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12,237 results for “2019”
Daily time series of spatially enhanced relative humidity for Europe at 1000 m 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 2015 - 2019. 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>The data have been reprojected to EU LAEA.</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> EU LAEA (EPSG: 3035)</p> <p>Spatial extent:<br> north: 6874000<br> south: -485000<br> west: 869000<br> east: 8712000</p> <p>Spatial resolution:<br> 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 Latitude-Longitude/WGS84 (EPSG: 4326) projection: <a href="http://https://doi.org/10.5281/zenodo.6344066">https://doi.org/10.5281/zenodo.6344066</a></p>
Daily lake area delineated from MODIS using GEEDiT for the year 2019
<p>Daily lake area delineated from MODIS using GEEDiT for the year 2019</p>
RCC Orthomosaics, 2017 - 2019
<p>Visible light orthomosaics of the study reaches in Red Canyon Creek were generated from UAV images and Structure from Motion photogrammetry. The orthomosaics show the study areas one year prior to beaver dam analogue (BDA) installation (2017), immediately after BDA installation (2018), and one year after the BDAs had been constructed (2019). These data are used in Davis et al. (2021), Evaluating the geomorphic channels response to beaver dam analog installation using unoccupied aerial vehicles (https://doi.org/10.1002/esp.5180). The rest of the data used in Davis et al. (2021) is available on CUAHSI HydroShare (http://www.hydroshare.org/resource/7c4ae80863174acfb571ccf8bdac8968).</p>
Dataset of micro-phytoplankton biodiversity in the Eastern Channel area from 1987 to 2019.
<p>Micro-phytoplankton communities are widely influenced by the environment and seasonal cycling. The French-English Channel is a system between the East Atlantic and the North Sea with contrasted coastal areas along the French coast. In a context of changing environment, it is crucial to understand the causes and consequences of environmental variability on marine compartments whose phytoplankton represent the first link.</p> <p>Thanks to various monitoring programs such as REPHY (REPHY – French Observation And Monitoring Program For Phytoplankton And Hydrology In Coastal Waters, 2021) and RHLN (Regional Observation and Monitoring programs for Phytoplankton and Hydrology in the eastern English Channel), datasets of micro-phytoplankton diversity are available since the late 80s at a national scale.</p> <p>Here, we present a subset of these datasets on micro-phytoplankton community. It gathers diversity and abundance from 1987 to 2019. Compared to the main dataset, a taxonomical review was done to ensure a correct spatial and temporal homogenization of the taxonomical denomination (description below). We also focus on 6 monitored stations along the French Channel coast; Antifer ponton pétrolier, Cabourg, Géfosse, Donville, At so and St Cast – Les Hébihens (gathering of two times series geographically close). A graphical visual of the sampling history (for the FLORTOT standard protocol) is available in the uploaded image below.</p> <p>The dataset comes from a national-wide standard protocol of water sampling (Neaud-Masson, 2016). All records are stored in the Quadrige² platform, the reference information system for coastal waters in France. A compilation of the various results obtained at the French national scale was done on the last 30 years of monitoring (Belin and Soudant, 2018). The sampling is done at water surface (0-1 meter) around the high tide period (+/- 2 hours), it is fixed through lugol acid on arrivals at the lab and analysed through reversed microscopy by experts trained through a similar 2 years learning process. The taxa are identified to the lowest taxonomic level possible and names are updated according to the WORMs’ denomination base. Since 2015, evaluations (International Phytoplankton Intercomparison - IPI) are being implemented at the European scale, coordinated by the Marine Institute of Galway, to reduce identification deviations and biases between laboratories.</p> <p>The columns are described as following:</p> <ul> <li><strong>#1 : Taxa_Name</strong></li> </ul> <p>Due to the extent of the initial dataset, both in space and time, a work of homogenization in the taxa’s denomination was applied. Therefore, associated with this dataset, we made available a table that summarizes changes applied in the nominations. It displays all the taxa initially present in the REPHY database (first column), the change in denomination applied if needed as they are not at the same resolution between stations’ laboratories, or because two species are now grouped into a common denomination. There is an “X” if the taxa is not relevant, rare, or not a phytoplankton or protist of interest and therefore it means the taxa was deleted and is not in the given dataset (second column) and potential comments or justifications (third column). It has been build and reviewed several times by the phytoplankton experts from the three Ifremer laboratories covering the stations (Dinard, Port en Bessin and Boulogne sur Mer).</p> <ul> <li><strong>#2 to 5 : Date, Day, Month, Year</strong></li> </ul> <p>Temporal description of the samples. Details of the sampling history are given in the image below.