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12,237 results for “2019”
ChinaHighCO: Daily Seamless 1 km Ground-Level CO Dataset for China (2019–Present)
<p>ChinaHighCO is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level CO dataset for China <strong>from 2019 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.80, a root-mean-square error (RMSE) of 0.29 mg m<sup>-3</sup>, and a mean absolute error (MAE) of 0.16 mg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighCO dataset in your scientific research, please cite the following reference (Wei et al., ACP, 2023):</p> <ul> <li> <p>Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-ACP-2023.pdf">Ground-level gaseous pollutants (NO<sub>2</sub>, SO<sub>2</sub>, and CO) in China: daily seamless mapping and spatiotemporal variations</a>. <em>Atmospheric Chemistry and Physics</em>, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023</p> </li> </ul> <p><strong>Note that the ChinaHighCO<sub> </sub>dataset is also available for periods prior to 2019, but at a spatial resolution of 10 km:</strong></p> <p> all (including <strong>daily</strong>) data for the years <strong>2013–2018 </strong>are accessible at: <strong><a href="https://doi.org/10.5281/zenodo.4641530">https://doi.org/10.5281/zenodo.4641530</a></strong></p> <p><strong>More CHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
ChinaHighSO₂: Daily Seamless 1 km Ground-Level SO₂ Dataset for China (2019–Present)
<p>ChinaHighSO<sub>2</sub> is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level SO<sub>2</sub> dataset for China <strong>from 2019 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.84, a root-mean-square error (RMSE) of 10.07 µg m<sup>-3</sup>, and a mean absolute error (MAE) of 4.68 µg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighSO<sub>2</sub> dataset in your scientific research, please cite the following reference (Wei et al., ACP, 2023):</p> <ul> <li> <p>Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-ACP-2023.pdf">Ground-level gaseous pollutants (NO<sub>2</sub>, SO<sub>2</sub>, and CO) in China: daily seamless mapping and spatiotemporal variations</a>. <em>Atmospheric Chemistry and Physics</em>, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023</p> </li> </ul> <p><strong>Note that the ChinaHighSO<sub>2 </sub>dataset is also available for periods prior to 2019, but at a spatial resolution of 10 km:</strong></p> <p> all (including <strong>daily</strong>) data for the years <strong>2013–2018 </strong>are accessible at: <strong><a href="https://doi.org/10.5281/zenodo.4641538">https://doi.org/10.5281/zenodo.4641538</a></strong></p> <p><strong>More CHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
S100 | PFASREACH | List of PFAS identified in REACH 2019
<p>This is the collection associated with list S100 PFASREACH List of PFAS identified in REACH 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>A list of 437 PFAS identified in Registration, Evaluation, Authorisation and Restriction of Chemicals <a href="https://eur-lex.europa.eu/eli/reg/2008/1272">(REACH) Reg. (EC) No 1272/2008</a> in September 2019. Of these, 17 are produced at >1000 tonnes/year and 84 at >10 tonnes per year for use in Europe. Collaborative effort between Hans Peter Arp (NGI) and Emma Schymanski (LCSB) within <a href="https://zenodo.org/communities/zeropm-h2020?page=1&size=20">ZeroPM</a> (EU H2020 grant 101036756).</p> <p>Updates: 14 Dec 2022: added 5 new CIDs following PubChem deposition. 16 April 2025: removed CID <a href="https://pubchem.ncbi.nlm.nih.gov/compound/10996402">10996402</a> and <a href="https://pubchem.ncbi.nlm.nih.gov/compound/21967041">21967041</a> as they are not PFAS, due to external report via PubChem. </p>
S60 | SWISSPEST19 | Swiss Pesticides and Metabolites from Kiefer et al 2019
<p>This is the collection associated with list S60 SWISSPEST19 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>Swiss pesticides (plant protection products) and metabolites from Kiefer et al 2019 (Eawag), Tables SI-B 1 and 2, DOI: <a href="https://doi.org/10.1016/j.watres.2019.114972">10.1016/j.watres.2019.114972</a></p> <p>Update 25 April 2020: fixed many naming issues in xlsx and csv file. No structural information changed. 25 Mar 2023: fixed date CAS. 25 May 2023: fixed non-live CIDs to <a href="https://gitlab.lcsb.uni.lu/eci/pubchem/-/commit/56abd8e2bacbbb6969c961101df792189d605bfd">live CIDs</a>. 6 Jul 2023: added <a href="https://gitlab.lcsb.uni.lu/eci/pubchem/-/commit/3ff26d704ad9ac831a5d276256c1c89020fb2e02">NOA 413161</a> structures and transformations file. 8 April 2025: fixed three CIDs that went non-live to match CIDs from NORMAN deposition. Mismatch of InChIKeys due to difference in treatment of the C=N-O groups between PubChem and Open Babel; files now match PubChem's output. 2 Jun 2025: updated TFA synonym. 31 Aug 2025: fixed triazole alanine structure for one entry (removing incorrect CID <span><a href="https://pubchem.ncbi.nlm.nih.gov/compound/139597001"><span>139597001</span></a></span>).</p>
Numerical weather simulation using COSMOiso in June 2019 during L-WAIVE field campaign: selected model output and post-processed data.
