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.
1,133
datasets available to search
ShareScore release 0.7.1
Dataset results
1,133 results for “Wetland”
UCSB SONGS Mitigation Monitoring: Wetland Performance Standard - Spartina Canopy
These data describe annual estimates of Spartina foliosa canopy architecture (measured as proportion of stems > 3 ft long) from four locations at two coastal wetlands as part of the SONGS San Dieguito Wetland Restoration mitigation monitoring program to track long-term patterns in Spartina size structure. This study began in 2012 in the San Dieguito Wetlands and Tijuana Estuary in San Diego County, CA. Beginning in 2024, Tijuana Estuary was replaced with Mugu Lagoon in Ventura County, CA.
UCSB SONGS Mitigation Monitoring: Wetland Performance Standard - Water Quality
These data describe annual estimates of wetland water quality, measured as the average duration of hypoxia (time dissolved oxygen concentration below 3 mg/l), collected as part of the SONGS San Dieguito Wetland Restoration mitigation monitoring program designed to evaluate compliance of the restoration project with conditions of the SONGS permit. This study began in 2012 in the San Dieguito Wetlands and Tijuana Estuary in San Diego County, CA, Carpinteria Salt Marsh in Santa Barbara County, CA, and Point Mugu Lagoon in Ventura County, CA. Beginning in 2024, Tijuana Estuary was replaced with Los Penasquitos Lagoon in San Diego County, CA.
UCSB SONGS Mitigation Monitoring: Wetland Performance Standard - Tidal Prism
These data describe estimates of tidal prism at the San Dieguito Wetland as part of the SONGS San Dieguito Wetland Restoration mitigation monitoring program to track long-term patterns of tidal prism. This study began in 2012.
UCSB SONGS Mitigation Monitoring: Wetland Performance Standard - Plant Reproductive Success
These data describe annual estimates reproductive success (measured by seed set) of salt marsh plants at the San Dieguito Wetland as part of the SONGS San Dieguito Wetland Restoration mitigation monitoring program designed to track long-term patterns in reproductive success of wetland plants. Monitoring began in 2012.
Periphyton, hydrological and environmental data in a coastal freshwater wetland (FCE), Florida Everglades National Park, USA (2014-2015)
The characteristic, calcareous periphyton mats of the Everglades, and particularly their diatom assemblages, provide an ideal community to study the patterns and mechanisms of community assembly along environmental gradients with ecotones. Understanding patterns and mechanisms of diatom community assembly along salinity and P gradients can be incorporated into tools for predicting changes in these gradients, and the location and movement of the "white zone" ecotone, caused by saltwater intrusion and water management outcomes in the Southern Everglades. Patterns of environmental variation and periphytic-diatom community structure along the freshwater-marine gradient of Everglades National Park, FL., USA were examined by sampling along a series of 7 transects extending from oligotrophic, freshwater marshes through the ecotone and down to the northern edge of the fringing mangrove forests. Seven transects spanning the west-east extent of the southeast Everglades, from the Main Park Road in the west to the Model Lands in the east, were sampled once in the dry season (May) and once in the wet season (November) of 2014 and 2015. These data are published in "Mazzei and Gaiser. 2018. Diatoms as tools for inferring ecotone boundaries in a coastal freshwater wetland threatened by saltwater intrusion. Ecological Indicators. 88:190-204."
Throw trap and electrofishing data collected during 1996–2022 from the Everglades, Florida, United States for the publication "Hydrology-mediated ecological function of a large wetland threatened by an invasive predator"
Asian swamp eels (Monopterus albus/javanensis complex) were first reported from Florida in 1997 and the Everglades in 2007; swamp eels have been established in Taylor Slough of Everglades National Park since 2014. This dataset incorporates plot-level mean densities (# of individuals per square meter) of common aquatic animals collected during 1996–2022 from 24 sites across four regions of the Everglades: Taylor Slough, Shark River Slough, Water Conservation Area 3, and the C-111 Panhandle. Prey species included are the six most common small fishes prior to swamp eel invasion of Taylor Slough (1996–2009) and the three common decapod species (two crayfish species and grass shrimp). An annual index of mean wet season electrofishing catch-per-unit-effort of swamp eels, Mayan cichlids (Mayaheros uruphthalmus), and the three other large 'top predator' fishes (Amia calva, Lepisosteus platyrhincus, Micropterus salmoides) is included for plots where electrofishing was performed from 1997-2021. Hydrologic measures used in analyses are included.
