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12,237 results for “2019”
Overview of social informatics scientific activities (1985–2019): Three datasets
<p>The <strong>dataset</strong> was created as part of the overview of social informatics scientific activities published from 1985 to 2019. The results of the overview are presented in the article <strong>Evolution of social informatics: Publications, research, and educational activities</strong>.</p> <p>Three files are included:</p> <ol> <li>The file <em>SI-dataset-SI-N.xslx</em> lists all peer-reviewed scientific publications containing the term <em>social informatics</em> in the title, abstract or keywords. Each listed publication is coded according to ten variables.</li> <li>The file <em>SI-dataset-SocInfo.xslx</em> lists all papers published in the Proceedings of the international conference on Social Informatics (SocInfo conference). Each listed publication is coded according to seven variables.</li> <li>The file <em>SI-dataset-Activities.xslx </em>lists all research and educational activities as well as conferences, journals and blogs that contain the term <em>social informatics</em> in their title or name.</li> </ol> <p>The methodology is detailed in the article.</p> <p>Vehovar, V., Smutny, Z., and Bartol, J. (2022). Evolution of social informatics: Publications, research, and educational activities. <em>The Information Society</em>, <em>38</em>(5), 307–333. <a href="https://doi.org/10.1080/01972243.2022.2092570">https://doi.org/10.1080/01972243.2022.2092570</a></p>
Seafloor pressure data from 2019 deployment at the Hikurangi subduction zone, New Zealand
<p>We include here hourly seafloor pressure time series (and locations) from a 2019 deployment at the Hikurangi subduction zone, used in "Using seafloor geodesy to detect vertical deformation at the Hikurangi subduction zone: insights from self-calibrating pressure sensors and ocean general circulation models", a paper submitted to JGR: Solid Earth in January 2022.</p> <p><strong>APG_hikurangi_2019.json/.mat: </strong>Seafloor pressure time series data (JSON and MATLAB format) from a deployment at the Hikurangi subduction zone in 2019. The files contain the hourly time series in datetime (UTC) and pressure in hectopascals, with the convention that a decrease in pressure is equivalent to a reduction in the height of the water column (seafloor uplift).</p> <p>The only processing that has been applied to the data is filtering using a 2-day corner lowpass filter for all sites, and the A-0-A correction for the POBS sensors (which are therefore drift corrected). All APGs not equipped with A-0-A still contain sensor drift. Each time series has been adjusted using the mean of the absolute data, which is why the time series for the sites plot about zero - amplitude has been preserved.</p> <p><strong>locations_APG_hikurangi_2019.csv: </strong>Locations of the seafloor pressure sites from a deployment at the Hikurangi subduction zone in 2019. Indicated for each site are the sensor’s institute (UTIG - University of Texas Institute for Geophysics, Austin, USA; GNS Science - GNS Science, New Zealand; LDEO - Lamont-Doherty Earth Observatory, Columbia University, USA; KU - Kyoto University and Tohoku University, Japan), A-0-A drift correction capability, deployment longitude, latitude, and depth, and whether there are usable data. The sensors without usable data either contained data logger issues or were not recovered, and are not included in APG_hikurangi_2019.json/.mat.</p>
Tropospheric delays and precipitable water vapor retrieved from global radiosonde observations from 2014 to 2019
<p>The data consists of a set of meteorological quantities including tropospheric delays (zenith wet delay, zenith hydrostatic delay, and zenith total delay), precipitable water vapor, and surface temperature and pressure. The data is retrieved from the observations of 414 globally distributed radiosonde stations from 2014 to 2019. In addition to the geographic information of radiosonde stations, the profiles of tropospheric delays and precipitable water vapor are contained in the data file. This data has a wide range of applications, e.g., validating the tropospheric delays and precipitable water vapor derived from other techniques, investigating the spatial-temporal variations of water vapor, and acting as training data of machine learning to build tropospheric delay models.</p> <p>In the manuscript "Machine Learning-based Model for Real-time GNSS Precipitable Water Vapor Sensing", this data is used to train a machine learning model to map the zenith total delays to precipitable water vapor. The data is split into training data and test data, where the data from 2014 to 2018 are employed for model training, and the data of 2019 are used for testing. The developed models and the results for the manuscript are saved in the directories of Models and Results, respectively.</p>
1 km Monthly Precipitation Dataset for China from 1952 to 2019 (ChinaClim_time-series)
