cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.3761562.v2043
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: 10.17043/wohlfarth-2017-preluca-tiganului-1
title: Lateglacial and Holocene paleoenvironmental change data from ancient Lake Preluca Tiganului, Romania
DOI: 10.17043/wohlfarth-2017-steregoiu-1
title: Lateglacial and early Holocene paleoenvironmental changes from ancient Lake Steregoiu, Romania
DOI: 10.17043/wohlfarth-2018-attekopsmosse-1
title: Lateglacial and early Holocene multi-proxy data set for the ancient lake at Atteköpsmosse, southern Sweden
DOI: 10.17043/wohlfarth-2018-kumphawapi-1
title: Holocene multi-proxy and multi-core data set for Lake Kumphawapi in northern Thailand
DOI: 10.17043/wohlfarth-2018-pa-kho-1
title: Multi-proxy record from Lake Pa Kho of Late Holocene climatic and environmental changes in northern Thailand
DOI: 10.6084/m9.figshare.13570559.v1
title: Data - Easter et al - Aggression-based social learning in zebra finches.zip

description: Data and additional, supplementary material for Easter et al., Aggression-based social learning in the zebra finch (<i>Taeniopygia guttata</i>).<br>In this study, we gathered data on the following types of social interaction between individuals in a population of 53 zebra finches housed in a free-flying aviary: feeding associations, dominance- and subordinate-related interactions, positive interactions, and matings. We introduced a novel foraging task to the population and used Network-Based Diffusion Analysis (NBDA) to establish whether learning events followed networks (or combinations of networks) representing the types of interaction outlined above. <br>To collect dominance-related data, we introduced a small platform with food to isolate any aggressive interactions into an easily observed area. Feeder and dominance data were collected by filming using GoPro cameras. Data on positive interactions and matings were recorded as live observations.<br><br>This dataset contains the R script used to carry out our analysis and all necessary data required. The data includes the following: - Data on visits to two feeders in the aviary, used to establish feeding associations between birds. - Live observations of positive interactions between pairs of birds. - Live observations of matings/nestings. - Dominance-related data (winners and losers of fights). - Data on task solves. - Suspected genders of birds, used as an individual-level variable (in absence of genetic information, we used observations of appearance and behaviour). - Solves of a previous task, the previous year, used as an individual-level variable. - Dominance ranks, used as an individual-level variable.<br>This dataset also includes information on identifying the birds in the population, including guides for how we identified unbanded birds and an Excel document with the IDs of all the birds along with additional information including each birds' matrix number (their position in the generated social matrices). These are located in the "Bird IDs and descriptions" folder.<br>This dataset also includes some of the Outputs obtained from our analysis, including the AIC table generated and the estimates for <i>s</i> and <i>propST </i>parameters (see paper for further information). These are located in the "Outputs" folder.<br>The "_NBDA Script" R file can be used to run the whole analysis. Please ensure R is set to the directory with all the data in.
DOI: 10.17043/wohlfarth-2021-pa-kho-1
title: Multi-proxy sediment record from Lake Pa Kho reconstructing MIS-3 climatic and environmental changes in northern Thailand
DOI: 10.17043/wohlfarth-2021-pa-kho
title: Multi-proxy sediment record from Lake Pa Kho reconstructing MIS-3 climatic and environmental changes in northern Thailand
DOI: 10.17043/zeppelin-aerosol-light-scattering-1
title: Aerosol light scattering properties at Zeppelin Observatory, Svalbard, 1999⁠ – ⁠2016, with back trajectory analysis parameters
DOI: 10.17043/zeppelin-aerosol-light-scattering
title: Aerosol light scattering properties at Zeppelin Observatory, Svalbard, 1999⁠ – ⁠2016, with back trajectory analysis parameters
DOI: 10.6084/m9.figshare.13456550.v1
title: EBSD data for synthetic polycrystalline pure water ice samples deformed at high homologous temperatures

description: EBSD data for synthetic polycrystalline pure water ice samples deformed at high homologous temperatures.
DOI: 10.6084/m9.figshare.17008922.v1
title: population genomics of cotton bollworm

description: population genomics of cotton bollworm
DOI: 10.6084/m9.figshare.14766102.v24
title: antspyt1w helper data

description: downloadable cloud data for testing and processing
DOI: 10.6084/m9.figshare.14766102.v23
title: antspyt1w helper data

description: downloadable cloud data for testing and processing
DOI: 10.6084/m9.figshare.12961817.v3
title: Figure 1 data supporting manuscript Intravital Deep-Tumor Single-Beam 3-Photon, 4-Photon and Harmonic Microscopy.

