Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
69
datasets available to search
ShareScore release 0.9.0
Dataset results
69 results for “lake level”
Figure. The Central Black Sea Region of Turkey and sampling sites. Sampling sites: 1. Amasya: Centrum, Firingiler, 40°41′15.9″N, 35°54′45.9″E, 378 m; 2. Amasya: Göynücek, Kışlabeyi Village, 40°23′25.2″N, 35°33′43.1″E, 542 m; 3. Amasya: Gümüşhacıköy, Keçi Village, 40°49′07.5″N, 35°15′35.4″E, 777 m; 4. Amasya: Merzifon, Yakacık Village, 40°53′48.6″N, 35°25′43.9″E, 877 m; 5. Amasya: Suluova, Centrum, 40°49′24.3″N, 35°37′18.0″E, 473 m; 6. Amasya: Suluova, Çayüstü Village, 40°48′43.4″N, 35°38′24.4″E, 495 m; 7. Amasya: Taşova, 40°44′55.5″N, 36°17′49.6″E, 242 m; 8. Amasya: Taşova, Güngörmüş Village, 40°43′41.8″N, 36°17′06.3″E, 279 m; 9. Çorum: Centrum, Güney Village, 40°37′47.6″N, 35°05′58.5″E, 1170 m; 10. Çorum: Laçin, Gökgözler Village, 40°48′48.6″N, 34°50′38.6″E, 434 m; 11. Çorum: Mecitözü, Centrum, 40°31′41.1″N, 35°18′22.3″E, 767 m; 12. Çorum: Mecitözü, Hıdırlı Village, 40°29′19.6″N, 35°15′10.9″E, 918 m; 13. Çorum: Ortaköy, Senemoğlu Village, 40°19′24.0″N, 35°21′37.2″E, 533 m; 14. Çorum: Uğurludağ, Eskiçeltek Village, 40°33′46.6″N, 34°27′00.0″E, 519 m; 15. Ordu: Akkuş, Gökçebayır, 40°43′06.0″ N, 37°01′33.5″E, 920 m; 16. Ordu: Fatsa, Ayazlı, 41°00′32.9″N, 37°27′06.9″E, 130 m; 17. Ordu: Gölköy, 40°40′18.9″N, 37°36′43.4″E, 850 m; 18. Ordu: İkizce, 41°06′07.9″N, 37°07′45.3″E, 50 m; 19. Ordu: Korgan, Terzili Village, 40°42′06.6″N, 37°17′39.2″E, 1246 m; 20. Ordu: Korgan, Yenipınar Village, 40°47′58.0″N, 37°21′31.6″E, 584 m; 21. Ordu: Mesudiye, Centrum, 40°27′42.7″N, 37°46′23.0″E, 1100 m; 22. Ordu: Perşembe, Yumrutaş Village, 41°06′07.7″ N, 37°45′38.3″E, 231 m; 23. Ordu: Ünye, Cevizdere Village, 41°06′26.4″ N, 37°20′10.2″E, sea level; 24. Samsun, Terme, Centrum, 41°12′22.4″N, 36°56′14.8″E, sea level; 25. Samsun:Ayvacık, Yenice Village, 41°03′05.5″N, 36°39′17.4″E, 70 m; 26. Samsun: Bafra, Karaköy, 41°31′26.1″N, 36°00′52.5″E, 21 m; 27. Samsun: Centrum, Ataköy, 41°15′22.9″N, 36°17′26.8″E, 150 m; 28. Samsun: Centrum, entrance of Yeşiltepe (Çorak Village), 