</p> <ul> <li><strong>#6 : Season</strong></li> </ul> <p>Defined as Spring = March to May; Summer = June to August; Autumn = September to November; Winter = December (Y) to February (Y+1)</p> <ul> <li><strong>#7 and 8 : Julian_days and week number</strong></li> <li><strong>#9 and 10 : Station_FullName and Station_Code</strong></li> </ul> <p><strong></strong>In the REPHY monitoring program, stations are both describe by full name locations and codes. The same level of detail was kept available in this version, with : Antifer ponton pétrolier (010-P-001), Cabourg (010-P-109), Géfosse (014-P-023), Donville (018-P-054), At so (006-P-001) and St Cast – Les Hébihens (022-P-002)</p> <ul> <li><strong>#11: Parameter</strong></li> </ul> <p>FLORTOT (standard samples, all micro-phytoplanktonic species are identified). FLORPAR (partially identified samples in area identified as under any sanitary risk). FLORIND (additional samples taken for toxic species monitoring, or for species reaching 100 000 cells per liter or producing toxins are identified).</p> <ul> <li><strong>#12 : Results </strong></li> </ul> <p>Abundance (in cells.L<sup>-1</sup>), whose level of detection (lowest value) through the microscopy approach is at 100 cells per liter.</p> <ul> <li><strong>#13 : Class.worms2019</strong></li> </ul> <p>The taxa’s class according to the World Register of Marine Species 2019 (https://www.marinespecies.org/)</p> <ul> <li><strong>#14 : Rank</strong></li> </ul> <p>Taxa's taxonomical rank : Species, Genus, Family, e-species (aka: group of similar species), e-genus (aka : group of similar genus), Class, subclass, Order, Phylum, Other.</p> <p> </p>
Kremlin.ru transcripts 1999–2019, Russian
<p>Document collection scraped from the Russian governmental website kremlin.ru. Includes all items listed on http://kremlin.ru/events/president/transcripts and following pages (e.g. http://kremlin.ru/events/president/transcripts/2) from 31 December 1999 until the end of 2019.</p> <p>10,723 documents. One document in each row.</p> <p>Columns:</p> <p>- Id: format Kremlin-1 <br> - Id_no: format 1 <br> - Date: Document date, format 1999-12-31 <br> - Title: Document title<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>Kremlin.ru content is licensed under Creative Commons Attribution 4.0 International.</p>
Electron microscopy of particles collected by different techniques from field measurements in the Moroccan Sahara during FRAGMENT 2019
<p>An intensive field campaign between 4-30 September 2019 was conducted at a major source region on the edge of the Saharan desert in Morocco (29.83 °N 5.87 °W) in the context of the FRontiers in dust minerAloGical coMposition and its Effects upoN climaTe (FRAGMENT) project. Samples were collected with three different sampling techniques, namely: flat-plate sampler (FPS), free-wing impactor (FWI), and a micro-orifice uniform deposit impactor (MOUDI). Substrates in the MOUDI and FWI were collected two times a day with a typical sampling duration of a few minutes to avoid overloading the substrate for individual particle analysis. For the flat-plate sampler, the average exposure time was half a day. Here we present dataset of the elemental composition and morphology of more than 300,000 freshly emitted individual particles by performing offline analysis in the laboratory using Scanning Electron Microscopy (SEM) coupled with Energy-Dispersive X-ray Spectrometry (EDX).</p>
Shorebird survey data from Foxe Basin and Rasmussen Lowlands, Nunavut - 1994-97 and 2019.
<p>Data used in the manuscript "Climate-related range shifts in Arctic-breeding shorebirds", submitted to Diversity and Distributions, June 2022. Data was funded and collected by Environment and Climate Change Canada</p> <p>Metadata:</p> <p>species - 4 letter codes denoting the observed shorebird species. See https://www.birdpop.org/docs/misc/Alpha_codes_eng.pdf</p> <p>binomial - Binomial nomenclature identifying the genus and species of the observed shorebird</p> <p>sti - Species Temperature Index. Calculated as the mean temperature with in the species breeding range. Temperature data came from the WorldClim 2.1 data set (Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315). Shorebird breeding range data came from BirdLife International (BirdLife International and Handbook of the Birds of the World. 2020. “Bird Species Distribution Maps of the World. Version 2020.1.” http://datazone.birdlife.org/species/requestdis.)</p> <p>plot - Plot identification number, as part of Environment and Climate Change Canada's Program for Regional and International Shorebird Monitoring program (PRISM).</p> <p>time_period - Denotes which time period the surveys took place in</p> <p>region - Denotes which region the surveys took place in</p> <p>plot_area_km2 - Area of the surveyed plot in km2</p> <p>plot_habitat - Surveyors estimated the proportion of each plot covered by upland and lowland habitat. Plots were the categorized as upland or lowland depending on which habitat type was predominant</p> <p>n_birds - The number of breeding birds of this species that were observed on plot during the survey.</p> <p>presence - A species was identified as present if one or more individuals of that species were present during the survey.</p> <p>latitude - Latitude of the plot corner</p> <p>longitude - Longitude of the plot corner</p> <p>coordinate_type - Which corner of the plot are the coordinates for?</p>
Filtered canopy top height estimates from GEDI LIDAR waveforms for 2019 and 2020
