Find research datasets worth reusing

openCC (other)Apr 2023View details →

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)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)Nov 2022View details →

Landscape Position Project at North Temperate Lakes LTER: Vertical Lake Profiles 1998 - 1999

Parameters characterizing the chemical limnology and spatial attributes of 45 lakes were surveyed as part of the Landscape Position Project. Parameters are measured at or close to the deepest part of the lake. A vertical profile of temperature, dissolved oxygen, and conductivity are collected at 1 meter increments Sampling Frequency: generally monthly for one summer; for some lakes, one or two samples in one summer Number of sites: 45

opencc-by-4.0Dec 2020View details →

Radar measurements for the article "Dynamic differential reflectivity calibration using vertical profiles in rain and snow"

Dataset documentation The archives in hdf5 format provided at this link contain the datasets used in the manuscript Dynamic differential reflectivity calibration using vertical profiles in rain and snow, submitted to Remote Sensing (MDPI) by Alfonso Ferrone and Alexis Berne in 2020.   File content Each file is structured as a table, with each column referring to a specific variable and each row containing a different realization (in space or time). The set of available variables is campaign dependent, and the possibilities are: <ul> <li> idx, and integer index that starts at 1 for the first scan of the dataset and increases by 1 for every successive scan; </li> <li> t, the timestamp of the scan, in seconds since seconds since Jan 01, 1970; </li> <li> r or rg (depending on the file), the distance from the radar in meters; </li> <li> az, the azimuth angle in degrees; </li> <li> el, the elevation angle in degrees; </li> <li> zdr, the uncalibrated differential reflectivity, in dB; </li> <li> zh, the horizontal reflectivity, in dBZ; </li> <li> rhovh or rho, the co-polar correlation coefficient, unitless; </li> <li> snr_h or snr, the signal to noise ratio for the horizontal channel in dB; </li> <li> snrv, the signal to noise ratio for the vertical channel, in dB, </li> <li> ngates, the number of unique range gates. </li> </ul> For the comparison of the data collected by MXPol and DX50 during the PAYERNE campaign, two auxiliary variables were added to the archives: <ul> <li> x the horizontal distance from the current radar, computed on a line passing through the location of two radars; </li> <li> z the vertical distance from the current radar. </li> </ul>   Usage The dataset are provided in the the Hierarchical Data Format version 5 (HDF5), an open source file format, supported by several programming language. They archives were created using the vaex library for Python 3: https://github.com/vaexio/vaex The function vaex.open from the same library can be used for accessing the archives and converting them to vaex.DataFrame.   Campaign-specific information Some of the parameters associated to the variables included in the archives may change depending on the campaign. The following subsection provide a summary of these information.   dataframe_HYMEX_2013_from_20130907-040344_to_20131105-175944.hdf5 Contains PPI scans at 90° elevation for the HYMEX campaign. Location information: <ul> <li> Latitude: 44.61° N </li> <li> Longitude: 4.55° E </li> <li> Altitude: 604 m.a.s.l. </li> </ul> Technical information: <ul> <li> Radar Frequency: 9.41 GHz </li> <li> Theoretical 3 dB angular beamwidth of the antenna: 1.45° </li> </ul> Scan setup: <ul> <li> Range resolution: 75 m </li> <li> Range to the first gate: 204.3 m </li> </ul> Processsing parameters: <ul> <li> Clutter filtering: OFF </li> <li> Minimum rhohv: 0.6 </li> <li> Attenuation correction: Z-PHI method </li> <li> Note on reflectivity calibration: The original manufacturer calibration