Stomach contents (1977-1981) and stable isotopes (1994) from the Everglades, Florida, USA from the publication "Fishes in a seasonally pulsed wetland show spatiotemporal shifts in diet and trophic niche but not shifts in trophic position"
Stomach contents of fishes (1977-1981) and stable isotopes of fishes, invertebrates, and basal resources (1994) were collected from spikerush marsh, sawgrass ridge, and alligator pond habitats in Shark River Slough, Everglades National Park, Florida, USA. These data were used to quantify diet, trophic niche area, trophic position, basal resource use and how these metrics vary among size classes, seasons, and habitats. Data collection is complete. These data support Flood et al. (2023). Associated R code will be made available through Peter Flood's GitHub: https://github.com/pjflood/historic_everglades_aquatic_food_web. References: Flood, Peter J., William F. Loftus, and Joel C. Trexler. "Fishes in a seasonally pulsed wetland show spatiotemporal shifts in diet and trophic niche but not shifts in trophic position." Food Webs 34 (2023): e00265. https://doi.org/10.1016/j.fooweb.2022.e00265
Environmental and periphyton composition data from Biscayne Bay Coastal Wetlands, Florida, USA, July 2022 - November 2022
Environmental and periphyton data were collected from transects in the Biscayne Bay Coastal Wetlands (BBCW) during the wet and dry seasons of 2022 to investigate the rate of carbonate sediment production by periphyton. Environmental data include surface water metrics (pH, salinity, conductivity, and water depth) and soil depths. Periphyton data include nutrient, production, and diatom species composition in samples collected from artificial substrates (periphytometers) placed in the field. Data collection for this project is complete, although the South Florida Management District continues to monitor these transects for a larger ongoing BBCW project.
Sulfate reductions rates in alpine wetlands, 2021.
Alpine ecosystems serve as crucial water resources for many areas of the world, and biogeochemical cycling in these regions can influence the chemistry of water flowing into downslope watersheds. Alpine and subalpine wetlands are understudied systems of particular interest since lowland wetlands are known to have high rates of biogeochemical activity that can disproportionally affect carbon (C) and nutrient uptake, sequestration, and transformations within the landscape. Wetland processes play a central role in sulfur (S) transformations and have conditions that can support sulfate reduction. Sulfate reduction determines the sequestration of S in wetlands and interacts closely with a multitude of other element cycles, including iron, carbon, nitrogen, and mercury. As alpine systems warm due to climate change, it is important to characterize the biogeochemical processes at these sites to predict how they may shift in response. Sulfate reduction rates were measured in three wetlands at the Niwot Ridge Long Term Ecological Research site. A new radioactive tracer method was adapted and streamlined to suit alpine soils. This work trials and assesses various methodological approaches, as well as documents sulfate reduction rates from these sites, the first time this process has been measured at Niwot Ridge. Reduction rates at one site were measured three times, to track changes across the Summer 2021 field season.
Alpine and subalpine wetland soil physicochemical characteristics, summer 2020.
To understand patterns in soil biogeochemistry of wetlands at Niwot Ridge, samples were collected and analyzed for a series of physicochemical characteristics during 2020-2021. Samples were collected from 8 wetland sites lying at different elevations from the Saddle into the subalpine. At each site, samples were collected at 4 depth intervals within 5 sampling nodes along transects from the dry edge to saturated center of each system. These soils were analyzed for a suite of physicochemical characteristics, including soil moisture, bulk density, extractable nitrate and ammonium, loss on ignition as a proxy for organic carbon content, pH, total carbon and nitrogen, and adsorbed sulfate. Data will be used to inform future studies on biogeochemistry patterns and processes in alpine wetlands and across the Niwot landscape.