description: Raw image data in ome.tiff format, supporting figure 1 from the manuscript Intravital Deep-Tumor Single-Beam 3-Photon, 4-Photon and Harmonic Microscopy. <br>Furthermore, the numerical data and analysis parameters supporting the plot in panel 1d are given in file Fig1d_2020-07-01_EmissionVSpowerInVivo.opju. This file can be opened with OriginPro2020 or with the freely distributed Origin viewer (https://www.originlab.com/viewer/).
DOI: 10.6084/m9.figshare.12961817.v2
title: Figure 1. Microscopy with simultaneous 2-, 3- and 4 photon processes excited in fluorescent skin tumor xenografts in vivo.

description: Raw image data in ome.tiff format, supporting figure 1. Furthermore, the numerical data and analysis parameters supporting the plot in panel 1e are given in file Fig1e_2020-07-01_EmissionVSpowerInVivo.opju. This file can be opened with OriginPro2020 or with the freely distributed Origin viewer (https://www.originlab.com/viewer/).
DOI: 10.6084/m9.figshare.13315913.v2
title: Amine composition in PM2.5 in Guangzhou during July 2018

description: Amine concentrations during a six-week measurement are included in this dataset. The analyses were performed by HPLC and quantitative concentrations were obtained. Concentrations of trace gases including SO2, NO2, NO, O3, PM10, PM2.5, PM1, and CO are included.
DOI: 10.6084/m9.figshare.13315913.v1
title: Amine composition in PM2.5 in Guangzhou during July 2018

description: Amine concentrations during a six-week measurement are included in this dataset. The analyses were performed by HPLC and quantitative concentrations were obtained. Concentrations of trace gases including SO2, NO2, NO, O3, PM10, PM2.5, PM1, and CO are included.
DOI: 10.6084/m9.figshare.17009087.v1
title: Total surface shortwave flux distributions 1901-2017 in support of carbon cycle modelling: Constant tropospheric aerosols, no stratospheric aerosols.

description: This dataset offers 6-hourly distributions of the total downward shortwave flux over the period 1901-2017. Radiative transfer calculations are based on monthly-averaged distributions of tropospheric aerosol optical depth, and 6-hourly distributions of cloud fraction. Methods follow those described in the Methods section of Mercado et al. (doi:10.1038/nature07949, 2009), but with updated input datasets. This version of the dataset excludes the radiative effects of stratospheric aerosols.<br><br>The time series of speciated tropospheric aerosol optical depth is taken from the historical and RCP8.5 simulations by the HadGEM2-ES climate model (Bellouin et al., doi:10.1029/2011JD016074, 2011). To correct for biases in HadGEM2-ES, tropospheric aerosol optical depths are scaled over the whole period to match the global and monthly averages obtained over the period 2003-2017 by the CAMS Reanalysis of atmospheric composition (Inness et al., doi:10.5194/acp-19-3515-2019, 2019), which assimilates satellite retrievals of aerosol optical depth. In this version of the dataset, monthly aerosol distributions are held fixed to their multi-annual means for 1901-1920.<br><br>The time series of cloud fraction is obtained by scaling the 6-hourly distributions simulated in the Japanese Reanalysis (JRA; Kobayashi et al., doi:10.2151/jmsj.2015-001, 2015) to match the monthly-averaged cloud cover in the CRU TS v4.03 dataset (Harris et al. doi:10.1038/s41597-020-0453-3, 2020).<br><br>Surface radiative fluxes account for aerosol-radiation interactions from both tropospheric and stratospheric aerosols, and for aerosol-cloud interactions from tropospheric aerosols, except mineral dust. Tropospheric aerosols are also assumed to exert interactions with cloud. The radiative effects of those aerosol-cloud interactions are assumed to scale with the radiative effects of aerosol-radiation interactions of tropospheric aerosols, using regional scaling factors derived from HadGEM2-ES.<br><br>Atmospheric constituent other than aerosols and clouds are set to a constant standard mid-latitude summer atmosphere.
DOI: 10.6084/m9.figshare.14428160.v2
title: Diffuse fraction distributions 1901-2017 in support of carbon cycle modelling: Constant aerosols.