41°14′29.6″N, 36°16′52.8″E, 32 m; 29. Samsun: Havza, entrance of Mürsel Village, 40°59′26.5″N, 35°43′20.9″E, 642 m; 30. Samsun: Kavak, İdrisli Village, 41°05′45.5″N, 35°59′36.0″E, 706 m; 31. Samsun: Ladik, Tatlıcak Village, 40°55′29.6″N, 35°58′13.1″E, 870 m; 32. Samsun: Ladik, the vicinity of Lake Ladik, 40°54′06.0″N, 35°59′49.9″E, 870 m; 33. Samsun: Ondokuz Mayıs, Yörükler, 41°31′14.8″N, 36°07′23.6″E, sea level; 34. Samsun: Tekkeköy, Kerpiçli Village, 41°09′26.9″N, 36°32′04.4″E, 152 m; 35. Samsun: Vezirköprü, Pazarcı Village, 41°04′18.5″ N, 35°30′23.2″E, 690 m; 36. Tokat: Almus, Centrum, 40°22′35.5″N, 36°54′42.5″E, 803 m; 37. Tokat: Artova, Centrum, 40°06′42.1″N, 36°18′14.3″E, 1170 m; 38. Tokat: Centrum, vicinity of Tokat Airport, 40°18′23.5″N, 36°20′12.0″E, 556 m; 39. Tokat: Erbaa, Dereçiftliği, 40°33′22.3″ N, 36°37′22.4″E, 384 m; 40. Tokat: Niksar, Şahinli Village, 40°35′09.2″N, 36°53′59.5″E, 270 m; 41. Tokat: Reşadiye, Centrum, 40°23′02.9″N, 37°20′06.3″E, 511 m; 42. Tokat: Turhal, 40°20′21.1″N, 36°08′41.2″E, 507 m; 43. Tokat: Turhal, Arzupınar Village, 40°19′43.7″N, 36°10′52.3″E, 608 m. in The Ceratopogonidae (Insecta: Diptera) fauna of the Central Black Sea Region in Turkey
Figure. The Central Black Sea Region of Turkey and sampling sites. Sampling sites: 1. Amasya: Centrum, Firingiler, 40°41′15.9″N, 35°54′45.9″E, 378 m; 2. Amasya: Göynücek, Kışlabeyi Village, 40°23′25.2″N, 35°33′43.1″E, 542 m; 3. Amasya: Gümüşhacıköy, Keçi Village, 40°49′07.5″N, 35°15′35.4″E, 777 m; 4. Amasya: Merzifon, Yakacık Village, 40°53′48.6″N, 35°25′43.9″E, 877 m; 5. Amasya: Suluova, Centrum, 40°49′24.3″N, 35°37′18.0″E, 473 m; 6. Amasya: Suluova, Çayüstü Village, 40°48′43.4″N, 35°38′24.4″E, 495 m; 7. Amasya: Taşova, 40°44′55.5″N, 36°17′49.6″E, 242 m; 8. Amasya: Taşova, Güngörmüş Village, 40°43′41.8″N, 36°17′06.3″E, 279 m; 9. Çorum: Centrum, Güney Village, 40°37′47.6″N, 35°05′58.5″E, 1170 m; 10. Çorum: Laçin, Gökgözler Village, 40°48′48.6″N, 34°50′38.6″E, 434 m; 11. Çorum: Mecitözü, Centrum, 40°31′41.1″N, 35°18′22.3″E, 767 m; 12. Çorum: Mecitözü, Hıdırlı Village, 40°29′19.6″N, 35°15′10.9″E, 918 m; 13. Çorum: Ortaköy, Senemoğlu