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12,237 results for “2019”
Solar and meteorological data collected from the Hahaya station (Comores) by the ENERGY-lab at the University of La Reunion between December 2019 and November 2022
<p>Scientific data provided by ENERGY-lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
Solar and meteorological data collected from the Amitie station (Seychelles) by the ENERGY-Lab at the University of La Reunion between November 2019 and December 2024
<p>Scientific data provided by ENERGY-Lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-Lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
Solar and meteorological data collected from the Anse Boileau station (Seychelles) by the ENERGY-Lab at the University of La Reunion between November 2019 and December 2024
<p>Scientific data provided by ENERGY-Lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-Lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
Solar and meteorological data collected from the Vacoas station (Mauritius) by the ENERGY-Lab at the University of La Reunion between October 2019 and December 2024
<p>Scientific data provided by ENERGY-Lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-Lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
GOES-Observed Fire Event Representation (GOFER) product for 28 California wildfires from 2019-2021
<p>The GOES-Observed Fire Event Representation (GOFER) algorithm uses geostationary satellite observations of active fires from GOES-East and GOES-West to map the hourly progression of large wildfires (over 50,000 acres or 202 sq. km). GOES observes North and South America with a spatial resolution of 2 km at the equator and at a frequency of 10-15 minutes for the full disk view. Along with the fire perimeter, we derive the active fire lines and fire spread rates. We tested the GOFER algorithm on a set of 28 wildfires in California from 2019-2021 and produced three versions of the product: GOFER-Combined, GOFER-East, and GOFER-West. GOFER-Combined uses both GOES-East and GOES-West observations, while GOFER-East and GOFER-West use only GOES-East and only GOES-West observations, respectively. We find that GOFER performs reasonably well compared to final perimeters from California's Fire and Resource Assessment Program (FRAP) and 12-hourly perimeters from the Fire Event Data Suite (FEDS), derived from 375-m active fire observations. See our <a href="https://globalfires.earthengine.app/view/gofer">GOFER Visualization</a> app on Earth Engine Apps for an overview of the dataset, alongside other datasets, such as FEDS and FRAP perimeters and 30-m burn severity from Monitoring Trends in Burn Severity (MTBS). Please refer to the corresponding <a href="https://github.com/tianjialiu/GOFER">GitHub repository</a> for the code, detailed dataset description, and version history.</p>
Global surface water quality data from 1980 - 2019, derived from the dynamical surface water quality model (DynQual) at 30 arcmin spatial resolution
<p>Global ~50km (30 arcmin) surface water quality data from the dynamical surface water quality model (DynQual) from 1980-2019, with annual, monthly and daily temporal resolution. Simulations are made following the ISIMIP3a protocol (https://protocol.isimip.org/#/ISIMIP3a).</p> <p>Output data includes:</p> <ul> <li>Salinity; as indicated by TDS concentrations (mg l-1)</li> <li>Organic pollution; as indicated by BOD concentrations (mg l-1)</li> <li>Pathogen/bacterial pollution; as indicated by FC concentrations (cfu 100ml-1)</li> </ul> <p>Simulations were originally made at 5-arcmin resolution and aggregated to 30 arcmin 0.5 degree by summing the in-stream (routed) loadings and channel storage over the aggregated area (at daily, monthly and annual timesteps), and subsequently calculating in-stream concentrations. Please note the aggregation technique is provisional and thus the data is subject to change.</p> <p>Note. A minimum discharge threshold of 0.1 m3 s-1 was used when computing TDS, BOD and FC concentrations, as uncertainties in absolute values of water availabilities have large impacts on resulting in-stream concentrations. Concentrations in these gridcells are assigned as NA.</p> <p>Hydrology and water quality simulations made at DynQuals native spatial resolution (5 arcmin) can be found at: <a href="https://zenodo.org/records/14673871">https://zenodo.org/records/14673871</a>.</p>
Agricultural Residue Burning Emissions 2019 v1