constant was 7.56 dBZ. The value used here derives from comparison with disdrometers during the HYMEX campaign. </li> </ul>   dataframe_PAYERNE_2014_from_20140321-160016_to_20140519-085808.hdf5 Contains PPI scans at 90° elevation performed by MXPol during the PAYERNE campaign. Location information: <ul> <li> Latitude: 46.81° N </li> <li> Longitude: 6.94° E </li> <li> Altitude: 496 m.a.s.l. </li> </ul> Technical information: <ul> <li> Radar Frequency: 9.41 GHz </li> <li> Theoretical 3 dB angular beamwidth of the antenna: 1.45° </li> </ul> Scan setup: <ul> <li> Range resolution: 75 m </li> <li> Range to the first gate: 204.3 m </li> </ul> Processsing parameters: <ul> <li> Clutter filtering: OFF </li> <li> Minimum rhohv: 0.6 </li> <li> Attenuation correction: None </li> </ul>   dataframe_DAVOS_2014_from_20140704-090224_to_20141231-105720.hdf5 Contains PPI scans at 90° elevation for the DAVOS campaign. Location information: <ul> <li> Latitude: 46.82° N </li> <li> Longitude: 9.82° E </li> <li> Altitude: 2220 m.a.s.l. </li> </ul> Technical information: <ul> <li> Radar Frequency: 9.41 GHz </li> <li> Theoretical 3 dB angular beamwidth of the antenna: 1.45° </li> </ul> Scan setup: <ul> <li> Range resolution: 75 m </li> <li> Range to the first gate: 204.3 m </li> </ul> Processsing parameters: <ul> <li> Clutter filtering: OFF </li> <li> Minimum rhohv: 0.6 </li> <li> Attenuation correction: None </li> </ul>   dataframe_APRES3_from_20151207-123944_to_20160129-125856.hdf5 Contains PPI scans at 90° elevation for the APRES3 campaign. Location information: <ul> <li> Latitude: 66.66 S </li> <li> Longitude: 140.00 E </li> <li> Altitude: 40 m.a.s.l. </li> </ul> Technical information: <ul> <li> Radar Frequency: 9.41 GHz </li> <li> Theoretical 3 dB angular beamwidth of the antenna: 1.45° </li> </ul> Scan setup: <ul> <li> Range resolution: 75 m </li> <li> Range to the first gate: 354.3 m </li> </ul> Processsing parameters: <ul> <li> Clutter filtering: OFF </li> <li> Minimum rhohv: 0.6 </li> <li> Attenuation correction: None </li> </ul>   dataframe_VALAIS_2016_from_20161104-154312_to_20170306-195912.hdf5 Contains PPI scans at 90° elevation for the VALAIS campaign. Location information: <ul> <li> Latitude: 46.12 N </li> <li> Longitude: 7.10 E </li> <li> Altitude: 460 m.a.s.l. </li> </ul> Technical information: <ul> <li> Radar Frequency: 9.41 GHz </li> <li> Theoretical 3 dB angular beamwidth of the antenna: 1.27° </li> </ul> Scan setup: <ul> <li> Range resolution: 30 m </li> <li> Range to the first gate: 226.95 m </li> </ul> Processsing parameters: <ul> <li> Clutter filtering: OFF </li> <li> Minimum rhohv: 0.6 </li> <li> Attenuation correction: None </li> </ul>   dataframe_DX50_from_20140501-020000_to_20140524-015752.hdf5 Contains PPI scans at 90° elevation performed by DX50 during the PAYERNE campaign. Location information: <ul> <li> Latitude: 46.84° N </li> <li> Longitude: 6.92° E </li> <li> Altitude: 450 m.a.s.l. </li> </ul> Technical information: <ul> <li> Radar Frequency: 9.459 GHz </li> <li> Theoretical 3 dB angular beamwidth of the antenna: 1.273° </li> </ul> Scan setup: <ul> <li> Range resolution: 75 m </li> <li> Range to the first gate: 0.0 m </li> </ul> Processsing parameters: <ul> <li> Clutter filtering: OFF </li> <li> Minimum rhohv: 0.0 </li> <li> Attenuation correction: None </li> </ul>   dataframe_DX50_RHI_201405.hdf5 Contains RHI scans from DX50 the PAYERNE campaign. The remaining information equal to the ones listed for dataframe_DX50_from_20140501-020000_to_20140524-015752.hdf5.   dataframe_MXPol_RHI_201405.hdf5 Contains RHI scans from MXPol the PAYERNE campaign. The remaining information is equal to the ones listed for dataframe_PAYERNE_2014_from_20140321-160016_to_20140519-085808.hdf5.

opencc-zeroOct 2023View details →

Vertical hydrography profiles from CTD rosette downcasts during PolarFront cruise 2023-08

