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97 results for “Water management”
Figure 2 in Developing an advanced information system to support ballast water management
Figure 2. The Ballast Water Information System provides services to Operational Team, Data Users, Vessel Operators, and System Administrators such as: 1) facilitating data entry, validation and verification for Ballast Water Reporting Forms (BWRFs); 2) providing a search functionality for BWRFs; 3) providing basic reports on BWRF data; and 4) implementing research on risk assessment to build decision-support reports.
Dataset: iShares MSCI Water Management Multisector ETF (IWTR) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Database of Pines from the Forests paper: "Intraspecific Variation in Pines from the Trans-Mexican Volcanic Belt Grown Under Two Watering Regimes: Implications for Management of Genetic Resources"
<p>Raw data from the Forests paper: "Intraspecific Variation in Pines from the Trans-Mexican Volcanic Belt Grown under Two Watering Regimes: Implications for Management of Genetic Resources" Forests <strong>2018</strong> <em>9</em>(2), 71. doi:<a href="http://dx.doi.org/10.3390/f9020071">10.3390/f9020071. </a></p> <p>The database correspond to seedlings of four Mexican pines: <em>P. oocarpa, P. patula</em> and <em>P. pseudostrobus</em>, that were submitted to two watering treatments: Field Capacity (FC) and Drought-Stress (DS), during 90 days. Growth and biomass, survival and ontogenetic score were measured.</p>
MADFORWATER: WP3: Adaptation of technologies for efficient water management and treated wastewater reuse in agriculture: Task3.1: Reduction of crop water requirement and tools for irrigation management with treated WW: Subtask 3.1.1: Plant Growth Promotion (PGP) bacteria to enhance crop resistance to water stress and salinity: Subset1
<p>This dataset contains the data underlying the following publication: Mouna Mahjoubi, Simone Cappello, Yasmine Souissi, Atef Jaouani and Ameur Cherif (February 7th 2018). Microbial Bioremediation of Petroleum Hydrocarbon– Contaminated Marine Environments, Recent Insights in Petroleum Science and Engineering Mansoor Zoveidavianpoor, IntechOpen, DOI: 10.5772/intechopen.72207</p> <p> </p>
Site characterization, water balance modeling, and regeneration attributes of managed and unmanaged ponderosa pine sites in the southwestern United States
<p>This dataset contains biotic and abiotic site characterization data and SOILWAT2 water balance model simulation outputs (two daily outputs: 1915-2011, 1980-2020) for 77 ponderosa pine forest sites in the southwestern United States. Data were collected in summer 2019 and summer 2021. Overviews of the sampling and modeling methodologies are detailed in the following publications:</p> <p>Pirtel NL, Bradford JB, Hubbard RM, Abella SR, Kolb TE, Litvak ME, Porter SL and Petrie MD. 2021. The aboveground and belowground growth characteristics of juvenile conifers in the southwestern United States, Ecosphere 12: e03839, doi:10.1002/ecs2.3839.</p> <p>Petrie MD, Hubbard RM, Bradford JB, Kolb TE, Moser WK, Noel A, Schlaepfer DR, Bowen MA, Fuller LR and Moser WK. 2023. Widespread regeneration failure in ponderosa pine forests of the southwestern United States, Forest Ecology and Management: in press.</p> <p> </p>
Integration of Renewable Energy Sources into the Water-Energy-Food (WEF) Nexus – Modelling a Demand Side Management Approach and Application to a Microgrid Farm in Morocco Dataset
<p>Here you can find the official data used for the publication "Integration of Renewable Energy Sources into the Water-Energy-Food (WEF) Nexus – Modelling a Demand Side Management Approach and Application to a Microgrid Farm in Morocco"</p> <p>If you want to run the model, please update line 11 in the run.jl file, to select the dataset from the scenario you want to look at. It is also recommended to change the result path, to a directory that corresponts to the current model run in order to find the results files quicker.</p> <p> </p> <p>To create a new plot a file called newPlot.jl can be found, that already take care of most data handling, only lines 136 and 141 need to be changed, in order to read in the result files of the results you want to investigate.</p>
Cost Efficiency Assessment of Four Pressure Management Methods in Water Distribution Systems (WRENG-5984)
<p>EPANET_3-PMX input data files for Hadımköy Water Distribution System accessible from</p> <p><a href="https://github.com/MehmetMelihKosucu/EPANET_3-PMX/tree/v3.1.1">https://github.com/MehmetMelihKosucu/EPANET_3-PMX/tree/v3.1.1</a></p>
Silicon concentrations and stoichiometry in two agricultural watersheds: implications for management and downstream water quality
Agriculture alters the biogeochemical cycling of nutrients such as nitrogen (N), phosphorus (P), and silicon (Si) which contributes to the stoichiometric imbalance among these nutrients in aquatic systems. Limitation of Si relative to N and P can facilitate the growth of non-siliceous, potentially harmful, algal taxa which has severe environmental and economic impacts. Planting winter cover crops can retain N and P on the landscape, yet their effect on Si concentrations and stoichiometry is unknown. We analyzed three years of biweekly concentrations and loads of dissolved N, P, and Si from subsurface tile drains and stream water in two agricultural watersheds in northern Indiana. Intra-annual patterns in Si concentrations and stoichiometry showed that cover crop vegetation growth did not reduce in-stream Si concentrations as expected, although, compared to fallow conditions, winter cover crops increased Si:N ratios to conditions more favorable for diatom growth. To assess the risk of non-siliceous algal growth, we calculated a stoichiometric index to quantify biomass growth facilitated by excess N and P relative to Si. Index values showed a divergence between predicted algal growth and what we observed in the streams, indicating other factors influence algal community composition. The stoichiometric imbalance was more pronounced at high flows, suggesting increased risk of harmful blooms as climate change increases the frequency and intensity of precipitation in the midwestern U.S. Our data include some of the first published measurements of Si within small agricultural watersheds and provide the groundwork for understanding the role of agriculture on Si export and stoichiometry.
