cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12656165.v31
title: COVID-19 Case Reports in China

description: Chinese prefectural level governments started to report daily confirmed COVID-19 cases online, starting from January 2020. The disclosures may contain the mobility, potential exposure scenario, epidemiological characteristics, and other useful information of individual cases. We organized a group of content coders since early March 2020, kept monitoring the information updates, manually extracted useful information from the public disclosures, and compiled these datasets.We welcome any form of collaborations with us and non-commercial reuse of our dataset. We highly encourage interested parties to examine the data, report errors in our coding, and help us to keep the data updated.The detailed data description can be found on SSRN preprint server https://dx.doi.org/10.2139/ssrn.3705815.
DOI: 10.17043/shibley-2020-finestructure-1
title: Acoustic and in-situ observations of double-diffusive finestructure in the Arctic Ocean, August to September 2016
DOI: 10.17043/shibley-2020
title: Acoustic and in-situ observations of double-diffusive finestructure in the Arctic Ocean, August to September 2016
DOI: 10.6084/m9.figshare.17303009.v1
title: High quantities of small microplastics presenting from surface ocean to abyssal depths in the South Atlantic Subtropical Gyre

description: MP particles were collected in the South Atlantic Gyre and identified with the FTIR imaging.
DOI: 10.17043/sjostrom-2017-south-africa-1
title: Biogenic and geochemical data covering the last 1700 years from a peat and sediment core from Mpumalanga Province, South Africa
DOI: 10.17043/sjostrom-2019-method-1
title: Loss-of-ignition, elemental carbon and mineral data from a peat composite – from a method study for organic matter removal
DOI: 10.17043/sjostrom-2020-sweden-1
title: Atmospheric mineral deposition and peat accumulation rates during the last 8300 years in south-central Sweden
DOI: 10.17043/sjostrom-2020
title: Atmospheric mineral deposition and peat accumulation rates during the last 8300 years in south-central Sweden
DOI: 10.17043/skelton-2016-islay-1
title: Stable isotope data from Neoproterozoic carbonate rocks, Islay, Scotland
DOI: 10.17043/soerensen-2018-mercury-1
title: Baltic Sea water column mercury profiles 2014–2016
DOI: 10.17043/steinthorsdottir-2020-miocene-1
title: CO₂ reconstruction for the Miocene Climatic Optimum based on fossil plants
DOI: 10.17043/steinthorsdottir-2020
title: CO₂ reconstruction for the Miocene Climatic Optimum based on fossil plants
DOI: 10.6084/m9.figshare.14864769.v3
title: clean_data

description: This item contains fish swimming speed (ucrit) and morphological measurement datasets. This data is currently submitted for publication in Scientific Data. Please wait for an updated citation before using this data in publications.
DOI: 10.6084/m9.figshare.14864769.v2
title: clean_data

description: This item contains fish swimming speed (ucrit) and morphological measurement datasets. This data is currently submitted for publication in Scientific Data. Please wait for an updated citation before using this data in publications.
DOI: 10.17043/stockholm-historical-barometer-1
title: Stockholm Historical Weather Observations — Barometer observations in original units since 1756
DOI: 10.17043/stockholm-historical-barometer
title: Stockholm Historical Weather Observations — Barometer observations in original units since 1756
DOI: 10.17043/stockholm-historical-cloud-amount-1
title: Stockholm Historical Weather Observations — Sub-daily cloud amount since 1756
DOI: 10.17043/stockholm-historical-cloud-amount
title: Stockholm Historical Weather Observations — Sub-daily cloud amount since 1756
DOI: 10.6084/m9.figshare.11860785.v1
title: Replication data - Shaping EU agencies’ rulemaking: Interest groups, national regulatory agencies and the European Aviation Safety Agency

description: Accompanying dataset for article 'Shaping EU agencies' rulemaking: Interest groups, national regulatory agencies and the European Aviation Safety Agency'
DOI: 10.17043/stockholm-historical-daily-slp-1
title: Stockholm Historical Weather Observations — Sub-daily sea level pressure since 1756
DOI: 10.17043/stockholm-historical-slp-daily
title: Stockholm Historical Weather Observations — Sub-daily sea level pressure since 1756
DOI: 10.17043/stockholm-historical-daily-temperature-1
title: Stockholm Historical Weather Observations — Daily mean air temperatures since 1756
DOI: 10.17043/stockholm-historical-daily-temperature-2
title: Stockholm Historical Weather Observations — Daily mean air temperatures since 1756
DOI: 10.17043/stockholm-historical-temps-daily-2
title: Stockholm Historical Weather Observations — Daily mean air temperatures since 1756
DOI: 10.17043/stockholm-historical-monthly-temperature-1
title: Stockholm Historical Weather Observations — Monthly mean air temperatures since 1756
DOI: 10.6084/m9.figshare.14832759.v3
title: Data availability.xlsx