PolarFront 2023-08 CTD profilesPressure, temperature, salinity, and other physical properties of seawater from 31 vertical profiles sampled during the PolarFront 2023-08 cruise. Only downcast values are used to avoid errors caused by turbulence on the upcast. Basic data processing were done using Sea-Bird Scientific software SBE Data Processing (v7.26): converting to physical units, filtering for outliers, and bin-averaging over 1 m bins. The final data accuracy is ±0.5 dbar for pressure, ±0.002˚C for sea water temperature and ±0.003 for sea water salinity.Example final data `head -n3 stnr1085.xls`:<blockquote>scan: Scan Count depSM: Depth [salt water, m] prDM: Pressure, Digiquartz [db] t090C: Temperature [ITS-90, deg C] c0S/m: Conductivity [S/m] sal00: Salinity, Practical [PSU] sigma-t00: Density [sigma-t, kg/m^3 ] svCM: Sound Velocity [Chen-Millero, m/s] flSP: Fluorescence, Seapoint sbeox0PS: Oxygen, SBE 43 [% saturation] seaTurbMtr: Turbidity, Seapoint [FTU] par/sat/log: PAR/Logarithmic, Satlantic [umol photons/m^2/sec] sbeox0ML/L: Oxygen, SBE 43 [ml/l] depSM: Depth [salt water, m], lat = 74.9995 potemp090C: Potential Temperature [ITS-90, deg C] sal00: Salinity, Practical [PSU] sigma-é00: Density [sigma-theta, kg/m^3] svCM: Sound Velocity [Chen-Millero, m/s] oxsolML/L: Oxygen Saturation, Garcia & Gordon [ml/l] flag: flag 243 3.955 4.000 8.9775 3.708699 34.9575 27.0886 1486.13 7.9014e-01 105.842 1.003 2.0359e+01 6.8367 3.958 8.9771 34.9576 27.0886 1486.13 6.45936 0.0000e+00 327 4.945 5.000 8.9787 3.708880 34.9577 27.0885 1486.15 7.4764e-01 99.487 0.991 1.4706e+01 6.4261 4.948 8.9782 34.9578 27.0886 1486.15 6.45918 0.0000e+00</blockquote> File typesEach station number has multiple data files from various steps of processing: `ls stnr1085*`: `stnr1085_bin.cnv stnr1085_bin.wmf stnr1085.bl stnr1085.btl stnr1085.btx stnr1085.cnv stnr1085.hdr stnr1085.hex stnr1085_RAW.cnv stnr1085.xls`Notice that the *.xls are plain ascii text files with tab-separated values. These may be converted to utf-8 using `iconv -f iso8859-1 -t utf8`<ul><li>*.bl = Bottle log information. Output bottle file, containing bottle firing sequence number and position, data, time, and beginning and ending scan numbers for each bottle closure. Beginning and ending scan numbers correspond to approximately 1.5-second duration for each bottle.</li><li>*.btl = Bottle files. Averaged data for each bottle.</li><li>*.cnv = Data converted to engineering units.</li><li>*hdr = Header information.</li><li>.hex = Hexadecimal data file.</li><li>*.xls = Converted data binned to 1 m as ascii text (tsv).</li><li>*.xlmcon = Instrument configuration.</li><li>*_bin.cnv = Converted data binned to 1 m as ascii data.</li><li>*_bin.wmf = Graphic of converted and 1m-binned data. *_RAW.cnv = Raw data as text file.</li></ul>

opencc-by-4.0Aug 2020View details →

OMPS-NPP L2 LP USask Ozone (O3) Vertical Profile swath daily V1.1

The USask OMPS-LP L2 2D Ozone v1.1 product provides ozone profile retrievals performed at the University of Saskatchewan for the central slit of the Ozone Mapping and Profiler Suite Limb Profiler (OMPS-LP) instrument on the Suomi-NPP satellite. The two-dimensional retrieval algorithm accounts for variation in the along orbital track dimension, retrieving an entire orbit simultaneously instead of treating each image independently. Ozone is retrieved from the thermal tropopause to 59 km on a 1 km grid with a vertical resolution of approximately 2 km. Each granule contains data from the daylight portion of each orbit measured for a full month. Spatial coverage is global (-82 to +82 degrees latitude), and there are about 14.5 orbits per day, each has typically 160 profiles with an along orbital track sampling of 125 km. The files are written using NetCDF4.

opencc-by-4.0Jul 2022View details →

Vertical profiles of urban wind speed, wind direction and turbulence measured by LiDAR on campus of University College Cork, Ireland