Village, 40°19′24.0″N, 35°21′37.2″E, 533 m; 14. Çorum: Uğurludağ, Eskiçeltek Village, 40°33′46.6″N, 34°27′00.0″E, 519 m; 15. Ordu: Akkuş, Gökçebayır, 40°43′06.0″ N, 37°01′33.5″E, 920 m; 16. Ordu: Fatsa, Ayazlı, 41°00′32.9″N, 37°27′06.9″E, 130 m; 17. Ordu: Gölköy, 40°40′18.9″N, 37°36′43.4″E, 850 m; 18. Ordu: İkizce, 41°06′07.9″N, 37°07′45.3″E, 50 m; 19. Ordu: Korgan, Terzili Village, 40°42′06.6″N, 37°17′39.2″E, 1246 m; 20. Ordu: Korgan, Yenipınar Village, 40°47′58.0″N, 37°21′31.6″E, 584 m; 21. Ordu: Mesudiye, Centrum, 40°27′42.7″N, 37°46′23.0″E, 1100 m; 22. Ordu: Perşembe, Yumrutaş Village, 41°06′07.7″ N, 37°45′38.3″E, 231 m; 23. Ordu: Ünye, Cevizdere Village, 41°06′26.4″ N, 37°20′10.2″E, sea level; 24. Samsun, Terme, Centrum, 41°12′22.4″N, 36°56′14.8″E, sea level; 25. Samsun:Ayvacık, Yenice Village, 41°03′05.5″N, 36°39′17.4″E, 70 m; 26. Samsun: Bafra, Karaköy, 41°31′26.1″N, 36°00′52.5″E, 21 m; 27. Samsun: Centrum, Ataköy, 41°15′22.9″N, 36°17′26.8″E, 150 m; 28. Samsun: Centrum, entrance of Yeşiltepe (Çorak Village), 41°14′29.6″N, 36°16′52.8″E, 32 m; 29. Samsun: Havza, entrance of Mürsel Village, 40°59′26.5″N, 35°43′20.9″E, 642 m; 30. Samsun: Kavak, İdrisli Village, 41°05′45.5″N, 35°59′36.0″E, 706 m; 31. Samsun: Ladik, Tatlıcak Village, 40°55′29.6″N, 35°58′13.1″E, 870 m; 32. Samsun: Ladik, the vicinity of Lake Ladik, 40°54′06.0″N, 35°59′49.9″E, 870 m; 33. Samsun: Ondokuz Mayıs, Yörükler, 41°31′14.8″N, 36°07′23.6″E, sea level; 34. Samsun: Tekkeköy, Kerpiçli Village, 41°09′26.9″N, 36°32′04.4″E, 152 m; 35. Samsun: Vezirköprü, Pazarcı Village, 41°04′18.5″ N, 35°30′23.2″E, 690 m; 36. Tokat: Almus, Centrum, 40°22′35.5″N, 36°54′42.5″E, 803 m; 37. Tokat: Artova, Centrum, 40°06′42.1″N, 36°18′14.3″E, 1170 m; 38. Tokat: Centrum, vicinity of Tokat Airport, 40°18′23.5″N, 36°20′12.0″E, 556 m; 39. Tokat: Erbaa, Dereçiftliği, 40°33′22.3″ N, 36°37′22.4″E, 384 m; 40. Tokat: Niksar, Şahinli Village, 40°35′09.2″N, 36°53′59.5″E, 270 m; 41. Tokat: Reşadiye, Centrum, 40°23′02.9″N, 37°20′06.3″E, 511 m; 42. Tokat: Turhal, 40°20′21.1″N, 36°08′41.2″E, 507 m; 43. Tokat: Turhal, Arzupınar Village, 40°19′43.7″N, 36°10′52.3″E, 608 m.