<p>Canopy top height (RH98) is estimated from GEDI L1B waveforms globally between 51.6° N & S from L1B Version 1 data for April-July 2019 and 2020. We refer to the original research article below for further information. The footprint data were filtered with respect to predictive uncertainty and MODIS non-vegetated probability.</p><p>The unfiltered data organized in hdf5 files corresponding to the orbit files of the GEDI L1B Version 1 data is available here:</p><p>April-July 2019: <a href="https://doi.org/10.5281/zenodo.5704852">https://doi.org/10.5281/zenodo.5704852</a></p><p>April-July 2020: <a href="https://doi.org/10.5281/zenodo.7737869">https://doi.org/10.5281/zenodo.7737869</a></p><p><strong>GEDI mission website</strong>: <a href="https://gedi.umd.edu/">https://gedi.umd.edu/</a>.</p><p><strong>Citation:</strong></p><p>Use of these data require citation of this dataset:</p><p>Lang, Nico, Kalischek, Nikolai, Armston, John, Schindler, Konrad, Dubayah, Ralph, & Wegner, Jan Dirk. (2021). Filtered canopy top height estimates from GEDI LIDAR waveforms for 2019 and 2020 (1.0) [Dataset]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7737946">https://doi.org/10.5281/zenodo.7737946</a></p><p>Original research article:</p><p>Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., & Wegner, J. D. (2022). Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. <i>Remote Sensing of Environment</i>, <i>268</i>, 112760.</p><p>This filtered dataset (2019 and 2020) was used to develop the global canopy height model fusing Sentinel-2 and GEDI that is presented in:</p><p>Lang, N., Jetz, W., Schindler, K., & Wegner, J. D. (2023). A high-resolution canopy height model of the Earth. Nature Ecology & Evolution, 1-12, <a href="https://doi.org/10.1038/s41559-023-02206-6">https://doi.org/10.1038/s41559-023-02206-6</a></p>
Multi-label Tweet Dataset for Textual Propaganda Detection related to anti-CAA protest in India (2019-2021)
<p>This is a collection of English Tweets about the Citizenship (Amendment) Bill protests that occurred in India in 2019-2021. The dataset contains tweet instances multiple labels for identified propaganda techniques. The data set consists of tweet ids, hashtags used, and corresponding propaganda techniques. Labels have been automatically generated using Weak Supervision. </p> <p>As of 2023, there are very limited textual propaganda detection dataset for Tweets. This dataset is released to facilitate future research as propaganda has become omnipresent in modern social media. </p> <p> </p> <p> </p>
Sea ice core biogeochemical data collected during the 2019 SCALE Winter Cruise
<p><strong>Title: </strong>Biogeochemical profiles of sea ice cores sampled during the Southern oCean seAsonal Experiment (SCALE) winter cruise in 2019.</p> <p> </p> <p><strong>Authors:</strong> Riesna R. Audh, Siobhan Johnson, Mark Hambrock, Hazel Little, Joshua Mirkin, Emmanuel Omatuku, Benjamin Hall, Tokoloho Rampai, Keith MacHutchon, Sebastian Skatulla, Sarah E. Fawcett, Marcello Vichi</p> <p> </p> <p><strong>Data Description:</strong></p> <p> </p> <p><strong>Abstract</strong></p> <p>Biogeochemical 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> <p> </p> <p>A total of four sea ice cores (cores) were sampled during the cruise. Two cores were collected overboard on a consolidated floe that was accessed via a personnel carrier suspended by the ship’s forward crane. Two cores were collected from a pancake that was lifted aboard the ship via a net that was attached to the ship’s aft crane and placed on the helideck for sampling. Profiles were obtained by cutting the cores using a bandsaw in a cold laboratory at -10 °C. The cores were cut into approximately 0.05 m segments, starting from the bottom of the core. These segments were allowed to melt in the dark in an insulated box. The meltwater was filtered for chlorophyll measurements (Welschmeyer, 1994) and the filtrate was analysed for oxygen isotopes (Walker and others, 2015), ammonium (Holmes et al., 1999), phosphate, nitrate, nitrite and silicate (using a SEAL AA500 segmented flow autoanalyser). These values are reported at the depth of the top of the segment in the core in μM. In order to facilitate comparison with the seawater concentrations below the ice, the in-ice nutrients (including NH4+) were salinity normalised using the equation of Fripiat and others (2017):</p> <p> </p> <p><em>C</em><em>norm</em><em> = C</em>SwS<em> </em><em> </em></p> <p> </p> <p>Where C is the measured bulk concentration, Sw is the salinity of the seawater, and S is the corresponding measured bulk salinity of the ice segment.</p> <p> </p> <p>Although sampling of the core occurred from the bottom of the core to the top of the core, the data are reported as the top of the core (snow/ice interface) being 0 m (depth=0 m). </p> <p> </p> <p><strong>This research has been funded by the National Research Foundation of South Africa (NRF)</strong></p> <p><br> </p> <p><strong>Cruise:</strong> VOY-038 (SCALE2019-WINTER) (URL: https://scale.org.za/)</p> <p><strong>Station(s):</strong> VOY-038-MIZ3A</p> <p>VOY-038-MIZ1D</p> <p><strong>Position(s):</strong> -58.13783 S; 0.00442 W</p> <p>-56.8017 S; 0.30262 E</p> <p><strong>Date/Time:</strong> 2019-07-27/10:38:00</p> <p>2019-07-28/09:15:00</p> <p><strong>Method(s):</strong> Overboard coring</p> <p>Pancake lifting via aft crane, on deck coring</p> <p><strong>Parameters:</strong><strong> </strong>Station Number (Station)</p> <p>Date/Time of station (Date/Time)</p> <p>Latitude of station (Latitude)</p> <p>Longitude of station (Longitude)</p> <p>Ice type (Ice Type)</p> <p>Core ID(Core), Pancake identifier A/B/C/D</p> <p>Oxygen isotopes (d18O)</p> <p>Chlorophyll (Chl-a)</p> <p>Ammonium (NH4)</p> <p>Nitrate + Nitrite (NO3+NO2)</p> <p>Nitrite (NO2)</p> <p>Phosphate (PO4)</p> <p>Silicate (Si)</p> <p>Nitrate (NO3)</p> <p>Salinity of the ice segment from physical cores (IceSalinity)</p> <p>Standard deviation of the salinity average from physical cores (IceSalinityStdev)</p> <p>Seawater salinity from CTD (SeawaterSalinity)</p> <p>Salinity normalised nitrate+nitrite (N03+N02_Avg_SalinityNormalised)</p> <p>Salinity normalised ammonium (NH4_SalinityNormalised)</p> <p>Salinity normalised nitrite (NO2_SalinityNormalised)</p> <p>Salinity normalised phosphate (PO4_SalinityNormalised)</p> <p>Salinity normalised silicate (Si_SalinityNormalised)</p> <p>Salinity normalised nitrate (NO3_SalinityNormalised)</p> <p><br> </p> <p> </p> <p><strong>Keywords: </strong>sea ice cores, Antarctica, pancake ice, sea ice, biogeochemistry, winter</p> <p> </p> <p><strong>References</strong><strong>:</strong></p> <p> </p> <p>Fripiat, F., Meiners, K.M., Vancoppenolle, M., Papadimitriou, S., Thomas, D.N., Ackley, S.F., Arrigo, K.R., Carnat, G., Cozzi, S., Delille, B. and Dieckmann, G.S., 2017. Macro-nutrient concentrations in Antarctic pack ice: Overall patterns and overlooked processes. Elementa: Science of the Anthropocene, 5. </p> <p> </p> <p>Holmes, R.M., Aminot, A., Kérouel, R., Hooker, B.A. and Peterson, B.J., 1999. A simple and precise method for measuring ammonium in marine and freshwater ecosystems. Canadian Journal of Fisheries and Aquatic Sciences, 56(10), pp.1801-1808. </p> <p> </p> <p>Walker, S.A., Azetsu‐Scott, K., Normandeau, C., Kelley, D.E., Friedrich, R., Newton, R., Schlosser, P., McKay, J.L., Abdi, W., Kerrigan, E. and Craig, S.E., 2016. Oxygen isotope measurements of seawater (18O/16O): A comparison of cavity ring‐down spectroscopy (CRDS) and isotope ratio mass spectrometry (IRMS). Limnology and Oceanography: Methods, 14(1), pp.31-38. </p> <p> </p> <p>Welschmeyer, N., 1994. A method for the determination of chlorophyll a in the presence of chlorophyll b and pheopigments. Limnology and Oceanography, 39, pp.1985-1992. </p> <p> </p>
Time series measurements of nitrogen fixation in the subtropical North Pacific (extended through 2019) (Reformatted)
<p>Rates of N2 fixation were measured using the 15N2 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 15N2 gas was first dissolved into seawater and 100 mL of the resulting 15N2-enriched water was added to 4.3 L polycarbonate sampling bottles. The resulting atom % enrichment of stocks of 15N2-enriched seawater was measured using a membrane inlet mass spectrometer. Incubation bottles amended with the 15N2 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). Dataset has been reformatted to meet submission requirements for Simons CMAP.</p>
Cartolabe arXiv 2019
<p>Cartolabe arXiv 2019 dataset is built with data extracted from the <a href="https://arxiv.org/">arXiv science repository.</a> It gives access to ~1.6m articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Only authors with at least 4 articles are kept. This map was generated with the data extracted from arXiv on 29/11/2019.</p>
DeepDance: Motion capture data of improvised dance (2019)
<p><em>When using this resource, please cite Wallace, B., Nymoen, K., Martin, C.P & Tøressen, J. DeepDance: Motion capture data of improvised dance (2019) (version 2.0). Zenodo 10.5281/zenodo.5838178</em></p> <p><strong>Abstract</strong></p> <p>This dataset comprises full-body motion capture of improvised dance as well as corresponding audio files. 30 dancers were recorded individually, improvising to six different audio files. The motion was captured in units of mm at 240Hz using a Qualisys infra-red optical system. The experiment was carried out at the University of Oslo in October 2019. For each dancer, 3 performances are recorded for each musical piece, resulting in 540 1-minute motion capture files. The dataset was collected for use as training data in deep learning for motion generation. This dataset also includes MATLAB code to visualize the motion capture files.</p> <p> </p> <p><strong>Music</strong></p> <ul> <li>Skarphedinsson, M. Wallace, B. (2019). “Song a”</li> <li>Skarphedinsson, M. Wallace, B. (2019). “Song b”</li> <li>Skarphedinsson, M. Wallace, B. (2019). “Song c”</li> <li>Skarphedinsson, M. Wallace, B. (2019). “Song d”</li> <li>Skarphedinsson, M. Wallace, B. (2019). “Song f”</li> <li>LaClair, J. Bounce. Jesse LaClair, (2018) <em>Referenced here as “Song e”</em></li> </ul> <p> </p> <p><strong>Data Description</strong></p> <p>The following data types are provided:</p> <ul> <li>Motion (marker position): Recorded with Qualisys Track Manager and saved as tab-separated .tsv files.</li> <li>Stimuli: audio .wav files containing 1 minute of the tracks described above.</li> <li>MATLAB script for animating the tsv files. (requires the MoCap Toolbox)</li> </ul> <p>Note: Recordings which contained errors such as missing markers have been replaced by subject 001. </p> <p><strong>Acknowledgements</strong></p> <p>This work was partially supported by the Research Council of Norway through its Centres of Excellence scheme, project number 262762.</p> <p> </p> <p><strong>Conflicts of Interest</strong></p> <p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p> <p> </p>
An ice-tethered, non-floating Trident Sensors Helix Beacon during SCALE 2019 Spring Cruise