description: This diffuse fraction dataset offers 6-hourly distributions of the diffuse fraction of surface shortwave fluxes over the period 1901-2017. This is a companion dataset of doi:10.6084/m9.figshare.14423690, where time variations in tropospheric aerosols are ignored, and stratospheric aerosols are not accounted for. Radiative transfer calculations are based on monthly-averaged distributions of tropospheric aerosol optical depth, and 6-hourly distributions of cloud fraction. Methods follow those described in the Methods section of Mercado et al. (doi:10.1038/nature07949, 2009), but with updated input datasets.<br><br>The time series of speciated tropospheric aerosol optical depth is taken from the historical and RCP8.5 simulations by the HadGEM2-ES climate model (Bellouin et al., doi:10.1029/2011JD016074, 2011). To correct for biases in HadGEM2-ES, tropospheric aerosol optical depths are scaled over the whole period to match the global and monthly averages obtained over the period 2003-2017 by the CAMS Reanalysis of atmospheric composition (Inness et al., doi:10.5194/acp-19-3515-2019, 2019), which assimilates satellite retrievals of aerosol optical depth. Monthly distributions of sulfate and carbonaceous optical depth are then averaged over the 1901-1920 period, and those multi-annual monthly averages are used for the whole 1901-2017 period. The only interannual changes in tropospheric aerosols therefore come from mineral dust and seasalt.<br><br>Stratospheric aerosol optical depth is not accounted for in the radiative transfer calculations.<br><br>The time series of cloud fraction is obtained by scaling the 6-hourly distributions simulated in the Japanese Reanalysis (JRA; Kobayashi et al., doi:10.2151/jmsj.2015-001, 2015) to match the monthly-averaged cloud cover in the CRU TS v4.03 dataset (Harris et al. doi:10.1038/s41597-020-0453-3, 2020).<br><br>Surface radiative fluxes account for aerosol-radiation and aerosol-cloud interactions by tropospheric aerosols, except mineral dust which only exerts aerosol-radiation interactions. The radiative effects of aerosol-cloud interactions are assumed to scale with the radiative effects of aerosol-radiation interactions of tropospheric aerosols, using regional scaling factors derived from HadGEM2-ES.<br><br>Diffuse fraction is assumed to be 1 in cloudy sky. Atmospheric constituents other than aerosols and clouds are set to a constant standard mid-latitude summer atmosphere, but their variations do not affect the diffuse fraction of surface shortwave fluxes.
DOI: 10.6084/m9.figshare.15138570.v9
title: 14-3-3 proteins promote synaptic localization of N-methyl d-aspartate receptors (NMDARs) in mouse hippocampal and cortical neurons

description: Data sets for manuscript, "14-3-3 proteins promote synaptic localization of N-methyl d-aspartate receptors (NMDARs) in mouse hippocampal and cortical neurons"
DOI: 10.6084/m9.figshare.15138570.v8
title: 14-3-3 proteins promote synaptic localization of N-methyl d-aspartate receptors (NMDARs) in mouse hippocampal and cortical neurons

description: Data sets for manuscript, "14-3-3 proteins promote synaptic localization of N-methyl d-aspartate receptors (NMDARs) in mouse hippocampal and cortical neurons"
DOI: 10.6084/m9.figshare.3761562.v2046
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: 10.6084/m9.figshare.3761562.v2045
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: 10.6084/m9.figshare.17029592.v2
title: Diffuse fraction distributions 1901-2017 in support of carbon cycle modelling: Constant tropospheric aerosols.

description: This diffuse fraction dataset offers 6-hourly distributions of the diffuse fraction of surface shortwave fluxes over the period 1901-2017. Radiative transfer calculations are based on monthly-averaged distributions of tropospheric and stratospheric aerosol optical depth, and 6-hourly distributions of cloud fraction. Methods follow those described in the Methods section of Mercado et al. (doi:10.1038/nature07949, 2009), but with updated input datasets.<br><br>The time series of speciated tropospheric aerosol optical depth is taken from the historical and RCP8.5 simulations by the HadGEM2-ES climate model (Bellouin et al., doi:10.1029/2011JD016074, 2011). To correct for biases in HadGEM2-ES, tropospheric aerosol optical depths are scaled over the whole period to match the global and monthly averages obtained over the period 2003-2017 by the CAMS Reanalysis of atmospheric composition (Inness et al., doi:10.5194/acp-19-3515-2019, 2019), which assimilates satellite retrievals of aerosol optical depth. In this version of the dataset, monthly distributions of sulfate and carbonaceous optical depth are then averaged over the 1901-1920 period, and those multi-annual monthly averages are used for the whole 1901-2017 period. The only interannual changes in tropospheric aerosols therefore come from mineral dust and seasalt.<br><br>The time series of stratospheric aerosol optical depth is taken from the climatology by Sato et al. (doi:10.1029/93JD02553, 1993), which has been updated to 2012. Years 2013-2017 are assumed to be background years so replicate background year 2010. That assumption is supported by the Global Space-based Stratospheric Aerosol Climatology time series (1979-2016; Thomason et al., doi:10.5194/essd-10-469-2018, 2018).<br><br>The time series of cloud fraction is obtained by scaling the 6-hourly distributions simulated in the Japanese Reanalysis (JRA; Kobayashi et al., doi:10.2151/jmsj.2015-001, 2015) to match the monthly-averaged cloud cover in the CRU TS v4.03 dataset (Harris et al. doi:10.1038/s41597-020-0453-3, 2020).<br><br>Surface radiative fluxes account for aerosol-radiation interactions from both tropospheric and stratospheric aerosols, and for aerosol-cloud interactions from tropospheric aerosols, except mineral dust. Tropospheric aerosols are also assumed to exert interactions with cloud. The radiative effects of those aerosol-cloud interactions are assumed to scale with the radiative effects of aerosol-radiation interactions of tropospheric aerosols, using regional scaling factors derived from HadGEM2-ES.<br><br>Diffuse fraction is assumed to be 1 in cloudy sky. Atmospheric constituent other than aerosols and clouds are set to a constant standard mid-latitude summer atmosphere, but their variations do not affect the diffuse fraction of surface shortwave fluxes.
DOI: 10.6084/m9.figshare.17029592.v1
title: Diffuse fraction distributions 1901-2017 in support of carbon cycle modelling: Constant tropospheric aerosols.