Surface and porewater chemistry and sulfur stable isotpes for alpine and subalpine wetland sites, 2021.
To understand patterns in aqueous biogeochemistry of wetlands at Niwot Ridge, samples were collected and analyzed for dissolved anions and stable isotopes of sulfur in 2021. Samples were collected from eight wetland sites representing three wetland types, from the Saddle into the subalpine. At each site, tension lysimeters were placed at visible surface inflow and outflow paths to collect porewater. Water samples were collected from the tension lysimeters and surface pools during four time points through the summer season. Water samples were measured for a suite of dissolved anions using ion chromatography, including dissolved chloride, nitrate, and sulfate. Samples were also measured for dissolved organic carbon. Sulfur from one surface water sample from each site at each time point was precipitated as barium sulfate, and precipitations were analyzed for stable sulfur isotope ratio (δ^34S-SO4^2-) at the Center for Stable Isotope Biogeochemistry at the University of California, Berkeley. Data was used in conjunction with soil biogeochemistry data from 2020 to evaluate patterns in reactants among wetland types.
Silver film response to sulfate reduction activity in alpine and subalpine wetlands, 2022.
Alpine ecosystems serve as crucial water resources for many areas of the world, and biogeochemical cycling in these regions can influence the chemistry of water flowing into downslope watersheds. Alpine and subalpine wetlands are understudied systems of particular interest since lowland wetlands are known to have high rates of biogeochemical activity that can disproportionally affect carbon (C) and nutrient uptake, sequestration, and transformations within the landscape. Wetland processes play a central role in sulfur (S) transformations and have conditions that can support sulfate reduction. Sulfate reduction determines the sequestration of S in wetlands and interacts closely with a multitude of other element cycles, including iron, carbon, nitrogen, and mercury. Previous work in Niwot alpine and subalpine wetlands noted large variability in sulfate reduction rates within wetland soils. Samples taken less than a meter away from each other sometimes showed almost 70x higher or lower rates (Rea, unpublished work). This work sought to adapt a silver film method to quantify sulfate reduction rates over small-scale spatial areas. The method proved valuable as a quick indicator of sulfate reduction activity and was able to visualize soil heterogeneity. However, the silver films were not sensitive enough to quantify sulfate reduction rates in situ.
Electron shuttling capacity and greenhouse gas production of soils for three high-elevation wetlands at Niwot Ridge, 2024.
High-elevation wetlands are important indicators of how mountain ecosystems may respond to global climate change. These wetlands also act as locations of disproportionate biogeochemical processing on the landscape, but they remain relatively understudied compared to lowland wetlands. This study aimed to characterize redox-active organic matter (RAOM) reduction, a known key control on carbon cycling in high-latitude peatland ecosystems, to better understand biogeochemical cycling in high elevation wetlands and carbon greenhouse gas production at Niwot Ridge LTER. Soils were collected from three different types of wetlands, a subalpine wetland, a periglacial solifluction lobe, and an alpine wet meadow. Samples were incubated at a common temperature in the laboratory to measure RAOM reduction, carbon dioxide production, and methane production over 63-d. This dataset reports the electron shuttling values, a measure of RAOM reduction, and the greenhouse gas production over the incubation period.
Year 2019-2021, 15 minute measurements of stage, water temperature in a small headwater stream draining draining a mainly forested catchment (55% forest + 19% wetland), Cart Cr., Newbury, MA.
Year 2019, 2020, and 2021 continuous measurements, every 15 minutes, were made of stage, water temperature in Cart Creek, Newbury, MA, a small headwater stream draining a mainly forested catchment (55% forest + 19% wetland) in the Parker River watershed. Discharge is determined from stage using discharge vs stage regressions.
