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12,237
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12,237 results for “2019”
A comprehensive and synthetic dataset for global, regional and national greenhouse gas emissions by sector 1970-2018 with an extension to 2019
<p>Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO<sub>2</sub> emissions from fossil fuel combustion and industry (FFI), CH<sub>4</sub> emissions, N<sub>2</sub>O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO<sub>2</sub> emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.</p>
WRR_SajoRiver_ADCP_cross_sections_2019
<p>This is an ADCP dataset used for the following manuscript submitted for consideration to the journal <strong>Water Resources Research</strong>:</p> <p><em>"Farhad Bahmanpouri, Anette Eltner, Silvia Barbetta, László Bertalan, Tommaso Moramarco - Applying the entropy concept to predict the discharge and velocity distribution relying on a single surface velocity measurement by UAV"</em></p>
October 2019 700 kHz multibeam echo sounder data used for Seasonal Change of Multifrequency Backscatter in three Baltic Sea Habitats
<p>The raw data used for the study</p> <p>Seasonal Change of Multifrequency Backscatter in three Baltic Sea Habitats</p> <p>by Schulze et al.; currently under review at Frontiers in Remote Sensing. </p> <p> </p> <p>Files are stored in the s7k-Format, and sorted by date of acquisition and frequency. 200 and 400 kHz data were manufacturer-calibrated. Correct absorption values have been applied duirng the export. Refer to the paper for further dataset information.</p> <p> </p> <p>This upload stores the 700 kHz data recorded in October 2019.</p>
Counts of shorebirds and environmental correlates at Roberts Bank, British Columbia (1991 to 2019)
<p>Data include counts conducted during northward migration of two species of shorebirds (Western Sandpiper Calidris mauri and Dunlin Calidris alpina) on the mudflats of Roberts Bank, British Columbia, 1991 to 2019. Count methodology detailed in Drever et al. (2014) and Canham et al. (2021). Data also include a set of covariates related to local conditions (precipitation, temperature, wind speed and direction, solar radiation, tidal amplitude, and water discharge rates from the Fraser River).</p>
SuperDARN data in netCDF format (2019-Oct)
<p>2019-Oct SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
SuperDARN data in netCDF format (2019-Dec)
<p>2019-Dec SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
SuperDARN data in netCDF format (2019-May)
<p>2019-May SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
SuperDARN data in netCDF format (2019-Aug)
<p>2019-Aug SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
SuperDARN data in netCDF format (2019-Apr)
<p>2019-Apr SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
SuperDARN data in netCDF format (2019-Sep)
<p>2019-Sep SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
SuperDARN data in netCDF format (2019-Feb)
<p>2019-Feb SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
SuperDARN data in netCDF format (2019-Jun)
<p>2019-Jun SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
SuperDARN data in netCDF format (2019-Mar)
<p>2019-Mar SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
SuperDARN data in netCDF format (2019-Jan)
<p>2019-Jan SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
SuperDARN data in netCDF format (2019-Jul)
<p>2019-Jul SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
Randomised trials in maternal and perinatal health in low- and middle-income countries from 2010 to 2019: A systematic scoping review
<p><strong>Objectives: </strong>To identify and map all trials in maternal health conducted in low- and middle-income countries (LMIC) over the 10-year period 2010-2019, to identify geographical and thematic trends, as well as compare to global causes of maternal death and pre-identified priority areas.</p> <p><strong>Design: </strong>Systematic scoping review.</p> <p><strong>Primary and secondary outcome measures: </strong>Extracted data included location, study characteristics and whether trials corresponded to causes of mortality and identified research priority topics.