cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2cj8ry
title: CCDC 2190390: Experimental Crystal Structure Determination

description: Related Article: Cornelia von Baeckmann, Sara Ruiz-Relaño, Inhar Imaz, Marcel Handke, Judith Juanhuix, Felipe Gándara, Arnau Carné-Sanchez, Daniel Maspoch|2023|Chem.Commun.|59|3423|doi:10.1039/D2CC06815J
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.24573637
title: Climate change is expected to severely impact Protected Designation of Origin olive growing regions over the Iberian Peninsula

description: Shapefiles with olive occurrences and the PDO polygons:olive_occurrences.zip - Shapefile with the olive tree in use for the ENM procedure.dop.zip - Shapefile with the "Protected Designation of Origin" regions.scripts_pdo_guise - R scripts with the data analysis.<br>
DOI: 10.6084/m9.figshare.24573637.v1
title: Climate change is expected to severely impact Protected Designation of Origin olive growing regions over the Iberian Peninsula

description: Shapefiles with olive occurrences and the PDO polygons:olive_occurrences.zip - Shapefile with the olive tree in use for the ENM procedure. dop.zip - Shapefile with the "Protected Designation of Origin" regions.<br>
DOI: 10.6084/m9.figshare.24573637.v2
title: Climate change is expected to severely impact Protected Designation of Origin olive growing regions over the Iberian Peninsula

description: Shapefiles with olive occurrences and the PDO polygons:olive_occurrences.zip - Shapefile with the olive tree in use for the ENM procedure.dop.zip - Shapefile with the "Protected Designation of Origin" regions.scripts_pdo_guise - R scripts with the data analysis.<br>
DOI: 10.25838/d5p-54
title: Genetic regulation of photoperiod-dependent flowering time in barley (Hordeum vulgare L.)

description: Supplemental Datasets S1 - S8 from the manuscript "FLOWERING LOCUS T1 is a pleiotropic regulator of reproductive development, plant architecture, and source-sink relations in barley", part of the dissertation "Genetic regulation of photoperiod-dependent flowering time in barley (Hordeum vulgare L.)".
DOI: 10.34740/kaggle/dsv/7074289
title: COVID Vaccination in World (updated daily)
DOI: 10.5281/zenodo.7437486
title: Dataset for publication: The Land-Atmosphere Feedback Observatory: A New Observational Approach for Characterizing Land-Atmosphere Feedback

description: Here are zip files containing the data sets for the figures of the publication "The Land-Atmosphere Feedback Observatory: A New Observational Approach for Characterizing Land-Atmosphere Feedback" by Späth et al., 2023, GI, doi: 10.5194/gi-12-25-2023.
Data collected from the Halo Doppler lidar (DL), Atmospheric Raman Temperature and HUmidity Sounder ARTHUS, scanning differential absorption lidar (DIAL), Eddy-Covariance stations (EC) and the Water and Temperature Sensor Network (WaTSeN) of the Land-Atmisphere Feedback Observatory LAFO at University of Hohenheim, Stuttgart, Germany. The data cover the temporal period as presented in the figures in Späth et al., 2023, GI, doi: 10.5194/gi-12-25-2023.
DOI: 10.5281/zenodo.7437485
title: Dataset for publication: The Land-Atmosphere Feedback Observatory: A New Observational Approach for Characterizing Land-Atmosphere Feedback

description: Here are zip files containing the data sets for the figures of the publication "The Land-Atmosphere Feedback Observatory: A New Observational Approach for Characterizing Land-Atmosphere Feedback" by Späth et al., 2023, GI, doi: 10.5194/gi-12-25-2023.
Data collected from the Halo Doppler lidar (DL), Atmospheric Raman Temperature and HUmidity Sounder ARTHUS, scanning differential absorption lidar (DIAL), Eddy-Covariance stations (EC) and the Water and Temperature Sensor Network (WaTSeN) of the Land-Atmisphere Feedback Observatory LAFO at University of Hohenheim, Stuttgart, Germany. The data cover the temporal period as presented in the figures in Späth et al., 2023, GI, doi: 10.5194/gi-12-25-2023.
DOI: 10.3886/e113166v1-143349
title: Replication data for: Do Recessions Accelerate Routine-Biased Technological Change? Evidence from Vacancy Postings