<p>This dataset consists of extracts from a simulation with the isotope-enabled regional numerical weather prediction model COSMOiso, which covers the timespan of the Lacustrine-Water vApor Isotope inVentory Experiment (L-WAIVE) field campaign taking place in June 2019 in the Annecy valley in the French Alps (Chazette et al. 2021).The simulation has a horizontal resolution of 0.1° (~10km) and 40 vertical levels.</p><p>This COSMOiso simulation is used in Thurnherr et al. (submitted) to compare stable water isotope measurements from various platforms. Here, we provide selected model outputs and post-processed data used in this comparison study. The post-processed data contain:</p><ol><li>COSMOiso output files for time steps 20190612_12, 20190613_12, 20190615_13, 20190616_13, 20190617_12, 20190622_12.</li><li>Pressure weighted total and subcolumn averages for time steps 20190612_12, 20190613_12, 20190615_13, 20190616_13, 20190617_12, 20190622_12.</li><li>Vertical cross section of selected variables at Annecy, the location of the L-WAIVE field campaign, for the simulation time window.</li><li>Interpolated time series of subcolumn and total column averages at Annecy, the location of the L-WAIVE field campaign, for the simulation time window.</li><li>Interpolated variables along the flight tracks from the L-WAIVE campaign (see Sodemann and Seidl, 2023).</li></ol><p>See also README files for more details on the provided data.</p><p>To access further model output and post-processed data, please contact the dataset authors.</p>
Local plot information observed on LandKlif plots during vegetation survey 2019
<p><span>LandKlif local plot information observed on site during vegetation survey 2019, including vegetation height, slope, aspect, proximity to hedge / forest edge / water, intensity of use (only for meadows), and further information on plot habitat.</span></p> <p><span>LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif). Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</span></p>
Plant Atlas 2020 — British and Irish vascular plant and charophyte 2 x 2 km grid square locations up to 2019
<p><span>This resource provides the data behind the 2 × 2 km grid square (tetrad) British and Irish distribution maps, for 3,431 taxa, presented in both the Plant Atlas 2020 book and website (</span><a href="http://www.plantatlas2020.org"><span><span>www.plantatlas2020.org</span></span></a><span><span>), up to 2019. These are presence-only data, indicating where a taxon was reported from a tetrad</span></span><span>. These 2 km square presences are based on over 30 million records, collected mainly by volunteer recorders of the Botanical Society of Britain and Ireland (BSBI) between 2000 and 2019, as well as previous nationwide surveys undertaken in the 1950s and 1990s.</span></p>
Plant Atlas 2020 — British and Irish vascular plant and charophyte 10 x 10 km grid square locations, subdivided by survey period, up to 2019
<p><span>This resource provides the data behind the 10 × 10 km grid square (hectad) British and Irish distribution maps, for 3,497 taxa, presented in both the Plant Atlas 2020 book and website (</span><a href="http://www.plantatlas2020.org"><span><span>www.plantatlas2020.org</span></span></a><span><span>), subdivided by time period<a><span>.</span></a> These are presence-only data, indicating where a taxon was reported from a hectad, within a given</span><span><span></span></span></span><span> multi-year period, up to 2019. These time periods cover the 20<sup>th</sup> Century, but also extend back to the earliest botanical records known for Britain and Ireland in the first period (pre-1930). These 10 km square presences are based on over 30 million records, collected mainly by volunteer recorders of the Botanical Society of Britain and Ireland (BSBI) between 2000 and 2019, as well as previous nationwide surveys undertaken in the 1950s and 1990s.</span></p>
