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.
247
datasets available to search
ShareScore release 0.7.1
Dataset results
247 results for “inland water”
LAGOS - Chlorophyll, TP, and water color summer epilimnetic concentrations and lake and catchment data for inland lakes in WI, MI, NY, and ME – a subset of lake data from LAGOSLimno v.1.040.1
This dataset includes lake total phosphorus (TP), true water color, and chlorophyll a (CHLa) concentrations from summer, epilimnetic water samples and is a subset of the larger LAGOS database (Lake multi-scaled geospatial and temporal database, described in Soranno et al. 2015). LAGOS compiles multiple, individual lake water chemistry datasets into an integrated database. We accessed LAGOSLIMNO version 1.040.0 for lake water chemistry data and LAGOSGEO version 1.02 for lake catchment geographic data. In the LAGOSLIMNO database, lake water chemistry data were collected from individual state agency sampling and volunteer programs designed to monitor lake water quality. Water chemistry analyses follow standard lab methods. In the LAGOSGEO database geographic data were collected from national scale geographic information systems (GIS) data layers. Lake catchments, defined as 'The area of land that drains directly into a lake, and into all upstream-connected, permanent streams to that lake exclusive of any upstream lake watersheds for lakes greater than or equal to 10 ha that are connected via permanent streams', were delineated for lakes greater than or equal to 4 ha. Lake-stream connectivity type was assigned to lakes greater than or equal to 4 ha using GIS tools that use the National Hydrology Dataset (See Soranno et al. 2015 for LAGOS geographic processing steps). A subset of lake and geographic data was created to examine spatial variation in TP and water color relationships with CHLa across broad geographic extents using spatially-varying coefficient models with a Bayesian framework. Lakes were selected that had complete records for summer epilimnetic total TP, true water color, and CHLa. In addition we selected lakes with surface area greater than or equal to 4 ha and less than 10,000 ha to exclude very small and very large lakes from the analyses. The resulting dataset includes 838 lakes in Wisconsin, Michigan, New York, and Maine with 7395 observations. The majo
Global dataset of nitrogen fixation rates across inland and coastal waters based on a coordinated synthesis effort
Biological nitrogen fixation converts inert di-nitrogen gas into bioavailable nitrogen and can be an important source of bioavailable nitrogen to organisms. This dataset synthesizes the aquatic nitrogen fixation rate measurements across inland and coastal waters. Data were derived from papers and datasets published by April 2022 and include rates measured using the acetylene reduction assay (ARA), 15N2 labeling, or the N2/Ar technique. The dataset is comprised of 4793 nitrogen fixation rates measurements from 267 studies, and is structured into four tables: 1) a reference table with sources from which data were extracted, 2) a rates table with nitrogen fixation rates that includes habitat, substrate, geographic coordinates, and method of measuring N2 fixation rates, 3) a table with supporting environmental and chemical data for a subset of the rate measurements when data were available, and 4) a data dictionary with definitions for each variable in each data table. This dataset was compiled and curated by the NSF-funded Aquatic Nitrogen Fixation Research Coordination Network (award number 2015825).