description: We show that skill requirements in job vacancy postings differentially increased in MSAs that were hit hard by the Great Recession, relative to less hard-hit areas. These increases persist through at least the end of 2015 and are correlated with increases in capital investments, both at the MSA and firm levels. We also find that effects are most pronounced in routine-cognitive occupations, which exhibit relative wage growth as well. We argue that this evidence is consistent with the restructuring of production toward routine-biased technologies and the more-skilled workers that complement them, and that the Great Recession accelerated this process.
DOI: 10.5281/zenodo.10213680
title: Freezing nucleus spectra for hailstone samples in China from droplet freezing experiments—Data

description: INP concentrations in hailstone samples from droplet freezing experiments.
DOI: 10.5281/zenodo.7597249
title: Freezing nucleus spectra for hailstone samples in China from droplet freezing experiments—Data

description: INP concentrations in hailstone samples from droplet freezing experiments.
DOI: 10.5281/zenodo.7597250
title: Ice Nucleating Particles in Hailstones in China - Data

description: INP concentrations in hailstone samples from droplet freezing experiments.
DOI: 10.5281/zenodo.10213601
title: Dataset for publication: Investigation of Hydrostatic Imbalance with Field Observations

description: Here is the data set for figure 8 of the publication "Investigation of Hydrostatic Imbalance with Field Observations" by Sun et al., 2023
Data collected from the Halo Doppler lidars (DL) and Eddy-Covariance station (EC) (EC south, EC-S) of the Land-Atmisphere Feedback Observatory LAFO at University of Hohenheim, Stuttgart, Germany (lafo.uni-hohenheim.de, Späth et al., 2023, doi: 10.5194/gi-12-25-2023). The data cover the temporal period of 23 August 2022 as presented in figure 8 in Sun et al., 2023, doi: ***.
DOI: 10.5281/zenodo.10213602
title: Dataset for publication: Investigation of Hydrostatic Imbalance with Field Observations

description: Here is the data set for figure 8 of the publication "Investigation of Hydrostatic Imbalance with Field Observations" by Sun et al., 2023
Data collected from the Halo Doppler lidars (DL) and Eddy-Covariance station (EC) (EC south, EC-S) of the Land-Atmisphere Feedback Observatory LAFO at University of Hohenheim, Stuttgart, Germany (lafo.uni-hohenheim.de, Späth et al., 2023, doi: 10.5194/gi-12-25-2023). The data cover the temporal period of 23 August 2022 as presented in figure 8 in Sun et al., 2023, doi: ***.
DOI: 10.34683/sr642
title: StoneRounds Case: SR642
title: Homocystinuria

description: StoneRounds Case SR642 was diagnosed with Homocystinuria likely caused by mutations in the gene: MTR.
DOI: 10.5281/zenodo.8112315
title: Anchorwhat dataset

description: In this research, we are interested in the use of what we call pan-scalar maps, i.e. interactive, zoomable, multi-scale maps such as Google Maps. It is frequent to feel disorientation when we use these pan-scalar maps, and the absence of consistent landmarks or anchors across scales can be one of the causes of this disorientation \citep{touya_where_2023}. As a consequence, within the virtual environment of a pan-scalar map, we make the hypothesis that map objects, parts of objects, or groups of objects can comport comparably to the qualities of anchors or landmarks in a real space for spatialization purposes.For that we designed a user study where participants were asked to draw on top of the memorable, salient landmarks they saw on the map. - RAW_DATA contains 2 CSV files: the first contains all drawings, the second all participations. - MAP_DRAWING contains all drawings split by view (location, style, zoom). - DRAWING_ANCHORS split drawings by view into pan-scalar anchors (Location, style, zoom, drawings_anchor). - ANCHORS contains the vector delineation of pan-scalar anchors (Location, style, zoom,anchor). -STATISTIC_DRAWING contains anchoress, presence... attribute information in xls of drawings (Location, style, zoom,drawings_statistics) -BOUNDED_ANCHOR contains vector data for anchor lines that have been drawn in the same hue (Location, style, zoom,bounded_anchor) -WORFLOW_ANCHOR : Contains all QGIS workflows used for AnchorWhat analysis. - ILLUSTATIONS contains some illustrations from the AnchorWhat analysis.
DOI: 10.5281/zenodo.8112488
title: Anchorwhat dataset