</p> <p><strong>Results: </strong>Our search identified 7,269 articles, 874 of which were included for analysis. Between 2010 and 2019, maternal health trials conducted in LMICs more than doubled (50 to 114). Trials were conducted in 61 countries – 231 trials (26.4%) were conducted in Iran. Only 225 trials (25.7%) were aligned with a cause of maternal mortality. Within these trials, pre-existing medical conditions, embolism, obstructed labour, and sepsis were all under-represented when compared with number of maternal deaths globally. Large numbers of studies were conducted on priority topics such as labour and delivery, obstetric haemorrhage, and antenatal care. Hypertensive disorders of pregnancy, diabetes, and health systems and policy – despite being high-priority topics – had relatively few trials.</p> <p><strong>Conclusion: </strong>Despite trials conducted in LMICs increasing from 2010 to 2019, there were significant gaps in geographical distribution, alignment with causes of maternal mortality, and known research priority topics. The research gaps identified provide guidance and insight for future research conducted in low-resource settings.</p>
UAV of Chutou gully during 2019 to 2021
<p>We release three sets of UAV images, which were obtained on 25 August 2019, 24 July 2020, and 10 November 2020, in Chutou gully, , Miansi Town, Wenchuan County, Sichuan Province, Southwestern China. These UAV images both can be viewed and edited in ArcGIS software.</p>
Transient Freeze-Thaw Responses to the 2018 and 2019 Fires near Batagaika Megaslump, Northeast Siberia
<p>This repository contains the dataset for the Transient Freeze-Thaw Responses to the 2018 and 2019 Fires near Batagaika Megaslump, Northeast Siberia. All files are formatted as CSV. </p> <p>TD_Burn14_site1.csv contains thaw depth data at the site1 in the 2014 fire scar. </p> <p>TD_Burn14_site2.csv contains thaw depth data at the site2 in the 2014 fire scar.</p> <p>TD_Burn18.csv contains thaw depth data in the 2018 fire scar.</p> <p>TD_Unburn-Burn19.csv contains thaw depth data along the transect across the 2019 fire scar and unburned control site.</p> <p>TD_Unburn18.csv contains thaw depth data in the unburned control site for the 2018 fire scar.</p> <p>Temperature_TidbiT_190924-210916.csv contains hourly air and surface temperature data from 24 September 2019 to 16 September 2016. All data were taken by the TidbiT logger. </p> <p> </p> <p> </p>
Soil biodiversity dataset in Hong Kong during Oct 2019 - Oct 2020
<p>The dataset includes the soil biodiversity of Hong Kong during Oct 2019 - Oct 2020. The information includes the taxonomy of sampled organisms, the geographic coordinates, collection location, date of collection and DNA barcodes (with NCBI Accession number).</p>
Multi-instrument observations of the ionospheric response to the 26 December 2019 solar eclipse over Indian and Southeast Asian longitudes
<p>Here, the impact of the 26 December 2019 solar eclipse on the equatorial and low latitude ionosphere has been investigated using ground and space-based observations over Indian and Southeast Asian longitudes. The high-resolution Ionosonde observations at Tirunelveli, GPS TEC observations from a chain of GPS receivers along and across the eclipse path, TIMED-SABER, and Ionospheric Connection Explorer (ICON) satellites were utilized to investigate the eclipse-induced variations in electron density and thermospheric cooling. We noticed a tremendous increase and decrease in the base height of the F-layer, resembling the nighttime Pre-Reversal Enhancement (PRE). Near the eclipse maximum, a strong blanketing sporadic E layer was observed at Tirunelveli with a top frequency of ~18 MHz for 1 hour and 26 minutes. Satellite traces (STs) and ‘U’ shaped ionograms were noticed for the first time over Tirunelveli during eclipse maximum and end phases. The ‘STs’ and ‘U’ shaped traces indicate the presence of short-period gravity waves or TID type of wave perturbations over the Indian region. A maximum of ~5-7 TECU (30-40%) decrease in TEC is observed on the eclipse day for iisc, hyde, and tiru stations. Periodogram analyses of TEC data showed the presence of wavelike structures with periodicities of 18-24 minutes for different stations. Simultaneous observations from the ICON satellite showed an increase and decrease in hmF2 and NmF2 which matches well with the ionosonde observations from Tirunelveli. The temperature profiles from TIMED-SABER and ICON satellites showed a reduction and enhancement in the lower and upper E regions respectively.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.