cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6860680
title: EEG-based Consumer Neuroscience and Neuromarketing Dataset

description: SciMAT dataset with 497 EEG-based Consumer Neuroscience and Neuromarketing articles from 2002 (Consumer Neuroscience field origin) to 2022. Three periods of study were established according to the frequency of EEG use in Consumer neuroscience. The first of these periods, 2002-2010, corresponds to the first decade in which we see the initial acceptance of EEG technology in the field. In the second period, 2011-2017, the use of EEG experiences a growth phase, and during the third period, 2018-2021, we see a substantial rise in the use of EEG in Consumer neuroscience and Neuromarketing studies.
DOI: 10.7294/23457491
title: Data and R Code to Accompany "Fast Range Expansion of the Red Imported Fire Ant in Virginia and Prediction of Future Spread in the United States"

description: Data set, R code and RMarkdown document that accompanies the publication "Fast Range Expansion of Red Imported Fire Ants in Virginia and Prediction of Future Spread in the United States" by Malone M, Ivanov K, Taylor SV & Schürch R in Ecosphere. The data set only contains our newly acquired data, and some derived data that can be used to save computing time. Shape files for US state boundaries can be downloaded from https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html.
DOI: 10.7294/23457491.v1
title: Data and R Code to Accompany "Fast Range Expansion of the Red Imported Fire Ant in Virginia and Prediction of Future Spread in the United States"

description: Data set, R code and RMarkdown document that accompanies the publication "Fast Range Expansion of Red Imported Fire Ants in Virginia and Prediction of Future Spread in the United States" by Malone M, Ivanov K, Taylor SV & Schürch R in Ecosphere. The data set only contains our newly acquired data, and some derived data that can be used to save computing time. Shape files for US state boundaries can be downloaded from https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html.
DOI: 10.48366/r592245
title: Greenhouse gas emission trends, projections and targets in the EU for the target year 2030, 1990-2050

description: This comparison provides an overview of the greenhouse gas emissions (GHG) of the EU-member states. It shows the trend of GHG-emissions for the historical (as-is) and two different future scenarios. The WAM future scenario takes into account the additional effects of planned measures reported by Member States. The WEM future scenario reflects the effects of all adopted and implemented measures at the time the projections were prepared. Both scenarios predict whether the target of the year 2030 will be achieved or not.
DOI: 10.48366/r592244
title: Greenhouse gas emission trends, projections and targets in the EU for the target year 2020, 1990-2050

description: This comparison provides an overview of the greenhouse gas emissions (GHG) of the EU-member states. It shows the trend of GHG-emissions for the historical (as-is) and two different future scenarios. The WAM future scenario takes into account the additional effects of planned measures reported by Member States. The WEM future scenario reflects the effects of all adopted and implemented measures at the time the projections were prepared. Both scenarios predict whether the target of the year 2020 will be achieved or not.
DOI: 10.6084/m9.figshare.23748780
title: Chamaecrista mimosoides genome (.fas), CDS (.cds), proteins (.pep), annotation file (.gff3), tRNA predicted genes (tRNA.gff3) and interproscan functionnal annotation (.tsv).

description: Files corresponding to the genome of Chamaecrista mimosoides.
DOI: 10.6084/m9.figshare.23748780.v1
title: Chamaecrista mimosoides genome (.fas), CDS (.cds), proteins (.pep), annotation file (.gff3), tRNA predicted genes (tRNA.gff3) and interproscan functionnal annotation (.tsv).

description: Files corresponding to the genome of Chamaecrista mimosoides.
DOI: 10.7488/5e17d5de-016c-4cf0-903a-290f0574a977
title: BBSRC funded projects

