This dataset relates to the analysis of the mechanical properties, the fibre structural properties and the mathematical models. The analyses were computed using four MS Excel spreadsheets:ANALYSIS Mech Prop ...xls: analysis of mechanical properties from each specimenANALYSIS Fibre Diam...xls: analysis of fibre diameterANALYSIS Pore Diam...xls: analysis of pore sizeanalysis_dib...xls: analysis of the mathematical models used in this paper to predict stiffness (E), fracture strength (sU) and water flux

Scientific and related management challenges in the water domain require synthesis of data from multiple domains. Many data analysis tasks are difficult because datasets are large and complex; standard formats for data types are not always agreed upon nor mapped to an efficient structure for analysis; water scientists may lack training in methods needed to efficiently tackle large and complex datasets; and available tools can make it difficult to share, collaborate around, and reproduce scientific work. Overcoming these barriers to accessing, organizing, and preparing datasets for analyses will be an enabler for transforming scientific inquiries. Building on the HydroShare repository’s established cyberinfrastructure, we have advanced two packages for the Python language that make data loading, organization, and curation for analysis easier, reducing time spent in choosing appropriate data structures and writing code to ingest data. These packages enable automated retrieval of data from HydroShare and the USGS’s National Water Information System (NWIS), loading of data into performant structures keyed to specific scientific data types and that integrate with existing visualization, analysis, and data science capabilities available in Python, and then writing analysis results back to HydroShare for sharing and eventual publication. These capabilities reduce the technical burden for scientists associated with creating a computational environment for executing analyses by installing and maintaining the packages within CUAHSI’s HydroShare-linked JupyterHub server. HydroShare users can leverage these tools to build, share, and publish more reproducible scientific workflows. The HydroShare Python Client and USGS NWIS Data Retrieval packages can be installed within a Python environment on any computer running Microsoft Windows, Apple MacOS, or Linux from the Python Package Index using the PIP utility. They can also be used online via the CUAHSI JupyterHub server (https://jupyterhub.cuahsi.org/) or other Python notebook environments like Google Collaboratory (https://colab.research.google.com/). Source code, documentation, and examples for the software are freely available in GitHub at https://github.com/hydroshare/hsclient/ and https://github.com/USGS-python/dataretrieval.

This presentation was delivered as part of the Hawai'i Data Science Institute's regular seminar series: https://datascience.hawaii.edu/event/data-science-and-analytics-for-water/

The Water Quality analysis simulation Program, an enhancement of the original WASP. This model helps users interpret and predict water quality responses to natural phenomena and man-made pollution for variious pollution management decisions.

The development and adoption of technologies by the water science community to improve our ability to openly collaborate and share workflows will have a transformative impact on how we address the challenges associated with collaborative and reproducible scientific research. Jupyter notebooks offer one solution by providing an open-source platform for creating metadata-rich toolchains for modeling and data analysis applications. Adoption of this technology within the water sciences, coupled with publicly available datasets from agencies such as USGS, NASA, and EPA enables researchers to easily prototype and execute data intensive toolchains. Moreover, implementing this software stack in a cloud-based environment extends its native functionality to provide researchers a mechanism to build and execute toolchains that are too large or computationally demanding for typical desktop computers. Additionally, this cloud-based solution enables scientists to disseminate data processing routines alongside journal publications in an effort to support reproducibility. For example, these data collection and analysis toolchains can be shared, archived, and published using the HydroShare platform or downloaded and executed locally to reproduce scientific analysis. This work presents the design and implementation of a cloud-based Jupyter environment and its application for collecting, aggregating, and munging various datasets in a transparent, sharable, and self-documented manner. The goals of this work are to establish a free and open source platform for domain scientists to (1) conduct data intensive and computationally intensive collaborative research, (2) utilize high performance libraries, models, and routines within a pre-configured cloud environment, and (3) enable dissemination of research products. This presentation will discuss recent efforts towards achieving these goals, and describe the architectural design of the notebook server in an effort to support collaborative and reproducible science

This was presented as an EPoster at the 2017 American Geophysical Union and can be found at: https://agu2017fallmeeting-agu.ipostersessions.com/default.aspx?s=2B-C4-70-3C-B8-A0-0D-77-35-04-7C-F2-A4-1B-36-10

