Find research datasets worth reusing

opencc-by-4.0Mar 2023View details →

Imaging Temperature and Thickness of Thin Planar Liquid Water Jets in Vacuum - data

Data set pertaining to the article "Imaging Temperature and Thickness of Thin Planar Liquid Water Jets in Vacuum", published in Struct. Dyn. 10, 034901 (2023), <a href="https://doi.org/10.1063/4.0000188" target="_blank" rel="noopener">https://doi.org/10.1063/4.0000188 </a>. The following data are provided: <table> <tbody> <tr> <td>(zip-)file/Folder</td> <td>Description</td> <td>Format</td> <td>Extension</td> </tr> <tr> <td>IR_images/calibration_data/vacuum</td> <td> Snapshots from a thermographic movie of our flat jet running in vacuum, at thirty different background temperature. (A snapshot shown in Fig. 3a, rhs.) </td> <td> temperature values per camera pixel (°C), 640 row * 480 columns, semicolon-separated ascii data </td> <td>.asc</td> </tr> <tr> <td>IR_images/calibration_data/1atm</td> <td>As above, for our flat jet running in atmosphere. (Three snapshots shown in Fig. 2a.)</td> <td>as above</td> <td>.asc</td> </tr> <tr> <td>IR_images/calibration_data/chipnozzle</td> <td>As above, for a flat jet produced from a chip nozzle, and running in atmosphere.</td> <td>as above</td> <td>.asc</td> </tr> <tr> <td>IR_images/raw_data</td> <td>As above, for various conditions of the flat jet environment as detailed in table exp_settings.csv.</td> <td>as above</td> <td>.asc</td> </tr> <tr> <td>IR_video</td> <td>Two thermographic movies recorded of our flat jet at varied conditions of the jet environment detailed in table chamber_pressure.pdf.</td> <td>Radiographic image stream, suitable for opening with free software Optris Pix Connect.</td> <td>.ravi</td> </tr> <tr> <td>FJ_cooling_2D.mph</td> <td>Input file for 2D finite element simulation of our flat jet.</td> <td>Input file suitable for Comsol software, proprietary format.</td> <td>.mph</td> </tr> <tr> <td>Y_Z_Temp_Comsol.txt</td> <td>Ascii representation of our simulated temperature profile (Fig. S7 (SI)).</td> <td>List of (y,z,T) tupels, with (y,z) in m and T in °C.</td> <td>.txt</td> </tr> </tbody> </table>   In case you have any questions regarding this data set please contact: Uwe Hergenhahn, uhe@fhi.mpg.de .

opencc-by-4.0Jun 2023View details →

Snow Water Equivalent Dataset for the South Fork of the San Joaquin River (2018/2021) and Senales (2019/2021)

The dataset is related to: Premier, V., Marin, C., Bertoldi, G., Barella, R., Notarnicola, C., & Bruzzone, L. (2022). Exploring the Use of Multi-source High-Resolution Satellite Data for Snow Water Equivalent Reconstruction over Mountainous Catchments. The Cryosphere Discussions, 1-42. It contains three hydrological seasons - from 1st of October 2018 to 30th of September 2021 - of snow water equivalent (SWE) for the South Fork of the San Joaquin river in California (USA) and two hydrological seasons - from 1st of October 2019 to 30th of September 2021 - for the Schnals/Senales basin in South Tyrol (Italy). The product is daily and with a spatial resolution of 25 m. SWE values are in mm. Snow cover area (SCA) can be derived from the same by thresholding pixel containing SWE greater than 0 mm. Further information about the reference system is contained in the attributes of the netcdf files. Please, contact the authors for further questions. Information about the methodology and the input data for producing these time series is contained in the related article.

Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Yearly time-series (2000-2011)

This data is part of the Monthly aggregated Water Vapor MODIS MCD19A2 (1 km) dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.General DescriptionThe monthly aggregated water vapor dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD19A2"><abbr title="MCD19A2 MODIS/Terra+Aqua daily product">MCD19A2 v061</abbr></a>. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:<ul><li>Monthly time-series: Derived from MCD19A2 v061, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the <abbr title="Moving Window Median">TMWM</abbr> algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values.</li><li>Yearly time-series: Derived from monthly time-series, this data provides a yearly time-series aggregated statistics of the monthly time-series data.</li><li>Long-term data (2000-2022): Derived from monthly time-series, this data provides long-term aggregated statistics for the whole series of monthly observations.</li></ul>Data Details<ul><li>Time period: 2000–2011</li><li>Type of data: Water vapor column above the ground (0.001cm)</li><li>How the data was collected or derived: Derived from MCD19A2 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li><li>Statistical methods used: Four statistics were derived: standard deviation, percentiles 25, 50, and 75.</li><li>Limitations or exclusions in the data: The dataset does not include data for Antarctica.</li><li>Coordinate reference system: EPSG:4326</li><li>Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.00081, 179.99994, 87.37000)</li><li>Spatial resolution: 1/120 d.d. = 0.008333333 (1km)</li><li>Image size: 43,200 x 17,924</li><li>File format: Cloud Optimized Geotiff (COG) format.</li></ul>SupportIf you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:<ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">GitLab Issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li></ul>Name conventionTo ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:<ol><li>generic variable name: wv = Water vapor</li><li>variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm</li><li>Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment</li><li>Spatial support: 1km</li><li>Depth reference: s = surface</li><li>Time reference begin time: 20000101 = 2000-01-01</li><li>Time reference end time: 20111231 = 2011-12-31</li><li>Bounding box: go = global (without Antarctica)</li><li>EPSG code: epsg.4326 = EPSG:4326</li><li>Version code: v20230619 = 2023-06-19 (creation date)</li></ol>

Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Monthly time-series (2012-2014)

This data is part of the Monthly aggregated Water Vapor MODIS MCD19A2 (1 km) dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.General DescriptionThe monthly aggregated water vapor dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD19A2"><abbr title="MCD19A2 MODIS/Terra+Aqua daily product">MCD19A2 v061</abbr></a>. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:<ul><li>Monthly time-series: Derived from MCD19A2 v061, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the <abbr title="Moving Window Median">TMWM</abbr> algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values.</li><li>Yearly time-series: Derived from monthly time-series, this data provides a yearly time-series aggregated statistics of the monthly time-series data.</li><li>Long-term data (2000-2022): Derived from monthly time-series, this data provides long-term aggregated statistics for the whole series of monthly observations.</li></ul>Data Details<ul><li>Time period: 2012–2014</li><li>Type of data: Water vapor column above the ground (0.001cm)</li><li>How the data was collected or derived: Derived from MCD19A2 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li><li>Statistical methods used: Four statistics were derived: mean, standard deviation, smoothed mean, smoothed standard deviation.</li><li>Limitations or exclusions in the data: The dataset does not include data for Antarctica.</li><li>Coordinate reference system: EPSG:4326</li><li>Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.00081, 179.99994, 87.37000)</li><li>Spatial resolution: 1/120 d.d. = 0.008333333 (1km)</li><li>Image size: 43,200 x 17,924</li><li>File format: Cloud Optimized Geotiff (COG) format.</li></ul>SupportIf you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:<ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">GitLab Issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li></ul>Name conventionTo ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:<ol><li>generic variable name: wv = Water vapor</li><li>variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm</li><li>Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment</li><li>Spatial support: 1km</li><li>Depth reference: s = surface</li><li>Time reference begin time: 20120101 = 2012-01-01</li><li>Time reference end time: 20141231 = 2014-12-31</li><li>Bounding box: go = global (without Antarctica)</li><li>EPSG code: epsg.4326 = EPSG:4326</li><li>Version code: v20230619 = 2023-06-19 (creation date)</li></ol>

Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Yearly time-series (2012-2022)