description: In this research, we are interested in the use of what we call pan-scalar maps, i.e. interactive, zoomable, multi-scale maps such as Google Maps. It is frequent to feel disorientation when we use these pan-scalar maps, and the absence of consistent landmarks or anchors across scales can be one of the causes of this disorientation \citep{touya_where_2023}. As a consequence, within the virtual environment of a pan-scalar map, we make the hypothesis that map objects, parts of objects, or groups of objects can comport comparably to the qualities of anchors or landmarks in a real space for spatialization purposes.For that we designed a user study where participants were asked to draw on top of the memorable, salient landmarks they saw on the map. - RAW_DATA contains 2 CSV files: the first contains all drawings, the second all participations. - MAP_DRAWING contains all drawings split by view (location, style, zoom). - DRAWING_ANCHORS split drawings by view into pan-scalar anchors (Location, style, zoom, drawings_anchor). - ANCHORS contains the vector delineation of pan-scalar anchors (Location, style, zoom,anchor). -STATISTIC_DRAWING contains anchoress, presence... attribute information in xls of drawings (Location, style, zoom,drawings_statistics) -BOUNDED_ANCHOR contains vector data for anchor lines that have been drawn in the same hue (Location, style, zoom,bounded_anchor) -WORFLOW_ANCHOR : Contains all QGIS workflows used for AnchorWhat analysis. - ILLUSTATIONS contains some illustrations from the AnchorWhat analysis.
DOI: 10.5281/zenodo.10213612
title: Anchorwhat dataset

description:  
- 01_RAW_DATA contains 2 CSV files: the first contains all drawings used for the analyse, the second all participations. We made attribute join on session
 
- 02_MAP_DRAWING contains all drawings split by view (location, style, zoom).
 
- 03_DRAWING_ANCHORS split drawings by view after manual selection and assignment  (Location, style, zoom, drawings_anchor).
 
- 04_ANCHORS contains the vector delineation of pan-scalar anchors (Location, style, zoom,anchor). See workflow_QGIS AllProcess.excalidraw with excalidraw website
 
- 05_STATISTIC_DRAWING contains statictical attribute information calculte in xls of drawings (Location, style, zoom,drawings_statistics) See workflow_QGIS AllProcess.excalidraw with excalidraw website
 
- 06_BOUNDED_ANCHOR contains vector data for anchor lines that have been drawn in the same hue (Location, style, zoom,bounded_anchor). See workflow_QGIS AllProcess.excalidraw- with excalidraw website
 
- 07_WORFLOW_ANCHOR : Contains all QGIS workflows used for AnchorWhat analysis + See workflow_QGIS AllProcess.excalidraw with excalidraw website  
 
- 08_ILLUSTATIONS contains most of the illustrations for the script
 
- 09_INITIAL_VIEWS: Contain all the view one wich participants were drawings
 
- 10_3D_VIEWS : Contain the views where drawing were extruded. The height of each pixel corresponds to the number of drawing divided by the number of participant See workflow_QGIS AllProcess.excalidraw- with excalidraw website
 
- 11_ANNOTED_VIEWS : Contain the 3D view annoted with the anchors.Linked to the illustration folder.
DOI: 10.5281/zenodo.8112314
title: Anchorwhat dataset

description:  
- 01_RAW_DATA contains 2 CSV files: the first contains all drawings used for the analyse, the second all participations. We made attribute join on session
 
- 02_MAP_DRAWING contains all drawings split by view (location, style, zoom).
 
- 03_DRAWING_ANCHORS split drawings by view after manual selection and assignment  (Location, style, zoom, drawings_anchor).
 