Vertical Profiles of Urban wind speed, wind direction and turbulence measured by LiDAR on campus of University College Cork, Ireland ================================= README version 1.3, 21/07/2022 ================================== Contact info: Paul Leahy, University College Cork paul.leahy@ucc.ie | +353 21 4902017 ================================   Contents 1. Measurement location and time period 2. What is measured (brief description) 3. Instrumentation 4. CSV file detailed descriptions ================================   1. Measurement location and time period: North roof of Kane Building, University College Cork (UCC), Ireland. Lat 51 d 53 m 34 s N. Long 8 d 29 m 39 s W. Roof is c. 39 m above sea level, and c. 26 m above ground level (ground level reference point is the car park West of the UCC Kane Building). The measurements were taken over a time period of several months in the years 2013 / 2014. ================================= 2. What is measured (brief description): * LiDAR Wind speed (horizontal and vertical), wind direction, turbulence intensity at 5  altitudes; reference point (0 m) for these altitudes is the top of the LiDAR instrument c. 1.2 m above roof level. * Air temperature, atmospheric pressure, relative humidity. * Wind speed and direction from an ultrasonic anemometer mounted on top of the instrument (c. 1.2 m above roof level). * 10-minute average values (2 files) and high-resolution (c. 23 sec) data (1 file) are provided. See 'CSV file detailed description' below for detailed information. * Diagnostic information. ================================= 2.1 Surrounding terrain: Surrounding area is urban/suburban. The aspect is northerly. To the West: 2-5 storey buildings, open spaces, suburban. To the South: 2-3 storey buildings, open spaces, trees, river. To the East: 2-3 storey buildings, open spaces. To the North: A higher section of the Kane Building roof (47 m asl), 1-3 storey buildings, suburban. ================================= 3. Instrumentation: ZephIR 175 continuous wave wind profiling LiDAR with integrated sonic anemometer, temperature, humidity, air temperature pressure sensors and GPS. ================================= 4. CSV files detailed description: 4.1 Data on 10-minute averages: Filename 05092013-03122013_10min_res.csv contains: 10 minute averaged data from 05/09/2013 to 03/12/2013. Measurement altitudes: 148 m, 90 m, 69 m, 44 m, 19m above instrument level.   Filename 03122013-07082014_10min_res.csv contains: 10 minute averaged data from:  03/12/2013 to 07/08/2014. Measurement altitudes:  148 m, 90 m,  50 m, 35 m,  15 m above instrument level. Note: from 19/06/2014 onwards, LiDAR data missing (MET data continues).   The first two rows contain header information. Row 1 contains location information (GPS record)) and the measurement altitudes for wind speeds. Sample GPS record: N51535775W8296590 = 51 d 53.5775 m North; 8 d 29.6590 m West. Row 2 contains the data column headers including units.   Wind speeds at each altitude are recorded: No of Packets (= number of scan units averaged over) [] Wind direction (mean) [deg] Horizontal wind speed (mean) & standard deviation [m/s] Vertical wind speed (mean) & standard deviation [m/s] Horizontal variance [m^2/s^2]                       Horizontal min [m/s]                               Horizontal max [m/s]                              TI (turbulence intensity) []   Other meteorological data: Air temperature [oC] Pressure [mbar] Rel. Humidity [%] Rain indicator  [unitless] Higher values indicate more rain during the averaging interval. Wind Speed [m/s] (column 'MET Wind Speed' measured at the top of the instrument by the ultrasonic anemometer) Wind direction [deg] (column 'MET Direction' measured at the top of the instrument by the ultrasonic anemometer).   Other housekeeping and diagnostic data: Instrument tilt [deg] Instrument bearing [deg] GPS data [degrees N, degrees W] Battery voltage [V]  Optics, electronics and battery temperature [oC]   =====================================================   4.2 Data with high time resolution (~23 s):   Filename 05092013-11112013_23s_res.csv contains: High resolution data from 05/09/2013 to 11/11/2013 Measurement altitudes: 148 m, 90 m, 69 m, 44 m, 19m.   Note on time resolution: The time resolution of processed wind measurements is c. 3 seconds per wind level, and around 8 seconds to reset to the first level. A full wind profile measurement at 5 altitudes therefore takes around (5 x 3) + 8 = 23 s to complete. The raw scanning resolution of the instrument is higher than this, as each wind measurement is an average of several values.   Row 1 contains location information (lat, long) and the vertical measurement levels for wind speeds. Row 2 contains the data column headers including units.   Wind speeds at each altitude are recorded: No of Packets (= scan units averaged over) [] Wind direction (mean) [deg] Horizontal wind speed (mean) & standard deviation [m/s] Vertical wind speed (mean) & standard deviation [m/s] Horizontal variance [m^2/s^2]    not defined as measurement interval is too short. Horizontal min [m/s]                    not defined as measurement interval is too short.                             Horizontal max [m/s]                    not defined as measurement interval is too short.                              TI (turbulence intensity) []           not defined as measurement interval is too short.   Other meteorological data: Air temperature [oC] Pressure [mbar] Rel. Humidity [%] Rain indicator  [unitless] Higher values indicate more rain during the scanning interval. Wind Speed [m/s] (column 'MET Wind Speed' measured at the top of the instrument by the ultrasonic anemometer) Wind direction [deg] (column 'MET Direction' measured at the top of the instrument by the ultrasonic anemometer.   Other housekeeping and diagnostic data: Instrument tilt [deg] Instrument bearing [deg] GPS data [degrees N, degrees W] Battery voltage [V]  Optics, electronics and battery temperature [oC]   ===================================================== 4.3 Quality control indicators:   9998                    atmospheric conditions which adversely affect LiDAR wind speed measurements e.g. fog 9999                    high quality wind speed measurement not possible e.g. very low wind speed or obscuration of optical path Status Flag         'Green' => good =======================================================