description: ## Access ## This dataset is held in the Edinburgh DataVault, directly accessible only to authorised University of Edinburgh staff. External users may request access to a copy of the data by contacting the Principal Investigator, Contact Person or Data Manager named on this page. University of Edinburgh users who wish to have direct access should consult the information about retrieving data from the DataVault at: https://www.ed.ac.uk/is/research-support/datavault .
description: The collection of studies funded by multiple BBSRC grants awarded to Dr Emily Clark currently a group leader at the Roslin Institute (previous Chancellor's Fellow of the University of Edinburgh). They have all been produced during (ISP 1&3 2017-2023) within Dr Emily Clark's lab. These data collections contain both published and unpublished genomic datasets (raw files and analysed data). The grant IDs are noted for each deposit separately. Further metadata related to these studies can be found at: https://www.wiki.ed.ac.uk/x/iM5zFg
DOI: 10.2908/tour_dem_expktw
title: Expenditure related to package trips (2012-2019)
title: Dépenses liées aux voyages à forfait (2012-2019)
title: Ausgaben im Zusammenhang mit Pauschalreisen (2012-2019)
DOI: 10.5281/zenodo.8179051
title: Checklist of the ultramafic flora of New Caledonia

description: Data from: How did the ultramafic soils shape the flora of the New Caledonian hotspot? The file contains an updated list of the ultramafic flora of New Caledonia. The list was first compiled from the 25 volumes of the Flora of New Caledonia (Aubréville et al. 1967-) and records from the herbaria of New Caledonia (http://publish.plantnet-project.org/project/nou), the Muséum National d’Histoire Naturelle de Paris (P), and the Missouri Botanical, Gardens (MO). An extensive survey of recent literature, herbarium records and field observations was conducted to ensure the checklist was as complete as<br> possible. The recently revised checklist of the New Caledonian flora "FLORICAL" Munzinger J, Morat P, Jaffré T, Gâteblé G, Pillon Y, Rouhan G, Bruy D, Veillon JM, Chalopin M. 2023. FLORICAL: Checklist of the vascular indigenous flora of New Caledonia. [accessed]. http://publish.plantnet-project.org/project/florical) was used to update the taxonomy.<br> To score the affinity of taxa to ultramafic soils, we used species description or occurrence localities. We assigned the category ultramafic-obligate (U) when habitat description or localities unambiguously states that a species is ultramafic- or serpentine-specialist. The category "ultramafic-facultative" (Uf) was assigned when adjectives such as "often", "sometimes", or "usually" were added to the description of species’ soil ecology, or when the taxa is known to occur (from locality) on different soil types.
DOI: 10.5281/zenodo.8183861
title: Checklist of the ultramafic flora of New Caledonia

description: Data from: How did the ultramafic soils shape the flora of the New Caledonian hotspot? The file contains an updated list of the ultramafic flora of New Caledonia. The list was first compiled from the 25 volumes of the Flora of New Caledonia (Aubréville et al. 1967-) and records from the herbaria of New Caledonia (http://publish.plantnet-project.org/project/nou), the Muséum National d’Histoire Naturelle de Paris (P), and the Missouri Botanical, Gardens (MO). An extensive survey of recent literature, herbarium records and field observations was conducted to ensure the checklist was as complete as<br> possible. The recently revised checklist of the New Caledonian flora "FLORICAL" Munzinger J, Morat P, Jaffré T, Gâteblé G, Pillon Y, Rouhan G, Bruy D, Veillon JM, Chalopin M. 2023. FLORICAL: Checklist of the vascular indigenous flora of New Caledonia. [accessed]. http://publish.plantnet-project.org/project/florical) was used to update the taxonomy.<br> To score the affinity of taxa to ultramafic soils, we used species description or occurrence localities. We assigned the category ultramafic-obligate (U) when habitat description or localities unambiguously states that a species is ultramafic- or serpentine-specialist. The category "ultramafic-facultative" (Uf) was assigned when adjectives such as "often", "sometimes", or "usually" were added to the description of species’ soil ecology, or when the taxa is known to occur (from locality) on different soil types.
DOI: 10.6084/m9.figshare.23748072
title: Data for careless responses

description: Datasets for careless responses
DOI: 10.6084/m9.figshare.23748072.v1
title: Data for careless responses

description: Datasets for careless responses
DOI: 10.5281/zenodo.8183843
title: Regional controls on the sea ice-mixed layer depth relationship in the West Antarctic Peninsula (WAP)