FUI Water Color product of inland waters in China at 30-m in 2015
<p>The first 30-meters FUI water color product of China. The product was developed using time-series Landsat 8 imagery and FUI water color retrieval method. Taking into account the huge amount of computational and storage space required for the national-scale water color mapping, the high-performance Google Earth Engine (GEE) cloud-based platform was introduced to support the computation. First, a cloud-free composite in China for the summer of 2015 was generated using time-series Landsat-8 imagery and the Best-Available-Pixel (BAP) compositing algorithm. Then, the first 30-merters FUI water color product of China was developed using the generated BAP composite and the Google Earth Engine computing platform. The first 30-meters FUI water color product can promote the understanding of the water color of water bodies in China, and provide very important information for preserving and restoring inland water quality.</p> <p>The details of the product is described in "<a href="https://zenodo.org/api/files/59060333-b9fc-45ad-b381-3b05a866de6c/FUI_WaterColor_2015China_Readme_V1.1.docx?versionId=44bb5bbb-407f-4dc8-b183-fa1a3843488f">FUI_WaterColor_2015China_Readme_V1.1.docx</a>".</p>
Data set associated to the manuscript entitled Carbon emissions from inland waters may be underestimated: evidence from European river networks fragmented by drying by López-Rojo et. al
<p>CO2 and CH4 emissions and several associated environmental variables were taken in 6 European drying river networks, in 20 river reaches per river network. The field work was carried across 3 sampling campaigns in 2021, coinciding with 3 hydrological seasons (pre-dry, dry and post-rewetting) to encompass most of the hydrological variability. Each time, measures were taken in the habitats available (flowing water, dry riverbeds, isolated pools).</p>
Supplementary dataset to the publication "Bi, S., and Hieronymi, M. (2024). Holistic optical water type classification for ocean, coastal, and inland waters. Limnology & Oceanography"
<p>The NetCDF data files contain the training dataset used to develop the Optical Water Type (OWT) framework proposed by Bi and Hieronymi (2024). The dataset is available in two spectral versions:</p> <p> 1. <code>owt_BH2024_training_data_hyper.nc</code>: This file includes training data with a spectral resolution of 2 nm, ranging from 400 to 900 nm.<br> 2. <code>owt_BH2024_training_data_olci.nc</code>: This file contains data formatted similarly to the hyperspectral version but aligned with the nominal Sentinel-3 OLCI wavebands.</p> <h2>Contents of the Dataset</h2> <p>For each version, the dataset includes spectral inherent and apparent optical properties such as:</p> <p> • Remote Sensing Reflectance (Rrs)<br> • Pure Water Absorption (aw)<br> • Absorption Coefficient of Detritus (ad)<br> • Total Absorption Coefficient without Pure Water (agp)<br> • Absorption Coefficient of Phytoplankton (aph)<br> • Backscattering Coefficient of Total Particulate Matter (bbp)<br> • Scattering Coefficient of Total Particulate Matter (bp)<br> • Scattering Coefficient of Pure Water (bw)</p> <p>Additionally, the dataset includes various environmental and biological parameters:</p> <p> • Chlorophyll a Concentration (Chl)<br> • Inorganic Suspended Matter Concentration (ISM)<br> • Colored Dissolved Organic Matter Absorption at 440 nm (ag440)<br> • Single-Scattering Albedo of Detritus at 550 nm (A_d)<br> • Power Law Exponent of Detritus Attenuation (G_d)<br> • Water Salinity (Sal)<br> • Water Temperature (Temp)<br> • Fraction for Diminished Coccolithophore Absorption (a_frac)<br> • Fraction of Coccolithophore Group (cocco_frac)</p> <h2>Optical Water Types</h2> <p>The training dataset includes 10 pre-defined optical water types, with 10,000 samples for each type. Detailed descriptions of these water types can be found in Table 1 of Bi and Hieronymi (2024) or as follows,</p> <table> <tbody> <tr> <td>OWT</td> <td>Desciption</td> </tr> <tr> <td>1</td> <td>Extremely clear and oligotrophic indigo-blue waters with high reflectance in the short visible wavelengths.</td> </tr> <tr> <td>2</td> <td>Blue waters with similar biomass level as OWT 1 but with slightly higher detritus and CDOM content.</td> </tr> <tr> <td>3a</td> <td>Turquoise waters with slightly higher phytoplankton, detritus, and CDOM compared to the first two types.</td> </tr> <tr> <td>3b</td> <td>A special case of OWT 3a with similar detritus and CDOM distribution but with strong scattering and little absorbing particles like in the case of Coccolithophore blooms. This type usually appears brighter and exhibits a remarkable ~490 nm reflectance peak.</td> </tr> <tr> <td>4a</td> <td>Greenish water found in coastal and inland environments, with higher biomass compared to the previous water types. Reflectance in short wavelengths is usually depressed by the absorption of particles and CDOM.</td> </tr> <tr> <td>4b</td> <td>A special case of OWT 4a, sharing similar detritus and CDOM distribution, exhibiting phytoplankton blooms with higher scattering coefficients, e.g., Coccolithophore bloom. The color of this type shows a very bright green.