Spatial distribution data set of wetlands in Baiyangdian Basin
<p>As one of the wetland systems in the northern plain of China, Baiyangdian plays a key role in ensuring the water resources security and good ecological environment of Xiong'an New Area. Understanding the current situation of the wetland ecosystem in Baiyangdian basin is also of great significance for the construction of the New Area and future scientific planning. Based on the 10 meter spatial resolution sentinel-2B image provided by ESA in September 2017, combined with Google Earth high resolution satellite image (resolution 0.23m), the network distribution map and water system distribution map of Baiyangdian basin wetland ecosystem in 2017 were drawn by artificial visual interpretation and machine automatic classification It provides the basis for the study of the connectivity (including hydrological connectivity and landscape connectivity).</p> <p>The boundary of Baiyangdian basin in this data set is from the basic geographic information map of Baiyangdian basin provided by Zhou Wei and others. The DEM is the GDEM digital elevation data with 30m resolution. The original image data of wetland remote sensing classification comes from the sentinel-2b remote sensing image provided by ESA on September 20, 2017. This data set uses the second, third, fourth and eighth bands of 10 meter resolution in the image, carries out radiation calibration, mosaic, mosaic and other preprocessing operations in SNAP and ArcGIS 10.2 software, and carries out supervised classification in ENVI 5.3 software. The data used for river channel extraction is based on Google Earth high resolution satellite images.</p> <p>The research and development steps of this dataset include: preprocessing sentinel-2B image, establishing wetland classification system and selecting samples, mapping the latest wetland ecosystem network distribution map of Baiyangdian basin by support vector machine classification; obtaining river network of Baiyangdian basin by visual interpretation based on Google Earth high resolution satellite image (resolution 0.23m).</p> <p>The spatial distribution data set of Baiyangdian Wetland includes vector data and raster data: (1) Baiyangdian basin boundary data (. SHP); Baiyangdian basin river network data (. shp); (2) Baiyangdian basin land use / cover classification data (including the classification data of the study area and the river 3 km buffer) (. tif); Baiyangdian basin constructed wetland and natural wetland distribution map (. shp); Baiyangdian basin slope map (. tif).</p> <p>According to the river network map of Baiyangdian basin obtained by manual visual interpretation, the total length of the river in Baiyangdian basin is about 2440 km and the total area is 514 km2. Among them, there are 177 km2 river channels in mountainous area, 866 km in length, distributed in Northeast southwest direction, mostly at the junction of forest land and cultivated land; and 337 km2 river channels in plain area, 1574 km in length.</p> <p>Baiyangdian basin is divided into eight types of land use / cover: river, flood plain, lake, marsh, ditch, cultivated land, forest land and construction land. The remote sensing monitoring results show that the wetland area of Baiyangdian basin accounted for 13.90 % in 2017. Among all wetland types, the area of marsh is the largest, followed by the area of flood plain, ditch accounts for about 1%, and the proportion of lake and river is less than 0.5%. Combined with the land use / cover classification map and the distribution of slope and elevation, it can be seen that nearly 60% of the area of woodland is distributed in 10 ° to 30 ° mountain area, and the rest of the land use / cover types are mainly distributed in 0 ° to 2 ° area. The elevation statistics show that nearly 80% of the lakes and large reservoirs are distributed in the height of 100 m to 300 m, the distribution of marsh is relatively uniform, mainly in the high altitude area of 20 m to 300 m, the types of construction land, flood area and cultivated land are mainly concentrated in the area of 20 m to 100 m, and rivers and ditches are mainly concentrated in the area of 0 m to 100 m.