description: <b>Dataset: </b><b>The sympathetic nervous system exacerbates carotid body sensitivity in hypertension. </b>
DOI: 10.17043/stockholm-historical-monthly-temperature-2
title: Stockholm Historical Weather Observations — Monthly mean air temperatures since 1756
DOI: 10.6084/m9.figshare.14832759.v2
title: Data availability.xlsx

description: <b>Dataset: </b><b>The sympathetic nervous system exacerbates carotid body sensitivity in hypertension. </b>
DOI: 10.17043/stockholm-historical-temps-monthly-2
title: Stockholm Historical Weather Observations — Monthly mean air temperatures since 1756
DOI: 10.17043/stockholm-historical-monthly-temperature-3
title: Stockholm Historical Weather Observations — Monthly mean air temperatures since 1756
DOI: 10.17043/stockholm-historical-temps-monthly-3
title: Stockholm Historical Weather Observations — Monthly mean air temperatures since 1756
DOI: 10.17043/stockholm-historical-obs-times-1
title: Stockholm Historical Weather Observations — Observation times since 1756
DOI: 10.17043/stockholm-historical-obs-times
title: Stockholm Historical Weather Observations — Observation times since 1756
DOI: 10.17043/stockholm-historical-pressure-1
title: Stockholm Historical Weather Observations — Air pressure
DOI: 10.17043/stockholm-historical-snow-depth-1
title: Stockholm Historical Weather Observations — Maximum snow depth each winter since 1904/05
DOI: 10.17043/stockholm-historical-snow-depth
title: Stockholm Historical Weather Observations — Maximum snow depth each winter since 1904/05
DOI: 10.6084/m9.figshare.16587311.v5
title: AREAdata updating output files

description: AREAdata output files for automatically updating climate averages. These are daily climate estimates based on the CDS ERA-5 reanalysis, see pearselab.github.io/areadata/.<br>File names begin with the climate variable given:temp = Temperaturespechumid = Specific Humidityrelhumid = Relative Humidityprecip = Precipiationuv = Ultraviolet<br>File names contain the administrative division level:<br>countries - highest administrative division<br>GID1 - lower administrative division, equivalent to states<br>GID2 - lower administrative division, equivalent to counties<br>Files downloadable as RDS files for direct use in the R statistical programming environment (appended .RDS), or as zip-compressed tab delimited files for other applications (appended .zip).<br>
DOI: 10.17043/stockholm-historical-thermometer-1
title: Stockholm Historical Weather Observations — Thermometer observations since 1756
DOI: 10.6084/m9.figshare.16587311.v4
title: AREAdata updating output files

description: AREAdata output files for automatically updating climate averages. These are daily climate estimates based on the CDS ERA-5 reanalysis, see pearselab.github.io/areadata/.<br>File names begin with the climate variable given:temp = Temperaturespechumid = Specific Humidityrelhumid = Relative Humidityprecip = Precipiationuv = Ultraviolet<br>File names contain the administrative division level:<br>countries - highest administrative division<br>GID1 - lower administrative division, equivalent to states<br>GID2 - lower administrative division, equivalent to counties<br>Files downloadable as RDS files for direct use in the R statistical programming environment (appended .RDS), or as zip-compressed tab delimited files for other applications (appended .zip).<br>
DOI: 10.17043/stockholm-historical-thermometer
title: Stockholm Historical Weather Observations — Thermometer observations since 1756
DOI: 10.17043/stordalen-ghg-sensors-2015-iot-1
title: Methane and carbon dioxide fluxes, temperature and relative humidity at Mellersta Harrsjön lake, Stordalen Mire, Abisko, 2015
DOI: 10.6084/m9.figshare.12130980.v120
title: MOOD Maps of Google community mobility change during the COVID-19 outbreak