description: This diffuse fraction dataset offers 6-hourly distributions of the diffuse fraction of surface shortwave fluxes over the period 1901-2017. Radiative transfer calculations are based on monthly-averaged distributions of tropospheric and stratospheric aerosol optical depth, and 6-hourly distributions of cloud fraction. Methods follow those described in the Methods section of Mercado et al. (doi:10.1038/nature07949, 2009), but with updated input datasets.<br><br>The time series of speciated tropospheric aerosol optical depth is taken from the historical and RCP8.5 simulations by the HadGEM2-ES climate model (Bellouin et al., doi:10.1029/2011JD016074, 2011). To correct for biases in HadGEM2-ES, tropospheric aerosol optical depths are scaled over the whole period to match the global and monthly averages obtained over the period 2003-2017 by the CAMS Reanalysis of atmospheric composition (Inness et al., doi:10.5194/acp-19-3515-2019, 2019), which assimilates satellite retrievals of aerosol optical depth. In this version of the dataset, monthly distributions of sulfate and carbonaceous optical depth are then averaged over the 1901-1920 period, and those multi-annual monthly averages are used for the whole 1901-2017 period. The only interannual changes in tropospheric aerosols therefore come from mineral dust and seasalt.<br><br>The time series of stratospheric aerosol optical depth is taken from the climatology by Sato et al. (doi:10.1029/93JD02553, 1993), which has been updated to 2012. Years 2013-2017 are assumed to be background years so replicate background year 2010. That assumption is supported by the Global Space-based Stratospheric Aerosol Climatology time series (1979-2016; Thomason et al., doi:10.5194/essd-10-469-2018, 2018).<br><br>The time series of cloud fraction is obtained by scaling the 6-hourly distributions simulated in the Japanese Reanalysis (JRA; Kobayashi et al., doi:10.2151/jmsj.2015-001, 2015) to match the monthly-averaged cloud cover in the CRU TS v4.03 dataset (Harris et al. doi:10.1038/s41597-020-0453-3, 2020).<br><br>Surface radiative fluxes account for aerosol-radiation interactions from both tropospheric and stratospheric aerosols, and for aerosol-cloud interactions from tropospheric aerosols, except mineral dust. Tropospheric aerosols are also assumed to exert interactions with cloud. The radiative effects of those aerosol-cloud interactions are assumed to scale with the radiative effects of aerosol-radiation interactions of tropospheric aerosols, using regional scaling factors derived from HadGEM2-ES.<br><br>Diffuse fraction is assumed to be 1 in cloudy sky. Atmospheric constituent other than aerosols and clouds are set to a constant standard mid-latitude summer atmosphere, but their variations do not affect the diffuse fraction of surface shortwave fluxes.
DOI: 10.6084/m9.figshare.17031899.v2
title: Diffuse fraction distributions 1901-2017 in support of carbon cycle modelling: No stratospheric aerosols