</p> <p>Based on the classification results of land use / cover within the river, it can be found that the main land use type is wetland. Specifically, the types of swamp, flood area and lake are the most, while the types of ditch and river are less. With the increase of the buffer area, the proportion of non wetland type gradually increased, while the proportion of wetland type gradually decreased. The main wetland types in 1-3km buffer zone on both sides of the river are swamp and flood zone. It is worth noting that nearly one third of the River belongs to cultivated land, that is, the river occupation is serious. In terms of area, about 1 / 3 rivers and 3 / 4 lakes are distributed in the river course. Most of the water bodies in the river course are controlled by human beings, but the marsh area in the river course only accounts for about 3% of the marsh area in the whole river course.</p> <p>River occupation will not only directly reduce the connectivity of wetlands in the basin, but also cause some environmental and economic problems such as water pollution. However, if the connectivity of wetlands is reduced, the ecological and environmental functions of wetlands will be destroyed, which will pose a great threat to the water security of the basin. Taking Baiyangdian basin as a whole, improving the connectivity of wetlands and enhancing the ecological and environmental functions of wetlands in the basin will help to improve the water ecological and environmental security of xiong'an new area and Baiyangdian basin.</p>
Change detection technique comparison in long-term wetland monitoring: datasets and maps of the Poitevin Marsh (France)
<h3>For a full description of the methodology and results, please see the following article:</h3> <div> <div>Demarquet, Q., Rapinel, S., Gore, O., Dufour, S., Hubert-Moy, L., 2024. Continuous change detection outperforms traditional post-classification change detection for long term monitoring of wetlands. <em>International Journal of Applied Earth Observation and Geoinformation </em>133, 104142. <a href="https://doi.org/10.1016/j.jag.2024.104142">https://doi.org/10.1016/j.jag.2024.104142</a></div> <div> </div> <div>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> </div> <h3># Datasets</h3> <p>Points datasets are projected in WGS84 (EPSG:4326), and are provided in the open source GeoPackage format.</p> <p>The first dataset (<strong>Dataset_1.gpkg</strong>) contains training and validation points for random forest classification of EUNIS habitats in the Poitevin Marsh. This dataset consists of 3360 training and 840 validation points (total: 4200).<br>Fields description:</p> <ul> <li>"<em>ID</em>": unique identifier</li> <li>"<em>CLASS</em>": EUNIS first level habitat type, classified as following:<br> <ul> <li>1: EUNIS habitat A</li> <li>2: EUNIS habitat B</li> <li>3: EUNIS habitat C1J5</li> <li>4: EUNIS habitat C3</li> <li>5: EUNIS habitat E</li> <li>6: EUNIS habitat G</li> <li>7: EUNIS habitat I</li> <li>8: EUNIS habitat J</li> </ul> </li> <li>"<em>DATE</em>": Date associated with EUNIS habitat sample</li> <li>"<em>LON</em>": Point longitude in decimal degrees</li> <li>"<em>LAT</em>": Point latitude in decimal degrees</li> <li>"<em>TYPE</em>": Either training ("<em>train</em>") or validation ("<em>test</em>") sample</li> </ul> <p>The second dataset (<strong>Dataset_2.gpkg</strong>) contains points for the Olofsson correction method. This dataset consists of 326 points where the change classes are classified as following: -10 (wetland loss), 10 (wetland gain), 100 (stable existing wetland), and 200 (stable damaged wetland).<br>Fields description:</p> <ul> <li>"<em>ID</em>": unique identifier</li> <li>"<em>LON</em>": Point longitude in decimal degrees</li> <li>"<em>LAT</em>": Point latitude in decimal degrees</li> <li>"<em>REFERENCE</em>": Change class reference</li> <li>"<em>CCDC</em>": Change class obtained from the Continuous Change Detection and Classification approach</li> <li>"<em>PCCD</em>": Change class obtained from the Post-Classification Change Detection approach</li> </ul> <p>Supplementary layout files (<strong>Dataset_1.qml</strong> and <strong>Dataset_2.qml</strong>) support formatting of the points in QGIS software.