Ocean Basin and Lake Polygons for Sea Level Grids
<p>This collection of files contains geographically registered polygons defining the shape of ocean basins and large lakes across the globe, designed for use in gridding and analysis of satellite-based observations of sea level.<span> </span>In particular, this data set was produced as part of the NASA-SSH (<a href="https://podaac.jpl.nasa.gov/NASA-SSH">https://podaac.jpl.nasa.gov/NASA-SSH</a>) effort, whose aim is to deliver continuously updated, climate-quality, global observations of sea level derived from radar altimeter observations.<span> </span></p> <p> </p> <p>The polygons were derived using products from free vector and raster map data Natural Earth (<a href="https://naturalearthdata.com">https://naturalearthdata.com</a>).<span> </span>Ocean basins were derived from version 5.1.0 of the 1:10m Marine Areas:</p> <p><a href="https://www.naturalearthdata.com/downloads/10m-physical-vectors/">https://www.naturalearthdata.com/downloads/10m-physical-vectors/</a></p> <p>and Lakes were derived from version 5.0.0 of the 1:50m Lakes and Rivers polygons:</p> <p><a href="https://www.naturalearthdata.com/downloads/50m-physical-vectors/">https://www.naturalearthdata.com/downloads/50m-physical-vectors/</a></p> <p> </p> <p>For the lakes, only the largest 26 lakes and inland seas were retained, as most smaller lakes are not typically sampled by traditional nadir altimeters.<span> </span>For the marine basins, some polygons were joined or sometimes split in order to simplify grouping altimeter data by regions where it is expected to be geographically correlated.<span> </span>For example, southern sections were split from the South Pacific and South Atlantic Oceans to simplify separation of these basins across the South American Peninsula.<span> </span></p> <p> </p> <p>In addition to the polygons themselves, a table listing connections between polygons is also provided.<span> </span>This allows users to select observations in regions that are likely to be correlated over time scales of days to weeks or longer. Each polygon carries a unique numerical identifier (the Arctic Ocean is 1, the Southern Ocean is 2, etc…).<span> </span>For each identifier, the connection table lists all of the other identifiers that polygon is connected to.<span> </span>This is used in the NASA-SSH gridding process to down-select data used to estimate sea level at a specific location.</p> <p> </p> <p>Both the ocean and lake polygons themselves, and the connection table can be easily visualized in Google Earth (or other geographic mapping software) using the KMZ file provided.<span> </span>The connection table provides sets of polygons connected to each individual feature.<span> </span>For example, the Arctic Ocean polygon is connected to the Beaufort Sea, the Greenland Sea, the Barents Sea, etc.<span> </span>These can be easily visualized by turning on subsets of features in the Basin Connections folder within the KMZ file.</p> <p> </p> <p>Files contained in this dataset include:</p> <p>basin_files.tar.gz – a tar gzip file that contains a .dbf, .prj, .shx, and .shp Shape file that can be loaded into a geographic mapping program such as QGIS or Google Earth.<span> </span>This contains the polygon definitions, including their names.</p> <p>basin_name_table.txt – an ascii text file containing a list of all the basin ID numbers and simplified version of the basin names, separated by a colon “:”.<span> </span>A few basins were created for this dataset and do not have common geographic names.<span> </span>These are given names based on their ID number for example “Feature ID: 240”.<span> </span></p> <p>basin_connection_table.txt – an ascii text file containing a list of all the basin ID numbers that are geographically connected to a given basin ID.<span> </span>The basin ID number in question is listed first on each row, and the connected ID numbers follow a colon “:”, in a comma separated list.</p> <p>NASA-SSH Basins.kmz – This KMZ file contains all of the polygon definitions, along with the set of polygons for each basin that shows which basins it is connected to.<span> </span>If loaded into Google Earth, it will create a folder in the Google Earth “Places” panel called “NASA-SSH Basins”.<span> </span>Below this, two subfolders will be created, one will be called “All Basin Polygons” and will contain all of the basins polygons colored red.<span> </span>Clicking on any one of the polygons will show the Basin ID number and name of this polygon.<span> </span>The second subfolder is called “Basin Connections” and contains a list of subfolders, one for each Basin ID.<span> </span>These can be turned on 1 at a time and will show a given basin polygon and all of the polygons it is connected to.</p> <p> </p> <p>If you use these data please cite:</p> <p>Willis, J.K., J. Sanchez, R. Santos, and S. Fournier,<span> </span>Ocean Basin and Lake Polygons for Sea Level Grids, at DOI:10.5281/zenodo.13910542.</p>