description: The MOOD project (MOnitoring Outbreak events for Disease surveillance in a data science context. H2020) has geo-referenced the data Google has published as a series of PDF files presenting reports on national and subnational human mobility levels relative to a baseline data of late January 2020. The details and the PDF files can be found at https://www.google.com/covid19/mobility/.<br>More detail on these files can be found at https://www.moodspatialdata.com/humanmobilityforcovid19 <br>The first set of data were released on April 2 2020 and have been revised weekly since then. The maps now utilise the CSV data released by Google. Please note that the maps figures use a mean of the previous three days, while the Google PDFs use a single days data so there will be differences between values in our maps when compare to the Google PDFs.<br>The authors have extracted the majority of these data into a series of excel spreadsheets. Each worksheet provides the data for % change in numbers of records at various types of location categories illustrated by: retail and recreation, grocery and pharmacy, parks and beaches, transit stations, workplaces and residential (columns f to K). A second set of columns calculates the difference of each value from the mean values for each category (columns L to P) Columns A to E contain geographical details. Column Q contains the names used to link to a mapping file.There are separate worksheets for the date of the data from each dated release (e.g. 2903, 0504 etc.) and separate worksheets calculating the changes between specific dates.<br>A second spreadsheet has been added calculating the 3 day moving mean of each day from the 15th of February. Each day is referenced by the Gregorian calendar day count. So day 48 = Feb 17th.<br>The maps (for EU & Global) display these data. We provide 600 dpi jpegs of the Global (“WD”) and European (“EU”) mapped values at the latest date available, for each of the mobility categories: retail and recreation (“retrec”) , grocery and pharmacy (“grocphar”) , parks (“parks”) , transit stations (“transit”), residential (“resid”) and workplaces (“work”). We also provide maps of the changes from the previous week (“ch”).<br>All data extracting and subsequent processing have been carried out by ERGO (Environmental Research Group Oxford, c/o Dept Zoology, University of Oxford) on behalf of the MOOD H2020 project. Data will be periodically updated. Additional maps can be obtained on request to the authors.
DOI: 10.17043/stordalen-ghg-sensors-2015-iot
title: Methane and carbon dioxide fluxes, temperature and relative humidity at Mellersta Harrsjön lake, Stordalen Mire, Abisko, 2015
DOI: 10.17043/stordalen-lake-temperatures-1
title: Temperatures in subarctic lakes on the Stordalen Mire, Abisko, Northern Sweden
DOI: 10.6084/m9.figshare.12130980.v119
title: MOOD Maps of Google community mobility change during the COVID-19 outbreak

