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123 results for “environmental water”
Data for: Water system simulation modeling with hydropower optimization and environmental flows: An example with Pywr
<p>This dataset was used in the CenSierraPywr model created for the project "Optimizing Hydropower Operations While Sustaining Ecosystem Functions in a Changing Climate", for the California Energy Commission. Specifically, this data is to support reproducibility of the article describing the basic methods (Rheinheimer et al., in review). The model was built in Pywr, an open-source, linear programming-based Python package for modeling basin-scale water systems in the Central Sierra Nevada, California. Here, we focus on the Stanislaus and Upper San Joaquin River basins as they have high elevation hydropower typically operated to maximize revenue. CenSierraPywr consists of daily water allocations that include both hydroeconomic drivers for hydropower and more advanced environmental flows. Piecewise linear electricity prices from simulated hourly price data are used to drive discretionary hydropower, while environmental flows include the addition of ramping rates. Hydrological inputs include runoff data at the sub-basin level, based on the historical (1950 to 2011) daily gridded (1/16 degree) runoff data generated by the Variable Infiltration Capacity (VIC) hydrologic model developed by Livneh et al. (2013), forced with observed meteorological data and bias-corrected using local gauge data. All data inputs for reproducibility of CenSierraPywr for the Stanislaus and Upper San Joaquin Rivers are included, including original and preprocessed electricity and hydrological data and management-related data specific to certain hydropower projects or facilities.</p>
Figure 1 in Shallow-water polychaete assemblages in the northwestern Mediterranean Sea and its possible use in the evaluation of good environmental state
Figure 1. (Upper left graph) Map of the studied zone. Blue circles represent sampled stations from the Gulf of Lions and red circles from the Northern Mediterranean Spanish coast. (Lower graph) Schematic diagram showing the distribution of the four studied communities in the mesoscale studied area
Figure 7 in The relationship between Sardinella aurita landings and the environmental factors in Moroccan waters (21°-26°N)
Figure 7. – GAM smoothing curves fit- ted to effects of SST, Chl-a, and UI on S. aurita landings at latitude 22°N. The solid lines are the estimated smoother and the dashed lines represent 95% confidence intervals around the main effects. The black lines at the bottom of each plot indicate where the data values lie.
Figure 5 in Interactions between environmental factors and the mesozooplankton community from the Romanian Black Sea waters
Figure 5. Analysis of the main components (PCA) for the environmental factors from the Romanian Black Sea, by sector and season, 2013–2020.
Figure 6 in Interactions between environmental factors and the mesozooplankton community from the Romanian Black Sea waters
Figure 6. Statistically significant correlation (p <0.05) between the mesozooplankton groups and environmental factors grouped by isobaths, 2013–2020.
Safe and Just Earth Systems Boundaries for Surface Water: Hydrologic Alteration of Environmental Flows
<p>Title: <strong>Safe and Just Earth Systems Boundaries for Surface Water: Hydrologic Alteration of Environmental Flows</strong> Author: Pamela A. Green (<a href="mailto:pg@pamelaagreen.com">pg@pamelaagreen.com</a>), Advanced Science Research Center, CUNY, New York, NY USA <a href="https://orcid.org/0009-0006-7803-8182">https://orcid.org/0009-0006-7803-8182</a></p> <p>The python Jupyter Notebook <strong>SafeJustEarthSysBnd_EstressCUNY-Griffith2022-23.ipynb</strong> and accompanying data sets represent spatial modelling for development of the safe and just surface water target for Working Group 3 of the Earth Commission for the Earth Commission Long Report and the "Safe and Just Earth Systems Boundaries" publication. The surface water target includes spatial modelling of the extent of global-scale hydrological alteration of environmental flows.</p> <p>All input datasets required to run the model are located under the <strong>ModelInput</strong> folder with raster data in zipped format to minimize space requirements. The code extracts the zipped files and then deletes the uncompressed files upon completion. All model outputs are located under the <strong>ModelOutput</strong> folder.</p> <p>Please reference the <strong>README.xlsx</strong> file for a full listing of the model input and output data files.</p>
Figure 5 in First data on water mite (Acari, Hydrachnidia) assemblages of Point Rosa Marsh, Harrison Township, Michigan, USA, and their use as environmental bioindicators of aquatic health