Coordinated Great Lakes lake-wide average monthly mean water levels
<div> <pre> </pre> <p>This dataset consists of lake-wide average monthly mean water levels for Lakes Superior, Michigan-Huron, Saint Clair, Erie, and Ontario. The data are presented in meters with reference to the International Great Lakes Datum (IGLD) 1985. The dataset is computed and stewarded by Environment and Climate Change Canada (ECCC) and the United States Army Corps of Engineers (USACE), under the auspices of the Coordinating Committee on Great Lakes Basic Hydraulic and Hydrologic Data (Coordinating Committee)<strong>1</strong>. Each agency independently computes and manages their own version of the dataset. The data are binationally coordinated annually, where ECCC and USACE compare their versions to verify that they are consistent. </p> <p> </p> </div> <div> <p>The lake-wide average monthly mean water levels are computed from daily hydrometric observations collected from a network of gauging stations located around each lake and on both sides of the Canada-United States border. The gauges are owned and maintained by the Canadian Hydrographic Service (CHS)<strong>2</strong> and the National Oceanic and Atmospheric Administration (NOAA)<strong>3</strong>. </p> <p> </p> </div> <div> <p>Detailed metadata and data files for each lake are provided within this dataset. The data files are in comma separated value format (CSV). The CSV data files contain both the lake-wide average monthly mean water level and concise metadata described in accordance with the Climate and Forecasting (CF) conventions. The detailed metadata is in PDF format and provides extended descriptions of data sources, computation methods, dataset history, and related information for each great lake. </p> <p> </p> <p>This dataset is updated annually with the addition of the most recent complete calendar year of data. The version number reflects the year of the last available data. However, note that as well as adding the most recent data, data from previous years may be adjusted based on updated information from those years.</p> </div> <div> <pre> </pre> <p><strong>1</strong>Coordinating Committee on Great Lakes Basic Hydraulic and Hydrologic Data <a href="https://www.greatlakescc.org/en/home/" target="_blank" rel="noreferrer noopener">https://www.greatlakescc.org/en/home/</a> </p> <p> </p> </div> <div> <p><strong>2</strong>DFO 2024. Marine Environmental Data Section Archive, <a href="https://meds-sdmm.dfo-mpo.gc.ca/" target="_blank" rel="noreferrer noopener">https://meds-sdmm.dfo-mpo.gc.ca</a>, Ecosystem and Oceans Science, Department of Fisheries and Oceans Canada. </p> <p> </p> </div> <div> <p><strong>3</strong>NOAA 2024. Tides and Currents, <a href="https://tidesandcurrents.noaa.gov/" target="_blank" rel="noreferrer noopener">https://tidesandcurrents.noaa.gov/</a>, Center for Operational Oceanographic Products and Services, National Oceanic and Atmospheric Administration. </p> </div>
Multiple lines and levels of evidence for avian zoochory promoting fish colonization of artificial lakes
<p>Understanding how obligate freshwater organisms colonize seemingly isolated ecosystems has long fascinated ecologists. While recent investigations reveal that fish eggs can survive the digestive tract of birds and successfully hatch once deposited, evidence for avian zoochory in natura are still lacking. Here, we used a multiple lines and levels of evidence approach to demonstrate plausible bird-mediated colonization of lakes by the European perch (<em>Perca</em> <em>fluviatilis</em>). We studied a set of newly-formed artificial lakes that the public is either prohibited to access because of gravel extraction or allow to access (mainly for angling). The motivating observation is that a large proportion of prohibited-access lakes (>80%) were colonized by European perch while stocking by anglers and managers never occurred. Three supplementary lines of evidence supported avian zoochory. First, European perch spawning occurs when waterfowl abundance is very high. Second, European perch lays sticky eggs at shallow depths where they can be eaten by waterfowls or attached to their bodies. Third, genetic analyses suggested that European perch actually migrate among lakes, and that distance moved matches with daily flight range of foraging waterfowls. Together, multiple lines of evidence point to avian zoochory as a pathway for fish colonizing remote or newly-formed freshwater ecosystems.</p>
Increasing marsh bird abundance in coastal wetlands of the Great Lakes (2011–2021) likely caused by increasing water levels
Open the record for dataset details and reuse information.
Multiple lines and levels of evidence for avian zoochory promoting fish colonization of artificial lakes
Open the record for dataset details and reuse information.