<p><strong>Brief data description</strong></p> <p>A Trident Sensors Helix Beacon identical to the one described in Womack et al. (2022), was deployed on sea ice at latitude 59.47<sup>o</sup> S and longitude 10.89<sup>o</sup> E, on 30 October 2019, as part of the <em>Southern oCean seAsonal Experiment</em> (SCALE; Ryan-Keogh and Vichi, 2022) aboard the SA Agulhas II.</p> <p>The region where the Trident was deployed (Antarctic marginal ice zone) consisted of first-year ice conditions, with an average thickness of 80-90 cm. The device was deployed by hand by three people, lowered by crane from the ship to the ice on a basket cradle.</p> <p>The temporal resolution was approximately four hours. The survival of the Trident depended on staying fixed to the ice floe and its battery life. The Trident recorded GPS position and air temperature, and transmitted data until 2 December 2019, where it sank due to sea-ice melting.</p> <p><strong>Buoy name and raw data:</strong></p> <p>Trident: Unit4.xlsx</p> <p><strong>Related code: </strong>The buoy data has been processed using https://github.com/mvichi/antarctic-buoys/.</p>
Polar Iridium Surface Velocity Profilers (p-iSVP), and standard Iridium Surface Velocity Profilers (iSVP) during SCALE 2019 Winter and Spring Cruises
<p><strong>Brief data description</strong></p> <p>In 2019, winter and spring scientific research expeditions aboard the SA Agulhas II were conducted along the Good-Hope line (0<sup>o</sup> E) to the Antarctic marginal ice zone (MIZ) in the north-eastern Weddell Sea region as part of the <em>Southern oCean seAsonal Experiment</em> (SCALE; Ryan-Keogh and Vichi, 2022).</p> <p>During the winter expedition, three polar Iridium Surface Velocity Profilers (p-iSVPs; MetOcean model) were deployed by the South African Weather Service (SAWS) between 27 July and 28 July 2019. These buoys were analysed in de Vos et al. (2022). The region of deployment consisted of pancake-ice conditions with an average ice thickness of 40-60 cm. The instruments were deployed by hand by three people, lowered by crane from the ship to the ice on a basket cradle. The first buoy (p-iSVP 1) was deployed in water, in between pancake ice floes, while the other two buoys (p-iSVP 2 and p-iSVP 3) were deployed on roughly circular ice floes > 3 m in diameter.</p> <p>These buoys were expendable devices that recorded GPS position, air and ice temperature, and barometric pressure. The temporal resolution is 30 minutes for p-iSVP 1 and hourly for p-iSVP 2 and p-iSVP 3. The survival of these sensors depended on their battery life, since p-iSVPs can continue to drift in the ocean after ice melting and can be further refrozen in between floes. p-iSVP 1 and p-iSVP 3 continued to transmit data until 15 October 2019. p-iSVP 2 stopped transmitting data on 25 August 2019.</p> <p>During the spring expedition, three standard Iridium Surface Velocity Profilers (iSVPs 4-6; Pacific Gyre model) were deployed by SAWS between 24 October and 28 October 2019 (de Vos et al., 2022). Specifically-designed frames were built around these three iSVPs to allow them to stand securely on the ice, without damaging the non-polar battery, and also to make sure they operated as Lagrangian ice trackers. These buoys were deployed during first-year ice conditions, with an average ice thickness of 80-90 cm. The instruments were deployed with the same protocol as the winter buoys.</p> <p>These buoys recorded GPS position, air temperature and barometric pressure, every hour. Their survival, like the winter p-iSVPs, also depended on their battery life, and therefore it was possible for them to continue to drift after ice melting. The iSVPs transmitted data until 19 December 2019.</p> <p><strong>Buoy names and raw data:</strong></p> <p>p-iSVP 1: 300234067003010-300234067003010-20191015T064320UTC.csv</p> <p>p-iSVP 2: 300234067002060-300234067002060-20191015T064316UTC.csv</p> <p>p-iSVP 3: 300234066992870-300234066992870-20191015T064314UTC.csv</p> <p>iSVP 4: 300234066433050.xlsx</p> <p>iSVP 5: 300234066433051.xlsx</p> <p>iSVP 6: 300234066433052.xlsx</p> <p><strong>Related code: </strong>The buoy data has been processed using https://github.com/mvichi/antarctic-buoys/. </p>
Marine macrobenthic assemblages off Bevano River mouth (2019)
<p>This dataset provides the abundance (ind. m<sup>-2</sup>) of marine macrobenthic invertebrate species at 39 random sampling points from 0.5 to 8 m depth along the coast (5 km) off the NATURA 2000 site IT4070009 "Ortazzo, Ortazzino e Foce del Torrente Bevano", sampled from 22 May to 4 July, 2019. Sediment grain size and organic matter are also provided.</p> <p>The dataset is provided in three formats: </p> <p>- Microsoft Excel XLSX file, including 3 sheets (Dataset, Fields and units, Taxonomy) </p> <p>- CSV files (UTF-8), 3 files corresponding to the 3 sheets of the Excel file </p> <p>- ESRI Shapefile (UTF-8, geometry point, EPSG:4326 - WGS 84)</p> <p>The dataset includes 39 records, one for each sampling point, and 111 fields. The first 12 fields are described in Table 4). The following fields concern the abundance of the identified taxa as individuals preserved in alcohol sorted and classified under microscope (ind. m<sup>-2</sup> ± 10). All the dataset fields are described in the file “Fields and units”, while the taxonomic related information for each taxon is provided in the file “Taxonomy”. Information extracted from the World Register of Marine Species (WoRMS; <a href="https://marinespecies.org/">https://marinespecies.org/</a>) is provided here.</p> <p>A total of 99 soft bottom taxa belonging to the Phyla Annelida (29), Arthropoda (28), Cnidaria (1), Echinodermata (2), Mollusca (37), Nemertea (1), and Phoronida (1) were identified. Of these, 51 have been recognized at species level.