Figure 5 Frequency of water mite genera collected from habitats surrounding Point Rosa Marsh including Lake St. Clair. Comparable samples were collected on ten collection dates during 2017, 2018 and 2019. Graphs are arranged (left to right, and then by row) in the order of the overall frequency of each genus. Each bar graph shows the number of taxa collected on the six collection dates with bars color-coded to assist in comparing graphs on various dates. Dark blue (B) [Oct. 18 2017], red (A) [Oct. 20 2017 (1)], light green (B) [Oct. 20 2017], dark green (D&C) [Oct. 27 2017], black (A) [Aug. 7 2018], orange (A) [Aug. 21 2018], grey (D&C) [Aug. 31 2018], yellow (A) [Sept. 16 2019], light
Figure 4 in First data on water mite (Acari, Hydrachnidia) assemblages of Point Rosa Marsh, Harrison Township, Michigan, USA, and their use as environmental bioindicators of aquatic health
Figure 4 Frequency of water mite genera collected in Point Rosa Marsh. Comparable samples were collected on nine collection dates during 2017, 2018 and 2019. Graphs are arranged (left to right, and then by row) in the order of the overall frequency of each genus. Each bar graph shows the number of taxa collected on the six collection dates with bars color-coded to assist in comparing graphs on various dates. Dark blue (1&2) [Oct. 18 2017], red (4) [Oct. 19 2017], light green (3) [Oct. 18 2017 (2)], no data (1&2) [Aug. 7 2018], black (1&2) [Aug. 21 2018],
Data for "Sr/Ca in shells of laboratory-grown bivalves (Arctica islandica) serves as a proxy for water temperature – Perspectives for (paleo)environmental research?"
<p>This repository contains all data generated for the publication "Sr/Ca in shells of laboratory-grown bivalves (<em>Arctica islandica</em>) serves as a proxy for water temperature – Perspectives for (paleo)environmental research?" currently under review.</p>
Distribution System Environmental and Sequencing Datasets for Assessing the Impacts of Lead Corrosion Control on the Microbial Ecology and Abundance of Drinking Water Associated Pathogens in a Full-Scale Drinking Water Distribution System
<p>The dataset of environmental parameters and sequence fastqs used to create figures and do analysis in the paper <strong>Assessing the Impacts of Lead Corrosion Control on the Microbial Ecology and Abundance of Drinking Water Associated Pathogens in a Full-Scale Drinking Water Distribution System </strong>submitted to Environmental Science & Technology</p>
Data for fitness analyses used in: Environmentally-induced DNA methylation is inherited across generations in water fleas (Daphnia magna)
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Genomic and environmental influences on resilience in a cold‐water fish near the edge of its range
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Data from: Sorting states of environmental DNA: Effects of isolation method and water matrix on recovery of membrane-bound, dissolved, and adsorbed states of eDNA
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Data for: Water system simulation modeling with hydropower optimization and environmental flows: An example with Pywr
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Different approaches to processing environmental DNA samples in turbid waters have distinct effects for fish, bacterial and archaea communities
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Semi-quantitative microcystin concentration measured in surface water samples collected by the Citizen-Led Environmental Observatory (CLEO) from multiple nearshore sites in Lake Lillinonah, Connecticut, USA, 2015-2018
Included in this data package are cyanobacterial toxin data from the Citizen-Led Environmental Observatory (CLEO), a volunteer water quality monitoring program run by Friends of the Lake (FOTL, friendsofthelake.org) and Fairfield University at Lake Lillinonah, Connecticut, USA. The program has been operational since 2008 (toxin data available form 2015-2018). Trained volunteer monitors collect routine surface water samples from multiple nearshore sites twice a month from Memorial Day through Labor Day. In addition, spot samples are taken whenever a significant algae bloom is present at the collection site. These samples are analyzed for microcystin concentrations. Routine water samples are also analyzed for levels of the total nitrogen and phosphorus concentrations. Additionally, CLEO volunteers collect data on water temperature, Secchi disk depth, water color, presence of floating woody debris, recreation potential, trash, particle type and surface scum every 1-3 days during the same period. These data are available in EDI packages EDI567 (general water quality) and EDI568 (nutrients).