Data from Phenocam (PHE) measurements of above-canopy vegetation (hartheim1) at Hartheim Forest Research Site (DE-Har) from 2019-01-01 to 2019-12-31 [RAW]
<p>Phenocam images from "hartheim1" at DE-Har separated into near-infrared (NIR) and visible (VIS) for the year 2019. </p> <p>Phenocam "hartheim1" shows the view from the main tower at 30m height towards N at the <a href="https://www.meteo.uni-freiburg.de/en/infrastructure/hartheim-forest-research-site?set_language=en">ICOS Associate Ecosyste Site DE-Har, Germany</a> recording the phenology and state of the top of the canopy consisting of pinus sylverstris and pinus nigra.</p>
2019-2020 woody plants pollen dataset from automatic particle detector in Šiauliai
<p>Dataset acquired from Rapid-E device by testing it with anemophilous woody plants pollen collected in Lithuania. Data is sorted by pollen type.</p> <p>The sampling methodology can be found in publication "Automatic pollen recognition with the Rapid-E particle counter: the first-level procedure, experience and next steps", Šaulienė Ingrida, et al. Atmospheric Measurement Techniques, 2019, 12.6: 3435-3452. <a href="https://doi.org/10.5194/amt-12-3435-2019">https://doi.org/10.5194/amt-12-3435-2019</a></p> <p>The authors would like to hear from you at realtime@sa.vu.lt if you use this dataset.</p>
2019-2020 herbaceous plants pollen dataset from automatic particle detector in Šiauliai
<p>Dataset acquired from Rapid-E device by testing it with anemophilous herbaceous plant pollen collected in Lithuania. Data is sorted by pollen type.</p> <p>The sampling methodology can be found in publication "Automatic pollen recognition with the Rapid-E particle counter: the first-level procedure, experience and next steps", Šaulienė Ingrida, et al. Atmospheric Measurement Techniques, 2019, 12.6: 3435-3452. <a href="https://doi.org/10.5194/amt-12-3435-2019">https://doi.org/10.5194/amt-12-3435-2019</a></p> <p>The authors would like to hear from you at realtime@sa.vu.lt if you use this dataset.</p>
Weather dataset from Otemma glacier forefield, Switzerland (from 14 July 2019 to 18 November 2021)
<p>Weather data collected in the Otemma forefield (Switzerland) from 14 July 2019 to 18 November 2021.<br> Data were collected by the research teams of Bettina Schaefli<sup>2</sup> and Stuart N. Lane<sup>1</sup>.</p> <p><sup>1</sup> Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland</p> <p><sup>2</sup> Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland</p> <p>For further information, please contact:<br> tom.muller.1@unil.ch<br> bettina.schaefli@giub.unibe.ch</p> <p><strong>Description of data : </strong>WeatherData.csv</p> <p>Time span of data : 14 July 2019 to 18 November 2021<br> Time step : homogenized 10 minutes-averaged data</p> <p>Location of data (Coordinate in SWISS LV95 (EPSG:2056)<br> - Glacier snout Station : 2598615 / 1087375<br> - Glacier center Station : 2600495 / 1088631<br> - Floodplain Station : 2598096 / 1087087</p> <p>STRUCTURE OF DATA : tidy dataframe with following headers :<br> - <em>date </em>: local date (UTC+01 with daylight saving time)<br> - <em>variable </em>: parameter of interest, with following classes :<br> Air_humidity : Air humidity in percent of air saturation [%]<br> Air_temperature : Air temperature in [°C]<br> Atm_pressure : Atmospheric pressure in [hPa]<br> Incoming_radiation : Incoming shortwave radiation in [W/m2]<br> Precipitation : Liquid precipitation measured [mm]<br> - <em>name </em>: location of data (see coordinates above)<br> - <em>dateUTC </em>: date with UTC timezone</p> <p>Device used for data acquisition :<br> - Air_humidity/Air_temperature/Atm_pressure : Decagon VP-4<br> - Incoming_radiation : Apogee Instruments SP-11<br> - Precipitation : Double tipping buckets rain gauge from Davis Instruments (resolution 0.2 mm)</p> <p> </p> <p><strong>Description of data : </strong>RainComposite_Otemma_Arolla.csv</p> <p>Time span of data : 01 July 2019 to 18 November 2021<br> Time step : homogenized 10 minutes-averaged data</p> <p>The dataset compiles the measured rain during summer at the closest weather station (Glacier snout).