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12,237 results for “2019”
Subset of #MeToo-tweets in English, German, Spanish from 2019 and 2021
<p>The zip contains two tables with tweets on #MeToo in English, Spanish and German from the months 2019-07, 2019-08, 2021-07 and 2021-08. Tweets were mostly annotated with evaluative categories (positive, negative, neutral, ambiguous) and whether they represent a concrete or a meta discourse.</p>
Vesuvio cGPS network – RINEX data quality control (2001 – 2019)
<p>For each cGPS station, the file contains a summary report with information about Rinex observation data. For each day, the summary line shows the following information:</p> <ol> <li>cGPS station name (Name),</li> <li>the start time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date) and GPS week (Week),</li> <li>the end time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date), and GPS week (Week),</li> <li>the start and end times of the window (time format is year month day hour min),</li> <li>window time laps (Hrs),</li> <li>observation interval (OI),</li> <li>the number of possible observations (#expt) above the elevation mask,</li> <li>the number of complete observations (#obs),</li> <li>the ratio of complete to possible observations as a percent (DCP),</li> <li>the RMS “multipath combinations” values MP1 and MP2, in meters, limited by the elevation mask (MP1, MP22) rounded to two decimal points,</li> <li>cycle slips (CS),</li> <li>the ratio of complete observations to cycle slips (obs/CS).</li> </ol> <p>A full description of cGPS network is reported in:<br> - De Martino P, Dolce M, Brandi G, Scarpato G, Tammaro U (2021). The Ground Deformation History of the Neapolitan Volcanic Area (Campi Flegrei Caldera, Somma–Vesuvius Volcano, and Ischia Island) from 20 Years of Continuous GPS Observations (2000–2019). Remote Sensing. 13(14):2725. doi:10.3390/rs13142725.</p> <p>Please cite this when using the dataset</p>
Campi Flegrei cGPS network – RINEX data quality control (2000 – 2019)
<p>For each cGPS station, the file contains a summary report with information about Rinex observation data. For each day, the summary line shows the following information:</p> <ol> <li>cGPS station name (Name),</li> <li>the start time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date) and GPS week (Week),</li> <li>the end time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date), and GPS week (Week),</li> <li>the start and end times of the window (time format is year month day hour min),</li> <li>window time laps (Hrs),</li> <li>observation interval (OI),</li> <li>the number of possible observations (#expt) above the elevation mask,</li> <li>the number of complete observations (#obs),</li> <li>the ratio of complete to possible observations as a percent (DCP),</li> <li>the RMS “multipath combinations” values MP1 and MP2, in meters, limited by the elevation mask (MP1, MP22) rounded to two decimal points,</li> <li>cycle slips (CS),</li> <li>the ratio of complete observations to cycle slips (obs/CS).</li> </ol> <p>A full description of cGPS network is reported in:<br> - De Martino P, Dolce M, Brandi G, Scarpato G, Tammaro U (2021). The Ground Deformation History of the Neapolitan Volcanic Area (Campi Flegrei Caldera, Somma–Vesuvius Volcano, and Ischia Island) from 20 Years of Continuous GPS Observations (2000–2019). Remote Sensing. 13(14):2725. doi:10.3390/rs13142725.</p> <p>Please cite this when using the dataset</p>
Ischia cGPS network – RINEX data quality control (2001 – 2019)