<p>The data provides the agricultural residue burning emission estimates over 11 agroclimatic zones of Madhya Pradesh, India, for the 2019 rabi season. It also includes supplementary information used in the emission estimation for the reader's reference. </p> <p>References:</p> <p>1. Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories: Reference Manual (Volume 3)https://www.ipcc-nggip.iges.or.jp/public/gl/invs6c.html </p> <p>2. Gupta, P. K.; Sahai, S.; Singh, N.; Dixit, C. K.; Singh, D. P.; Sharma, C.; Tiwari, M. K.; Gupta, R. K.; Garg, S. C. Residue Burning in Rice-Wheat Cropping System: Causes and Implications. Curr. Sci. 2004, 87 (12).</p> <p>3. Jain, N.; Bhatia, A.; Pathak, H. Emission of Air Pollutants from Crop Residue Burning in India. Aerosol Air Qual. Res. 2014, 14 (1).<a href="https://doi.org/10.4209/aaqr.2013.01.0031"> https://doi.org/10.4209/aaqr.2013.01.0031</a>.</p> <p>4. Venkatramanan, V.; Shah, S.; Rai, A. K.; Prasad, R. Nexus Between Crop Residue Burning, Bioeconomy and Sustainable Development Goals Over North-Western India. Front. Energy Res. 2021, 8.<a href="https://doi.org/10.3389/fenrg.2020.614212"> https://doi.org/10.3389/fenrg.2020.614212</a>.</p> <p>5. Van Der Werf, G. R.; Randerson, J. T.; Giglio, L.; Collatz, G. J.; Mu, M.; Kasibhatla, P. S.; Morton, D. C.; Defries, R. S.; Jin, Y.; Van Leeuwen, T. T. Global Fire Emissions and the Contribution of Deforestation, Savanna, Forest, Agricultural, and Peat Fires (1997-2009). Atmos. Chem. Phys. 2010, 10 (23). <a href="https://doi.org/10.5194/acp-10-11707-2010">https://doi.org/10.5194/acp-10-11707-2010</a>.</p>
Svalbox-DOM_2019-0003_Vardeborg
<b>Model description: </b><br>Distant digital outcrop model of Vardeborg (Winter). <br><br><b>Data availability:</b><br>All uploaded archives are to be extracted to the same directory for a functional working environment, i.e., at the very least consisting of a project folder with the data and metashape subdirectories. The included processing report specifies the processing parameters that were used as input for the processing software (Agisoft Metashape, v1.7.1.11797).<br>Exported model data use CRS epsg:32633.<br><br><b>Show and visualise on <a href="http://svalbox.no/?page_id=3674">Svalbox.no</a>:</b><br>Through Svalbox we aim to take geoscientific research and education to the next level – with the simple motto of placing all key information and data sets in their correct spatial position. We bring modern technology, in particular cost-effective digital outcrops, to complement more than 200 years of geoscientific research on Svalbard. Finally, Svalbox provides a sustainable platform for sharing results, workflows and teaching material with the wider geoscientific community.<br><br>For more information, contact the Svalbox team at svalbox[at]gmail.com.
Svalbox-DOM_2019-0004_Productustoppen
<b>Model description: </b><br>Distant 3D model of Productustoppen.<br><br><b>Data availability:</b><br>All uploaded archives are to be extracted to the same directory for a functional working environment, i.e., at the very least consisting of a project folder with the data and metashape subdirectories. The included processing report specifies the processing parameters that were used as input for the processing software (Agisoft Metashape, v1.7.1.11797).<br>Exported model data use CRS epsg:32633.<br><br><b>Show and visualise on <a href="http://svalbox.no/?page_id=3680">Svalbox.no</a>:</b><br>Through Svalbox we aim to take geoscientific research and education to the next level – with the simple motto of placing all key information and data sets in their correct spatial position. We bring modern technology, in particular cost-effective digital outcrops, to complement more than 200 years of geoscientific research on Svalbard. Finally, Svalbox provides a sustainable platform for sharing results, workflows and teaching material with the wider geoscientific community.<br><br>For more information, contact the Svalbox team at svalbox[at]gmail.com.
Svalbox-DOM_2019-0005_Munindalen
<b>Model description: </b><br>3D Model of Munindalen and Mimerdalen at the water reservoirs of Pyramiden. <br><br><b>Data availability:</b><br>All uploaded archives are to be extracted to the same directory for a functional working environment, i.e., at the very least consisting of a project folder with the data and metashape subdirectories. The included processing report specifies the processing parameters that were used as input for the processing software (Agisoft Metashape, v1.7.1.11797).<br>Exported model data use CRS epsg:32633.<br><br><b>Show and visualise on <a href="http://svalbox.no/?page_id=3682">Svalbox.no</a>:</b><br>Through Svalbox we aim to take geoscientific research and education to the next level – with the simple motto of placing all key information and data sets in their correct spatial position. We bring modern technology, in particular cost-effective digital outcrops, to complement more than 200 years of geoscientific research on Svalbard. Finally, Svalbox provides a sustainable platform for sharing results, workflows and teaching material with the wider geoscientific community.<br><br>For more information, contact the Svalbox team at svalbox[at]gmail.com.