<br> For the winter period (and few gaps during the summer), the solid precipitations (snow) from the closest MeteoSwiss weather stations (SwissMetNet) were used.<br> Gaps where filled with 1) MeteoSwiss Otemma and if data were still missing filled with 2) MeteoSwiss Arolla<br> <br> Location of data (Coordinate in SWISS LV95 (EPSG:2056)<br> - Glacier snout Station : 2598615 / 1087375<br> - Otemma camp Station : 2597508 / 1086653<br> - MeteoSwiss Station : 2596476 / 1085864<br> - MeteoSwiss Arolla : 2603507 / 1095832</p> <p>STRUCTURE OF DATA : tidy dataframe with following headers :<br> - date : local date (UTC+01 with dst)<br> - variable : parameter of interest<br> Precipitation : Liquid and solid precipitation [mm]. Composite dataset composed of melted snow (snow Water Equivalent, in mm, from MeteoSwiss station) and Rain (in mm from Glacier station).<br> - Location : location of data (see above)<br> - dateUTC : date in UTC timezone<br> <br> Device used for data acquisition :<br> - Glacier snout Station : Double tipping buckets rain gauge from Davis Instruments (resolution 0.2 mm)<br> - Otemma camp Station : Double tipping buckets rain gauge, Spectrum WatchDog 1120 (resolution 0.25 mm)<br> - MeteoSwiss : see <a href="https://www.meteoswiss.admin.ch/home/measurement-and-forecasting-systems/land-based-stations/automatisches-messnetz.html">SwissMetNet</a> project</p> <p> </p> <p><strong>Description of data :</strong> Otemma_weather_Plot_alldata.html</p> <p>An interactive plot generated with python plotly (open in web browser) containing all above described data.</p>
Stream discharge, stage, electrical conductivity & temperature dataset from Otemma glacier forefield, Switzerland (from July 2019 to October 2021)
<p>Stream data collected in the Otemma forefield (Switzerland) from July 2019 to Ocober 2021.<br> Data were collected by the research teams of Bettina Schaefli<sup>2</sup> and Stuart N. Lane<sup>1</sup>.</p> <p><sup>1</sup> Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland</p> <p><sup>2</sup> Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland</p> <p>For further information, please contact:</p> <ul> <li>tom.muller.1@unil.ch</li> <li>floreana.miesen@unil.ch</li> </ul> <p><strong>Description of data </strong></p> <p>A detailed description of the dataset is provided in the <strong>data_description_analysis.pdf</strong> file. In particular, the methodology and stage-discharge rating curves are provided in this file. Stream data were measured in three locations from glacier snout (Station 1); after the outwash plain (Station 2) and at the end of the glacier forefield (Station 3) (<strong>see overview_GS.png</strong>). A <strong>shapefile </strong>is also provided (coordinate system LV95).</p> <p>2 datasets are available in the data.zip file:</p> <ul> <li> <p><strong>River_2019_2021_10T.csv</strong> : contains the measured River Electrical conductivity (EC) [μS/cm], Stage [meters] and Temperature [°C] data for all stations in a tidy data format (see pdf for detailed description), with a 10 minutes timestep.</p> </li> <li> <p><strong>Discharge2020_10T.csv </strong>&<strong> Discharge2021_10T.csv </strong>: contains the estimated discharge [m<sup>3</sup>/s] at Station 1 and Station 2 from July 2020 to October 2021 and estimated error (2 standard deviations) in a tidy data format (see pdf for detailed description), with a 10 minutes timestep.</p> </li> </ul> <p>Additionally, the point discharge measurements covering peak summer discharge to minimal winter baseflow are provided in the <strong>Point_discharge_measurements_2020_2021.xlsx</strong> file.</p> <p>Plots of river parameters and discharge are also provided in data.zip for vizualisation.</p> <p> </p>