<p>For each cGPS station, the file contains a summary report with information about Rinex observation data. For each day, the summary line shows the following information:</p> <ol> <li>cGPS station name (Name),</li> <li>the start time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date) and GPS week (Week),</li> <li>the end time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date), and GPS week (Week),</li> <li>the start and end times of the window (time format is year month day hour min),</li> <li>window time laps (Hrs),</li> <li>observation interval (OI),</li> <li>the number of possible observations (#expt) above the elevation mask,</li> <li>the number of complete observations (#obs),</li> <li>the ratio of complete to possible observations as a percent (DCP),</li> <li>the RMS “multipath combinations” values MP1 and MP2, in meters, limited by the elevation mask (MP1, MP22) rounded to two decimal points,</li> <li>cycle slips (CS),</li> <li>the ratio of complete observations to cycle slips (obs/CS).</li> </ol> <p>A full description of cGPS network is reported in:<br> - De Martino P, Dolce M, Brandi G, Scarpato G, Tammaro U (2021). The Ground Deformation History of the Neapolitan Volcanic Area (Campi Flegrei Caldera, Somma–Vesuvius Volcano, and Ischia Island) from 20 Years of Continuous GPS Observations (2000–2019). Remote Sensing. 13(14):2725. doi:10.3390/rs13142725.</p> <p>Please cite this when using the dataset</p>
Madrid datasets (air quality, meteorological and traffic data) of 2019 and 2022
<p>The datasets consist of air quality, meteorological, and traffic data from January to June 2019 and from January to June 2022. The following are the features of the datasets: Nitrogen dioxide, Wind speed, Wind direction (in degrees), Temperature, Humidity, Pressure, Solar irradiance, Intensity, Occupancy time, Load, Average traffic speed, Wind direction (after converting to categorical data (north, northeast, east, southeast, south, southwest, west, northwest) and passing through One Hot Encoder). The following are the names of the above features (the names of the columns in the files): 'NO2', 'windSpeed', 'windDir', 'Temp', 'Humidity', 'Pressure', 'SolarRad', 'intensidad', 'ocupacion', 'carga', 'vmed', 'windDir_Categ_east', 'windDir_Categ_north', 'windDir_Categ_northeast', 'windDir_Categ_northwest', 'windDir_Categ_south', 'windDir_Categ_southeast', 'windDir_Categ_southwest', 'windDir_Categ_west'.</p> <p><a href="https://zenodo.org/api/files/5af044df-ea61-41ee-a43f-4d095f43ca55/Mad_2019_winddir.csv">Mad_2019_winddir.csv</a> and <a href="https://zenodo.org/api/files/5af044df-ea61-41ee-a43f-4d095f43ca55/Mad_2019_winddir.csv">Mad_2022_winddir.csv</a> include the above features for the defined grid in the city of Madrid for January-June 2019 and January-June 2022, respectively, with the following dimension: 4344×340×19: (January-June 2019); 4343 × 340 × 19 (January-June 2022).</p> <p><a href="https://zenodo.org/api/files/5af044df-ea61-41ee-a43f-4d095f43ca55/Mad_Station_2019.csv">Mad_Station_2019.csv</a> and <a href="https://zenodo.org/api/files/5af044df-ea61-41ee-a43f-4d095f43ca55/Mad_Station_2019.csv">Mad_Station_2022.csv</a> include the above features with the following dimension: 4344 × 24 × 19: (January-June 2019); 4343 × 24 × 19 (January-June 2022), extracted from the defined grid for cells only where air quality monitoring stations exist.</p>
Energy-related CO2 Emission Accounts and Datasets for 40 Emerging Economies in 2010 - 2019
<p>The Carbon Emission Accounts and Datasets for emerging economies (CEADs, <a href="https://ceads.net">https://ceads.net</a>) aims to provide transparent, verifiable, open-access data on the CO<sub>2</sub> emissions of 40 emerging economies (accounting for 17.5% of the emissions and 12.9% of GDP of the world) for the period 2010-2019. Given the variety of statistical capacity among these countries, we standardized sectors using relevant data on energy and economic statistics and compiled emissions inventories at the regional level using sub-national statistics. For energy-related data, we included emissions from burning biomass, given its significance as an emissions source in these economies. The emissions dataset covers 47 economic sectors and 8 major energy categories in the 40 emerging economies, and in 28 we provide a subnational inventory. The emissions inventories are compiled for 47 economic sectors and 8 major categories.</p>
Escales Vaixells 2019 - Port de Tarragona
<p>Dataset de les escales de vaixells al Port de Tarragona durant el 2019 obtingut a través del scraper del repositori https://github.com/arnaugiribet/scrapings</p>