</p> <p>This dataset comes from the project "Characterization of the mouth area of the Bevano River and identification of strategies for the conservation and enhancement of nursery areas for protected species of commercial interest", carried out by the Interdepartmental Research Center for Environmental Sciences (CIRSA) of the Alma Mater Studiorum University of Bologna. The project was financed by the Emilia-Romagna Region (call FLAG Costa dell'Emilia-Romagna 2018) with funds from the European Union (FEAMP 2014/2020, Action 2.A.a, "Marine and lagoon habitats - Studies and research"), and took place from January to August 2019 (Abbiati et al., 2019 DOI: <a href="http://doi.org/10.5281/zenodo.4016598">10.5281/zenodo.4016598</a>). This dataset has been revised and completed within the project "Ecosystem for Sustainable Transition in Emilia-Romagna" (Code: ECS_00000033 - CUP: B33D21019790006; Mission 04 Education and research - Component 2 From research to business Investment 1.5 - NextGenerationEU) .</p> <p> </p> <p>First 12 fields in the dataset.</p> <table> <tbody> <tr> <td> <p><strong>Field</strong></p> </td> <td> <p><strong>Darwin Core term</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> <td> <p><strong>Precision</strong></p> </td> <td> <p><strong>Note</strong></p> </td> </tr> <tr> <td> <p>locationID</p> </td> <td> <p>locationID</p> </td> <td> <p>NA</p> </td> <td> <p>NA</p> </td> <td> <p>Sampling location identifier (ID) specific to the data set</p> </td> </tr> <tr> <td> <p>samplingDate</p> </td> <td> <p>eventDate</p> </td> <td> <p>YYYY-MM-DD</p> </td> <td> <p>NA</p> </td> <td> <p>Conforms to ISO 8601-1:2019</p> </td> </tr> <tr> <td> <p>samplingTime</p> </td> <td> <p>eventTime</p> </td> <td> <p>HH:MM</p> </td> <td> <p>± 10 min</p> </td> <td> <p>Central European Summer Time CEST (UTC+2) conforms to ISO 8601-1:2019</p> </td> </tr> <tr> <td> <p>decimalLatitude</p> </td> <td> <p>decimalLatitude</p> </td> <td> <p>decimal degrees</p> </td> <td> <p>± 0.00001</p> </td> <td> <p>WGS84 (EPSG: 4326) - WAAS/EGNOS enabled GPS position</p> </td> </tr> <tr> <td> <p>decimalLongitude</p> </td> <td> <p>decimalLongitude</p> </td> <td> <p>decimal degrees</p> </td> <td> <p>± 0.00001</p> </td> <td> <p>WGS84 (EPSG: 4326) - WAAS/EGNOS enabled GPS position</p> </td> </tr> <tr> <td> <p>Depth</p> </td> <td> <p>maximumDepthInMeters</p> </td> <td> <p>m</p> </td> <td> <p>± 0.1</p> </td> <td> <p>Mean Lower Low Water - measured with echosounder or depth gauge corrected by tide gauge of Porto Corsini (RA)</p> </td> </tr> <tr> <td> <p>SamplingGear</p> </td> <td> <p>NA</p> </td> <td> <p>NA</p> </td> <td> <p>NA</p> </td> <td> <p>Van Veen grab operated from boat or bailer manually operated by diver inside a cylindrical frame</p> </td> </tr> <tr> <td> <p>SamplingArea</p> </td> <td> <p>NA</p> </td> <td> <p>m^2</p> </td> <td> <p>± 0.001</p> </td> <td> <p>Sampler size</p> </td> </tr> <tr> <td> <p>Mud</p> </td> <td> <p>NA</p> </td> <td> <p>% dry mass</p> </td> <td> <p>± 0.1%</p> </td> <td> <p><63 µ wet sieved recovered on Whatman filter paper and then dried at 80°C for 24 hours before weighing at ± 0.00001 g</p> </td> </tr> <tr> <td> <p>FineSand</p> </td> <td> <p>NA</p> </td> <td> <p>% dry mass</p> </td> <td> <p>± 0.1%</p> </td> <td> <p>250-63 µ wet sieved recovered on Whatman filter paper and then dried at 80°C for 24 hours before weighing at ± 0.00001 g</p> </td> </tr> <tr> <td> <p>MediumSand</p> </td> <td> <p>NA</p> </td> <td> <p>% dry mass</p> </td> <td> <p>± 0.1%</p> </td> <td> <p>>250 µ wet sieved recovered on Whatman filter paper and then dried at 80°C for 24 hours before weighing at ± 0.00001 g</p> </td> </tr> <tr> <td> <p>OrganicMatter</p> </td> <td> <p>NA</p> </td> <td> <p>% dry mass</p> </td> <td> <p>± 0.1%</p> </td> <td> <p>Loss on Ignition (LOI%) at 450°C 8h and weighted at ± 0.00001 g</p> </td> </tr> <tr> <td> <p>Actiniaria</p> </td> <td> <p>NA</p> </td> <td> <p>ind. m^-2</p> </td> <td> <p>± 10</p> </td> <td> <p>Individuals preserved in alcohol sorted and classified under microscope</p> </td> </tr> </tbody> </table>
Striped venus clam (Chamelea gallina) abundance, size, and biomass off Bevano River mouth (2019)
<p>This dataset provides the abundance (ind. m<sup>-2</sup>) of the striped venus clams, <em>Chamelea gallina</em> (Linnaeus, 1758), at 71 random sampling points (Fig. 3) from 0.5 to 8 m depth along the coast (5 km) off the NATURA 2000 site IT4070009 "Ortazzo, Ortazzino e Foce del Torrente Bevano", sampled from 22 May to 4 July, 2019. Where available, the mean and standard deviation of shell length (i.e. the maximum distance between anterior and posterior margins) of <em>Chamelea gallina </em>determined to the nearest 0.01 mm using a manual calliper, and the wet biomass per square metre (g m<sup>-2</sup>), estimated on the basis of the mean shell length, by length–weight relationship (according to <a href="https://doi.org/10.1080/24750263.2019.1668066">Petetta et al., 2019</a>), were provided. Depth, sediment grain size, and organic matter at each point are also provided. </p> <p>The dataset is provided in three formats: </p> <ul> <li>Microsoft Excel XLSX file, including 3 sheets (Dataset, Fields and units, Parameters) </li> <li>CSV files (UTF-8), 3 files corresponding to the 3 sheets of the Excel file </li> <li>ESRI Shapefile (UTF-8, geometry point, EPSG:4326 - WGS 84)</li> </ul> <p> </p> <p>The dataset includes 71 records, one for each sampling point, and 17 fields, which are described in the Excel sheet/CSV file “Fields and units” (see also Table 6). The Excel sheet/CSV file provides details and coefficients of the length–weight relationship (log W= log a + b log L; where: W=wet weight (g), L=length (mm), Log base=10) used to estimate the wet biomass from the mean lengths and abundances (the calculation formulas are present in the Excel sheet).