Phylum level phytoplankton composition and FTIR spectra for body wash microplastics and plant-based scrub particles from a 7-day summer 2016 surface mesocosm experiment in Otsego Lake, NY, USA
We tested the effects of two types of microplastics, 50 µm polystyrene (PS) calibration beads and polylactic acid (PLA) plastic body wash scrub particles, and one type of plant-derived body wash scrub particle on a natural phytoplankton assemblage through a 7-day mesocosm incubation experiment in a temperate, mesotrophic lake (Otsego Lake, Otsego County, NY, USA) in summer 2016.
Measurements of water levels and water temperature profiles in Lake Kinneret, in summer, during the period (2000 – 2020)
<p><em>The datasets include measurements of water levels and water temperature profiles in Lake Kinneret, in summer, during the period (2000 – 2020). </em><em>Lake Kinneret is located in Israel. </em>Shipboard measurements of water temperature profiles were taken at the deepest point of ~40 m near the lake center (32.82 N; 35.60 E). The <em>AML MINOS X probe was used (specifications are available online at <a href="https://geo-matching.com/ctd-systems/minos-x-ctd/svp">https://geo-matching.com/ctd-systems/minos-x-ctd/svp</a>). </em><em>Data of water temperature profiles are associated with the Kinneret Limnological Laboratory, Israel Oceanographic and Limnological Research (</em><a href="http://kinneret.ocean.org.il/ar_3d_vb.aspx"><em>http://kinneret.ocean.org.il/ar_3d_vb.aspx</em></a><em> ). Data of Lake Kinneret water levels are associated with the Israel Water and Sewage Authority (</em><a href="https://www.gov.il/en/departments/water_authority/govil-landing-page">https://www.gov.il/en/departments/water_authority/govil-landing-page</a><em> ). Data format: xlsx file. </em>There are two sheets in the Excel file: the first sheet includes water temperature profiles, while the second sheet includes lake water levels, in summer, during the period (2000 – 2020). </p>
Salinity variation with water level in shallow lakes
<p>This dataset and associated R code describes water level and salinity data collected by the Department of Biodiversity Conservation and Attractions and its predecessors as part of the South-West Wetland Monitoring Program (SWWMP). SWWMP measured 152 lakes on multiple occassions between 1977 - 2019. The R code presents analyses of this data and associated groundwater quality and streamflow. The dataset consists of the following:</p> <p><strong>Water_Data.csv</strong></p> <p>A comma delimited ascii file containing the following columns of data.</p> <p>Code: A string denoting the lake identifier.</p> <p>Date: dd/mm/yyyy</p> <p>Depth (m): Lake water depth, relative to its deepest point.</p> <p>Salinity (ppt): g/L estimated from electrical conductivity measurments. </p> <p><strong>Salinity_variation_shallow_lakes.R</strong></p> <p>An R script for the analysis and interpreation of salinity vs water level data, utilising the data files included here.</p> <p><strong>Dulbinning_StageDischarge.csv</strong></p> <p>A comma delimited ascii file containing the following columns of data.</p> <p>Date,Depth (m),Salinity (mg/L)</p> <p>Date: dd/mm/yyyy</p> <p>Depth (m): Lake water depth, relative to its deepest point.</p> <p>Salinity (mg/L): Stream salinity.</p> <p>Q (Ml/day): Daily discharge from Department of Water and Environmental Regualtion stream gauge.</p> <p>EC (uS/cm): Stream electrical conductivity.</p> <p>d: Adjusted lake depth (m) relative to threshold depth at which streamflow is initiated.</p> <p>Q: Stream discharge (ML/day)</p> <p><strong>SiteSummary.xlsx</strong></p> <p>A Microsoft excel file with four sheets.</p> <p>Sheet 1: Data, containing the following columns.</p> <p>Code: A string denoting the lake identifier.</p> <p>Name: A string. The full lake name.</p> <p>Reserve_Name: A string. The full name of the nature reserve (if any) containing the lake.</p> <p>Easting: UTM easting in meters.</p> <p>Northing: UTM northing in meters.