<p>ChinaClim_time-series is a monthly temperatures and precipitation dataset in China for the period of 1952-2019 of 1km spatial resolution, the data was generated by superimposing monthly anomaly surface and baseline climatology surface (ChinaClim_baseline) based on climatologically aided interpolation (CAI). The scale factor of the data is 0.1.</p>
1 km Monthly Maximum Temperature Dataset for China from 1952 to 2019 (ChinaClim_time-series)
<p>ChinaClim_time-series is a monthly temperatures and precipitation dataset in China for the period of 1952-2019 of 1km spatial resolution, the data was generated by superimposing monthly anomaly surface and baseline climatology surface (ChinaClim_baseline) based on climatologically aided interpolation (CAI). The scale factor of the data is 0.1.</p>
1 km Monthly Average Temperature Dataset for China from 1952 to 2019 (ChinaClim_time-series)
<p>ChinaClim_time-series is a monthly temperatures and precipitation dataset in China for the period of 1952-2019 of 1km spatial resolution, the data was generated by superimposing monthly anomaly surface and baseline climatology surface (ChinaClim_baseline) based on climatologically aided interpolation (CAI). The scale factor of the data is 0.1.</p>
1 km Monthly Minimum Temperature Dataset for China from 1952 to 2019 (ChinaClim_time-series)
<p>ChinaClim_time-series is a monthly temperatures and precipitation dataset in China for the period of 1952-2019 of 1km spatial resolution, the data was generated by superimposing monthly anomaly surface and baseline climatology surface (ChinaClim_baseline) based on climatologically aided interpolation (CAI). The scale factor of the data is 0.1.</p>
Flowing days Derived from CubeSat Imagery and Ground Observations in Hassayampa River (HR), Arizona from 2019-2021 (Water Years)
<p>Flowing days derived from CubeSat imagery and ground observations in Hassayampa River (HR), Arizona from October 2018 to September 2021. This database supports the following paper:</p> <div> <div>Wang, Z., & Vivoni, E. R. (2022). Detecting streamflow in dryland rivers using CubeSats. <em>Geophysical Research Letters</em>, 49, e2022GL098729. <a href="https://doi.org/10.1029/2022GL098729">https://doi.org/10.1029/2022GL098729</a></div> </div> <div> <div> </div> </div> <p>This database includes three folders.</p> <ol> <li>GroundObservations: Ground observations along the HR retrieved from USGS website (https://waterdata.usgs.gov/nwis) and Flood Control District of Maricopa County using Single Sensor Data Reports tool (https://alert.fcd.maricopa.gov/showrpts_mc.html): <ol> <li><strong>Precip</strong>: Records of daily precipitation amount.</li> <li><strong>Stream</strong>: Records of daily streamflow amount.</li> </ol> </li> <li>GIS: GIS layers used in deriving flowing days. <ol> <li><strong>Subreach</strong>: Shapefiles of nine sub reaches along the HR.</li> <li><strong>Buffer segment</strong>: Buffer zones perpendicular to HR reaches at 90 m resolution. Column ID is sorted in ascending order along the HR.</li> <li><strong>Channel masks: </strong>Channel masks of HR derived from Planet data.</li> </ol> </li> <li>Results <ol> <li><strong>BufferSegment_Flowdays.xlsx:</strong> Days with flow determined using the NIR difference threshold for water years (WY) 2019 to 2021 for each 90 m buffer areas. The relative location of each buffer areas can be found in the Buffer Segment GIS layer. ‘NaN’ suggests no data.</li> </ol> </li> </ol> <div> <div> </div> </div>
Figures - MIDA Project, April 2019
<p><strong>MIDA Figures</strong></p> <p>Figures created for the MIDA Project application document. </p> <p> </p>
Eurostat dataset weekly deaths from 2019
<p>European Deaths broken down by week, sex and NUTS 3 published by Eurostat</p> <p>First version data up to W13 2021</p> <p>Second Version data up to W3 2022</p>
World Risk Index - Vulnerability Component 2019
<p>Indicators for assessing human vulnerability to climatic hazards and natural hazards based on the most recent available international data in 2019. The overall indicator system is based on the concept of WRI methodology published in 2011 (https://weltrisikobericht.de/wp-content/uploads/2016/08/WorldRiskReport_2011.pdf). See also for conceptual understanding: https://doi.org/10.1142/S2345737615500037; https://doi.org/10.1142/S2345737616500056</p> <p>Data used in following publications:</p> <p>https://doi.org/10.1016/j.scitotenv.2021.150065</p> <p>https://doi.org/10.1088/1748-9326/ac1f43</p> <p> </p>
Data from Test Performance Study Euphresco project 2019-A-327
<p>Data of the test performance study organised in the framework of the Euphresco project 2019-A-327 'Validation of molecular tests for the detection of tomato brown rugose fruit virus<em> </em>(ToBRFV) in seed of tomato and pepper'</p>
Daily indices of heat at select U.S. military installations (1990 - 2019)