</p> <p>Finally, ESRI Shapefile provides users with direct upload in any Geographic Information System (GIS). Nevertheless, due to this file format limitations, dataset field names have been truncated and/or renamed to fit 10 characters.</p> <p>Fields in the dataset (NA=not available).</p> <table> <thead> <tr> <th scope="col"> <p><strong>Field</strong></p> </th> <th scope="col"> <p><strong>Darwin Core term</strong></p> </th> <th scope="col"> <p><strong>Unit</strong></p> </th> <th scope="col"> <p><strong>Precision</strong></p> </th> <th scope="col"> <p><strong>Note</strong></p> </th> </tr> </thead> <tbody> <tr> <td> <p>locationID</p> </td> <td> <p>locationID</p> </td> <td> <p>NA</p> </td> <td> <p>NA</p> </td> <td> <p>Sampling location identifier (ID) specific to the data set</p> </td> </tr> <tr> <td> <p>samplingDate</p> </td> <td> <p>eventDate</p> </td> <td> <p>YYYY-MM-DD</p> </td> <td> <p>NA</p> </td> <td> <p>Conforms to ISO 8601-1:2019</p> </td> </tr> <tr> <td> <p>samplingTime</p> </td> <td> <p>eventTime</p> </td> <td> <p>HH:MM</p> </td> <td> <p>± 10 min</p> </td> <td> <p>Central European Summer Time CEST (UTC+2) conforms to ISO 8601-1:2019</p> </td> </tr> <tr> <td> <p>decimalLatitude</p> </td> <td> <p>decimalLatitude</p> </td> <td> <p>decimal degrees</p> </td> <td> <p>± 0.00001</p> </td> <td> <p>WGS84 (EPSG: 4326) - WAAS/EGNOS enabled GPS position</p> </td> </tr> <tr> <td> <p>decimalLongitude</p> </td> <td> <p>decimalLongitude</p> </td> <td> <p>decimal degrees</p> </td> <td> <p>± 0.00001</p> </td> <td> <p>WGS84 (EPSG: 4326) - WAAS/EGNOS enabled GPS position</p> </td> </tr> <tr> <td> <p>Depth</p> </td> <td> <p>maximumDepthInMeters</p> </td> <td> <p>m</p> </td> <td> <p>± 0.1</p> </td> <td> <p>Mean Lower Low Water - measured with echosounder or depth gauge corrected by tide gauge of Porto Corsini (RA)</p> </td> </tr> <tr> <td> <p>SamplingGear</p> </td> <td> <p>NA</p> </td> <td> <p>NA</p> </td> <td> <p>NA</p> </td> <td> <p>Van Veen grab operated from boat or bailer manually operated by diver inside a cylindrical frame</p> </td> </tr> <tr> <td> <p>SamplingArea</p> </td> <td> <p>NA</p> </td> <td> <p>m2</p> </td> <td> <p>± 0.001</p> </td> <td> <p>Sampler size</p> </td> </tr> <tr> <td> <p>Mud</p> </td> <td> <p>NA</p> </td> <td> <p>% dry mass</p> </td> <td> <p>± 0.1%</p> </td> <td> <p>Sediment particles <63 µ wet sieved recovered on Whatman filter paper and then dried at 80°C for 24 hours before weighing at ± 0.00001 g</p> </td> </tr> <tr> <td> <p>FineSand</p> </td> <td> <p>NA</p> </td> <td> <p>% dry mass</p> </td> <td> <p>± 0.1%</p> </td> <td> <p>Sediment particles 250-63 µ wet sieved recovered on Whatman filter paper and then dried at 80°C for 24 hours before weighing at ± 0.00001 g</p> </td> </tr> <tr> <td> <p>MediumSand</p> </td> <td> <p>NA</p> </td> <td> <p>% dry mass</p> </td> <td> <p>± 0.1%</p> </td> <td> <p>Sediment particles >250 µ wet sieved recovered on Whatman filter paper and then dried at 80°C for 24 hours before weighing at ± 0.00001 g</p> </td> </tr> <tr> <td> <p>OrganicMatter</p> </td> <td> <p>NA</p> </td> <td> <p>% dry mass</p> </td> <td> <p>± 0.1%</p> </td> <td> <p>Sediment organic matter content obtained by Loss of weight on Ignition (LOI%) at 450°C 8h and weighted at ± 0.00001 g</p> </td> </tr> <tr> <td> <p>Individuals</p> </td> <td> <p>NA</p> </td> <td> <p>ind. sample-1</p> </td> <td> <p>± 1</p> </td> <td> <p>Individuals of <em>Chamelea gallina </em>retrieved in each sample, preserved in alcohol sorted and classified under microscope</p> </td> </tr> <tr> <td> <p>Abundance</p> </td> <td> <p>NA</p> </td> <td> <p>ind. m-2</p> </td> <td> <p>± 10</p> </td> <td> <p>Abundance of <em>Chamelea gallina</em> per square meter estimated on the basis of the sampling area</p> </td> </tr> <tr> <td> <p>MeanLength</p> </td> <td> <p>NA</p> </td> <td> <p>mm</p> </td> <td> <p>± 0.01</p> </td> <td> <p>Mean shell length (i.e. the maximum distance between anterior and posterior margins) of <em>Chamelea gallina</em> determined to the nearest 0.01 mm using a manual calliper</p> </td> </tr> <tr> <td> <p>SDLength</p> </td> <td> <p>NA</p> </td> <td> <p>mm</p> </td> <td> <p>± 0.01</p> </td> <td> <p>Standar deviation of mean shell length of <em>Chamelea gallina</em></p> </td> </tr> <tr> <td> <p>WetMass</p> </td> <td> <p>NA</p> </td> <td> <p>g m-2</p> </td> <td> <p>± 1</p> </td> <td> <p>Wet biomass per square meter of <em>Chamelea gallina</em> estimated on the basis of the mean shell length, by length–weight relationship (according to <a href="https://doi.org/10.1080/24750263.2019.1668066">Petetta et al., 2019</a> DOI:10.1080/24750263.2019.1668066), and abundnce</p> </td> </tr> </tbody> </table> <p> </p> <p>This dataset comes from the project "Characterization of the mouth area of the Bevano River and identification of strategies for the conservation and enhancement of nursery areas for protected species of commercial interest", carried out by the Interdepartmental Research Center for Environmental Sciences (CIRSA) of the Alma Mater Studiorum University of Bologna. The project was financed by the Emilia-Romagna Region (call FLAG Costa dell'Emilia-Romagna 2018) with funds from the European Union (FEAMP 2014/2020, Action 2.A.a, "Marine and lagoon habitats - Studies and research"), and took place from January to August 2019 (<a href="https://doi.org/10.5281/zenodo.4016598">Abbiati et al., 2019</a>). Finally, this dataset has been revised and completed within the project “Ecosystem for Sustainable Transition in Emilia-Romagna” (ECOSISTER, Code: ECS_00000033 - CUP: B33D21019790006).</p> <p> </p>