Provide data analysis for environmental element monitoring Air Water resources and marine environ

Long-term freshwater quality data from federal and federal-provincial sampling sites throughout Canada's aquatic ecosystems are included in this dataset. Measurements regularly include physical-chemical parameters such as temperature, pH, alkalinity, major ions, nutrients and metals. Collection includes data from active sites, as well as historical sites that have a period of record suitable for trend analysis. Sampling frequencies vary according to monitoring objectives. The number of sites in the network varies slightly from year-to-year, as sites are adjusted according to a risk-based adaptive management framework. The Great Lakes are sampled on a rotation basis and not all sites are sampled every year. Data are collected to meet federal commitments related to transboundary watersheds (rivers and lakes crossing international, inter-provincial and territorial borders) or under authorities such as the Department of the Environment Act, the Canada Water Act, the Canadian Environmental Protection Act, 1999, the Federal Sustainable Development Strategy, or to meet Canada's commitments under the 1969 Master Agreement on Apportionment.

The U.S. Geological Survey (USGS), in cooperation with the Missouri Department of Natural Resources (MDNR), collects data pertaining to the surface-water resources of Missouri. These data are collected as part of the Missouri Ambient Water-Quality Monitoring Network (AWQMN) and are stored and maintained by the USGS National Water Information System (NWIS) database. These data constitute a valuable source of reliable, impartial, and timely information for developing an improved understanding of the water resources of the State. Water-quality data collected between water years 1993 and 2017 were analyzed for long term trends and the network was investigated to identify data gaps or redundant data to assist MDNR on how to optimize the network in the future. This is a companion data release product to the Scientific Investigation Report: Richards, J.M., and Barr, M.N., 2021, General water-quality conditions, long-term trends, and network analysis at selected sites within the Ambient Water-Quality Monitoring Network in Missouri, water years 1993–2017: U.S. Geological Survey Scientific Investigations Report 2021–5079, 75 p., https://doi.org/10.3133/sir20215079. The following selected tables are included in this data release in compressed (.zip) format: AWQMN_EGRET_data.xlsx -- Data retrieved from the USGS National Water Information System database that was quality assured and conditioned for network analysis of the Missouri Ambient Water-Quality Monitoring Network AWQMN_R-QWTREND_data.xlsx -- Data retrieved from the USGS National Water Information System database that was quality assured and conditioned for analysis of flow-weighted trends for selected sites in the Missouri Ambient Water-Quality Monitoring Network AWQMN_R-QWTREND_outliers.xlsx -- Data flagged as outliers during analysis of flow-weighted trends for selected sites in the Missouri Ambient Water-Quality Monitoring Network AWQMN_R-QWTREND_outliers_quarterly.xlsx -- Data flagged as outliers during analysis of flow-weighted trends using a simulated quarterly sampling frequency dataset for selected sites in the Missouri Ambient Water-Quality Monitoring Network AWQMN_descriptive_statistics_WY1993-2017.xlsx -- Descriptive statistics for selected water-quality parameters at selected sites in the Missouri Ambient Water-Quality Monitoring Network The following selected graphics are included in this data release in .pdf format. Also included in this data release are web pages accessible for people with disabilities provided in compressed .zip format. The web pages present the same information as the .pdf files: Annual and seasonal discharge trends.pdf -- Graphics of discharge trends produced from the EGRET software for selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Annual_and_seasonal_discharge_trends_htm.zip -- Compressed web page presenting graphics of discharge trends produced from the EGRET software for selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Graphics of simulated quarterly sampling frequency trends.pdf -- Graphics of results of simulated quarterly sampling frequency trends produced by the R-QWTREND software at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Graphics_of_simulated_quarterly_sampling_frequency_trends_htm.zip -- Compressed web page presenting graphics of results of simulated quarterly sampling frequency trends produced by the R-QWTREND software at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Graphics of median parameter values.pdf -- Graphics of median values for selected parameters at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Graphics_of_median_parameter_values_htm.zip -- Compressed web page presenting graphics of median values for selected parameters at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Parameter value versus time.pdf -- Scatter plots of the value of selected parameters versus time at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Parameter_value_versus_time_htm.zip -- Compressed web page presenting scatter plots of the value of selected parameters versus time at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Parameter value versus discharge.pdf -- Scatter plots of the value of selected parameters versus discharge at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Parameter_value_versus_discharge_htm.zip -- Compressed web page presenting scatter plots of the value of selected parameters versus discharge at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot of parameter value distribution by season.pdf -- Seasonal boxplots of selected parameters from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Seasons defined as Winter (December, January, and February), Spring (March, April, and May), Summer (June, July, and August), and Fall (September, October, and November). Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot_of_parameter_value_distribution_by_season_htm.zip -- Compressed web page presenting seasonal boxplots of selected parameters from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Seasons defined as Winter (December, January, and February), Spring (March, April, and May), Summer (June, July, and August), and Fall (September, October, and November). Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot of sampled discharge compared with mean daily discharge.pdf -- Boxplots of the distribution of discharge collected at the time of sampling of selected parameters compared with the period of record discharge distribution from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot_of_sampled_discharge_compared_with_mean_daily_discharge_htm.zip -- Compressed web page presenting boxplots of the distribution of discharge collected at the time of sampling of selected parameters compared with the period of record discharge distribution from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot of parameter value distribution by month.pdf -- Monthly boxplots of selected parameters from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot_of_parameter_value_distribution_by_month_htm.zip -- Compressed web page presenting monthly boxplots of selected parameters from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report.