Baliles Center (Hull Springs) Aimes Creek water depth data from 2021-10-19 to 2019-11-11
<p>General Metadata for Hull Springs Dock-YSI Sampling Station</p> <p>Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <pre><code>HS_YSI_YYYY-MM-DD_metadata.txt HS_dock_pressure_trans_YYYY-MM-DD_metadata.txt</code></pre> <p>Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>NOTE: The metadata in the above file is collected from the data logger and does not have all of fields present in the final data set, because some were created during data cleaning.</p> <p>File Created</p> <ul> <li>2021-07-14 by KF</li> </ul> <p>File Modified</p> <ul> <li>2021-07-22 by KF - added metadata for pressure transducer</li> </ul> <p>Description</p> <p>These data are from the sampling station in Aimes Creek on the Camp House dock (38.125367, -76.659537).</p> <pre><code>* Physical and chemical water data are collected with a YSI EXO2 sonde. * Temperature (dC) and Pressure (KPa) are collected with an Onset HOBO U20-001-01-Ti Water Level Logger</code></pre> <p>All data are CC-BY and should be cited using the DOI available at <a href="https://zenodo.org/communities/leo/">https://zenodo.org/communities/leo/</a></p> <p>Station Specifics</p> <p>The sensors are sampled every 15 minutes</p> <p>Measurements Parameters, units, and Variable Names</p> <ul> <li>date.time - the date and time that the record was collected, reported in POSIX standard time (YYYY-MM-DD HH:MM:SS)</li> <li>date.time.adj - the date and time that the pressure was measured adjusted to match the time that barometric pressure was collected. (YYYY-MM-DD HH:MM:SS)</li> <li>observation - the number of the observation.</li> <li>timestamp - the date and time as reported by the data logger (MM/DD/YY HH:MM:SS AM/PM)</li> <li>date - the date that the record was collected (MM/DD/YYYY)</li> <li>time - the time that the record was collected (HH:MM:SS)</li> <li>site_name - the ID name provided by the KOR software.</li> <li>unit_ID - the sonde identification number.</li> <li>user_ID - the user that created the sampling template.</li> <li>Temp_dC - the water temperature in degrees C.</li> <li>DO_perc - the dissolved oxygen percent saturation (%).</li> <li>DO_percL - the "local" dissolved oxygen percent saturation where the calibration is locally always 100% saturated independent of the barometric pressure (%).</li> <li>DO_mg-L - the concentration of dissolved oxygen (mg/L).</li> <li>SPC_uS-cm - the specific conductance (uS/cm).</li> <li>C_uS-cm - the conductivity (uS/cm).</li> <li>nLFC_uS-cm</li> <li>TDS_mg-L - the total dissolved solids (mg/L).</li> <li>SAL_PSU - the salinity of the water (PSU).</li> <li>pH - the pH of the water.</li> <li>pH_mV - the millivolt measurement of the pH meter (mV).</li> <li>FNU - the turbidity of the water (FNU).</li> <li>TSS_mg-L - the total suspended solids (mg/L).</li> <li>BGA_PC_RFU - the phycocyanin fluorescence level which is an indicator of blue-green algae (RFU).</li> <li>BGC_PC_ug-L - the concentration of phycocyanin in the water, which is an indicator of blue-green algae (ug/L).</li> <li>Chl_RFU - the chlorophyll fluorescence level, which is an indicator of phytoplankton biomass (RFU).</li> <li>Chl_ug-L - the concentration of chlorophyll in the water, which is an indicator of phytoplankton biomass (ug/L).</li> <li>fDOM_RFU - the fluorescent DOM fluorescence level (RFU).</li> <li>fDOM_QSU - the standardized fluorescent DOM concentration (QSU).</li> <li>Wiper_V - the voltage output of the wiper (V).</li> <li>Cabel_V - the voltage output of the cable (V).</li> <li>Batt_V - the voltage output of the internal batteries (V).</li> <li>Pressure - the pressure of the water above the pressure transducer (KPa)</li> <li>Temp - the water temperature reported by the pressure transducer (dC)</li> <li>press_mmHg - the pressure of the water above the pressure transducer (mm Hg)</li> <li>BP_mmHg - the barometric pressure recorded by the weather station at the Yellow House (mm Hg)</li> <li>Z_press_trans - the depth of the water above the pressure transducer (cm)</li> <li>Z - the depth of the water above the sediments (cm)</li> </ul>
Svalbox-DOM_2019-0006_Fortet