description: The MOOD project (MOnitoring Outbreak events for Disease surveillance in a data science context. H2020) has geo-referenced the data Google has published as a series of PDF files presenting reports on national and subnational human mobility levels relative to a baseline data of late January 2020. The details and the PDF files can be found at https://www.google.com/covid19/mobility/.<br>More detail on these files can be found at https://www.moodspatialdata.com/humanmobilityforcovid19 <br>The first set of data were released on April 2 2020 and have been revised weekly since then. The maps now utilise the CSV data released by Google. Please note that the maps figures use a mean of the previous three days, while the Google PDFs use a single days data so there will be differences between values in our maps when compare to the Google PDFs.<br>The authors have extracted the majority of these data into a series of excel spreadsheets. Each worksheet provides the data for % change in numbers of records at various types of location categories illustrated by: retail and recreation, grocery and pharmacy, parks and beaches, transit stations, workplaces and residential (columns f to K). A second set of columns calculates the difference of each value from the mean values for each category (columns L to P) Columns A to E contain geographical details. Column Q contains the names used to link to a mapping file.There are separate worksheets for the date of the data from each dated release (e.g. 2903, 0504 etc.) and separate worksheets calculating the changes between specific dates.<br>A second spreadsheet has been added calculating the 3 day moving mean of each day from the 15th of February. Each day is referenced by the Gregorian calendar day count. So day 48 = Feb 17th.<br>The maps (for EU & Global) display these data. We provide 600 dpi jpegs of the Global (“WD”) and European (“EU”) mapped values at the latest date available, for each of the mobility categories: retail and recreation (“retrec”) , grocery and pharmacy (“grocphar”) , parks (“parks”) , transit stations (“transit”), residential (“resid”) and workplaces (“work”). We also provide maps of the changes from the previous week (“ch”).<br>All data extracting and subsequent processing have been carried out by ERGO (Environmental Research Group Oxford, c/o Dept Zoology, University of Oxford) on behalf of the MOOD H2020 project. Data will be periodically updated. Additional maps can be obtained on request to the authors.
DOI: 10.17043/stordalen-lake-temperature
title: Temperatures in subarctic lakes on the Stordalen Mire, Abisko, Northern Sweden
DOI: 10.17043/stordalen-lake-temperatures-2
title: Temperatures in subarctic lakes on the Stordalen Mire, Abisko, Northern Sweden
DOI: 10.17043/stordalen-lake-temperature-2
title: Temperatures in subarctic lakes on the Stordalen Mire, Abisko, Northern Sweden
DOI: 10.17043/stordalen-lake-temperatures-3
title: Temperatures in subarctic lakes on the Stordalen Mire, Abisko, Northern Sweden
DOI: 10.17043/stordalen-lake-temperature-3
title: Temperatures in subarctic lakes on the Stordalen Mire, Abisko, Northern Sweden
DOI: 10.17043/stordalen-lake-temperatures-4
title: Temperatures in subarctic lakes on the Stordalen Mire, Abisko, Northern Sweden
DOI: 10.17043/stordalen-lake-temperature-4
title: Temperatures in subarctic lakes on the Stordalen Mire, Abisko, Northern Sweden
DOI: 10.17043/stordalen-lakes-ch4-conc-1
title: Methane concentrations from subarctic lakes on the Stordalen Mire, Abisko, Northern Sweden
DOI: 10.17043/stordalen-lakes-ch4-conc
title: Methane concentrations from subarctic lakes on the Stordalen Mire, Abisko, Northern Sweden
DOI: 10.17043/stordalen-lakes-ch4-diff-1
title: Methane diffusive fluxes from subarctic lakes on the Stordalen Mire, Abisko, Northern Sweden
DOI: 10.17043/stordalen-lakes-ch4-diff
title: Methane diffusive fluxes from subarctic lakes on the Stordalen Mire, Abisko, Northern Sweden
DOI: 10.17043/tarfala-1
title: Data from Tarfala Research Station, Kebnekaise Mountains, Northern Sweden
DOI: 10.17043/tarfala-kirchner-2017-bathymetry-1
title: Bathymetry of Lake Tarfala, northern Sweden
DOI: 10.17043/tarfala-kirchner-2017-bathymetry-2
title: Bathymetry of Lake Tarfala, northern Sweden
DOI: 10.17043/tarfala-kirchner-2020-lake-temperature-1
title: Water temperatures from the arctic-alpine Lake Tarfala, Northern Sweden, 2016⁠ – ⁠2019
DOI: 10.17043/kirchner-2020
title: Water temperatures from the arctic-alpine Lake Tarfala, Northern Sweden, 2016⁠ – ⁠2019
DOI: 10.17043/tarfala-ice-off-2020
title: Time lapse imagery of the ice-off event at Lake Tarfala, Kebnekaise Mountains, northern Sweden, summer 2020
DOI: 10.17043/tarfala-kirchner-2021-ice-off-1
title: Time lapse imagery of the ice-off event at Lake Tarfala, Kebnekaise Mountains, northern Sweden, summer 2020
DOI: 10.17043/thorslund-2018-hydroclimate-1
title: Long-term hydroclimatic data for 22 globally distributed wetlandscape sites
DOI: 10.6084/m9.figshare.17303009
title: High quantities of small microplastics presenting from surface ocean to abyssal depths in the South Atlantic Subtropical Gyre

description: MP particles were collected in the South Atlantic Gyre and identified with the FTIR imaging.
DOI: 10.17043/thurner-2018-sapwood-1
title: Sapwood biomass carbon in northern boreal and temperate forests
DOI: 10.17043/thurner-2018-sapwood-1.1
title: Sapwood biomass carbon in northern boreal and temperate forests
DOI: 10.6084/m9.figshare.17303009.v2
title: High quantities of small microplastics presenting from surface ocean to abyssal depths in the South Atlantic Subtropical Gyre

description: MP particles were collected in the South Atlantic Gyre and identified with the FTIR imaging.
DOI: 10.17043/thurner-2020
title: Sapwood biomass carbon in northern boreal and temperate forests
DOI: 10.17043/west-2019-amino-acid-1
title: Amino acid racemization data from the Yermak Plateau, Arctic Ocean, 0⁠ – 227 ka
DOI: 10.6084/m9.figshare.13673845.v2
title: Supplementary code, raw data and figure plots from "Lattice defects induced by microtubule-stabilizing agents exert a long-range effect on microtubule growth by promoting catastrophes"