- 04_ANCHORS contains the vector delineation of pan-scalar anchors (Location, style, zoom,anchor). See workflow_QGIS AllProcess.excalidraw with excalidraw website
 
- 05_STATISTIC_DRAWING contains statictical attribute information calculte in xls of drawings (Location, style, zoom,drawings_statistics) See workflow_QGIS AllProcess.excalidraw with excalidraw website
 
- 06_BOUNDED_ANCHOR contains vector data for anchor lines that have been drawn in the same hue (Location, style, zoom,bounded_anchor). See workflow_QGIS AllProcess.excalidraw- with excalidraw website
 
- 07_WORFLOW_ANCHOR : Contains all QGIS workflows used for AnchorWhat analysis + See workflow_QGIS AllProcess.excalidraw with excalidraw website  
 
- 08_ILLUSTATIONS contains most of the illustrations for the script
 
- 09_INITIAL_VIEWS: Contain all the view one wich participants were drawings
 
- 10_3D_VIEWS : Contain the views where drawing were extruded. The height of each pixel corresponds to the number of drawing divided by the number of participant See workflow_QGIS AllProcess.excalidraw- with excalidraw website
 
- 11_ANNOTED_VIEWS : Contain the 3D view annoted with the anchors.Linked to the illustration folder.
DOI: 10.5281/zenodo.8120550
title: Anchorwhat dataset

description: In this research, we are interested in the use of what we call pan-scalar maps, i.e. interactive, zoomable, multi-scale maps such as Google Maps. It is frequent to feel disorientation when we use these pan-scalar maps, and the absence of consistent landmarks or anchors across scales can be one of the causes of this disorientation \citep{touya_where_2023}. As a consequence, within the virtual environment of a pan-scalar map, we make the hypothesis that map objects, parts of objects, or groups of objects can comport comparably to the qualities of anchors or landmarks in a real space for spatialization purposes.For that we designed a user study where participants were asked to draw on top of the memorable, salient landmarks they saw on the map. - RAW_DATA contains 2 CSV files: the first contains all drawings, the second all participations. - MAP_DRAWING contains all drawings split by view (location, style, zoom). - DRAWING_ANCHORS split drawings by view into pan-scalar anchors (Location, style, zoom, drawings_anchor). - ANCHORS contains the vector delineation of pan-scalar anchors (Location, style, zoom,anchor). -STATISTIC_DRAWING contains anchoress, presence... attribute information in xls of drawings (Location, style, zoom,drawings_statistics) -BOUNDED_ANCHOR contains vector data for anchor lines that have been drawn in the same hue (Location, style, zoom,bounded_anchor) -WORFLOW_ANCHOR : Contains all QGIS workflows used for AnchorWhat analysis. - ILLUSTATIONS contains some illustrations from the AnchorWhat analysis.
DOI: 10.5281/zenodo.10000128
title: Last Glacial Maximum planktonic foraminifera species assemblages

description: Last Glacial Maximum planktonic foraminifera species assemblages used in Jonkers et al. "Strong temperature gradients in the ice age North Atlantic Ocean revealed by plankton biogeography", Nature Geoscience, 2023, https://www.nature.com/articles/s41561-023-01328-7. The data are split by ocean basin and contain extensive metadata and saved as a list. The data can be opened using the open source software R. This file contains 2,085 assemblages from 647 globally distributed sites.
An update to this synthesis with more data is available at PANGAEA.
DOI: 10.5281/zenodo.10000129
title: Last Glacial Maximum planktonic foraminifera species assemblages

description: Last Glacial Maximum planktonic foraminifera species assemblages used in Jonkers et al. "Strong temperature gradients in the ice age North Atlantic Ocean revealed by plankton biogeography", Nature Geoscience, 2023, https://www.nature.com/articles/s41561-023-01328-7. The data are split by ocean basin and contain extensive metadata and saved as a list. The data can be opened using the open source software R. This file contains 2,085 assemblages from 647 globally distributed sites.
An update to this synthesis with more data is available at PANGAEA.
DOI: 10.5281/zenodo.10212981
title: Silica dissolution under a flow of pure water at pressure and temperature conditions relevant to geothermal energy extraction