This data is part of the Monthly aggregated Water Vapor MODIS MCD19A2 (1 km) dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.General DescriptionThe monthly aggregated water vapor dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD19A2"><abbr title="MCD19A2 MODIS/Terra+Aqua daily product">MCD19A2 v061</abbr></a>. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:<ul><li>Monthly time-series: Derived from MCD19A2 v061, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the <abbr title="Moving Window Median">TMWM</abbr> algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values.</li><li>Yearly time-series: Derived from monthly time-series, this data provides a yearly time-series aggregated statistics of the monthly time-series data.</li><li>Long-term data (2000-2022): Derived from monthly time-series, this data provides long-term aggregated statistics for the whole series of monthly observations.</li></ul>Data Details<ul><li>Time period: 2012–2022</li><li>Type of data: Water vapor column above the ground (0.001cm)</li><li>How the data was collected or derived: Derived from MCD19A2 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li><li>Statistical methods used: Four statistics were derived: standard deviation, percentiles 25, 50, and 75.</li><li>Limitations or exclusions in the data: The dataset does not include data for Antarctica.</li><li>Coordinate reference system: EPSG:4326</li><li>Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.00081, 179.99994, 87.37000)</li><li>Spatial resolution: 1/120 d.d. = 0.008333333 (1km)</li><li>Image size: 43,200 x 17,924</li><li>File format: Cloud Optimized Geotiff (COG) format.</li></ul>SupportIf you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:<ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">GitLab Issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li></ul>Name conventionTo ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:<ol><li>generic variable name: wv = Water vapor</li><li>variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm</li><li>Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment</li><li>Spatial support: 1km</li><li>Depth reference: s = surface</li><li>Time reference begin time: 20120101 = 2012-01-01</li><li>Time reference end time: 20221231 = 2022-12-31</li><li>Bounding box: go = global (without Antarctica)</li><li>EPSG code: epsg.4326 = EPSG:4326</li><li>Version code: v20230619 = 2023-06-19 (creation date)</li></ol>

Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Long-term data (2000-2022)

This data is part of the Monthly aggregated Water Vapor MODIS MCD19A2 (1 km) dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.General DescriptionThe monthly aggregated water vapor dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD19A2"><abbr title="MCD19A2 MODIS/Terra+Aqua daily product">MCD19A2 v061</abbr></a>. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:<ul><li>Monthly time-series: Derived from MCD19A2 v061, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the <abbr title="Moving Window Median">TMWM</abbr> algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values.</li><li>Yearly time-series: Derived from monthly time-series, this data provides a yearly time-series aggregated statistics of the monthly time-series data.</li><li>Long-term data (2000-2022): Derived from monthly time-series, this data provides long-term aggregated statistics for the whole series of monthly observations.</li></ul>Data Details<ul><li>Time period: 2000–2022</li><li>Type of data: Water vapor column above the ground (0.001cm)</li><li>How the data was collected or derived: Derived from MCD19A2 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li><li>Statistical methods used: Four statistics were derived: standard deviation, percentiles 25, 50, and 75.</li><li>Limitations or exclusions in the data: The dataset does not include data for Antarctica.</li><li>Coordinate reference system: EPSG:4326</li><li>Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.00081, 179.99994, 87.37000)</li><li>Spatial resolution: 1/120 d.d. = 0.008333333 (1km)</li><li>Image size: 43,200 x 17,924</li><li>File format: Cloud Optimized Geotiff (COG) format.</li></ul>SupportIf you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:<ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">GitLab Issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li></ul>Name conventionTo ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:<ol><li>generic variable name: wv = Water vapor</li><li>variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm</li><li>Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment</li><li>Spatial support: 1km</li><li>Depth reference: s = surface</li><li>Time reference begin time: 20000101 = 2000-01-01</li><li>Time reference end time: 20221231 = 2022-12-31</li><li>Bounding box: go = global (without Antarctica)</li><li>EPSG code: epsg.4326 = EPSG:4326</li><li>Version code: v20230619 = 2023-06-19 (creation date)</li></ol>

Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Monthly time-series (2018-2020)