</td> </tr> <tr> <td>5a</td> <td>Green eutrophic water, with significantly higher phytoplankton biomass, exhibiting a bimodal reflectance shape with typical peaks at ~560 and ~709 nm.</td> </tr> <tr> <td>5b</td> <td>Green hyper-eutrophic water, with even higher biomass than that of OWT 5a (over several orders of magnitude), displaying a reflectance plateau in the Near Infrared Region, NIR (vegetation-like spectrum).</td> </tr> <tr> <td>6</td> <td>Bright brown water with high detritus concentrations, which has a high reflectance determined by scattering.</td> </tr> <tr> <td>7</td> <td>Dark brown to black water with very high CDOM concentration, which has low reflectance in the entire visible range and is dominated by absorption.</td> </tr> </tbody> </table> <h2>Additional Information</h2> <p>The detailed description of the data simulation can be found in the supporting information of Bi and Hieronymi (2024). The models used for simulating the data are available on GitHub:</p> <p> • Component IOP Model: <a href="https://github.com/bishun945/IOPmodel" target="_blank" rel="noopener">Bio-geo-optical modelling of natural waters by Bi, Hieronymi, and Röttgers (2023)</a><br> • OWT Package: <a href="https://github.com/bishun945/pyOWT" target="_blank" rel="noopener">pyOWT</a></p> <h2>References</h2> <p> 1. OWT Framework: Bi, S., and Hieronymi, M. (2024). Holistic optical water type classification for ocean, coastal, and inland waters. Limnology & Oceanography, lno.12606. doi: 10.1002/lno.12606<br> 2. Component IOP Model: Bi, S., Hieronymi, M., and Röttgers, R. (2023). Bio-geo-optical modelling of natural waters. Front. Mar. Sci. 10, 1196352. doi: 10.3389/fmars.2023.1196352<br> 3. Pure Water IOP Model: Röttgers, R., Doerffer, R., McKee, D., and Schönfeld, W. (2016). The Water Optical Properties Processor (WOPP): Pure Water Spectral Absorption, Scattering and Real Part of Refractive Index Model. Technical Report No WOPP-ATBD/WRD6. Available at: https://calvalportal.ceos.org/tools<br> 4. Rrs Model: Lee, Z., Du, K., Voss, K. J., Zibordi, G., Lubac, B., Arnone, R., et al. (2011). An inherent-optical-property-centered approach to correct the angular effects in water-leaving radiance. Appl. Opt. 50, 3155. doi: 10.1364/AO.50.003155</p> <h2>Authors and Contact</h2> <p> • Author: Shun Bi, Martin Hieronymi, Rüdiger Röttgers<br> • Creator: Shun Bi, Shun.Bi@hereon.de</p> <h2>Example Python Code to Read Data</h2> <p>Here is an example of how to read the NetCDF data using Python and the <code>xarray</code> library:</p> <pre><code>import xarray as xr # Load the dataset data_hyper = xr.open_dataset("path_to_your_file/owt_BH2024_training_data_hyper.nc") # Print the dataset to see its structure print(data_hyper) # Access a specific variable, e.g., remote sensing reflectance (Rrs) rrs = data_hyper['Rrs'] # Plot a sample of Rrs import matplotlib.pyplot as plt # Select a sample ID, for example the first sample sample_id = 0 plt.plot(data_hyper['wavelen'], rrs[sample_id, :]) plt.xlabel('Wavelength (nm)') plt.ylabel('Rrs (1/sr)') plt.title(f'Remote Sensing Reflectance for Sample ID {sample_id}') plt.show()</code></pre>
LakeSST: Lake Skin Surface Temperatures in French inland water bodies
<p>The data set LakeSST contains skin surface temperature data for 442 French water bodies for the period 1999-2016 obtained from archives of Landsat 5 and Landsat 7 thermal infrared images. The overall accuracy of the satellite-derived temperature measurements is about 1.2 ºC, similar to other applications of satellite images to estimate freshwater surface temperatures. The spatial and temporal coverage of the data set makes it an ideal resource for studies on the temporal evolution of lake surface temperatures and for geographical studies of temperature patterns.</p>
Water Body Checklists 2019: Seto Inland Sea Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Seto Inland Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Water Body Checklists: Seto Inland Sea Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Seto Inland Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
OLIGOTREND, a global database of multi-decadal timeseries of chlorophyll-a and nutrient concentrations in inland and transitional waters, 1986-2023
The Oligotrend database is a collection of multi-decadal chlorophyll-a and nutrient timeseries in inland and transitional waters. The objective of this Data Package was to explore how inland and transitional aquatic ecosystems respond to oligotrophication trends. Overall, the Oligotrend L1 database is made of 4.3 million valid observations originating from 1,894 stations. There are 238, 687 and 969 stations located in estuaries, lakes and rivers, respectively. The top 3 largest sources of data are the French national water quality monitoring (775 stations), the global database of lake datasets from Naderian et al. 2024 (378 stations), and the Chesapeake Bay Program (199 stations). The data was harmonized through a reproducible data processing pathway. In this Data Package, quality-checked level L1 data is provided, together with data sources, geographical coordinates of the stations, and the output of a trend analysis of all timeseries (level L2).