description: Diffuse fraction distributions 1901-2017 in support of carbon cycle modelling: No stratospheric aerosols<br><br>This diffuse fraction dataset offers 6-hourly distributions of the diffuse fraction of surface shortwave fluxes over the period 1901-2017. Radiative transfer calculations are based on monthly-averaged distributions of tropospheric aerosol optical depth, and 6-hourly distributions of cloud fraction. Methods follow those described in the Methods section of Mercado et al. (doi:10.1038/nature07949, 2009), but with updated input datasets.<br><br>The time series of speciated tropospheric aerosol optical depth is taken from the historical and RCP8.5 simulations by the HadGEM2-ES climate model (Bellouin et al., doi:10.1029/2011JD016074, 2011). To correct for biases in HadGEM2-ES, tropospheric aerosol optical depths are scaled over the whole period to match the global and monthly averages obtained over the period 2003-2017 by the CAMS Reanalysis of atmospheric composition (Inness et al., doi:10.5194/acp-19-3515-2019, 2019), which assimilates satellite retrievals of aerosol optical depth.<br><br>This dataset does not account for stratospheric aerosols.<br><br>The time series of cloud fraction is obtained by scaling the 6-hourly distributions simulated in the Japanese Reanalysis (JRA; Kobayashi et al., doi:10.2151/jmsj.2015-001, 2015) to match the monthly-averaged cloud cover in the CRU TS v4.03 dataset (Harris et al. doi:10.1038/s41597-020-0453-3, 2020).<br><br>Surface radiative fluxes account for aerosol-radiation interactions from tropospheric aerosols, and for aerosol-cloud interactions from tropospheric aerosols, except mineral dust. Tropospheric aerosols are also assumed to exert interactions with cloud. The radiative effects of those aerosol-cloud interactions are assumed to scale with the radiative effects of aerosol-radiation interactions of tropospheric aerosols, using regional scaling factors derived from HadGEM2-ES.<br><br>Diffuse fraction is assumed to be 1 in cloudy sky. Atmospheric constituent other than aerosols and clouds are set to a constant standard mid-latitude summer atmosphere, but their variations do not affect the diffuse fraction of surface shortwave fluxes.
DOI: 10.6084/m9.figshare.17031899.v1
title: Diffuse fraction distributions 1901-2017 in support of carbon cycle modelling: No stratospheric aerosols

description: Diffuse fraction distributions 1901-2017 in support of carbon cycle modelling: No stratospheric aerosols<br><br>This diffuse fraction dataset offers 6-hourly distributions of the diffuse fraction of surface shortwave fluxes over the period 1901-2017. Radiative transfer calculations are based on monthly-averaged distributions of tropospheric aerosol optical depth, and 6-hourly distributions of cloud fraction. Methods follow those described in the Methods section of Mercado et al. (doi:10.1038/nature07949, 2009), but with updated input datasets.<br><br>The time series of speciated tropospheric aerosol optical depth is taken from the historical and RCP8.5 simulations by the HadGEM2-ES climate model (Bellouin et al., doi:10.1029/2011JD016074, 2011). To correct for biases in HadGEM2-ES, tropospheric aerosol optical depths are scaled over the whole period to match the global and monthly averages obtained over the period 2003-2017 by the CAMS Reanalysis of atmospheric composition (Inness et al., doi:10.5194/acp-19-3515-2019, 2019), which assimilates satellite retrievals of aerosol optical depth.<br><br>This dataset does not account for stratospheric aerosols.<br><br>The time series of cloud fraction is obtained by scaling the 6-hourly distributions simulated in the Japanese Reanalysis (JRA; Kobayashi et al., doi:10.2151/jmsj.2015-001, 2015) to match the monthly-averaged cloud cover in the CRU TS v4.03 dataset (Harris et al. doi:10.1038/s41597-020-0453-3, 2020).<br><br>Surface radiative fluxes account for aerosol-radiation interactions from tropospheric aerosols, and for aerosol-cloud interactions from tropospheric aerosols, except mineral dust. Tropospheric aerosols are also assumed to exert interactions with cloud. The radiative effects of those aerosol-cloud interactions are assumed to scale with the radiative effects of aerosol-radiation interactions of tropospheric aerosols, using regional scaling factors derived from HadGEM2-ES.<br><br>Diffuse fraction is assumed to be 1 in cloudy sky. Atmospheric constituent other than aerosols and clouds are set to a constant standard mid-latitude summer atmosphere, but their variations do not affect the diffuse fraction of surface shortwave fluxes.
DOI: 10.6084/m9.figshare.14251409.v8
title: IPV_attachment study

description: IPV Methodology/supplementary file for study 1 and 2.
DOI: 10.6084/m9.figshare.14251409.v7
title: IPV_attachment study

description: IPV Study 1 datasetIPV Study 2 datasetIPV Methodology/supplementary file for study 1 and 2.