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <h3># EUNIS habitat</h3> <p>Maps are projected in WGS84 (EPSG:4326), and are provided in the GeoTiff format at 30m of spatial resolution. </p> <p>Habitat maps are given for the two approaches in years 1984 and 2022:</p> <ul> <li>CCDC: Continuous Change Detection and Classification (<strong>CCDC_HABITAT_1984.tif</strong> and <strong>CCDC_HABITAT_2022.tif</strong>)</li> <li>PCCD: Traditional post-classification approach (<strong>PCCD_HABITAT_1984.tif </strong>and <strong>PCCD_HABITAT_2022.tif</strong>)</li> </ul> <p>Supplementary layout files (<strong>CCDC_HABITAT_1984.qml, CCDC_HABITAT_2022.qml, PCCD_HABITAT_1984.qml, PCCD_HABITAT_2022.qml</strong>) support formatting of raster layers in QGIS software.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <h3># Change detection during the 1984-2022 period</h3> <p>Maps are projected in WGS84 (EPSG:4326), and are provided in the GeoTiff format at 30m of spatial resolution. Raster values follow the classification scheme used in Dataset_2.</p> <p>Change detection maps are given for the two approaches:</p> <ul> <li>CCDC: Continuous Change Detection and Classification (<strong>CCDC_CHANGE_1984_2022.tif</strong>)</li> <li>PCCD: Traditional post-classification approach (<strong>PCCD_CHANGE_1984_2022.tif</strong>)</li> </ul> <p>Supplementary layer files (<strong>CCDC_CHANGE_1984_2022.qml</strong> and<strong> PCCD_CHANGE_1984_2022.qml</strong>) support formatting of the raster layers in QGIS software.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <h3># GEE repository</h3> <p>To get direct access to GEE scripts and assets, please follow those two links:</p> <p>https://code.earthengine.google.com/?accept_repo=users/demarquetquentin/CCDC_Poitevin</p> <p>https://code.earthengine.google.com/?asset=projects/ee-quen-dem/assets/CCDC_Poitevin</p>
2019_Herbaceous_Wetlands_Copernicus
<p>Copernicus global land cover, herbaceous wetlands, 100m, and proportion herbaceous wetland (1km) in 2019. </p> <p><strong>Abstract</strong>:</p> <p>Landuse/landcover datasets are provided through the Copernicus climate data service, (Buchhorn, M.; Smets, B.; Bertels, L.; De Roo, B.; Lesiv, M.; Tsendbazar, N.E., Linlin, L., Tarko, A. (2020): Copernicus Global Land Service: Land Cover 100m: Version 3 Globe 2015-2019: Product User Manual; Zenodo, Geneve, Switzerland, September 2020; doi: 10.5281/zenodo.3938963).</p> <p>This 100m resolution product has been windowed to the MOOD extent (erprobaherbwet100m.tif). and then aggregated to 1km resolution version which contains the proportion of each pixel that is assigned as herbaceous wetland (erprobapropherbwet1km.tif)</p> <p> </p> <p><strong>File naming scheme:</strong> </p> <p>This 100m resolution product has been windowed to the MOOD extent (erprobaherbwet100m.tif). and then aggregated to 1km resolution version which contains the proportion of each pixel that is assigned as herbaceous wetland (erprobapropherbwet1km.tif)</p> <p><strong>Projection + EPSG code:</strong><br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p><strong>Spatial extent:</strong><br>Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716</p> <p><strong>Spatial resolution:</strong><br>100-meter and 1000-meter</p> <p><strong>Temporal resolution:</strong><br>The year 2019</p> <p><strong>Pixel values:</strong><br> The proportion of each pixel that is assigned as herbaceous wetland</p> <p><strong>Source: </strong><br>The Copernicus climate data service</p> <p><strong>Software used:</strong><br>The software used for map production is ESRI ArcMap 10.8</p> <p><strong>License: </strong>CC-BY-SA 4.0<br><strong>Processed by:</strong><br>ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project</p>
Low-Cost Sensors and Multitemporal Remote Sensing for Operational Turbidity Monitoring in an East African Wetland Environment - Measurements and Locations