Coupled atmosphere-wave-ocean simulation of Hurricane Dorian (2019)
<p><strong>Description</strong></p> <p>This dataset provides the output of the coupled atmosphere-wave-ocean simulation of Hurricane Dorian from August 29 to September 7, 2019. The simulation is a composite of two separate simulations:</p> <ol> <li>From 00 UTC August 29 to 00 UTC September 1, 2019</li> <li>From 00 UTC September 1 to 00 UTC September 7, 2019</li> </ol> <p>The first simulation serves as "spin-up" for the hurricane and its environment prior to landfall. The second simulation is initialized from the output of the first simulation, while relocating the Dorian vortex to its correct position on September 1. Due to the size of the dataset only the surface fields are made available.</p> <p><strong>Model configuration</strong></p> <ul> <li><strong>Atmosphere</strong>: Weather Research and Forecasting (WRF, https://github.com/wrf-model/WRF) model v4.2.2, with the Advanced Research WRF (ARW) dynamical core. The model has a 3-km resolution grid over the parent domain and a 1-km resolution nest over the Bahamas region (September 1-7 only), both with 45 vertical layers. Initial and boundary conditions are based on 6-hourly ERA-5 dataset.</li> <li><strong>Ocean Waves</strong>: University of Miami Wave Model (UMWM, https://umwm.org). The model is configured at the same 3-km as the atmosphere model, and has 36 directional bins and 37 frequency bins that are logarithmically spaced from 0.0313 to 2 Hz.</li> <li><strong>Ocean Circulation</strong>: HYbrid Coordinate Ocean Model (HYCOM, https://github.com/HYCOM) v2.3.01, configured at 0.01 degree resolution and 41 vertical layers. Initial and boundary conditions are based on daily GOFS 3.1 41-layer HYCOM + NCODA Global 1/12° Analysis, daily. K-Profile Parameterization for vertical mixing.</li> <li><strong>Coupling</strong>: Earth System Modeling Framework (ESMF, https://github.com/esmf-org/esmf) v8.0.1</li> </ul> <p><strong>File Description</strong></p> <ul> <li>blkdat.input - HYCOM (ocean circulation) configuration file</li> <li>dorian2019_atmosphere_1km_2019090100.nc - Atmosphere at 1-km resolution dataset</li> <li>dorian2019_atmosphere_waves_3km_2019082900.nc - Atmosphere and waves at 3-km resolution dataset, Aug 29 - Sep 1.</li> <li>dorian2019_atmosphere_waves_3km_2019090100.nc - Atmosphere and waves at 3-km resolution dataset, Sep 1-7</li> <li>dorian2019_ocean_1km_2019082900.nc - Ocean circulation at 1-km resolution dataset</li> <li>main.nml - UMWM (waves) configuration file</li> <li>namelist.input - WRF (atmosphere) configuration file</li> <li>regional.depth.[ab] - HYCOM (ocean circulation) bathymetry files</li> <li>regional.grid.[ab] - HYCOM (ocean circulation) grid files</li> <li>umwm.gridtopo - UMWM (waves) grid and bathymetry file</li> <li>wrfbdy_d01 - WRF (atmosphere) boundary conditions file</li> <li>wrfinput_d01.2019082900 - WRF (atmosphere) initial conditions file for parent domain on Aug 29</li> <li>wrfinput_d01.2019090100 - WRF (atmosphere) initial conditions file for parent domain on Sep 1</li> <li>wrfinput_d02.2019090100 - WRF (atmosphere) initial conditions file for inner nest on Sep 1</li> </ul> <p><strong>Coupled model source code</strong></p> <p>The model source code has not yet been released. We plan to open source it upon publication of the paper describing the simulation. When the source code is released, we will add the link to this repository.</p>
Chiral Conglomerate Crystals in the CSD (v5.41; Nov 2019)
<p>The list of chiral conglomerate crystals found by manual search of the CSD (Version 5.41; November 2019) and literature search.<br> Dataset is associated with the following manuscript: <a href="https://doi.org/10.26434/chemrxiv-2022-3g59b">https://doi.org/10.26434/chemrxiv-2022-3g59b</a></p>
