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12,237 results for “2019”
A Weakly-Labeled Stance Dataset during the 2019 South American Protests
<p>Research across different disciplines has documented the expanding polarization in social media. However, much of it focused on the US political system or its culturally controversial topics. In this work, we explore polarization on Twitter in a different context, namely the protest that paralyzed several countries in the South American region in 2019. By leveraging users’ endorsement of politicians' tweets and hashtag campaigns with defined stances towards the government of each country (for or against), we construct a weakly labeled stance dataset with hundreds of thousands of users. Moreover, through the synergistic usage of network-focused methods applied on news sharing patterns and language-focused methods, we validate our labeling methodology by showing that these stances partition the users into meaningful communities. That is, we show that polarization in users' news sharing patterns was consistent with their stances towards the government and that polarization in their language mainly manifested along ideological, political, or protest-related lines.</p>
NASA's Airborne Topographic Mapper (ATM) ground calibration data for the Arctic Spring campaign 2019
<p>The Airborne Topographic Mapper (ATM) was a scanning lidar developed and used by NASA for observing the Earth’s topography for several scientific applications, foremost of which was the measurement of changing Arctic and Antarctic ice sheets, glaciers and sea ice. ATM measured topography to an accuracy of better than 5 centimeters by incorporating measurements from GPS (global positioning system) receivers and inertial navigation system (INS) attitude sensors.</p> <p>This particular data set was used in a publication by Studinger <em>et al.</em>, 2022 (<a href="https://doi.org/10.5194/tc-16-3649-2022">https://doi.org/10.5194/tc-16-3649-2022</a>) that developed methods for estimating water depths of supraglacial lakes on the Greenland Ice Sheet.</p> <p>In pressurized aircraft the transmitted laser pulse travels thru the aircraft’s optical window close to the scan mirror. The optical delay fiber that is necessary to separate the transmit pulse and window reflection as well as other system components introduce a laser time-of-flight range bias that needs to be determined from ground calibration measurements. This data set includes ATM waveform data from the T6 and T7 lidars, as well as the true ranges and a MATLAB® function to read ground test waveform data.</p> <p><strong><strong>See also:</strong> </strong></p> <p>NASA's Airborne Topographic Mapper (ATM) ground calibration data for waveform data products: <a href="https://doi.org/10.5281/zenodo.7225936">https://doi.org/10.5281/zenodo.7225936</a></p> <p>User guide for NASA's Airborne Topographic Mapper HDF5 Waveform Data: Products:<a href="https://doi.org/10.5281/zenodo.7246097"> https://doi.org/10.5281/zenodo.7246097</a></p> <p>Collection of MATLAB® functions for working with ATM (Airborne Topographic Mapper, laser altimetry data products in HDF5 waveform format: <a href="https://github.com/mstudinger/ATM-waveform-tools">https://github.com/mstudinger/ATM-waveform-tools</a></p> <p>Airborne Topographic Mapper (ATM) Bathymetry Toolkit (MATLAB® functions): <a href="https://doi.org/10.5281/zenodo.6341229">https://doi.org/10.5281/zenodo.6341229</a></p>
A global spatiotemporally seamless daily mean land surface temperature from 2003 to 2019
<p>The global daily mean land surface temperature product (GADTC product) was generated based on the improved ADTC-based framework (termed IADTC framework) which basically combines the annual temperature cycle and diurnal temperature cycle model. </p> <p>The GADTC product is organized by year and each .tif image contains the global spatiotemporally seamless daily mean land surface temperature (LST) for each day with the unit of Kelvin. </p> <p>The demo code of the IADTC framework is available at https://github.com/faluhong/IADTC-framework. </p>
Data and outputs for chapter 'The impacts of the 2019-20 wildfires on Australian fungi ' in 'Australia's Megafires: Biodiversity Impacts and Lessons from 2019-2020
<p><strong>Dataset includes raw data downloaded from the following sources: fungi_data.csv</strong></p> <ul> <li>Atlas of Living Australia occurrence download: https://doi.org/10.26197/ala.9e0ca388-9da2-4096-b1a3-26e2aaa51d8a. Accessed 2021-09-16. GBIF.org (16 September 2021)</li> <li>GBIF Occurrence Download https://doi.org/10.15468/dl.secenk</li> <li>Fungimap (https://fungimap.org.au/ (data obtained directly from Fungimap Inc.)</li> <li>MycoPortal (https://mycoportal.org/portal/index.php)</li> <li>iNaturalist (https://www.inaturalist.org/home)</li> </ul> <p><strong>Output files from point and polygon overlap with fire layer:</strong></p> <ul> <li>Fungi and fire analysis point overlap.xlsx</li> <li>Fungi and fire analysis polygon overlap.xlsx</li> </ul> <p> </p> <p> </p>