<b>Model description: </b><br>3D Model of paleokarst breccias dominated by Gypsum below Fortet. Situated in the Minkinfjellet Formation.<br><br><b>Data availability:</b><br>All uploaded archives are to be extracted to the same directory for a functional working environment, i.e., at the very least consisting of a project folder with the data and metashape subdirectories. The included processing report specifies the processing parameters that were used as input for the processing software (Agisoft Metashape, v1.6.1.10009).<br>Exported model data use CRS epsg:32633.<br><br><b>Show and visualise on <a href="http://svalbox.no/?page_id=3731">Svalbox.no</a>:</b><br>Through Svalbox we aim to take geoscientific research and education to the next level – with the simple motto of placing all key information and data sets in their correct spatial position. We bring modern technology, in particular cost-effective digital outcrops, to complement more than 200 years of geoscientific research on Svalbard. Finally, Svalbox provides a sustainable platform for sharing results, workflows and teaching material with the wider geoscientific community.<br><br>For more information, contact the Svalbox team at svalbox[at]gmail.com.
Svalbox-DOM_2019-0008_Fortet-South-East
<b>Model description: </b><br>Virtual Outcrop Model of the Fortet Southeast outcrop section. Cropping out in this section are both sedimentary paleokarst breccias and chaotic collapse breccias near the top of the cave.<br><br><b>Data availability:</b><br>All uploaded archives are to be extracted to the same directory for a functional working environment, i.e., at the very least consisting of a project folder with the data and metashape subdirectories. The included processing report specifies the processing parameters that were used as input for the processing software (Agisoft Metashape, v1.7.1.11797).<br>Exported model data use CRS epsg:32633.<br><br><b>Show and visualise on <a href="http://svalbox.no/?page_id=3735">Svalbox.no</a>:</b><br>Through Svalbox we aim to take geoscientific research and education to the next level – with the simple motto of placing all key information and data sets in their correct spatial position. We bring modern technology, in particular cost-effective digital outcrops, to complement more than 200 years of geoscientific research on Svalbard. Finally, Svalbox provides a sustainable platform for sharing results, workflows and teaching material with the wider geoscientific community.<br><br>For more information, contact the Svalbox team at svalbox[at]gmail.com.
Svalbox-DOM_2019-0010_Fortet
<b>Model description: </b><br>Virtual Outcrop Model of a solitary block east of Fortet. This block shows chaotic breakdown breccias in direct contact to paleo-cave roof lithilogies of the Wordiekammen Formation.<br><br><b>Data availability:</b><br>All uploaded archives are to be extracted to the same directory for a functional working environment, i.e., at the very least consisting of a project folder with the data and metashape subdirectories. The included processing report specifies the processing parameters that were used as input for the processing software (Agisoft Metashape, v1.6.1.10009).<br>Exported model data use CRS epsg:32633.<br><br><b>Show and visualise on <a href="http://svalbox.no/?page_id=3739">Svalbox.no</a>:</b><br>Through Svalbox we aim to take geoscientific research and education to the next level – with the simple motto of placing all key information and data sets in their correct spatial position. We bring modern technology, in particular cost-effective digital outcrops, to complement more than 200 years of geoscientific research on Svalbard. Finally, Svalbox provides a sustainable platform for sharing results, workflows and teaching material with the wider geoscientific community.<br><br>For more information, contact the Svalbox team at svalbox[at]gmail.com.
Svalbox-DOM_2019-0009_Fortet
<b>Model description: </b><br>Virtual Outcrop Model of the eastward facing side of Fortet. Both sedimentarz and chaotic collapse breccias are cropping out in this section. <br><br><b>Data availability:</b><br>All uploaded archives are to be extracted to the same directory for a functional working environment, i.e., at the very least consisting of a project folder with the data and metashape subdirectories. The included processing report specifies the processing parameters that were used as input for the processing software (Agisoft Metashape, v1.7.1.11797).<br>Exported model data use CRS epsg:32633.<br><br><b>Show and visualise on <a href="http://svalbox.no/?page_id=3737">Svalbox.no</a>:</b><br>Through Svalbox we aim to take geoscientific research and education to the next level – with the simple motto of placing all key information and data sets in their correct spatial position. We bring modern technology, in particular cost-effective digital outcrops, to complement more than 200 years of geoscientific research on Svalbard. Finally, Svalbox provides a sustainable platform for sharing results, workflows and teaching material with the wider geoscientific community.<br><br>For more information, contact the Svalbox team at svalbox[at]gmail.com.