Water table elevation and groundwater temperature from the outwash plain of the Otemma glacier forefield (Switzerland) from 2019 to 2021
<p><strong>Water table elevation and groundwater temperature from the outwash plain of the Otemma glacier forefield (Switzerland) from 2019 to 2021.</strong><br> Data were collected by the research teams of Bettina Schaefli<sup>1,2</sup>, Stuart N. Lane<sup>1</sup>.</p> <p><sup>1</sup> Institute of Earth Surface Dynamics (IDYST), University of Lausanne, 1015 Lausanne, Switzerland</p> <p><sup>2</sup> Institute of Geography (GIUB), University of Bern, 3012 Bern, Switzerland</p> <p>For further information, please contact:</p> <ul> <li>tom.muller.1@unil.ch</li> </ul> <p><strong>This dataset is first referenced and discussed in the research paper by Müller et al., 2022.</strong></p> <p>------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Data Description</strong></p> <p>9 piezometers consisting of fully screened plastic tubes were installed at an averaged depth of 1.5 to 2m in the outwash plain of the Otemma glacier forefield (WGS84 : 45.93434 / 7.41209). They cover four transects perpendicular to the stream from downstream (A) to upstream to (D).</p> <p>Water table elevation and temperature were recorded in each well at a 10 minute interval using SparkFun MS5803-14BA pressure sensors. Sensor resolution is 1 mm and 0.01 °C, sensor accuracy is ± 2 cm and ± 0.8°C. Sensor bias was verified and corrected by bi-monthly manual groundwater stage measurements. Water temperature was not manually corrected and may be subject to some unidentified bias.</p> <p>Piezometer location can be visualized in<strong><em> overview_piezo.jpg</em> </strong></p> <p>Piezometer coordinates are available in<strong> </strong>shapefile <strong><em>GPS_piezometers.zip</em></strong> (coordinate system: swiss LV95 (EPSG:2056))</p> <p>Piezometers name matches the labelling used in Müller et al. 2022 (A1,A2 to D1,D2). Two additionnal piezometers (BinjUp & BinjDown) used for a specific salt tracing analysis (see <em>ERT_timelapse_salt_tracer.gif </em>at <a href="https://zenodo.org/record/6342767#.YjBRmTXjJlh">https://zenodo.org/record/6342767#.YjBRmTXjJlh</a>) are also available. Finally an additional piezometer (B1-2) located between B1 and B2 is also available although it was not used in Müller et al. 2022.</p> <p><strong>Data Structure</strong></p> <p><strong>df_piezometers.csv</strong> is formated as a tidy dataframe with 10 minute interval and following headers :<br> - <em>date </em>: local date (UTC+01 with daylight saving time)<br> - <em>variable </em>: parameter of interest, with following classes :<br> w_elevation: Water table elevation [m. asl]<br> w_temperature : Groundwater temperature [°C]<br> - <em>name </em>: name of piezometer (see coordinates in GPS_piezometers.zip)<br> - <em>dateUTC </em>: date with UTC timezone</p> <p>Data can be quickly vizualized in<strong> plot_piezo.png</strong> or interactively in a browser using <strong>plot_piezo_interactive.html</strong></p>
MetFrag Local CSV: CompTox (7 March 2019 release) MetaData File
<p>These are the CSV files that can be used as a local database in MetFrag (https://msbi.ipb-halle.de/MetFrag/), for those who wish to integrate this into the command line version.</p> <p>Note that this file is TOO LARGE to be uploaded via the web interface, updated versions of these files are integrated in the web interface.</p> <p>This upload includes the SelectMetaData version of the CompTox MetFrag file from the 7 March 2019 release (DOI:<a href="https://doi.org/10.23645/epacomptox.7525199.v2">10.23645/epacomptox.7525199.v2</a>).</p>
Measurements of benzene and toluene in underway surface seawater and ambient air in the Atlantic sector of the Southern Ocean on cruise ANDREXII/JR18005 between February and April 2019.