</p> <p>Zone: UTM zone.</p> <p>Longitude: decimal degrees.</p> <p>Latitude: decimal degrees.</p> <p>Bathy_Beta: Bathymetry exponent.</p> <p>StreamOutflow Level (m): estimate of level (relative to lake bottom) stream outflow is inititiated from bathymetry data.</p> <p>Lake Overflow level (m): estimate of level (relative to lake bottom) lake overtops its banks.</p> <p>model: prior expectation of the pattern of salinity variation with water level as estimated visually from the data.</p> <p>Soil_types: String describing the soil classes adjacent the lake described by Department of Primary Industries and Regional Development</p> <p>Inflow stream: Whether a stream geomorphic feature was seen in aerial imagery flowing into the lake and its type, None if no stream seen, perrenial if observed as wide and full of water, ephemeral if a dry stream feature with riparian vegetation seen.</p> <p>Outflow stream: Whether a stream geomorphic feature was seen in aerial imagery flowing from the lake and its type, None if no stream seen, perrenial if observed as wide and full of water, ephemeral if a dry stream feature with riparian vegetation seen. Outflow DWER gauge: Department of Water and Environmental Regulation stream gauge identifier.</p> <p>Aridity: ratio of annual potentia evaporation to annual rainfall.</p> <p>P_ann: Annual rainfall in mm.</p> <p>E_ann: Annual potential evaporation in mm.</p> <p> </p> <p>Sheet 2: Data Description is a briefer version of the data description for Sheet 1 as detailed above.</p> <p> </p> <p>Sheet 3: BEVE n-Q</p> <p>Lake water level data from Lake Beverly and DWER gauged streamflow at the outlet of Lake Beverly.</p> <p> </p> <p>Sheet 4: STAT n-Q</p> <p>Lake water level data from Station Lake and DWER gauged streamflow at the outlet of Station Lake.</p>
Lake Levelling Data from Lake Taupō, 1979-2021
<p>This dataset contains lake levelling measurements from Lake Taupō covering the period 1979-2021. It also contains three hand drawn versions of trendlines for the entire dataset (see associated paper for details).</p> <p>If you use this data please cite:</p> <p>Otway, P., Illsley-Kemp, F., Mestel, E.R.H. Taupō volcano’s restless nature revealed by 42 years of deformation surveys, 1979-2021. New Zealand Journal of Geology and Geophysics, 2022. </p>
Evaluation of reconstructed multi-decadal lake levels using ICESat, ICESat-2, and Landsat satellites
<p>Overview: This dataset contains lake hypsometry and time-varying lake levels (1992-2018) for 342 lakes worldwide with sizes ranging from 1 to 81,844 km<sup>2</sup>. The method is described in detail in Yao et al., <em>Water Resources Research</em> (in review), but a quick overview is provided here.</p> <p>1. We developed a proxy-based approach to reconstruct global-scale, multi-decadal lake levels using water areas and hypsometric curves.</p> <p>2. We depended on elevation measurements from laser altimeters ICESat and ICESat-2 and water areas from Landsat imagery to construct lake hypsometry.</p> <p>3. We used dynamic water masks to reduce the uncertainties in water levels derived from ICESat and ICESat-2.</p> <p>4. We refined water areas mapped from Landsat based on topographic constraints on lake growth and shrinkage and levels from ICESat and ICESat-2.</p> <p>5. We validated the method on 342 lakes against observed long-term water levels from in-situ gauges or satellite radar altimeters.</p> <p> </p> <p> </p> <p>This dataset includes:</p> <p>1. Lake hypsometry from Landsat, ICESat, and ICESat-2 satellites</p> <p>2. Reconstructed water levels using Landsat-derived water areas and hypsometric curves</p> <p>3. Reference level data</p>
Data from: Congruence, but no cascade - pelagic biodiversity across 3 trophic levels in Nordic lakes
Open the record for dataset details and reuse information.
Population asynchrony within and between trophic levels have contrasting effects on consumer community stability in a subtropical lake
Open the record for dataset details and reuse information.