Baliles Center (Hull Springs) Aimes Creek YSI Data 2019-08-10 to 2019-09-29
<p>General Metadata for Hull Springs Dock-YSI Sampling Station</p> <p>Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <pre><code>HS_YSI_YYYY-MM-DD_metadata.txt HS_dock_pressure_trans_YYYY-MM-DD_metadata.txt</code></pre> <p>Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>NOTE: The metadata in the above file is collected from the data logger and does not have all of fields present in the final data set, because some were created during data cleaning.</p> <p>File Created</p> <ul> <li>2021-07-14 by KF</li> </ul> <p>File Modified</p> <ul> <li>2021-07-22 by KF - added metadata for pressure transducer</li> </ul> <p>Description</p> <p>These data are from the sampling station in Aimes Creek on the Camp House dock (38.125367, -76.659537).</p> <pre><code>* Physical and chemical water data are collected with a YSI EXO2 sonde. * Temperature (dC) and Pressure (KPa) are collected with an Onset HOBO U20-001-01-Ti Water Level Logger</code></pre> <p>All data are CC-BY and should be cited using the DOI available at <a href="https://zenodo.org/communities/leo/">https://zenodo.org/communities/leo/</a></p> <p>Station Specifics</p> <p>The sensors are sampled every 15 minutes</p> <p>Measurements Parameters, units, and Variable Names</p> <ul> <li>date.time - the date and time that the record was collected, reported in POSIX standard time (YYYY-MM-DD HH:MM:SS)</li> <li>date.time.adj - the date and time that the pressure was measured adjusted to match the time that barometric pressure was collected. (YYYY-MM-DD HH:MM:SS)</li> <li>observation - the number of the observation.</li> <li>timestamp - the date and time as reported by the data logger (MM/DD/YY HH:MM:SS AM/PM)</li> <li>date - the date that the record was collected (MM/DD/YYYY)</li> <li>time - the time that the record was collected (HH:MM:SS)</li> <li>site_name - the ID name provided by the KOR software.</li> <li>unit_ID - the sonde identification number.</li> <li>user_ID - the user that created the sampling template.</li> <li>Temp_dC - the water temperature in degrees C.</li> <li>DO_perc - the dissolved oxygen percent saturation (%).</li> <li>DO_percL - the "local" dissolved oxygen percent saturation where the calibration is locally always 100% saturated independent of the barometric pressure (%).</li> <li>DO_mg-L - the concentration of dissolved oxygen (mg/L).</li> <li>SPC_uS-cm - the specific conductance (uS/cm).</li> <li>C_uS-cm - the conductivity (uS/cm).</li> <li>nLFC_uS-cm</li> <li>TDS_mg-L - the total dissolved solids (mg/L).</li> <li>SAL_PSU - the salinity of the water (PSU).</li> <li>pH - the pH of the water.</li> <li>pH_mV - the millivolt measurement of the pH meter (mV).</li> <li>FNU - the turbidity of the water (FNU).</li> <li>TSS_mg-L - the total suspended solids (mg/L).</li> <li>BGA_PC_RFU - the phycocyanin fluorescence level which is an indicator of blue-green algae (RFU).</li> <li>BGC_PC_ug-L - the concentration of phycocyanin in the water, which is an indicator of blue-green algae (ug/L).</li> <li>Chl_RFU - the chlorophyll fluorescence level, which is an indicator of phytoplankton biomass (RFU).</li> <li>Chl_ug-L - the concentration of chlorophyll in the water, which is an indicator of phytoplankton biomass (ug/L).</li> <li>fDOM_RFU - the fluorescent DOM fluorescence level (RFU).</li> <li>fDOM_QSU - the standardized fluorescent DOM concentration (QSU).</li> <li>Wiper_V - the voltage output of the wiper (V).</li> <li>Cabel_V - the voltage output of the cable (V).</li> <li>Batt_V - the voltage output of the internal batteries (V).</li> <li>Pressure - the pressure of the water above the pressure transducer (KPa)</li> <li>Temp - the water temperature reported by the pressure transducer (dC)</li> <li>press_mmHg - the pressure of the water above the pressure transducer (mm Hg)</li> <li>BP_mmHg - the barometric pressure recorded by the weather station at the Yellow House (mm Hg)</li> <li>Z_press_trans - the depth of the water above the pressure transducer (cm)</li> <li>Z - the depth of the water above the sediments (cm)</li> </ul>
Svalbox-DOM_2019-0012_Fortet