<p>Benzene and toluene cycling iin the unpolluted marine environment s poorly understood. Due to a paucity of measurements, the role of the ocean in the atmospheric budgets of atmospheric benzene and toluene is unknown. In order to quantify the air-sea fluxes of these gases and obtain insights to their biogeochemical cycling, we measured their seawater concentrations (surface and depth profiles) and air mixing ratios in the Atlantic sector of the Southern Ocean, along a ~11000 km long transect at approximately 60o S in Feb-Apr 2019. The measurements were made using a Proton Transfer Reaction Mass Spectrometer coupled to a Segmented Flow Coil Equilibrator. Concentrations, oceanic saturations and calculated fluxes benzene and toluene are presented here. </p> <p> </p> <p>The data is further presented and discussed in a manuscript: </p> <p>Marine biogenic benzene and toluene emissions and their impact on secondary organic aerosol in the polar regions. Charel Wohl, Qinyi Li, Carlos A. Cuevas, Rafael P. Fernandez, Mingxi Yang, Alfonso Saiz-Lopez, Rafel Simó<span>, </span>Submitted to Atmospheric Atmospheric Chemistry and Physics, 2022</p> <p> </p> <p>Computation of the air-sea gas fluxes is explained in detail in the linked manuscript about benzene and toluene. <br> Positive values indicate oceanic outgassing, thus sea to air flux.</p> <p> </p> <p>Definitions of acronyms, site abbreviations, or other project-specific designations:<br> deg = degree <br> SW = seawater concentration</p> <p>ATM= atmosphere</p> <p>SAT = saturation</p> <p>flux= air-sea flux in (micro)umol_m^(2)_d^(-1)<br> nM = nano Molar seawater concentration defined as nmol dm^(-3)</p> <p>LAT, LONG = Latitude, Longitude. (negative indicates west and south)</p> <p>The timestamp indicates sampling time in UTC, expressed as DD/MM/YYYY_HH:MM</p> <p>Empty data cells/points are listed as an impossible number of -999. Interruptions in the measurements are due to calibrations and other instrument maintenance.Interruptions in the calculated flux are due to missing auxiliary data at those sampling points e.g. no wind speed or underway auxiliary data.</p> <p>Fluxes and saturations computed using the interpolated air mixing ratio (see linked manuscript) are indicated with the suffix "_2"</p> <p> </p> <p>Negative values correspond to readings below the blank and detection limit. <br> They are effectively zero and are included here as the computed negative concentration to avoid skewing the mean.</p> <p> </p> <p>Data last modified 06.05.2022. Version 1 uploaded on that date. No further maintenance planned. This is the final data.</p> <p> </p>
ICESat-2 Arctic Sea Ice Surface Topography from the University of Maryland-Ridge Detection Algorithm: April 2019, 2020, and 2021
<p>This dataset is derived from the ICESat-2 (IS-2) Global Geolocated Photon Height Product (ATL03) using the University of Maryland-Ridge Detection Algorithm (UMD-RDA). The UMD-RDA is applied to ATL03 on a per-shot basis, nominally resulting in elevation measurements at IS-2's maximum along-track resolution of ~0.7 m. From these elevation measurements, the UMD-RDA can measure various sea ice parameters including, but not limited to, individual ridge crests and their respective sail heights, the distance between ridges, and sea ice surface roughness.</p> <p><strong>********Changes in Version 2********</strong></p> <p><em>Version 2 includes a column for time (seconds since 2018-01-01) in all parameter files in addition to longitude, latitude, and parameter value.</em></p> <p><em>The full resolution UMD-RDA derived elevation data was too large to host here, but is available upon request. If you need a particular track or segment for your research please contact me with your request by email: kd</em><em>uncan at umd dot edu</em></p>
Plant Atlas 2020 — British and Irish species weekly apparency (including by-latitude breakdown for Britain), 2000–2019
<p>Plant Atlas 2020 is the most comprehensive survey of plants (flowering plants, ferns and charophytes) ever undertaken in Britain and Ireland. It is based on over 30 million records, collected mainly by volunteer recorders of the Botanical Society of Britain and Ireland (BSBI) between 2000 and 2019, as well as previous nationwide surveys undertaken in the 1950s and 1990s. This resource contains the species weekly “apparency” metrics presented within the Plant Atlas book and website, including a breakdown of species apparency by latitude for Britain. Apparency at the scale presented here (2 x 2 km spatially, smoothed over the period 2000–2019) combines aspects of recorder activity and detectability, with the latter being primarily influenced by phenology at this spatio-temporal scale.</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.