<p>R data files for annual indices of heat of 30 Continental U.S. (CONUS) U.S. military installations from 1990-2019.</p> <p>Daily indices were derived from hourly meteorological estimates from the North American Land Data Assimilation System 2 (NLDAS-2) forcing dataset served as the primary source of weather and atmospheric data. We extracted parameters from NLDAS grid cells containing the centroid of each installation based on shapefiles from the Department of Defense (DoD) Military Installations, Ranges, and Training Areas (MIRTA) Dataset. We calculated relative humidity from specific humidity, temperature, and atmospheric pressure; heat index (HI) from temperature and relative humidity based on a US National Weather Service algorithm; and outdoor WBGT from air temperature, relative humidity, solar irradiance, barometric pressure, and wind speed using the method of Liljegren <em>et al.</em></p> <p>`daily_indices.Rmd` (328402 observations of 18 variables) contains indices for daily maximum, mean, minimum, and 95th percentile values of temperature (deg F), heat index (deg F), and WBGT (deg C) with urban and non-urban inputs to the Liljegren model.</p> <p>`morning_indices.Rmd` (328364 observations of 5 variables) contains indices for daily 0600 hour (local time) values of temperature (deg F), heat index (deg F), and WBGT (deg C, urban model). </p>
Storyline data used in the paper "The July 2019 European heatwave in a warmer climate: Storyline scenarios with a coupled model using spectral nudging"
<p>We provide the storyline data (in NetCDF format) used in the paper: “The July 2019 European heatwave in a warmer climate: Storyline scenarios with a coupled model using spectral nudging” published in Journal of Climate. The data is structured in four .tar.gz files (Preindustrial, Present, 2 and 4 K warmer climates) containing all variables used in this each climate. The data from the five ensemble members (E1 to E5) have been included separately in 3-months files.</p> <p>Atmospheric variables (Files are named as: {variable}_E{ensemble member}_{starting month}{year}.nc:</p> <ul> <li> <p>Latent heat flux (ahfl)</p> </li> <li> <p>Sensible heat flux (ahfs)</p> </li> <li> <p>Monthly Global Mean 2m Temperature (GMTT2mMonthly)</p> </li> <li> <p>Maximum 2m Temperature (t2max)</p> </li> <li> <p>Mean 2m Temperature (t2mean)</p> </li> <li> <p>Minimum 2m Temperature (t2max)</p> </li> <li> <p>Soil Wetness (ws)</p> </li> </ul> <p> Only for present climate:</p> <ul> <li> <p>850 hPa Temperature (T850)</p> </li> <li> <p>Total Cloud Cover (TCC)</p> </li> <li> <p>500 hPa Geopotential Height (Z500)</p> </li> </ul> <p>Five layers soil moisture (Only for present climate, Files are named as: From20172019in2017Climatessp370{ensemble member}_{year}{starting month}.01_jsbid.nc) </p> <p>Oceanic variables (from FESOM, Files are named as: {variable}_E{ensemble member}_{year}{starting month}01.nc:</p> <ul> <li> <p>Sea Ice Concentration (SIC)</p> </li> <li> <p>Sea Surface Temperature (SST)</p> </li> </ul> <p><strong>Please, note that FESOM uses an unstructured mesh.</strong></p>
Figure 11. Female Matinta Ruiz & Maddison 2019 in The Ecuadorean jumping spiders of Andreas Kay (Araneae: Salticidae)
Figure 11. Female Matinta Ruiz & Maddison 2019, in Ruiz, Maddison & Galiano 2019 (Tribe Amycini).
Supplementary dataset for "High-resolution Finite Fault Slip Inversion of the 2019 Ridgecrest Earthquake using 3D Finite Element Modeling."
<p>Supplementary dataset for "High-resolution Finite Fault Slip Inversion of the 2019 Ridgecrest Earthquake using 3D Finite Element Modeling." </p>
Atmospheric data measured in Qvidja, Finland, from Apr to Jun 2019
<p>Time series data of gaseous and particle components and environmental variables measured at the Qvidja farm from Apr to Jun 2019.</p>
Drivers of extreme wildfire years in the 1965–2019 fire regime of the Tłı̨chǫ First Nation territory, Canada
<p>Datasets, metadata and Rscript used to describe 1965-2019 wildfire regime and extreme wildfire years in central NWT.</p> <p> </p>
Reports about the tools for georeferencing IFC delivered within the GeoBIM benchmark 2019 - Task 2
<p>Answers delivered through the online forms, about the tests performed by participants within the Task 2 - georeferencing IFC files of the GeoBIM benchmark 2019, funded as a Scientific Initiative 2019 by the International Society of Photogrammetry and Remote Sensing (ISPRS) and co-funded by the European association for Spatial Data Research (EuroSDR).</p> <p>Full details and additional resources about the project are available in the project website: https://3d.bk.tudelft.nl/projects/geobim-benchmark/</p> <p>The dataset results from the collaboration of the authors with all the participants to the benchmark, listed at https://3d.bk.tudelft.nl/projects/geobim-benchmark/participants.html</p> <p>It is composed by 2 files:</p> <p>- the answers to the delivered online forms, organised in excel sheet;</p> <p>- the same answers organised in a more human-readable PDF, with reliable images and links.</p>
Lekima_2019_Wenzhou_Taizhou_Radar_GRL_Paper
<p>Radar data supporting the Paper: Microphysical Characteristics of the Phase-locking VRW-induced Asymmetric Convection in the Outer Eyewall of Super Typhoon Lekima (2019)</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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.