description: Supplementary code, raw data and figure plots from the paper:<br><i>"Lattice defects induced by microtubule-stabilizing agents exert a long-range effect on microtubule growth by promoting catastrophes"</i><br>Ankit Rai, Tianyang Liu,<br> Eugenie A. Katrukha, Juan Estévez-Gallego,<br> Szymon W. Manka, Ian Paterson, J Fernando Díaz,<br> Lukas C Kapitein, Carolyn A. Moores, Anna Akhmanova<br><br>
DOI: 10.17043/west-2019
title: Amino acid racemization data from the Yermak Plateau, Arctic Ocean, 0⁠ – 227 ka
DOI: 10.17043/wild-2019-carbon-1
title: Content of carbon isotopes (¹³C and ¹⁴C) in organic carbon pools in terrestrial Siberia
DOI: 10.6084/m9.figshare.13673845.v1
title: Supplementary code, raw data and figure plots from "Microtubule lattice defects promote catastrophes"

description: Supplementary code, raw data and figure plots from the paper:<br><i>"Microtubule lattice defects promote catastrophes"</i><br>Ankit Rai, Tianyang Liu, Eugene A. Katrukha, Juan Estévez-Gallego, J. Fernando Díaz, Lukas C. Kapitein, Carolyn A. Moores and Anna Akhmanova<br>
DOI: 10.17043/winiger-2016-aerosol-abisko-1
title: Concentration and carbon isotope data of elemental carbon aerosols, Abisko, Sweden, September 2011 to March 2013
DOI: 10.6084/m9.figshare.16821187.v2
title: Table S1.xlsx - Annual mean geometric day length (h); annual mean global solar radiation (MJ m-2 day-1); annual mean effective day length up to 500 lx; annual mean effective day length at 1000 lx; annual mean effective day length up to 5000 lx; annual mean effective day length up to 10000 lx.

description: The data are relative to the Brazilian territory, the fifth largest country in the world, and help in the discussion of concepts and interpretations of environmental photoperiod. The data available are: latitude, longitude, solar radiation and average monthly and annual day length of the 5,569 Brazilian cities. Light exposure in Brazil. Annual mean geometric day length (h); annual mean global solar radiation (MJ m-2 day-1); annual mean effective day length up to 500 lx; annual mean effective day length at 1000 lx; annual mean effective day length up to 5000 lx; annual mean effective day length up to 10000 lx.
DOI: 10.17043/winiger-2016-aerosol-svalbard-1
title: Concentration and carbon isotope data of elemental carbon aerosols, Svalbard, Jan–Mar 2009
DOI: 10.17043/winiger-2018-bc-tiksi-1
title: Observation (carbon isotopes) and model data for Arctic black carbon, Tiksi, Russia, April 2012 to March 2014
DOI: 10.17043/wohlfarth-2008-varve-1
title: Clay-varve data sets for southeastern Sweden
DOI: 10.17043/wohlfarth-2016-chashnitsy-1
title: Lateglacial and Holocene paleoenvironmental change data from Lake Chashnitsy, Russia
DOI: 10.6084/m9.figshare.11881395.v2
title: DownloadForm.zip

description: absdd
DOI: 10.17043/wohlfarth-2016-les-echets-1
title: Multi-proxy dataset from Les Échets, France
DOI: 10.6084/m9.figshare.11881395.v1
title: DownloadForm.zip

description: absdd
DOI: 10.17043/wohlfarth-2016-nero-1
title: Lateglacial and Holocene paleoenvironmental change data from Lake Nero, Russia
DOI: 10.17043/wohlfarth-2016-pichozero-1
title: Lateglacial and Holocene paleoenvironmental change data from Lake Pichozero, Russia
DOI: 10.17043/wohlfarth-2016-tambichozero-1
title: Lateglacial and Holocene paleoenvironmental change data from Lake Tambichozero, Russia
DOI: 10.17043/wohlfarth-2016-zaozere-1
title: Lateglacial and Holocene paleoenvironmental change data from Lake Zaozer'e, Russia
DOI: 10.6084/m9.figshare.17003545.v2
title: Total surface shortwave flux distributions 1901-2017 in support of carbon cycle modelling: 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.<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: 10.17043/wohlfarth-2017-hasseldala-1
title: Lateglacial and early Holocene multi-core and multi-proxy data set for the ancient lake at Hässeldala Port, southern Sweden
DOI: 10.6084/m9.figshare.3761562.v2044
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-medvedevskoye-1
title: Lateglacial and early Holocene environmental changes from Lake Medvedevskoye, Russia
DOI: 10.17043/wohlfarth-2017-pastorskoye-1
title: Lateglacial and early Holocene paleoenvironmental change data from Lake Pastorskoye, Russia