<p>Many wetlands in East Africa are farmed and wetland reservoirs are used for irrigation, livestock, and fishing. Water quality and agriculture have a mutual influence on each other. Turbidity is a principal indicator of water quality and can be used for, otherwise, unmonitored water sources. Low-cost turbidity sensors improve in situ coverage and enable community engagement. The availability of high spatial resolution satellite images from the Sentinel-2 multispectral instrument and of bio-optical models, such as the Case 2 Regional CoastColor (C2RCC) processor, has fostered turbidity modeling. However, these models need local adjustment, and the quality of low-cost sensor measurements is debated. We tested the combination of both technologies to monitor turbidity in small wetland reservoirs in Kenya. We sampled ten reservoirs with low-cost sensors and a turbidimeter during five Sentinel-2 overpasses. Low-cost sensor calibration resulted in an R² of 0.71. The models using the C2RCC C2X-COMPLEX (C2XC) neural nets with turbidimeter measurements (R² = 0.83) and with low-cost measurements (R² = 0.62) performed better than the turbidimeter-based C2X model. The C2XC models showed similar patterns for a one-year time series, particularly around the turbidity limit set by Kenyan authorities. This shows that both the data from the commercial turbidimeter and the low-cost sensor setup, despite sensor uncertainties, could be used to validate the applicability of C2RCC in the study area, select the better-performing neural nets, and adapt the model to the study site. We conclude that combined monitoring with low-cost sensors and remote sensing can support wetland and water management while strengthening community-centered approaches.</p> <p>The provided dataset includes a point shapefile with the studied reservoirs in central Kenya and a data table with the sampling date (Sentinel-2 overpass plus/minus one day), low-cost sensor setup number, reservoir ID, sampling location within the reservoir, the voltage measurements of the three respective low-cost sensor heads for sensor setups A and B, the averaged voltage, and the turbidimeter measured turbidity value in nephelometric turbidity units (NTU).</p> <p>The study is available in (please cite):</p> <div> <div>Steinbach, S., Rienow, A., Chege, M.W., Dedring, N., Kipkemboi, W., Thiong’o, B.K., Zwart, S.J., Nelson, A., 2024. Low-Cost Sensors and Multitemporal Remote Sensing for Operational Turbidity Monitoring in an East African Wetland Environment. <em>IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing</em> <em>17</em>, 8490–8508. <a href="https://doi.org/10.1109/JSTARS.2024.3381756">https://doi.org/10.1109/JSTARS.2024.3381756</a></div> </div> <p>This research was supported in part by the German Federal Ministry of Education and Research (BMBF) through the Project “Participatory Approach to Environmental Conservation of the Muringato Catchment Area for Sustainable Management and Enhanced Ecosystem Health” (CITGI4Muringato) under Grant Agreement No. 01DG20022.</p>
Benthic diatoms of the Ebro Delta coastal wetlands (NW Mediterranean)
<p>This dataset includes 24 sites encompassing diatom counts and associated water chemistry parameters from wide range of wetland habitat types (coastal lagoons, salt and brackish marshes, shallow bays, microbial mats and nearshore marine waters) from the Ebro Delta (Spain). Diatom data represents sediment surface samples collected over three seasons (winter, spring and summer) from 2012-2013 years. The dataset has been formatted following the standards of the Tropical South American Diatom Database (<a href="https://zenodo.org/records/5721364">https://zenodo.org/records/5721364</a>)--a database constituent of Neotoma (<a href="https://www.neotomadb.org/">www.neotomadb.org</a>), a global community-curated database by regional experts for multiple types of paleoecological data--and as part of the project "DiatomS mEEt Databases: resources and practices to enable large-scale ecological research (SEED)" funded by the International Society for Diatom Research (<a href="https://isdr.org/early-career-networking-award/">https://isdr.org/early-career-networking-award/</a>).</p> <p> </p>
Plot Observations of Wetland Vegetation in Sub-Saharan Africa
<p>An R-Image containing plot-observations, Cocktail definitions and syntaxonomy using the packages <a href="https://docs.ropensci.org/taxlist/">taxlist</a> and <a href="https://github.com/kamapu/vegtable">vegtable</a>.</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.