Presence of Common Scales: Everyday Discrimination Scale<br>
DOI: 10.5281/zenodo.7478423
title: Data for "Clouds increasingly influence Arctic sea surface temperatures as CO2 rises" part 2

description: Data for "Clouds increasingly influence Arctic sea surface temperatures as CO2 rises" submitted to Geophysical Research Letters. Data are included from three fully-coupled CESM2 simulations with variable CO2 concentrations: pre-industrial climate ('control'), 424 ppm CO2 ('yr40'), and 1139 ppm CO2 ('yr140'). Data in all files are restricted to 40-90<sup>o</sup>N. Variables include surface temperature (TS), sea ice concentration (ICEFRAC), CALIPSO total cloud fraction (CLDTOT_CAL), total grid cell cloud liquid water path (TGCLDLWP), sea surface temperature (SST), surface downwelling shortwave radiation (FSDS), surface downwelling longwave radiation (FLDS), surface net shortwave radiation (FSNS), clear-sky surface net shortwave radiation (FSNSC), and clear-sky surface downwelling longwave radiation (FLDSC).
DOI: 10.5281/zenodo.7478422
title: Data for "Clouds increasingly influence Arctic sea surface temperatures as CO2 rises" part 2

description: Data for "Clouds increasingly influence Arctic sea surface temperatures as CO2 rises" submitted to Geophysical Research Letters. Data are included from three fully-coupled CESM2 simulations with variable CO2 concentrations: pre-industrial climate ('control'), 424 ppm CO2 ('yr40'), and 1139 ppm CO2 ('yr140'). Data in all files are restricted to 40-90<sup>o</sup>N. Variables include surface temperature (TS), sea ice concentration (ICEFRAC), CALIPSO total cloud fraction (CLDTOT_CAL), total grid cell cloud liquid water path (TGCLDLWP), sea surface temperature (SST), surface downwelling shortwave radiation (FSDS), surface downwelling longwave radiation (FLDS), surface net shortwave radiation (FSNS), clear-sky surface net shortwave radiation (FSNSC), and clear-sky surface downwelling longwave radiation (FLDSC).
DOI: 10.5281/zenodo.6762792
title: Data for "Clouds increasingly influence Arctic sea surface temperatures as CO2 rises" part 1

description: Data for "Clouds increasingly influence Arctic sea surface temperatures as CO2 rises" submitted to Geophysical Research Letters. Data are included from three fully-coupled CESM2 simulations with variable CO2 concentrations: pre-industrial climate ('control'), 424 ppm CO2 ('yr40'), and 1139 ppm CO2 ('yr140'). Data in all files are restricted to 40-90<sup>o</sup>N. Variables include surface temperature (TS), sea ice concentration (ICEFRAC), CALIPSO total cloud fraction (CLDTOT_CAL), total grid cell cloud liquid water path (TGCLDLWP), sea surface temperature (SST), surface downwelling shortwave radiation (FSDS), surface downwelling longwave radiation (FLDS), surface net shortwave radiation (FSNS), clear-sky surface net shortwave radiation (FSNSC), and clear-sky surface downwelling longwave radiation (FLDSC).
DOI: 10.5281/zenodo.6762793
title: Data for "Clouds increasingly influence Arctic sea surface temperatures as CO2 rises"

description: Data for "Clouds increasingly influence Arctic sea surface temperatures as CO2 rises" submitted to Geophysical Research Letters. Data are included from three fully-coupled CESM2 simulations with variable CO2 concentrations: pre-industrial climate ('control'), 424 ppm CO2 ('yr40'), and 1139 ppm CO2 ('yr140'). Data in all files are restricted to 40-90<sup>o</sup>N. Variables include surface temperature (TS), sea ice concentration (ICEFRAC), CALIPSO total cloud fraction (CLDTOT_CAL), total grid cell cloud liquid water path (TGCLDLWP), sea surface temperature (SST), surface downwelling shortwave radiation (FSDS), and surface downwelling shortwave radiation (FLDS).