Dynamic Rupture Modeling of the 2019 Ridgecrest Earthquakes
<p>This repository contains files needed to reproduce the 2019 Ridgecrest earthquake sequence dynamic rupture models using SeisSol (<a href="https://github.com/SeisSol/SeisSol">https://github.com/SeisSol/SeisSol</a>).</p>
The UAV-derived orthomosaics and DEMs of 23K Glacier and 24K Glacier in southeastern Tibetan Plateau (2019-2020)
<p>The UAV-derived orthomosaics and DEMs of 23K Glacier and 24K Glacier in southeastern Tibetan Plateau (Agu. 2019, Oct. 2019, Agu. 2020, Oct. 2020). </p>
MUSES Leaf Area Index (LAI) 8-Day Global 1km SIN Grid in 2019
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global LAI product at 1km spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES LAI product in 2019. <em>Please <a href="https://zenodo.org/record/7485123#.Y6p12n1ByUl"><strong>click here</strong></a> to download the MUSES LAI product <strong>in 2018</strong></em>, and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2020</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2019</li> <li>Spatial Resolution: 1km</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318.</li> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2019 (001–177)
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 250m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 250m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FVC product in 2019 (001–177). Please <em><a href="https://zenodo.org/record/7500321#.Y7S1dNVBw2y"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2018 (185–361)</strong></em>, and <em><a href="https://zenodo.org/record/7499330#.Y7OTKdVBw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2019 (185–361</strong></em><em><strong>)</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2019 (001–177)</li> <li>Spatial Resolution: 250m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 100</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
Honeybee lifetime tracking data 2019
<p>Data from barcode-based tracking of honeybees during summer of 2019, taken in an observation hive located at University of Konstanz, Germany.</p> <p>See <a href="https://github.com/jacobdavidson/bees_lifetimetracking_2019data">table of experiments and manipulations performed</a></p> <p>Associated GitHub repositories:<br> <a href="https://github.com/jacobdavidson/bees_lifetimetracking_2019data">https://github.com/jacobdavidson/bees_lifetimetracking_2019data</a><br> <a href="https://github.com/jacobdavidson/bees_drones_2019data">https://github.com/jacobdavidson/bees_drones_2019data</a></p> <p>Data usage example is on <a href="https://github.com/jacobdavidson/bees_lifetimetracking_2019data/blob/main/Data%20usage%20example%20-%20updated.ipynb">Github</a></p> <p><strong>File contents: </strong></p> <p><strong><a href="https://zenodo.org/record/7298798/files/2019_Quantity_Dates_Cohorts_DaysLived.xlsx?download=1">2019_Quantity_Dates_Cohorts_DaysLived.xlsx</a></strong></p> <p>List and description of quantities contained in the single-day summary metrics, and the metrics calculated at shorter intervals (hour, 5 minute, 1 minute). Also lists day number definitions, cohort birthdates, and estimated days lived per bee.</p> <p><strong><a href="https://zenodo.org/record/7298798/files/trajectories_000-019.zip?download=1">trajectories_000-019.zip</a>, <a href="https://zenodo.org/record/7298798/files/trajectories_020-039.zip?download=1">trajectories_020-039.zip</a>, <a href="https://zenodo.org/record/7298798/files/trajectories_040-059.zip?download=1">trajectories_040-059.zip</a>, <a href="https://zenodo.org/record/7298798/files/trajectories_060-079.zip?download=1">trajectories_060-079.zip</a>, <a href="https://zenodo.org/record/7298798/files/trajectories_080-099.zip?download=1">trajectories_080-099.zip</a>, <a href="https://zenodo.org/record/7298798/files/trajectories_100-114.zip?download=1">trajectories_100-114.zip</a></strong></p> <p>Single-day hdf files, each containing x-y trajectory data in the form:</p> <table> <thead> <tr> <th>daynum</th> <th>framenum</th> <th>uid</th> <th>x</th> <th>y</th> <th>camera</th> <th>theta</th> </tr> </thead> </table> <ul> <li>daynum: the day number of the observation period. See 'Day numbers' sheet in <a href="https://zenodo.org/record/7298798/files/2019_Quantity_Dates_Cohorts_DaysLived.xlsx?download=1">2019_Quantity_Dates_Cohorts_DaysLived.xlsx</a></li> <li>framenum: using the camera frame rate of 3fps, the frame number with respect to that particular day</li> <li>uid: the Unique ID of each bee</li> <li>x,y: pixel coordinate values of each bee. Conversion is 80 pixels/cm</li> <li>camera: which camera the bee was detected on: 0=exit side (with dance floor - shown at right in comb map images), 1=back side (show at left in comb map images)</li> <li>theta: orientation of the bee in the hive </li> </ul> <p><strong><a href="https://zenodo.org/record/7298798/files/comb-contents-images2019.zip?download=1">comb-contents-images2019.zip</a></strong></p> <p>Comb content color-coded png image files, as well as pkl files for using the comb data type (see <a href="https://github.com/jacobdavidson/bees_lifetimetracking_2019data/blob/main/Data%20usage%20example%20-%20updated.ipynb">Data Usage Example</a>)</p> <p><strong><a href="https://zenodo.org/record/7298798/files/daydatamat.csv?download=1">daydatamat.csv</a></strong></p> <p>Summary behavioral metrics calculated for each tracked bee on each day of the experiment. See 2018_Quantity_Descriptions.xlsx for full list and detail of quantities. Data is in the form:</p> <table> <thead> <tr> <th>Age</th> <th>Day number</th> <th>Bee unique ID</th> <th>Cohort ID</th> <th><metric1></th> <th><metric2></th> <th>...</th> </tr> </thead> </table> <p><strong><a href="https://zenodo.org/record/7298798/files/df_day1min.zip?download=1">df_day1min.zip</a>, <a href="https://zenodo.org/record/7298798/files/df_day5min.zip?download=1">df_day5min.zip</a>, <a href="https://zenodo.org/record/7298798/files/df_dayhour.zip?download=1">df_dayhour.zip</a></strong></p> <p>Behavioral metrics calculated over different time intervals: 1 minute, 5 minute, or 1 hour divisions. See 2018_Quantity_Descriptions.xlsx for full list and detail of quantities. Files are organized as a single .hdf file for each day. Each file contains data in the form:</p> <p>Per-hour data:</p> <table> <thead> <tr> <th>Age</th> <th>Day number</th> <th>Bee unique ID</th> <th>Cohort ID</th> <th>Hour</th> <th><metric1></th> <th><metric2></th> <th>...</th> </tr> </thead> </table> <p>5-minute or 1-minute data: </p> <table> <thead> <tr> <th>Age</th> <th>Day number</th> <th>Bee unique ID</th> <th>Cohort ID</th> <th>timedivision</th> <th><metric1></th> <th><metric2></th> <th>...</th> </tr> </thead> </table> <p><strong><a href="https://zenodo.org/record/7298798/files/dfxy_dayhour.zip?download=1">dfxy_dayhour.zip</a></strong></p> <p>x-y histogram data, calculated by using a 2cmx2cm grid and binning for each hour of each day. One file per day, and each file contains data in the form:</p> <table> <thead> <tr> <th>Day number</th> <th>Bee unique ID</th> <th>Cohort ID</th> <th>Hour</th> <th>hist0</th> <th>hist1</th> <th>...</th> <th>hist1434</th> </tr> </thead> </table> <p>Convert the flattened histogram structure into a shape of [41,35] in order to view as shown in the paper or in the <a href="https://github.com/jacobdavidson/bees_lifetimetracking_2019data/blob/main/Data%20usage%20example%20-%20updated.ipynb">Data Usage Example</a></p>
Mid.ru press documents 2003–2019, Russian
<p>Document collection scraped from the Russian governmental website mid.ru. Includes all items (transcripts, comments etc.) listed on:<br> - https://mid.ru/ru/press_service/minister_speeches/,<br> - https://mid.ru/ru/press_service/deputy_ministers_speeches/,<br> - https://mid.ru/ru/press_service/telefonnye-razgovory-ministra/,<br> - https://mid.ru/ru/press_service/spokesman/briefings/,<br> - https://mid.ru/ru/press_service/spokesman/official_statement/,<br> - https://mid.ru/ru/press_service/spokesman/answers/,<br> - https://mid.ru/ru/press_service/spokesman/kommentarii/,<br> and on their following pages (e.g. https://mid.ru/ru/press_service/minister_speeches/?PAGEN_1=2) from the first documents (4 January 2003) until the end of 2019.