Simulated Geomorphically Relevant Palaeoclimate Variables for the Late Cenozoic (from Mutz and Ehlers, 2019)
<p><strong>Contents Description: (see more details in file header)</strong><br> The files contains long term means of several variables derived from an ECHAM5 palaeoclimate simulation. Details about the type of means and simulations are given in the file name (see explanation below). For information about the simulation setup and boundary conditions, we refer the user to the Mutz et al. (2018) publication. For details about the construction of contained variables, we refer the reader to the Mutz and Ehlers (2019) publication. Data package <em>alterm</em> contains long term annual means, whereas package <em>mlterm</em> contains long term monthly means. The variables included in the data packages are:</p> <p>- csfd: consecutive freezing days (days)<br> - fthd: freeze thaw days (days)<br> - cswd: consecutive wet days (days)<br> - csdd: consecutive dry days (days)<br> - t2am: 2m air temperature amplitude (°C)<br> - tsam: surface temperature amplitude (°C)<br> - pamp: precipitation amplitude (mm/day)<br> - pmax: maximum daily precipitation (mm/day)</p> <p><strong>Publications:</strong><br> <em>Derived Variables (contained in these files):</em><br> Mutz S.G. and Ehlers T. A. (2019). Detection and Explanation of Spatiotemporal Patterns in Late Cenozoic Palaeoclimate Change Relevant to Earth Surface Processes. Earth Surface Dynamics. doi.org/10.5194/esurf-7-663-2019</p> <p><em>Original Simulations:</em><br> Mutz S.G., Ehlers T. A., Werner M., Lohmann G., Stepanek C., Li J., (2018). Estimates of Late Cenozoic climate change relevant to Earth surface processes in tectonically active orogens. Earth Surface Dynamics. doi.org/10.5194/esurf-6-271-2018</p> <p><strong>Authors:</strong><br> Mutz S.G., Ehlers T. A.</p> <p><strong>License:</strong><br> This work is distributed under the Creative Commons Attribution 4.0 International License</p> <p><strong>Format:</strong><br> netCDF (.nc)</p> <p><strong>File Names (nc):</strong><br> [publication]_[experiment ID]_[time period]_[horizontal resolution][vertical resolution]_[means].nc<br> </p> <table> <tbody> <tr> <td>experiment ID</td> <td>usually a letter followed by 3-5 digits, e007_2 (pre-industrial), e008 (Mid-Holocene), e009 (Last Glacial Maximum), e010 (Pliocene)</td> </tr> <tr> <td>time period</td> <td>PI (pre-industrial), MH (Mid-Holocene), LGM (Last Glacial Maximum), PLIO (Pliocene)</td> </tr> <tr> <td>horizontal resolution</td> <td>spectral resolution, t followed by a number, e.g. t159</td> </tr> <tr> <td>vertical resolution</td> <td>number of vertical levels, l followed by a number, e.g. l31</td> </tr> <tr> <td>means</td> <td>alterm (annual long term means) or mlterm (monthly long term means)</td> </tr> </tbody> </table> <p><br> <strong>Correspondence:</strong><br> Sebastian G. Mutz (sebastian@sebastianmutz.com)</p> <p> </p>
Timeline of government interventions and events regarding the COVID-19 pandemic in Sweden December 31, 2019, to May 5, 2023.
<p>The Swedish approach to managing the COVID-19 pandemic has received significant attention in international scholarly work and the press. For this dataset, we have reviewed governmental and media archives to build a detailed timeline that chronicles significant policies, interventions, and events in the Swedish management of COVID-19. The dataset contains summary descriptions of what took place, when it happened, and who the principal actors involved were. Links to primary sources are provided for each entry. Because of the level of detail and saturation, the dataset offers a detailed account of Swedish pandemic governance and will benefit anyone working on Swedish pandemic management or doing comparative work between Sweden and other jurisdictions.</p> <p>The dataset contains details on the date an event took place (column 1), tags to facilitate navigation (column 2), details on the principal actors involved in the event (column 3), a summary description of what took place and who was involved (column 4), and links to primary materials (e.g., archival entries) (columns 4-12). Through a structured and detailed outline, the dataset provides a saturated account of policy interventions and events in Sweden during the COVID-19 pandemic for the period 2020-2023 until it was no longer considered a public health emergency of international concern by the WHO and complements existing and less detailed timelines published at earlier points in the period.</p>
S106 | AQUAlity19 | EU AQUAlity's water analysis list 2019
<p>This is the collection associated with list S106 AQUAlity19 EU AQUAlity's water analysis list 2019 on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>List of compounds found in surface and wastewater sampling and analysis by LC-HRMS (Orbitrap) within the European project AQUAlity in 2019.<a href="http://https://www.aquality-etn.eu/"> AQUAlity</a> is a project funded by the European Union under the Marie Skłodowska-Curie Actions (MSCA) – Innovative Training Networks (Call: H2020-MSCA-ITN-2017) Project N. 765860.</p> <p>The list was kindly provided by Azziz Assoumani (INERIS, France)<br> </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.