description: This dataset is a published product of the 'REFLECT' Project - a Horizon Europe project which aims to inform the processes of geothermal energy extraction by determining the effect of relevant fluid properties and reactions in order to enhance predictive geochemical modelling and thus the energy exploitation and life-time of geothermal power plants.
The dataset records the concentration of silica measured in water that had been passed through a packed column of quartz grains at temperatures from 200 to 450°C and pressures from 150 to 450 bar.  Concentrations are reported as g/ml SiO2, measured photometrically. The reader is referred to the full report for deliverable 1.4 of the REFLECT project for details of the experimental set up and interpretation of the data.
Silica concentrations marked with (a) are believed to be artificially reduced compared to the rest of the dataset due to a reduction in the surface density of active sites during some of the highest dissolution experiments.  The final column lists the chronological order in which the measurements were taken to assist with interpretation of this factor.
The density marked with (b) represents the density of water at 150 bar and 342°C, rather than the measured condition of 148 bar and 344°C.  This is to reflect the fact that the solubility measurement suggests the presence of a liquid phase - the conditions chosen represent the closest point on the phase boundary to the measured conditions.  The discrepancy may reflect a small shift in the phase envelope due to silica dissolution in addition to any uncertainty in the pT measurements.
DOI: 10.5281/zenodo.10212982
title: Silica dissolution under a flow of pure water at pressure and temperature conditions relevant to geothermal energy extraction

description: This dataset is a published product of the 'REFLECT' Project - a Horizon Europe project which aims to inform the processes of geothermal energy extraction by determining the effect of relevant fluid properties and reactions in order to enhance predictive geochemical modelling and thus the energy exploitation and life-time of geothermal power plants.
The dataset records the concentration of silica measured in water that had been passed through a packed column of quartz grains at temperatures from 200 to 450°C and pressures from 150 to 450 bar.  Concentrations are reported as g/ml SiO2, measured photometrically. The reader is referred to the full report for deliverable 1.4 of the REFLECT project for details of the experimental set up and interpretation of the data.
Silica concentrations marked with (a) are believed to be artificially reduced compared to the rest of the dataset due to a reduction in the surface density of active sites during some of the highest dissolution experiments.  The final column lists the chronological order in which the measurements were taken to assist with interpretation of this factor.
The density marked with (b) represents the density of water at 150 bar and 342°C, rather than the measured condition of 148 bar and 344°C.  This is to reflect the fact that the solubility measurement suggests the presence of a liquid phase - the conditions chosen represent the closest point on the phase boundary to the measured conditions.  The discrepancy may reflect a small shift in the phase envelope due to silica dissolution in addition to any uncertainty in the pT measurements.
DOI: 10.6084/m9.figshare.23808459
title: Data from: Tropical forest birds after 20 years of the establishment of a reservoir: Extinction, colonization, and recolonization events

description: Original data from:Bohada-Murillo, M., F.E. Fontúrbel & G.J. Castaño-Villa. 2023. Tropical forest birds after 20 years of the establishment of a reservoir: Extinction, colonization, and recolonization events. Global Ecology and Conservation.
DOI: 10.6084/m9.figshare.23808459.v1
title: Data from: Tropical forest birds after 20 years of the establishment of a reservoir: Extinction, colonization, and recolonization events

description: Original data from:Bohada-Murillo, M., F.E. Fontúrbel & G.J. Castaño-Villa. 2023. Tropical forest birds after 20 years of the establishment of a reservoir: Extinction, colonization, and recolonization events. Global Ecology and Conservation.
DOI: 10.2909/88ea692c-d3df-40d2-b8f1-2447f0230121
title: VPP quality flag 2017-present (raster 10m and 100m), Europe, yearly, Nov. 2022 - adjusted to the HRL grid

description: The Quality Flag (QFLAG), one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories, which is a filtered time series with regular 10-day time step. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The Seasonal Productivity is one of the 13 parameters. The full list is available in the table 3 of the Product User Manual in the below link section. The Quality Flag (QFLAG) is a quality indicator for the above set of 13 Vegetation Phenology and Productivity (VPP) parameters and provides a confidence level, that is described in table 4 of the same manual. The QFLAG dataset is made available as raster files with 10 x 10m and 100 x 100m resolutions, in ETRS89-LAEA projection corresponding to the HRL grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2017 onwards. It is updated in the first quarter of each year.
DOI: 10.60619/5xra-ms03
title: 3D model of the VC haloscope for the tuning study