description: Data corresponding to "Regional controls of the sea ice-mixed layer depth relationship in the West Antarctic Peninsula (WAP)" (Bischof et al., in prep.). model_setup contains the input and code directories needed to run our model using MITgcm. data contains all model output used to plot the figures contained in the paper.
DOI: 10.5281/zenodo.8183842
title: Regional controls on the sea ice-mixed layer depth relationship in the West Antarctic Peninsula (WAP)

description: Data corresponding to "Regional controls of the sea ice-mixed layer depth relationship in the West Antarctic Peninsula (WAP)" (Bischof et al., in prep.). model_setup contains the input and code directories needed to run our model using MITgcm. data contains all model output used to plot the figures contained in the paper.
DOI: 10.25430/researchdata.cab.unipd.it.00000941
title: Experimental nest cooling reveals dramatic effects of heatwaves on reproduction in a Mediterranean bird of prey

description: Data corresponding to the manuscript GCB-23-0747 titled "Experimental nest cooling reveals dramatic effects of heatwaves on reproduction in a Mediterranean bird of prey"
DOI: 10.6084/m9.figshare.23739792
title: Getting the most out of the API and the Reporting Database materials

description: Materials for the "Getting the most out of the API and the Reporting Database" webinar with Symplectic Elements, 20230724
DOI: 10.6084/m9.figshare.23739792.v4
title: Getting the most out of the API and the Reporting Database materials

description: Materials for the "Getting the most out of the API and the Reporting Database" webinar with Symplectic Elements, 20230724
DOI: 10.6084/m9.figshare.23739792.v3
title: Getting the most out of the API and the Reporting Database materials

description: Materials for the "Getting the most out of the API and the Reporting Database" webinar with Symplectic Elements, 20230724
DOI: 10.34740/kaggle/dsv/6193575
title: Mahanagar Yatayat Dataset
DOI: 10.17028/rd.lboro.23593530
title: Supplementary information files for Temperature dependency of diagnostic methods in lithium-ion batteries

description: Supplementary files for article Temperature dependency of diagnostic methods in lithium-ion batteries Accurate estimation of battery performance and capacity is essential for assessing the capabilities of different batteries and determining state of health (SOH). Typically, diagnostic methods such as constant current charge/discharge, current interrupt, and electrochemical impedance spectroscopy (EIS) are demonstrated in laboratory tests with controlled environmental conditions. In real-world applications, such as electric vehicles, batteries are subjected to wide temperature ranges and temperature control for performance and SOH estimation is often not possible. This study investigates how battery diagnostic techniques are influenced by small temperature variations in order to determine suitable temperature windows over which different diagnostic methods can be performed without negatively influencing results. A lithium-ion 18650 cell is subjected to constant current charge, pulse discharge and EIS between 0 °C and 40 °C in 2 °C increments. The influence of variations in operating temperature on the diagnostic abilities of incremental capacity analysis (ICA), differential voltage analysis (DVA) and equivalent circuit fitting is explored. The findings of this study indicate that small temperature variations between diagnostic tests can lead to significant errors in the ability to diagnose degradation mechanisms, particularly at low temperatures (&lt;10 °C). If temperature variations of ±4 °C or greater are expected between characterisation tests, then temperature regulation will be required to ensure accurate diagnostics. EIS based diagnostics was shown to be less temperature sensitive than voltage-based analysis techniques such as ICA.
DOI: 10.17028/rd.lboro.23593530.v1
title: Supplementary information files for Temperature dependency of diagnostic methods in lithium-ion batteries