Time series measurements of nitrogen fixation in the subtropical North Pacific (extended through 2019)
<p>Rates of N<sub>2</sub> fixation were measured using the <sup>15</sup>N<sub>2</sub> isotopic tracer technique. Sampling occurred during near-monthly Hawaii Ocean Time-series cruises. Whole seawater samples from six discrete depths (5, 25, 45, 75, 100, and 125 m) were subsampled into acid-washed 4.3 L polycarbonate bottles. The <sup>15</sup>N<sub>2</sub> gas was first dissolved into seawater and 100 mL of the resulting <sup>15</sup>N<sub>2</sub>-enriched water was added to 4.3 L polycarbonate sampling bottles. The resulting atom % enrichment of stocks of <sup>15</sup>N<sub>2</sub>-enriched seawater was measured using a membrane inlet mass spectrometer. Incubation bottles amended with the <sup>15</sup>N<sub>2</sub> tracer were attached to a free-drifting array and incubated at the discrete depths from which samples had been collected. The array was deployed before dawn and samples were incubated at in situ light and temperature for 24 h. After recovery of the array, the entire volume from each bottle was filtered onto a pre-combusted glass microfiber filter (Whatman 25 mm GF/F) and filters were placed onto pre-combusted pieces of foil in Petri dishes and stored frozen at -20°C. Filters were dried for 24 h at 60°C, pelleted, and the total mass of N and its isotopic signature on each filter were analyzed on an elemental analyzer-isotope ratio mass spectrometer (Carlo-Erba EA NC2500 coupled with ThermoFinnigan Delta S). </p>
Daily time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (Set 4: 2015 - 2019) derived from ERA5-Land data
<p>Overview:<br> ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Processing steps:<br> The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically:<br> 1. spatially aggregate CHELSA to the resolution of ERA5-Land<br> 2. calculate difference of ERA5-Land - aggregated CHELSA<br> 3. interpolate differences with a Gaussian filter to 30 arc seconds<br> 4. add the interpolated differences to CHELSA</p> <p>Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 07/2021.</p> <p>Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997):</p> <p><code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code></p> <p><code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code></p> <p><code>relative humidity = actual water pressure / maximum water pressure</code></p> <p>Data provided is the daily averages of relative humidity. This set provides data for the years 2000 - 2004. For other time periods, please see further linked data sets.</p> <p>Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000].</p> <p>File naming scheme (YYYY = year; MM = month; DD = day):<br> <code>ERA5_land_rh2m_avg_daily_YYYYMMDD.tif</code></p> <p>Projection + EPSG code:<br> Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br> north: 82:00:30N<br> south: 18N<br> west: 32:00:30W<br> east: 70E</p> <p>Spatial resolution:<br> 30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br> Daily</p> <p>Pixel values:<br> Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0</p> <p>Original ERA5-Land dataset license:<br> <a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a></p> <p>CHELSA climatologies (V1.2):<br> Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br> Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC</p> <p>Data is also available in EU LAEA (EPSG: 3035) projection: <a href="https://zenodo.org/record/7434447">https://zenodo.org/record/7434447</a></p>
Paired Sentinel-1 and Sentinel-2 Images for 2 Locations in Scotland and India for 2019 and 2020
<p>The dataset contains two years of coverage (2019 and 2020) for two distant geographical areas in India and in Scotland.</p> <p>If using this dataset, please cite the paper where it has been introduced:</p> <pre><code>@article{rs14061342, author = {Czerkawski, Mikolaj and Upadhyay, Priti and Davison, Christopher and Werkmeister, Astrid and Cardona, Javier and Atkinson, Robert and Michie, Craig and Andonovic, Ivan and Macdonald, Malcolm and Tachtatzis, Christos}, title = {Deep Internal Learning for Inpainting of Cloud-Affected Regions in Satellite Imagery}, journal = {Remote Sensing}, volume = {14}, year = {2022}, number = {6}, article-number = {1342}, url = {https://www.mdpi.com/2072-4292/14/6/1342}, ISSN = {2072-4292}, DOI = {10.3390/rs14061342} }</code></pre> <p> </p>
Second release of the data associated with the paper entitled 'Cluster-enhanced ensemble learning for mapping global monthly surface ozone from 2003 to 2019'