This data is part of the Monthly aggregated Water Vapor MODIS MCD19A2 (1 km) dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.General DescriptionThe monthly aggregated water vapor dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD19A2"><abbr title="MCD19A2 MODIS/Terra+Aqua daily product">MCD19A2 v061</abbr></a>. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:<ul><li>Monthly time-series: Derived from MCD19A2 v061, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the <abbr title="Moving Window Median">TMWM</abbr> algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values.</li><li>Yearly time-series: Derived from monthly time-series, this data provides a yearly time-series aggregated statistics of the monthly time-series data.</li><li>Long-term data (2000-2022): Derived from monthly time-series, this data provides long-term aggregated statistics for the whole series of monthly observations.</li></ul>Data Details<ul><li>Time period: 2018–2020</li><li>Type of data: Water vapor column above the ground (0.001cm)</li><li>How the data was collected or derived: Derived from MCD19A2 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li><li>Statistical methods used: Four statistics were derived: mean, standard deviation, smoothed mean, smoothed standard deviation.</li><li>Limitations or exclusions in the data: The dataset does not include data for Antarctica.</li><li>Coordinate reference system: EPSG:4326</li><li>Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.00081, 179.99994, 87.37000)</li><li>Spatial resolution: 1/120 d.d. = 0.008333333 (1km)</li><li>Image size: 43,200 x 17,924</li><li>File format: Cloud Optimized Geotiff (COG) format.</li></ul>SupportIf you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:<ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">GitLab Issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li></ul>Name conventionTo ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:<ol><li>generic variable name: wv = Water vapor</li><li>variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm</li><li>Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment</li><li>Spatial support: 1km</li><li>Depth reference: s = surface</li><li>Time reference begin time: 20180101 = 2018-01-01</li><li>Time reference end time: 20201231 = 2020-12-31</li><li>Bounding box: go = global (without Antarctica)</li><li>EPSG code: epsg.4326 = EPSG:4326</li><li>Version code: v20230619 = 2023-06-19 (creation date)</li></ol>

openCC (other)Jul 2019View details →

LAGOS-NE-LIMNO v1.087.3: A module for LAGOS-NE, a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of U.S. Lakes: 1925-2013

This data package, LAGOS-NE-LIMNO v1.087.3, is 1 of 5 data packages associated with the LAGOS-NE database-- the LAke multi-scaled GeOSpatial and temporal database. With this release, only this data package is being updated and users are expected to use prior releases of the other types of data. Please see the attached additional documentation for a full description of the changes that have been made for this new release.The data packages that make up LAGOS-NE include the following information on lakes and reservoirs in 17 lake-rich states in the Northeastern and upper Midwestern U.S. (1) LAGOS-NE-LOCUS v1.01: lake location and physical characteristics for all lakes greater than one hectare. (2) LAGOS-NE-GEO v1.05: ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes and for all spatial resolutions, also called ‘zones’ (i.e., ecoregions, states, counties). These geospatial data were created by processing national-scale and publicly-accessible datasets to quantify numerous metrics at multiple spatial resolutions. (3) LAGOS-NE-LIMNO v1.087.3: in-situ measurements of lake water quality from the past three decades for approximately 2,600-12,000 lakes, depending on the variable. This module was created by harmonizing 87 water quality datasets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. This module includes variables that are most commonly measured by state agencies and researchers for studying eutrophication. For each water quality data value, we also include metadata related to the sampling program, methods, qualifiers with data flags from the original program (qual, not standardized for LAGOS-NE), censor codes from our quality control procedures (censorcode, standardized for LAGOS-NE), and the date of each sample. (4) LAGOS-NE-GIS v1.0: the GIS data layers for lakes, wetlands, and streams, as well as the spatial resolutions that were used to create the LAGOS-N

openCC0Nov 2021View details →

Concentration of dissolved organic carbon in water samples taken from the Upper Clark Fork River (Montana, USA) during water year 2019 (1 Oct 2018 - 30 Sep 2019)

These data were collected to support monitoring of the Upper Clark Fork River restoration, and data collection was funded by the US NSF Long Term Research in Environmental Biology (LTREB) program and the US NSF EPSCoR funded Montana Consortium for Research on Environmental Water Systems. The LTREB monitoring project consists of monthly or bi-weekly water quality monitoring across a 200-km restoration gradient contaminated by historic mining practices to monitor inorganic phosphorus and nitrogen concentrations, biotic standing stocks, and heavy metal contamination. The original analytical intent for these data was to assess the response of river dissolved organic carbon to the floodplain restoration. Data are Aurora Total Organic Carbon combustion analyses of the concentration of organic carbon dissolved in filtered samples of well-mixed river thalweg water. Data are from the 2019 water year (1 Oct 2018 to 30 Sep 2019). Data were collected on the Upper Clark Fork River (USGS HUC 17010201) at project sites distributed along the river from the vicinity of Anaconda to Missoula, Montana, USA.