FUI Water Color product of inland waters in China at 30-m in 2015
<p>The first 30-meters FUI water color product of China. The product was developed using time-series Landsat 8 imagery and FUI water color retrieval method. Taking into account the huge amount of computational and storage space required for the national-scale water color mapping, the high-performance Google Earth Engine (GEE) cloud-based platform was introduced to support the computation. First, a cloud-free composite in China for the summer of 2015 was generated using time-series Landsat-8 imagery and the Best-Available-Pixel (BAP) compositing algorithm. Then, the first 30-merters FUI water color product of China was developed using the generated BAP composite and the Google Earth Engine computing platform. The first 30-meters FUI water color product can promote the understanding of the water color of water bodies in China, and provide very important information for preserving and restoring inland water quality.</p>
Fig.ç6.Ec hinoderes ohtsukai sp. nov., holotype, male (ZIHU 3976), Nomarski photomicrographs. A, Segments 1 and 2, ventral view; B, segments 4 and 5, ventral view. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; lvt, lateroventral tubule; pac, pachycyclus; pf, pectinate fringe; rss, rounded sensory spot. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç6.Ec hinoderes ohtsukai sp. nov., holotype, male (ZIHU 3976), Nomarski photomicrographs. A, Segments 1 and 2, ventral view; B, segments 4 and 5, ventral view. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; lvt, lateroventral tubule; pac, pachycyclus; pf, pectinate fringe; rss, rounded sensory spot.
Fig.ç5.Ec hinoderes ohtsukai sp. nov., holotype, male (ZIHU 3976), Nomarski photomicrographs. A, Segments 1 and 2, dorsal view; B, segment 4, dorsal view. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; mds, middorsal spine; pac, pachycyclus; pf, pectinate fringe; ps, perforation site; rss, rounded sensory spot. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç5.Ec hinoderes ohtsukai sp. nov., holotype, male (ZIHU 3976), Nomarski photomicrographs. A, Segments 1 and 2, dorsal view; B, segment 4, dorsal view. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; mds, middorsal spine; pac, pachycyclus; pf, pectinate fringe; ps, perforation site; rss, rounded sensory spot.
Fig.ç7.Ec hinoderes ohtsukai sp. nov., paratype, female (ZIHU 3980), Nomarski photomicrographs. A, Segments 5 and 6, ventral view; B, segments 8 and 9, ventral view. Abbreviations: dss, droplet-shaped sensory spot; gco2, modi ed glandular cell outlet type II; lvt, lateroventral tubule; si, sieve plate; sp, sternal plate; tp, tergal plate. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç7.Ec hinoderes ohtsukai sp. nov., paratype, female (ZIHU 3980), Nomarski photomicrographs. A, Segments 5 and 6, ventral view; B, segments 8 and 9, ventral view. Abbreviations: dss, droplet-shaped sensory spot; gco2, modi ed glandular cell outlet type II; lvt, lateroventral tubule; si, sieve plate; sp, sternal plate; tp, tergal plate.
Fig.ç4.Ec hinoderes ohtsukai sp. nov., paratype, female (ZIHU 3983), scanning electron micrographs. A, Mouth cone, lateral view; B, introvert, lateral view. Abbreviations: oo, outer oral styles; sc, scalids; sp, spinoscalids; tr, trichoscalids. Digits a er the labels refer to introvert ring numbers. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç4.Ec hinoderes ohtsukai sp. nov., paratype, female (ZIHU 3983), scanning electron micrographs. A, Mouth cone, lateral view; B, introvert, lateral view. Abbreviations: oo, outer oral styles; sc, scalids; sp, spinoscalids; tr, trichoscalids. Digits a er the labels refer to introvert ring numbers.