Genome-scale target capture of mitochondrial and nuclear environmental DNA from water samples
<p>Environmental DNA (eDNA) provides a promising supplement to traditional sampling methods for population genetic inferences, but current studies have almost entirely focused on short mitochondrial markers. Here, we develop one mitochondrial and one nuclear set of target capture probes for the whale shark (<i>Rhincodon typus</i>) and test them on seawater samples collected in Qatar to investigate the potential of target capture for eDNA-based population studies. The mitochondrial target capture successfully retrieved ~235x (90x-352x per base position) coverage of the whale shark mitogenome. Using a minor allele frequency of 5%, we find 29 variable sites throughout the mitogenome, indicative of at least five contributing individuals. We also retrieved numerous mitochondrial reads from an abundant non-target species mackerel tuna<i> </i>(<i>Euthynnus affinis</i>), showing a clear relation between sequence similarity to the capture probes and the number of captured reads. The nuclear target capture probes retrieved only few reads and polymorphic variants from the whale shark, but we successfully obtained millions of reads and thousands of polymorphic variants with different allele frequencies from <i>E</i>. <i>affinis</i>. We demonstrate that target capture of complete mitochondrial genomes and thousands of nuclear loci is possible from aquatic eDNA samples. Our results highlight that careful probe design, taking into account the range of divergence between target and non-target sequences as well as presence of non-target species at the sampling site, is crucial to consider. Environmental DNA sampling coupled with target capture approaches provide an efficient means with which to retrieve population genomic data from aggregating and spawning aquatic species.</p>
Odonata records and environmental data from 20 ponds in the Eddleston Water catchment (Scotland, summer 2023)
<p>This set contains data collected in 2023 in the Eddleston Water catchment for a study on the contribution of flood management ponds to dragonfly (Insecta: Odonata) diversity. Ten ponds created for Natural Flood Management as part of the <a href="https://tweedforum.org/our-work/projects/the-eddleston-water-project/">Eddleston Water Project</a> (funded by the Scottish Government and led by Tweed Forum) and ten reference ponds were surveyed. </p> <p>The following files are available (in both .csv and .txt formats):</p> <p>- "Odonata_AllStages" contains all Odonata data;<br>- "PondEnv" contains information on the surveyed ponds, including basic environmental data (e.g. surface, perimeter) and vegetation variables;<br>- "LandUse" contains the percentage of different land use categories in buffers of 5 and 100 m around the ponds;<br>- "Metadata" describes all variables found in the files mentioned above.</p> <p>Adult Odonata surveys were conducted by following the protocol of the pond biodiversity index "IBEM" (see Indermuehle et al. 2010. <a href="https://doi.org/10.23818/limn.29.08">https://doi.org/10.23818/limn.29.08</a>; and <a href="http://campus.hesge.ch/ibem/odonates.asp">http://campus.hesge.ch/ibem/odonates.asp</a>).</p> <p>Larval Odonata were sampled on a subset of 6 ponds in early June 2023. Qualitative samples were taken with a sweep net (mesh size: 500 µm, 25*18 cm frame size) close to the shore, near the land-water interface. Twenty sweeps of 1 m were conducted in each sampled pond, with the sweeps distributed among the different mesohabitats in proportion to their coverage. The content of the net was then emptied into a white tray and the aquatic macroinvertebrates were directly sorted in the field. Only Odonata larvae were picked and fixated with 70% ethanol, to be identified under the microscope. Larvae were identified to species level where possible, and to genus or family level where this was not possible.</p> <p>Land use and several vegetation variables were collected following the same protocol as in the <a href="https://ponderful.eu/">PONDERFUL</a> project.</p> <p> </p> <p> </p> <p> </p>
Environmental watering volumes (ML) used (held and planned environmental water) on the Murray-Darling Basin between 2007-08 and 2020-21
<p>An assessment of the volumes of environmental water released in the Murray-Darling Basin, Australia, by catchment, between 2007-08 and 2020-21</p>
Effects of water source and technology on energy use and environmental impacts of rice production in northern Iran
<p>To analyze the energy flow and greenhouse gas emissions, a total of 200 paddy fields were selected based on the water source utilized for irrigation (river as the surface water source and well as the underground water source), transplanting methods (traditional and mechanical) and grain yield (low- and high-yielding rice cultivars) as a factorial experimental design (2×2×2=8 conditions). Data were collected in 2020 from 200 rice growers in northern Iran using a face-to-face questionnaire survey.</p>
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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.