</p> <p>11,857 documents. One document in each row. Columns:</p> <p>- ID: format MID-1 <br> - ID_no: format 1 <br> - Date: Document date, format 2019-12-31 <br> - Title: Document title<br> - Type: One of the following: Брифинги; Выступления заместителей Министра; Выступления Министра; Комментарии; Ответы на вопросы СМИ; Официальные заявления; Телефонные разговоры Министра<br> - Text: Document text including title <br> - URL: URL from which the document is downloaded <br> - Downloaded: Date of download, format 2019-12-31</p> <p>Formats: rds and json.</p> <p>Version 1.1: edited column names.</p> <p>---</p> <p>From www.mid.ru:</p> <p>Materials on the website of the Russian Ministry of Foreign Affairs are generally accessible and open for non-commercial use (personal, family, education, research, etc.).<br> Their reprinting, as well as any quoting in the mass media is allowed only with a reference to the website of the Russian Ministry of Foreign Affairs as a source of the information.</p>
Global Mangrove Watch: Monthly Alerts of Mangrove Loss for Africa 2019--2022
<p>This results from a prototype change alert system (Bunting et al., 2023) that has been developed to identify mangrove losses on a monthly basis. Implemented on the Microsoft Planetary Computer, the Global Mangrove Watch v3.0 mangrove baseline extent map (<a href="https://doi.org/10.3390/rs14153657">Bunting et al., 2022</a>) for 2018 was refined and used to define the mangrove extent mask under which potential losses would be identified. The study period was 2018-2022 due to the availability of the Copernicus Sentinel-2 imagery used for the study. The alert system is based on optimised NDVI thresholds used to identify mangrove losses and a temporal scoring system used to filter false positives. The alert system was found to have an estimated overall accuracy of 92.1 %, with the alert commission and omission estimated to be 10.4 % and 20.6 %, respectively. The alert system is presently limited to Africa, where significant losses were identified in the study period, with 90 % of the loss alerts identified in Nigeria, Guinea-Bissau, Madagascar, Mozambique and Guinea. The drivers of those losses vary, with West Africa primarily driven by economic activities such as agricultural conversion and infrastructure development. At the same time, East Africa is dominated by climatic drivers, primarily storm frequency and intensity. Production of the monthly loss alerts for Africa will be continued as part of the wider Global Mangrove Watch project, and the spatial coverage is expected to be expanded over the coming months and years. Future updates of the mangrove loss alerts will be via the Global Mangrove Watch portal: <a href="https://www.globalmangrovewatch.org">https://www.globalmangrovewatch.org</a></p>
Annotation of Facebook ads in 2019 EU parliamentary elections
<p>This dataset contains manual annotation for ads from parties from UK, Italy, Germany, Spain, and Poland that ran ads on Facebook during the 2019 EU parliamentary elections and that were defined as "populist" by popu-list.org. For each ad, we qualitatively identified a primary issue, a sub-issue, and a stance with respect to the sub-issue. The annotation was performed through an open coding procedure. More information is reported in our paper "The Thin Ideology of Populist Advertising on Facebook during the 2019 EU Elections".<br> More information about the same ads is reported in: https://zenodo.org/record/6597765<br> <br> This TSV file contains the following columns:<br> - "state": the country of the ad.<br> - "ad_id": the original id of the ad.<br> - "link": a link to the ad.<br> - "page_name": name of the page running the ad.<br> - "is_local": whether the ad is related to local elections or not (if not, it's related to the 2019 EU Elections).<br> - "issue": a comma-separated list of the main issues of the ad.<br> - "sub-issue": a comma-separated list of sub-issues of the ad.<br> - "stance": a comma-separated list of stances of the ad.</p>
Google Street View vehicle-based mobile air quality observations in Salt Lake City (May 2019-March 2020)