description: Supplementary files for article Temperature dependency of diagnostic methods in lithium-ion batteries Accurate estimation of battery performance and capacity is essential for assessing the capabilities of different batteries and determining state of health (SOH). Typically, diagnostic methods such as constant current charge/discharge, current interrupt, and electrochemical impedance spectroscopy (EIS) are demonstrated in laboratory tests with controlled environmental conditions. In real-world applications, such as electric vehicles, batteries are subjected to wide temperature ranges and temperature control for performance and SOH estimation is often not possible. This study investigates how battery diagnostic techniques are influenced by small temperature variations in order to determine suitable temperature windows over which different diagnostic methods can be performed without negatively influencing results. A lithium-ion 18650 cell is subjected to constant current charge, pulse discharge and EIS between 0 °C and 40 °C in 2 °C increments. The influence of variations in operating temperature on the diagnostic abilities of incremental capacity analysis (ICA), differential voltage analysis (DVA) and equivalent circuit fitting is explored. The findings of this study indicate that small temperature variations between diagnostic tests can lead to significant errors in the ability to diagnose degradation mechanisms, particularly at low temperatures (&lt;10 °C). If temperature variations of ±4 °C or greater are expected between characterisation tests, then temperature regulation will be required to ensure accurate diagnostics. EIS based diagnostics was shown to be less temperature sensitive than voltage-based analysis techniques such as ICA.
DOI: 10.26050/wdcc/ar6.c6cmcecc1
title: IPCC DDC: CNRM-CERFACS CNRM-CM6-1 model output prepared for CMIP6 CMIP

description: Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.CNRM-CERFACS.CNRM-CM6-1' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CNRM-CM6-1 climate model, released in 2017, includes the following components: aerosol: prescribed monthly fields computed by TACTIC_v2 scheme, atmos: Arpege 6.3 (T127; Gaussian Reduced with 24572 grid points in total distributed over 128 latitude circles (with 256 grid points per latitude circle between 30degN and 30degS reducing to 20 grid points per latitude circle at 88.9degN and 88.9degS); 91 levels; top level 78.4 km), atmosChem: OZL_v2, land: Surfex 8.0c, ocean: Nemo 3.6 (eORCA1, tripolar primarily 1deg; 362 x 294 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: Gelato 6.1. The model was run by the CNRM (Centre National de Recherches Meteorologiques, Toulouse 31057, France), CERFACS (Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique, Toulouse 31057, France) (CNRM-CERFACS) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.
DOI: 10.26050/wdcc/ar6.c6cmcecc2
title: IPCC DDC: CNRM-CERFACS CNRM-CM6-1-HR model output prepared for CMIP6 CMIP

description: Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.CNRM-CERFACS.CNRM-CM6-1-HR' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CNRM-CM6-1-HR climate model, released in 2017, includes the following components: aerosol: prescribed monthly fields computed by TACTIC_v2 scheme, atmos: Arpege 6.3 (T359; Gaussian Reduced with 181724 grid points in total distributed over 360 latitude circles (with 720 grid points per latitude circle between 32.2degN and 32.2degS reducing to 18 grid points per latitude circle at 89.6degN and 89.6degS); 91 levels; top level 78.4 km), atmosChem: OZL_v2, land: Surfex 8.0c, ocean: Nemo 3.6 (eORCA025, tripolar primarily 1/4deg; 1442 x 1050 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: Gelato 6.1. The model was run by the CNRM (Centre National de Recherches Meteorologiques, Toulouse 31057, France), CERFACS (Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique, Toulouse 31057, France) (CNRM-CERFACS) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, atmosChem: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km.
DOI: 10.26050/wdcc/ar6.c6cmcece1
title: IPCC DDC: CNRM-CERFACS CNRM-ESM2-1 model output prepared for CMIP6 CMIP

description: Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.CNRM-CERFACS.CNRM-ESM2-1' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CNRM-ESM2-1 climate model, released in 2017, includes the following components: aerosol: TACTIC_v2, atmos: Arpege 6.3 (T127; Gaussian Reduced with 24572 grid points in total distributed over 128 latitude circles (with 256 grid points per latitude circle between 30degN and 30degS reducing to 20 grid points per latitude circle at 88.9degN and 88.9degS); 91 levels; top level 78.4 km), atmosChem: REPROBUS-C_v2, land: Surfex 8.0c, ocean: Nemo 3.6 (eORCA1, tripolar primarily 1deg; 362 x 294 longitude/latitude; 75 levels; top grid cell 0-1 m), ocnBgchem: Pisces 2.s, seaIce: Gelato 6.1. The model was run by the CNRM (Centre National de Recherches Meteorologiques, Toulouse 31057, France), CERFACS (Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique, Toulouse 31057, France) (CNRM-CERFACS) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.