<b>Model description: </b><br>Virtual Outcrop Model of outcrop section Fortet North. Fortet is a paleokarst breccia outcrop at the southern end of Wordiekammen. At Fortet North the western section is characterized by stratified sedimentary breccias deposited "inversly" onlapping to the paleo-cave ceiling. Towards the east of the stratified breccia we find chaotic breakdown breccia which are truncating the stratfied breccias. The chaotic breackdown breccia resemble a second phase of dissolution and collapse of a paleokarst system.<br><br><b>Data availability:</b><br>All uploaded archives are to be extracted to the same directory for a functional working environment, i.e., at the very least consisting of a project folder with the data and metashape subdirectories. The included processing report specifies the processing parameters that were used as input for the processing software (Agisoft Metashape, v1.7.1.11797).<br>Exported model data use CRS epsg:32633.<br><br><b>Show and visualise on <a href="http://svalbox.no/?page_id=3743">Svalbox.no</a>:</b><br>Through Svalbox we aim to take geoscientific research and education to the next level – with the simple motto of placing all key information and data sets in their correct spatial position. We bring modern technology, in particular cost-effective digital outcrops, to complement more than 200 years of geoscientific research on Svalbard. Finally, Svalbox provides a sustainable platform for sharing results, workflows and teaching material with the wider geoscientific community.<br><br>For more information, contact the Svalbox team at svalbox[at]gmail.com.
Svalbox-DOM_2019-0011_Fortet
<b>Model description: </b><br>Virtual Outcrop Model of the Fortet South outcrop section. This model is focusing on the central part of the southfacing side of Fortet. The east and west are characterized by a large fault surface marked by a steep orange surface. The central part is characterized by a gully which is mainly constituing chaotic breakdown breccia made of large Wackestone/Packstone blouders. <br><br><b>Data availability:</b><br>All uploaded archives are to be extracted to the same directory for a functional working environment, i.e., at the very least consisting of a project folder with the data and metashape subdirectories. The included processing report specifies the processing parameters that were used as input for the processing software (Agisoft Metashape, v1.6.1.10009).<br>Exported model data use CRS epsg:32633.<br><br><b>Show and visualise on <a href="http://svalbox.no/?page_id=3741">Svalbox.no</a>:</b><br>Through Svalbox we aim to take geoscientific research and education to the next level – with the simple motto of placing all key information and data sets in their correct spatial position. We bring modern technology, in particular cost-effective digital outcrops, to complement more than 200 years of geoscientific research on Svalbard. Finally, Svalbox provides a sustainable platform for sharing results, workflows and teaching material with the wider geoscientific community.<br><br>For more information, contact the Svalbox team at svalbox[at]gmail.com.
Optical polarimetric observations of low-mass X-ray black hole binary MAXI J1820+070 during 2019-2021
<p>The dataset contains raw polarimetric FITS images of the low-mass X-ray black hole binary <a href="https://www.astronomerstelegram.org/?read=11399">MAXI J1820+070</a> (and surrounding field), obtained by the <a href="https://doi.org/10.3847/1538-3881/abc74f">DIPol-UF </a>optical CCD polarimeter in three (BVR) filters while mounted on the 2.56m <a href="https://www.not.iac.es">Nordic Optical Telescope</a>. The data were collected over 5 observing runs throughout 2019--2021. During each observing night, a set of calibration images were also obtained. These typically include 7 dark and 7 bias images per filter per night (sometimes more if weather conditions or instrument settings changed during observations). Bias and dark FITS files have `_bias` or `_dark` labels in their names, as well as FITS key `IMAGETYP` set to either `Bias Frame` or `Dark Frame`, respectively.</p>
Data for PAN at SemEval 2019 Task 4: Hyperpartisan News Detection
<p>Training, validation, and test data for the <a href="https://webis.de/events/semeval-19/">PAN @ SemEval 2019 Task 4: Hyperpartisan News Detection</a>.</p> <p>See the README for details.</p>
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