<p>Google Street View (GSV) mobile air quality data collected in Salt Lake City as part of an Environmental Defense Fund-supported project that took place between May 2019 and March 2020. <br> The data collection, resolved at 1-second frequency, was carried out with two GSV vehicles.</p> <p>The data is in a single CSV (comma-separated value) text file. </p>
Health records from hospitalized adults with malaria during 2019 at Bo Government Hospital, Sierra Leone
<p class="MsoNormal"><span>We performed a chart review of adults admitted to Bo Government Hospital during 2019. Of 893 admissions,149 (59% female, mean age 58.5 years) had a laboratory diagnosis of malaria and 22 (14.8%) died. Mortality was significantly higher among patients with severe malaria compared with those who had non-severe malaria (6/20 [30%] versus 16/129 [12.4%], <em>p</em>=0.031). Our data suggest that malaria is a common cause of death in hospitalized Sierra Leonean adults. </span></p>
Patterns in avian reproduction in the Prudhoe Bay Oilfield, Alaska, 2003–2019
<p>The Arctic Coastal Plain is one of the most important avian breeding grounds in the world; however, many species are in decline. Arctic-breeding birds contend with short breeding seasons, harsh climatic conditions, and now, rapidly changing, variable, and unpredictable environmental conditions caused by climate change. Additionally, those breeding in industrial areas may be impacted by human activities. It is difficult to separate the impacts of industrial development and climate change, however, long-term datasets can help show patterns over time. We evaluated factors influencing reproductive parameters of breeding birds at Prudhoe Bay, Alaska, 2003–2019, by monitoring 1265 shorebird nests, 378 passerine nests, and 231 waterfowl nests. We found that nest survival decreased significantly nearer high-use infrastructure for all guilds. Temporally, passerine nest survival declined across the 17 years of the study, while there was no significant evidence of change in their nest density. Shorebird nest survival did not vary significantly across years, nor did nest density. Waterfowl nest density increased over the course of the study, but we could not estimate nest survival in all years. Egg predator populations varied across time; numbers of gulls and ravens increased in the oilfields from 2003–2019, while Arctic fox decreased, and jaeger numbers did not vary significantly. Long-term datasets are rare in the Arctic, but they are crucial for understanding impacts to breeding birds from both climate change and increasing anthropogenic activities. We show that nest survival was lower for birds nesting closer to high-use infrastructure in Arctic Alaska, which was not detected in earlier, shorter-term studies. Additionally, we show that Lapland longspur nest survival decreased across time, which is concerning considering the continent-wide declines in many passerine species. The urgency to understand these relationships cannot be expressed strongly enough, given change is continuing to happen and the potential impacts are large.</p>
MUSES Leaf Area Index (LAI) Monthly Global 500m SIN Grid in 2019
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global LAI product at 500m spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES LAI product in 2019. Please <em><a href="https://zenodo.org/record/7739325#.ZBLQNHZByUk"><strong>click here</strong></a> to download the MUSES LAI product <strong>in 2018</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2019</li> <li>Spatial Resolution: 500m</li> <li>Temporal Resolution: 1 month</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318.</li> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
Catalog of icequakes recorded in March 2019, in the Van Mijen Fjord, in Svalbard (Norway)
<p>The files contain a catalog of icequakes waveforms recorded in March 2019, at Vallunden lake, in the Van Mijen Fjord, in Svalbard (Norway). The three components of velocity are available. </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.