Wisconsin Department Natural Resources Historical Lake Level Data from 1918 to 2000 in Wisconsin
The lake level data collected by Wisconsin Department of Natural Resources. It covers 751 lakes. The time span is from 1918 to 2000. Historically, the Wisconsin Department of Natural Resources surveyed lake level elevations over time in many lakes across the state. The date and lake level were recorded on paper and filed by lake and county. Associated materials for each lake include detailed descriptions, maps, and survey elevations for benchmarks. The full set of files are stored on the fourth floor of the GEF2 building in downtown Madison.
Waushara County Lake Level Data from 1931 to 2015 in Waushara County, Wisconsin
The lake level data from Waushara County Wisconsin. It contains 21 lakes in that county. The data were collected and are stored by the Zoning and Land Conservation Department of Waushara County. County staff survey the water level elevation on these lakes once each year near the end of summer.
Decades of water column temperature, lake level and meteorological data of Lake Lacawac, a pristine glacial lake at Lacawac Biological Field Station in the Pocono mountains, Pennsylvania USA (1992-2019)
Lake Lacawac weather and the lake water column were monitored to observe seasonal and interannual temperature and lake level patterns in response to solar heating, wind-driven water column mixing, precipitation, evaporation, watershed runoff and seepage, and outflow (L. Lacawac has an outflow stream but no inflow stream, and is surrounded on one side by peat bogs). Several studies have shown the lake to have a slow exchange by seepage (slightly more seeping in than out except during dry months). Over the years the raft data have been used to calibrate heat flux, mixing, and evaporation models for the lake, to study zooplankton and phytoplankton distribution and dissolved organic matter flux (photobleaching in the water column, influx from the watershed, and exchange with bottom sediments) and associated UV transparency. The data have also been used to accompany experimental manipulations of lake organisms (algae, zooplankton, fish) in relation to exposure to UV radiation. More recently these lake data have been useful in developing lake indices of climate change.
In-situ lake level observations on the western Tibetan Plateau
<p>Lake level has been monitored at six lakes (Lumajiangdong Co, Memar Co, Jieze Caka, Longmu Co, Luotuo Lake, Aksaiqin Lake) on the western TP. HOBO water level loggers (U20-001-01) or Solist water level loggers were installed in the littoral zone of the lakes. To validate lake level changes, two HOBO water-level loggers were installed in different regions of Lumajiangdong Co (Fig. S1). Because water levels were recorded as changes in pressure (less than 0.5 cm water level equivalent), air pressure data were subtracted from the level loggers to obtain pressure changes related to water column variations. The loggers can still work efficiently at the beginning of ice cover. However, there is no data available when the lake ice was thick enough and loggers were frozen up. Daily lake-level changes were used in this study. At Lumajiangdong Co, lake level data are available between October 2016 and September 2021. At Memar Co, lake level data are only available between October 2017 and September 2019 and between October 2020 and September 2021. For Jieze Caka, Longmu Co and Luotuo Lake, there is only one year’s data available. Water depth of the loggers was measured during fieldwork to validate the logger data.</p>
Data from: Integrating water quality monitoring and diatom community trends to determine landscape-level change in protected lakes.
<p>These data represent water quality measurements and diatom counts from surface sediments collected from lakes in five National Park units in the Great Lakes Region (USA) between 2005 and 2018. Water quality measurements were made three times annually during the growing season; data are presented as growing season means. Surface sediment samples were collected from the same lakes every 3-5 years over more than a decade to analyze diatom community turnover. Diatom data are presented as proportions relative to total diatom counts in each sample; only taxa occuring at 2% or greater in a given park were included. </p>
Water level of Qinghai Lake based on multi-source satellite altimetry data
Open the record for dataset details and reuse information.
Effects of periodic fluctuation of water level on solute transport in seasonal lakes in Poyang floodplain system
<p>The purpose of this study is to investigate the solute transport in seasonal lakes within floodplain systems by means of laboratory experiments and numerical simulations.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.