<p>This is the second release of the data associated with the paper entitled 'Cluster-enhanced ensemble learning for mapping global monthly surface ozone from 2003 to 2019'.</p> <p>The paper was published in Geophysical Research Letters. We provide the data that has been smoothed by moving filter and not. The data can be loaded by the <em>raster </em>package in <em>R.</em> Note that the unit is ppmv.</p> <p>Please note that both of these files must be in the same directory to open in <em>R</em> properly<em>.</em></p> <p>Please get in touch with the authors if you have any issues, email: xliu21@smail.nju.edu.cn or wanghk@nju.edu.cn</p>
Biological data science courses at UMONS, Belgium: student's activity for 2019-2020
<p>Progression of the students in the different exercises of the biological data science courses at the University of Mons, Belgium for the academic year 2019-2020.</p> <p>Activity of the students was recorded to monitor their individual progression in asynchronous exercises. The courses were taught in flipped classroom by Philippe Grosjean (<a href="mailto:philippe.grosjean@umons.ac.be">philippe.grosjean@umons.ac.be</a>) and Guyliann Engels (<a href="mailto:guyliann.engels@umons.ac.be">guyliann.engels@umons.ac.be</a>) the University of Mons. These authors designed almost all the teaching material, the exercises, and the related software. The courses were also taught at the Campus Charleroi by Raphaël Conotte (<a href="mailto:raphael.conotte@umons.ac.be">raphael.conotte@umons.ac.be</a>) that also contributed to a part of the learnr exercises and of the inline course.</p> <p><strong>How to use these data?</strong></p> <p>The README file provides detailed information on the purpose, collection and management of the data. The data are presented in tabular format in CSV files. Metadata in the `datapackage.json` document the different tables and their fields. It is in the Frictionless data format (<a href="https://frictionlessdata.io/">https://frictionlessdata.io</a>). You can get a view of a part of these metadata by uploading the file `datapackage.json` into the inline data package creator at <a href="https://create.frictionlessdata.io/">https://create.frictionlessdata.io</a>. There is a large set of libraries and tools for different programming languages available at <a href="https://frictionlessdata.io/tooling/libraries/">https://frictionlessdata.io/tooling/libraries/</a>. Otherwise, any CSV library should import the data in your favourite software. Please, note that encoding is UTF8. For R, the {learnitdown} package provides specific functions to import these data and/or convert them in a SQLite database (<a href="https://www.sciviews.org/learnitdown/">https://www.sciviews.org/learnitdown/</a>).</p> <p>For any question, send an email at <a href="mailto:sdd@sciviews.org">sdd@sciviews.org</a>.</p>
Automatic weather station data from the debris-covered Kennicott Glacier, Alaska (May-Aug 2019)
<p>Data from an automatic weather station installed on the debris-covered<strong> Kennicott Glacier</strong> in the Wrangell St. Elias National Park, Alaska, USA, spanning most of the 2019 ablation season (28 May - 22 August 2019).</p> <p>Brief description of the uploaded datasets:</p> <ul> <li><em><strong>KEN_2019_60.csv</strong></em>: Hourly aggregated measurements of air temperature, relative humidity, wind speed and direction, atmospheric pressure, incoming and reflected shortwave radiation, incoming and outgoing longwave radiation and precipitation</li> <li><em><strong>KEN_2019_AWS_Metadata.pdf</strong></em>: Metedata file describing the station location, sensor setup and measurements</li> <li><em><strong>KEN_2019_debris.csv</strong></em>: Debris properties assumed to be potentially applicable for this site (not measured but taken from literature)</li> <li><strong><em>KEN_2019_VAL_debristemperature.csv</em></strong>: Hourly time series of temperature measurements at known depths within the debris cover close to the station location</li> <li><strong><em>KEN_2019_VAL_subdebrismelt.csv</em></strong>: Monthly stake measurements of surface height change (sub-debris ice melt) close to the station location spanning the ablation season</li> </ul> <p>We recorded additional meteorological data at the weather station (e.g. vertical profiles of wind speed/direction, relative humidity and temperature; 3D ultrasonic anemometer) as well as subdebris melt, dGPS-surface change and temperature profiles at additional transects for the same period. Please contact the authors in case of interest in any additional data.</p>
Theia OSO Land Cover Map 2019
<p>Land cover map of France based on Sentinel-2 satellite images with iota² chain (https://framagit.org/iota2-project/iota2/)</p>
Perspectives on Medical Education Journal Data and Supplemental Files (2012 - 2019)
<p>This is the supplemental data, figures, and tables for <em>Joining the meta-research movement: A bibliometric case study of Perspectives on Medical Education</em>. </p> <p>For Figures 2-4 from the manuscript, to open the network maps, use both the network and map file for each figure in VoS viewer - https://www.vosviewer.com/</p>