opencc-by-4.0May 2024View details →

Data for: Impact of SO2 injection profiles on simulated volcanic forcing for the Sarychev 2009 eruptions - investigating the importance of using high vertical resolution methods when compiling SO2 data

The files are data assosicated with the study High-resolution stratospheric volcanic SO2 injections in WACCM. The files are associated with four differnt simulaions described in the paper: M16, S21-1D, S21-3D and No-Volc. The files with "input" in the name are the SO2 input files used in the WACCM (Whole Atmosphere Community Climate Model) simulations in the paper. The files with "monthly_averages" in the filenames are monthly averages of model output data the variables used in the paper.  The CALIOP_monthly_averages.nc file is monthly average of the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) satellite data used in the study to evaluate the WACCM simulations.

openCC (other)Sep 2024View details →

The Jefferson Project 2021 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 2021, 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 2021. These vertical profiler stations are named VP_AnthonysNose, VP_HarrisBay, and VP_TeaIsland. The water quality data are collected by a YSI EXO2 Multi-parameter sonde sensors. The sensors collect data at 1 meter or less 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)Jul 2025View details →

The Jefferson Project 2022 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 2022, 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 2022. These vertical profiler stations are named VP_HarrisBay and VP_TeaIsland. The water quality data are collected by a YSI EXO2 Multi-parameter sonde sensors. The sensors collect data at 1 meter or less 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.

openCC0Dec 2023View details →

Limnological data from nearly 400 lakes across the Americas and New Zealand with a focus on vertical profiles of temperature, UV radiation, and optical properties

Two and a half decades of limnological data have been collected from nearly 400 lakes, encompassing a wide range of systems and a broad range of geography. This data set comprises one of the largest and most complete sets of measurements of underwater ultraviolet (UV) transparency available in the world. The data include a suite of 36 variables, with a focus on the optical characteristics. Lakes range from pristine natural lakes to manmade reservoirs. The systems represented in this data set are largely located in North America, from the northeastern United States to Alaska, and alpine and subalpine lakes in the Rocky Mountains of the United States and Canada. Lakes included range from iconic Lake Tahoe, and Castle Lake in northern California, to lakes in the South American Patagonian region, as well as New Zealand. Data were most often collected during the summer, and in some lakes span multiple years (with year-round data since 2006 in Lake Tahoe). The data here are contained in four files, including LakeData.csv, SiteInformation.csv, Methods.csv, and Variables.csv. The main data are in LakeData.csv. SiteInformation.csv, Methods.csv, and Variables.csv support the main data file with descriptions of the sampling sites, methods by which samples were processed, and descriptions of the variables that were measured, respectively. This data set complements the site-intensive limnological data that we published in EDI on 30+ years of data from 3 lakes in the Poconos Mountains region of Pennsylvania, USA. This complementary data set can be accessed at https://portal.edirepository.org/nis/mapbrowse?scope=edi&identifier=186

openCC0Sep 2022View details →

Global data set of long-term summertime vertical temperature profiles in 153 lakes