Fig.ç3.Ec hinoderes ohtsukai sp. nov., scanning electron micrographs. A, B, Paratype, female (ZIHU 3983); C–E, paratype, male (ZIHU 3982). A, General habitus, lateral view; B, neck and segments 1–4, lateral view; C, enlargement of segment 7, lateral view; D, enlargement of segment 9, lateral view; E, enlargement of segments 10 and 11, lateroventral view. Abbreviations: ch, cuticular hair; dss, droplet-shaped sensory spot; gco2, modi ed glandular cell outlet type II; ldt, laterodorsal tubule; pf, pectinate fringe; po, pore; ps1, penile spine 1; ps2, penile spine 2; ps3, penile spine 3; rss, rounded sensory spot; si, sieve plate; ss, sensory spot. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç3.Ec hinoderes ohtsukai sp. nov., scanning electron micrographs. A, B, Paratype, female (ZIHU 3983); C–E, paratype, male (ZIHU 3982). A, General habitus, lateral view; B, neck and segments 1–4, lateral view; C, enlargement of segment 7, lateral view; D, enlargement of segment 9, lateral view; E, enlargement of segments 10 and 11, lateroventral view. Abbreviations: ch, cuticular hair; dss, droplet-shaped sensory spot; gco2, modi ed glandular cell outlet type II; ldt, laterodorsal tubule; pf, pectinate fringe; po, pore; ps1, penile spine 1; ps2, penile spine 2; ps3, penile spine 3; rss, rounded sensory spot; si, sieve plate; ss, sensory spot.
Fig.ç2.Ec hinoderes ohtsukai sp. nov., camera lucida drawings. A, B, Holotype, male (ZIHU 3976), entire animal, dorsal and ventral view, respectively; C, D, allotype, female (ZIHU 3977), segments 9–11, dorsal and ventral view, respectively. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; ldt, laterodorsal tubule; lts, lateral terminal spine; lvt, lateroventral tubule; mds, middorsal spine; ne, neck; ps, penile spine; rss, rounded sensory spot; si, sieve plate. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç2.Ec hinoderes ohtsukai sp. nov., camera lucida drawings. A, B, Holotype, male (ZIHU 3976), entire animal, dorsal and ventral view, respectively; C, D, allotype, female (ZIHU 3977), segments 9–11, dorsal and ventral view, respectively. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; ldt, laterodorsal tubule; lts, lateral terminal spine; lvt, lateroventral tubule; mds, middorsal spine; ne, neck; ps, penile spine; rss, rounded sensory spot; si, sieve plate.
Fig.ç1.M aps and photograph showing the sampling locality for Echinoderes ohtsukai sp. nov. A, Map of eastern Asia; B, enlargement of the rectangle in A; C, enlargement of the area indicated by the black circle in B; D, photograph of the sampling locality; white arrow indicates the Kamogawa River and dotted circle indicates the sampling site. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç1.M aps and photograph showing the sampling locality for Echinoderes ohtsukai sp. nov. A, Map of eastern Asia; B, enlargement of the rectangle in A; C, enlargement of the area indicated by the black circle in B; D, photograph of the sampling locality; white arrow indicates the Kamogawa River and dotted circle indicates the sampling site.
Fig.ç8.Ec hinoderes ohtsukai sp. nov., holotype, male (ZIHU 3976), Nomarski photomicrographs. A, Segments 10 and 11, dorsal view; B, segments 10 and 11, ventral view. Abbreviations: ldt, laterodorsal tubule; lts, lateral terminal spine; ps1, penile spine 1; ps2, penile spine 2. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç8.Ec hinoderes ohtsukai sp. nov., holotype, male (ZIHU 3976), Nomarski photomicrographs. A, Segments 10 and 11, dorsal view; B, segments 10 and 11, ventral view. Abbreviations: ldt, laterodorsal tubule; lts, lateral terminal spine; ps1, penile spine 1; ps2, penile spine 2.
Figure 20 in An updated and annotated checklist of the Malacostraca (Crustacea) species inhabited Turkish inland waters
Figure 20. Distribution of the members of Hippolytidae, Atyidae, Crangonidae and Palaemonidae in Turkish inland waters.
Figure 18 in An updated and annotated checklist of the Malacostraca (Crustacea) species inhabited Turkish inland waters
Figure 18. Distribution of the members of Diogenidae, Grapsidae, Astacidae, Eriphiidae, Carcinidae and Portunidae in Turkish inland waters.
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.