Data from the field experiment on katabatic winds on a steep slope (Grand Colon, French Alps), February 2019
<p>These are the data from the field experiment described in the paper 'Katabatic winds over steep slopes: overview of a field experiment designed to investigate slope-normal velocity and near surface turbulence' by CHARRONDIERE, C., BRUN, C., COHARD, J.M., SICART, J.E., OBLIGADO, M., BIRON, R., COULAUD, C. & GUYARD, H. (2022), Boundary-Layer Meteorol. 187, 29-54.</p> <p> </p>
Long-term annual soil nitrogen surplus across Europe (1850 – 2019)
<p>This dataset consists of annual long-term reconstruction of total N surplus (both agricultural and non-agricultural soils) across Europe at a 5 arcmin spatial resolution for the period 1850 to 2019. The dataset consists of 16 N surplus estimates that account for the uncertainties resulting from input data sources and methodological choices in major components of the N surplus.This dataset offers the flexibility of aggregating the N surplus at any spatial scale of relevance to support water and land management strategies.<br> </p> <p><strong>Data description:</strong></p> <p>1. Gridded N surplus data (NetCDF format): 16 files, each of them containing 170 years (1850-2019) of gridded data N surplus</p> <p>2. Aggregated N surplus at European NUTS level (csv format) : 3 files (NUTS 1, NUTS 2, NUTS 3), each of them containing 170 years (1850-2019) of N surplus (mean and standard deviation of 16 estimates). Additionally, a readme file is provided for the NUTS ID's.</p> <p>3. Aggregated N surplus at European river basins (csv format) : 1 file, each of them containing 170 years (1850-2019) of N surplus (mean and standard deviation of 16 estimates). Additionally, a readme file is provided for the river basin ID's.</p> <p>Unit: kg/ha/yr (ha = physical area of grid cell/NUTS/river basins)</p> <p>Time period: 1850-2019</p> <p><strong>Further information:</strong></p> <p>Details/Citation: Long-term annual soil nitrogen surplus across Europe (1850-2019) by M. Batool, F.J.Sarrazin, S. Attinger, N.B.Basu, K.Van Meter and R. Kumar.</p> <p>Further queries regardig these datasets can be directed to Masooma Batool (masooma.batool@ufz.de) and Rohini Kumar (rohini.kumar@ufz.de).</p>
GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2019
<p><strong># GPM_API 2019</strong></p> <p>GPM_API data root: <a href="https://zenodo.org/record/6489998">https://zenodo.org/record/6489998</a></p> <p><strong># Related article:</strong></p> <p>Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566.</p> <p>Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a></p> <p>Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a></p>
Facebook pages for parties in 2019 EU parliamentary elections
<p>This dataset contains information about the parties from UK, Italy, Germany, Spain, and Poland that ran ads on Facebook during the 2019 EU parliamentary elections. For each party, we manually associated the corresponding Facebook page that ran the ads. We also report the PopuList tags as per https://popu-list.org.</p> <p>"2019 EU elections parties.csv" contains aggregate data:</p> <ul> <li>State: the country of the party</li> <li>Party: acronym of the party</li> <li>Name: extended name of the party</li> <li>Percentage: share of votes in the election</li> <li>Facebook page_name: name of the Facebook page that ran the ads</li> <li>Facebook page_id: id of the Facebook page that ran the ads</li> <li>populist: binary label indicating whether the party is populist according to PopuList</li> <li>farright: binary label indicating whether the party is far right according to PopuList</li> <li>farleft: binary label indicating whether the party is far left according to PopuList</li> <li>eurosceptic: binary label indicating whether the party is eurosceptic according to PopuList</li> <li>found on populist: binary label indicating whether the party is on any list on PopuList</li> <li>seat: whether the party got a seat in any previous national election (selection criterion from PopuList)</li> </ul> <p>"FB ads impressions cost populist demographic votes" contains detailed data:</p> <ul> <li>ad_id: Facebook ad id</li> <li>page_id: id of the Facebook page who published the ad</li> <li>page_name: name of the Facebook page who published the ad</li> <li>ad_creative_body: text of the ad</li> <li>ad_creative_*: other properties of the ad (caption, links, title)</li> <li>ad_delivery_start_time,ad_delivery_stop_time: time of beginning and end of the ad campaign</li> <li>funding_entity: declared party who financed the ad</li> <li>impressions_min, impressions_max: range of impressions for the ad</li> <li>spend_min, spend_max: range of expenditure for the ad</li> <li>ad_url: URL of the ad</li> <li>state: state of the party</li> <li>impression_mean, spend_mean: average number of impressions and expenditure</li> <li>imp_per_day: number of impressions per day</li> <li>populist, farright, farleft, eurosceptic, popu-list: binary tags from PopuList (refer to the description above)</li> <li>female_*, male_*, unknown_*: fraction of impressions for each demographic bucket (defined as gender_age)</li> <li>party: name of the party</li> <li>percentage: share of votes in the election</li> </ul>