The Jefferson Project 2017 water quality data from two vertical profiler stations in Lake George, NY, USA.

The Jefferson Project at Lake George – a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association – combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake’s food web and overall water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2017, The Jefferson Project deployed two vertical profiler stations on the lake, collecting data on water quality and meteorology. Meteorological data have been included with the Jefferson Project Weather Station dataset for 2017. These vertical profiler stations are named VP_AnthonysNose and VP_TeaIsland. The water quality data are collected by EXO2 Multi-parameter sonde sensors. The sensors collect data at 1 meter depth increments, starting at 1 meter and proceeding to 2 meters off bottom. The data is transferred in near real-time to an off-site database for monitoring and review. The data provided have undergone data correction by Jefferson Project researchers.

openCC (other)Dec 2024View details →

Water properties of Arco Lake, Budd Lake, Deming Lake, and Josephine Lake in Itasca State Park from 2006-2009 and 2019-et seq.

Depth profiles of water column chemical and physical properties were assessed with seasonal-scale frequency from four lakes in the Itasca State Park from 2006-2009 and from 2019-et seq. The data was used to assess the mixing status and major geochemical constituents within the lakes. Several parameters were routinely measured with deployable probes at meter or sub-meter resolution at the deepest location in each lake. Water samples were also collected for laboratory analysis. Bathymetry data collected in 2022 is supplied as rasters.

openCC (other)Jan 2024View details →

Marcell Experimental Forest 30-minute water table elevation and temperature from transects of wells in the S2 and S6 peatlands, 2018-ongoing

This data publication contains 30-minute water table elevation and temperature data collected along bog to lagg transects within two watersheds at the Marcell Experimental Forest (MEF) in Itasca County, Minnesota. The bog to lagg transects are located on the north and south sides of S2 and S6 peatlands and contain three surface water wells each. The water table elevations provide information to calculate the hydraulic gradients that drive flow to and from the bogs. The collection of these data was funded by the US Department of Energy. The research program at Marcell Experimental Forest is managed by the USDA Forest Service Northern Research Station.

openCC (other)Jul 2022View details →

Daily water temperature (C) in the Yolo Bypass and Sacramento River, 1998-2019

This data is an integration of raw logger data as well as relevant California Data Exchange Center (CDEC) data (Pien et al. 2020, hourly) and water quality data collected during the Yolo Bypass Fish Monitoring Program’s (YBFMP) fish collection (Pien and Kwan 2022) to produce a daily water temperature dataset for the Yolo Bypass and Sacramento River at Sherwood Harbor and Rio Vista Bridge. The raw YBFMP’s water temperature data was collected by loggers attached to the rotary screw trap (STTD) and Sherwood Harbor (SHR). Logger data ranged from daily means (1998) to a fifteen-minute collection interval (2013-2017 for the Yolo Bypass, 2009-2019 for Sherwood Harbor). A daily mean, maximum, minimum, standard deviation and coefficient of variation in water temperature was produced as well as columns for sample size (n, number of measurements per day), method (data collection or estimation), category, length (number of consecutive missing dates) and site. Daily water temperature data for the full extent of the YBFMP’s fish collection is valuable for a variety of purposes. For example, variation in water temperature during inundation and comparisons between temperatures in the Sacramento River and Yolo Bypass have been used as metrics of habitat complexity and linked to life history diversity in salmon (Goertler et al. 2017).

openCC (other)Aug 2022View details →

Marcell Experimental Forest chemistry of surface water draining the S2 catchment, 1986 - ongoing