Derived data and code for Deines et al. 2020, Agricultural Water Management
<p>This codebase accompanies the paper:</p> <p>Deines, J.M., M.E. Schipanski, B. Golden, S.C. Zipper, S. Nozari, C. Rottler, B. Guerrero, & V. Sharda. 2020. Transitions from irrigated to dryland agriculture in the Ogallala Aquifer: Land use suitability and regional economic impacts. Agricultural Water Management 233:106061. DOI: <a href="https://doi.org/10.1016/j.agwat.2020.106061">https://doi.org/10.1016/j.agwat.2020.106061</a></p> <p>Data needed to reproduce the figures from Deines et al. 2020 can be found in the <code>data</code> folder.</p> <p>See the Readme.md or <a href="https://github.com/jdeines/Deines_etal_2020_agwat_OgallalaTransistions">https://github.com/jdeines/Deines_etal_2020_agwat_OgallalaTransistions</a> for more information.</p>
Figure 6 in Analysis of Carapas Width and Weight on Maturity Level of Swimming Crab (Portunus pelagicus) as Basis for Sustainable Resource Management in the Eastern Waters of Surabaya
Figure 6: Percentage of maturity level of female swimming crabs
Figure 5 in Analysis of Carapas Width and Weight on Maturity Level of Swimming Crab (Portunus pelagicus) as Basis for Sustainable Resource Management in the Eastern Waters of Surabaya
Figure 5. Percentage of spawning females Phase
Figure 4 in Analysis of Carapas Width and Weight on Maturity Level of Swimming Crab (Portunus pelagicus) as Basis for Sustainable Resource Management in the Eastern Waters of Surabaya
Figure 4: Sex ratio distribution of swimming crab
Figure 1 in Analysis of Carapas Width and Weight on Maturity Level of Swimming Crab (Portunus pelagicus) as Basis for Sustainable Resource Management in the Eastern Waters of Surabaya
Figure 1: Maturity level composition of swimming crab based on carapace width
Figure 3 in Analysis of Carapas Width and Weight on Maturity Level of Swimming Crab (Portunus pelagicus) as Basis for Sustainable Resource Management in the Eastern Waters of Surabaya
Figure 3: Average weight of swimming crab
Figure 2 in Analysis of Carapas Width and Weight on Maturity Level of Swimming Crab (Portunus pelagicus) as Basis for Sustainable Resource Management in the Eastern Waters of Surabaya
Figure 2: Average Carapace Width of Swimming Crab
Data and Software for "Probabilistic Trade-offs Analysis for Sustainable and Equitable Management of Climate-Induced Water Risks"
<p><span>Research data supporting the study "Probabilistic trade-offs analysis for sustainable and equitable management of climate-induced water risks"</span></p> <p><span>This repository provides data of the Stochastic Dual Dynamic Programming (SDDP) model, and the output results of the simulations of the various policies and climate scenarios considered in this study, as well as the code used for postprocessing and visualizing the results.</span></p> <p><strong><span>Contents</span></strong></p> <ol> <li><strong><span>Data: Model Inputs</span></strong><span><br>This folder contains the physical river network, reservoir and water demand, and economic data derived from the observed database.<br>The key files are:</span></li> <ul> <li><span>Input_HydrologicalData</span></li> <li><span>Input_SystemData</span></li> </ul> <li><strong><span>Results: Model Output Analysis</span></strong><span><br>This folder includes outputs from the Stochastic Dual Dynamic Programming (SDDP) model under various policies and climate scenarios. The results showcase optimized sectoral water use, including irrigated areas, hydropower generation, and allocations for agriculture, energy, and urban demands across spatial locations (upstream and downstream).<br>Key files include:</span></li> <ul> <li><strong><span>SDDP Model Outputs</span></strong><span> (MATLAB format): </span></li> <ul> <li><span>EnergyPriority_Baseline.mat</span></li> <li><span>EnergyPriority_2070.mat</span></li> <li><span>EnergyPriority_2100.mat</span></li> <li><span>AgriculturePriority_Baseline.mat</span></li> <li><span>AgriculturePriority_2070.mat</span></li> <li><span>AgriculturePriority_2100.mat</span></li> </ul> <li><strong><span>Extracted Model Results</span></strong><span> (Excel format): </span></li> <ul> <li><span>Organized for each policy and climate scenario to facilitate analysis.</span></li> </ul> </ul> <li><strong><span>Software: Data Analysis and Visualization</span></strong><span><br>Python scripts designed for outputs data analysis and visualization are included to reproduce the primary figures from the study.<br>Scripts provided:</span></li> <ul> <li><span>CDF_outflow.py</span><span>: Analyzes cumulative distribution functions for river discharge.</span></li> <li><span>CDF_sectors.py</span><span>: Examines sectoral water use distributions.</span></li> <li><span>PCP_SI.py</span><span>: Generates Parallel Coordinate Plots for trade-offs analysis.</span></li> </ul> <li><strong><span>Instructions: README File</span></strong><span><br>A comprehensive README file explains:</span></li> <ul> <li><span>Details of model input data.</span></li> <li><span>Instructions to interpret the SDDP model outputs.</span></li> </ul> </ol> <p><strong><span>Instructions:</span></strong><span><br></span><span>The Python scripts process Excel files from the model output results folder to generate and visualize the figures for the paper. Each step is clearly documented within the scripts.</span></p>