Climate change and other anthropogenic stressors have led to long-term changes in the thermal structure, including surface temperatures, deepwater temperatures, and vertical thermal gradients, in many lakes around the world. Though many studies highlight warming of surface water temperatures in lakes worldwide, less is known about long-term trends in full vertical thermal structure and deepwater temperatures, which have been changing less consistently in both direction and magnitude. Here, we present a globally-expansive data set of summertime in-situ vertical temperature profiles from 153 lakes, with one time series beginning as early as 1894. We also compiled lake geographic, morphometric, and water quality variables that can influence vertical thermal structure through a variety of potential mechanisms in these lakes. These long-term time series of vertical temperature profiles and corresponding lake characteristics serve as valuable data to help understand changes and drivers of lake thermal structure in a time of rapid global and ecological change.

openCustomJan 2020View details →

Monthly vertical profiles of salinity, temperature, pressure, oxygen and photosynthetically-available radiation in Sapelo River, Doboy Sound, Duplin River and Altamaha River transect surveys from April 2008 to April 2015

Monthly hydrographic surveys were performed along the Sapelo River, Doboy Sound, Duplin River and Altamaha River from April 2008 to April 2015. Vertical CTD profiles were collected from the surface to bottom during low and high tide phases at fixed stations chosen to coincide with long-term GCE-LTER moorings. Conductivity, temperature, pressure, photosynthetically-available radiation (PAR) and oxygen concentration were measured at 8 Hz using a SeaBird Electronics SBE-37 instrument, and depth, salinity and sigma-t and oxygen saturation were calculated using UNESCO algorithms. Data values collected during the upcast were deleted. This data set was collected as part of the Georgia Coastal Ecosystems LTER monthly hydrographic monitoring program, and will be updated approximately annually to include the latest observations.

openCC (other)Apr 2025View details →

Conductivity, temperature, and depth (CTD) vertical profiles from Lake Joyce, McMurdo Dry Valleys, Antarctica, November 2014

Vertical profiles of conductivity, temperature, and depth (CTD) were collected from Lake Joyce, a perennially ice-covered lake in the McMurdo Dry Valleys of Antarctica. Water column observations were made in November 2014 using a YSI 6600 multiparameter probe in the deepest region of the lake, as identified by Mackey et al. (2018). The water column was accessed by drilling a 10-inch wide hole through the perennial ice cover using a Jiffy drill. This data package includes CTD profiles, as well as measurements of salinity, total dissolved solids (TDS), dissolved oxygen (DO), pH, and pressure.

openCC (other)Dec 2025View details →

Limno run codes, dates, and locations associated with vertical lake profile data collected in the McMurdo Dry Valleys, Antarctica (1991-2025, ongoing)

This data package provides a summary of "limno runs" performed each season in lakes located throughout the McMurdo Dry Valleys of Antarctica as part of the McMurdo Dry Valleys Long Term Ecological Research program. Each limno run is identified by unique code that allows one to compile a complete set of limnological data from a particular lake and location at specific time points. There are usually two or three limno runs performed per lake per austral summer field season, although the number of runs may vary by lake and by season depending on site access, site conditions, and other external factors.

openCC (other)Mar 2025View details →

Underwater photosynthetically active radiation (PAR) vertical profiles collected from lakes in the McMurdo Dry Valleys, Antarctica (1993-2025, ongoing)

As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, lakes are monitored for photosynthetic active radiation (PAR) levels. This data set exemplifies the vertical profile of underwater PAR profiles in perennial ice covered lakes and ambient PAR measured at the air-ice interface. Profile data can be used to calculate extinction coefficients for the water column and ice layer.

openCC (other)Oct 2025View details →

Conductivity, temperature, and depth (CTD) vertical profiles collected from lakes in the McMurdo Dry Valleys, Antarctica (1993-2025, ongoing)

As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a series of Taylor Valley lakes have been monitored for conductivity, temperature, and depth (CTD). A Seabird instrument was used to record CTD profiles in these perennial ice covered lakes.