This data set is a record since 1986 of chemistry for surface water draining the S2 catchment at the Marcell Experimental Forest (MEF) in Itasca County, Minnesota. Unfiltered water is usually collected every one or two weeks as part of the long-term monitoring program of the S2 catchment. Some samples were collected more often for various other studies and are included in this data set. Samples are routinely measured for pH, specific conductivity, anions (chloride, sulfate), cations (calcium, magnesium, potassium, sodium, aluminum, iron, manganese, strontium), silicon, nutrients (ammonium, nitrate+nitrite, soluble reactive phosphorus, total nitrogen, total phosphorus), and total organic carbon. Occasionally, stable water and mercury isotopes as well as concentrations of dissolved organic carbon (DOC), bacterial respiration of dissolved organic matter, biodegradable DOC (BDOC), ferrous and ferric iron, total mercury (filtered or unfiltered), methylmercury (filtered or unfiltered), and lead were measured. Ultraviolet (UV) absorbance, a measure of water color or dissolved organic matter optical properties, was also measured for some samples. More solutes and values will be added as additional metadata are documented (pre-1986 to 1992), water samples are collected and analyzed (concentrations and isotopes), or archived water samples are analyzed for stable water isotopes. The MEF is operated and maintained by the USDA Forest Service, Northern Research Station.

The Jefferson Project 2018 hydrologic, water quality, and soil quality data from 11 Tributary Stations within the Lake George basin, NY, USA.

The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at <https://jeffersonproject.rpi.edu/> In 2018, The Jefferson Project had eleven tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which underwent data correction and downsampling to an hourly frequency.

The Jefferson Project 2019 hydrologic, water quality, and soil quality data from 12 Tributary Stations within the Lake George basin, NY, USA.

The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2019, The Jefferson Project had twelve tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_Sucker, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which underwent data correction and downsampling to an hourly frequency.

openCC (other)Sep 2024View details →

The Jefferson Project 2018 water quality data from two vertical profiler stations in Lake George, NY, USA.

The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2018, The Jefferson Project deployed two vertical profiler stations on the lake, collecting data on water quality and weather. Meteorological data have been included with the Jefferson Project Weather Station dataset for 2018. These vertical profiler stations are named VP_AnthonysNose and VP_TeaIsland. The water quality data are collected by YSI EXO2 Multi-parameter sonde sensors. The sensors collect data at 1 meter depth increments, starting at 1 meter and proceeding to 2 meters off bottom. The data is transferred in near real-time to an off-site database for monitoring and review. The data provided have undergone data correction by Jefferson Project researchers.

openCC (other)Sep 2024View details →

The Jefferson Project 2019 water quality data from three vertical profiler stations in Lake George, NY, USA.

The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2019, The Jefferson Project deployed three vertical profiler stations on the lake, collecting data on water quality and weather. Meteorological data have been included with the Jefferson Project Weather Station dataset for 2019. These vertical profiler stations are named VP_AnthonysNose, VP_CalvesPen, and VP_TeaIsland. The water quality data are collected by a YSI EXO2 Multi-parameter sonde sensors. The sensors collect data at 1 meter depth increments, starting at 1 meter and proceeding to 2 meters off bottom. The data are transferred in near real-time to an off-site database for monitoring and review. The data provided here have undergone data correction by Jefferson Project researchers.

openCC (other)Oct 2022View details →

Water quality and watershed attributes of 41 Pampean streams in Argentina, 12 years later (2003-2015).

This database consists of water chemistry (pH, conductivity, dissolved oxygen, nutrients, and carbonates) and catchment attributes for 41 streams of Buenos Aires province, Argentina. Water quality was measured in 2003/4 and 12 years later (2015/16). Sampling were made in May (autumn), November (spring), and February (summer) at baseflow condition. Some physico-chemical parameters were measured in situ. Parameters determined at laboratory were nutrients and salts. And catchment attributes were determined (physiographic parameters, land use, soil type and geology).

The Jefferson Project 2020 hydrologic, water quality, and soil quality data from 12 Tributary Stations within the Lake George basin, NY, USA.

The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2020, The Jefferson Project had twelve tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_Sucker, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data are transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. The data provided here are level 4 data which underwent data correction and downsampling to an hourly frequency.