Coupling nitrogen removal and watershed management to improve global lake water quality
<p>Updated dataset and code for the article "Coupling nitrogen removal and watershed management to improve global lake water quality" after removing deep lakes.</p>
Data from: Predicting habitat suitability and connectivity for management and conservation of urban wildlife: A real-time web application for grassland water voles
<ol> <li>Natural habitats in urban areas provide benefits for both humans and biodiversity. However, to achieve biodiversity gains we require new techniques to determine habitat suitability and ecological connectivity that will inform urban planning and development.</li> <li>Using an example of an urban population of water voles (<i>Arvicola amphibius</i>) we developed a habitat suitability model and a resistance-surface-based model of landscape connectivity to identify potential connectivity between areas of suitable habitat. We then updated the environmental variables according to new urban development plans and used our models to generate spatially explicit predictions of both habitat suitability and connectivity.</li> <li>To make models accessible to urban and conservation planners we developed an interactive mapping tool that provided users with a graphical user interface (GUI) to inform conservation planning for this species.</li> <li>The model found that habitat suitability for water voles was related to distance from key environmental variables, such as built-up areas and urban green spaces, while the connectivity model identified important corridors connecting areas of potential distribution for this species.</li> <li>Future development plans altered the potential spatial distribution of the water vole population, reducing the extent of suitable habitat in some core areas. The interactive mapping tool made available suitable habitat and connectivity maps for conservation managers to assess new planning applications and for the development of a conservation action plan for water voles.</li> <li>Synthesis and applications: We believe this approach provides a framework for future development of nature conservation tools that can be used by planners to inform ecological decision making, increase biodiversity and reduce human-wildlife conflict in urban environments.</li> </ol>
NIOO-QingZ/Geertruidenberg_Mesocosms: Towards climate-robust water quality management: testing the efficacy of different eutrophication control measures during a heat
<p>Data and Codes used in the following open-access publication: </p> <p>https://www.sciencedirect.com/science/article/pii/S0048969722015145</p>
Mitigating ecosystem service tradeoffs in rangelands by using grazing duration and timing to manage water quality
<p>1. Mitigating ecosystem service (ES) tradeoffs is a key management goal in locations where stakeholders value different and potentially conflicting ecosystem services (ESs). However, studies are not often designed to examine how local management actions address ES tradeoffs, and therefore do not provide options that can alleviate conflict.</p> <p>2. In semi-arid rangelands, we examined the potential for managers to mitigate tradeoffs between livestock production and water quality. To move away from solutions that offer cattle removal as a singular management strategy, we examined how cattle presence, plus two elements of rotational grazing - the length of time cattle spend on rangeland (i.e., duration), and the season grazed (i.e., timing), affected stream Escherichia coli (E. coli concentrations). We also modeled how grazing duration and timing affected the ability to meet regulatory benchmarks for water quality throughout a grazing season.</p> <p>3. Grazing duration controlled the length of time E. coli concentrations were high in streams. In short- and medium-duration systems, E. coli concentrations were high for shorter periods of time than in long-duration systems, resulting in fewer violations of national and state water quality standards.</p> <p>4. Stream E. coli concentrations showed a consistent seasonal pattern, starting low in spring, peaking in summer, and declining towards fall. Thus, grazing during spring or fall, rather than in summer, reduced the number of days that E. coli levels exceeded water quality standards.</p> <p>5. Our results suggest that reducing the grazing duration and shifting its timing are complementary strategies that can mitigate the tradeoffs between livestock grazing and water quality without fencing-off riparian areas or removing cattle from pastures with streams.</p> <p>6. Synthesis and applications. In this study, we found grazing duration and timing can be used as tools to mitigate ES tradeoffs between cattle production and water quality in rangeland streams. Shorter grazing durations reduced the number of days <i>E. coli</i> levels were above regulatory limits, as did grazing that occurred either early or late in the season. These results support the idea that rotational grazing can be an effective strategy to manage water quality in semi-arid rangelands. They also highlight the need for more grazing studies that incorporate gradients of duration and timing into study designs.</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.