Automatic Number Plate Recognition Data from Bristol, England for Apr-Jun 2019, 2020 and 2021
<p>These files contain data derived from Automatic Number Plate Recognition (ANPR) data collected by Bristol City Council as part of their continuous traffic monitoring work. The data was used in a project examining the impact of Covid-19 on travel behaviour in Bristol and the implications for air quality. </p> <p>Number plate data cannot be shared, therefore the files contain anonymised vehicle identifiers. Also, specific information about the locations of the ANPR cameras cannot be provided under the data sharing agreement we have with the data owners. Figures 4 and 19 in the project report (see uploaded pdf file) indicate the approximate locations of the ANPR cameras. </p> <p>The following files have been uploaded: </p> <p>- a metadata file containing descriptions of variables in the vehicle and trip files </p> <p>- a copy of the report which came out of this project. </p> <p>- BCCANPR21_REG_descrip_withID_NOVRM.csv: This file includes vehicle information for all vehicles observed in the trip data. The anonymised identification numbers can be used to merge this data with the trip files. </p> <p>- BCCANPR21_2019AprMayJun_REG_idv2.csv: Three files of this format contain trip data. Each file contains trips in April to June of the year specified in the file name. The number plate data was processed to chain together observations that were part of the same trip and this file only contains the location of the first and last ANPR camera at which this trip was recorded. </p> <p>This project was funded by the TRANSITION Clean Air Network through the first round of its Discovery & Innovation Fund in 2021. TRANSITION is a UK-wide network, led by the University of Birmingham in collaboration with nine universities and over 20 cross-sector partners, aiming to optimise the air quality and health outcomes of transport decarbonisation. The network (NERC ref. NE/V002449/1) is itself funded by UK Research & Innovation through its Clean Air Strategic Priorities Fund, administered by the Natural Environment Research Council. </p> <p>The number plate data used in this research was provided by the Bristol Traffic Control Service at Bristol City Council. The vehicle data was obtained through the DVLA’s Vehicle Enquiry Service API. </p>
Shipborne ADCP data in the Thwaites Gyre region, 2019
<p>This dataset contains the velocity profiles in the Thwaites Gyre region collected by the shipborne Acoustic Doppler Current Profiler (ADCP) onboard RV Nathaniel B Palmer in 2019 under the project Thwaites-Amundsen Regional Survey and Network Integrating Atmosphere-Ice-Ocean Processes (TARSAN). Ocean current velocities over the upper 980 m of the water column were obtained using a 75 kHz Ocean Surveyor (OS75) and a 38 kHz Ocean Surveyor (OS38) shipboard Acoustic Doppler Current Profiler (sADCP). Common Ocean Data Access System (CODAS) processing has been run to generate the NetCDF files. The authors made additional quality control including, 1. removing data with velocity values higher than 0.6 m/s and other obvious outliners, and 2. removing data below 400 m for OS75 and data below 760 m for OS38 as the last several levels in the velocity profiles are believed to be potentially affected by the water turbidity and bubbles passing below the sADCP etc. The data are stored in .txt format and .mat MATLAB format.</p>
chiaraadornetto/dati-tv-elezioni-ue-2019: Comunicare l'Unione Europea: dati sull'analisi della copertura delle elezioni europee del 2019 nella televisione italiana
<p>Dati sull'analisi della copertura delle elezioni europee del 2019 nella televisione italiana. Analisi dei contenuti trasmessi da "#cartabianca" (RAI) e "Dritto e Rovescio" (Mediaset) dal 2019-03-05 al 2019-05-30. Dataset allegato alla tesi triennale di Chiara Adornetto (Political science and International relations - Università di Catania)</p>
Sea ice core temperature and salinity data collected during the 2019 SCALE Winter Cruise
<p>Temperature and salinity profiles of sea ice cores extracted from in situ sea ice floes and lifted pancakes were measured in the Atlantic sector of the Antarctic Marginal Ice Zone during the Southern oCean seAsonal Experiment (SCALE) winter cruise in 2019 (<a href="http://www.scale.org.za">www.scale.org.za</a>) aboard the SA Agulhas II.</p>
ScienceDex guides
Understand access before you commit
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.