Water is vital for life and local water pollution can damage the environment and affect human health. Governments and private institutions monitor and regulate water quality to protect the environment and populations. The consequences of pollution can reach far and wide, costing companies significant amounts in cleanup costs and loss of reputation. Most countries have official accredited laboratories and sampling teams that use varied technology, global expertise and local knowledge to provide water quality monitoring for different types of water and different and varied sampling locations. However, one of the main problems associated with monitoring and assessing water quality and meeting minimum standards of potability or usability is the analysis of samples based on local data. The problem lies in the fact that in many cases the data, due to the methodology or technique used or the expertise of the human resource that handles the samples, ends up configured in sets that have a large amount of missing information or data without information. This implies a problem depending on the analysis to be carried out. If you want to estimate a water quality index based on the samples, then you may have biased calculations due to the loss of information.

This dataset has been used for the generation of the manuscript: Efficient improvement for water quality analysis with large amount of missing data. D. Sierra-Porta,M. Tobón-Ospino. This manuscript is being submitted to Sustainable Production and Consumption (2022 Elsevier), Publication of the Institution of Chemical Engineers.

North America Water Consumption Market is Segmented by Source of Water Procurement (Potable Water, Non-Potable/Treated Greywater, Alternate Sources), Data Center Type (Enterprise, Colocation, Cloud Service Provider), Data Center Size (Mega, Massive, Large, and More), Cooling Technology (Air-Based, and More), and Country (United States, Canada, Mexico). The Market Forecasts are Provided in Terms of Volume (Liters).

An objective review (Asquith and others, 2018 and 2020) of the distribution of the first two significant figures of a water-level measurement (depth below land surface) was done on the 10,295 measurements (one per well) that met the threshold criteria. The purpose of this review was to ascertain the degree to which substantial rounding of values might exist in the dataset. It was evident that the dataset has a large number of values rounded to the nearest integer foot with a tendency for more rounding towards even integers. For values between 10 and 99 feet, there is a large number of values rounded to the even 10 feet and for values less than about 35 feet, there are an excessive number of values rounded the nearest. There are also numerous values of 12 and 14. This suggests that considerable estimation or rounding of water levels have been made probably by use of apparatus other than graduate tapes. Systematic review of original data sources is not possible and insufficient metadata exist for a manual of each value in the data set. However, the database is large and offers an opportunity for data mining and machine learning to foster further review. A large, objective, technically-demanding, and rigorous spatial-temporal review of the water-level data, expressed in altitude, was made for the 10,295 water level records comprising this data release.

Provide statistical analysis of water conservancy in each county and city.

The California Water Boards’ Water Data Center is proud to present the CA Water Quality Status Report. This report is an annual data-driven snapshot of the Water Board's water quality and environmental data. This inaugural version of the report is based solely on the surface water datasets available via the Surface Water Ambient Monitoring Program (SWAMP) and in future years we hope to expand this to include the groundwater, drinking water and water resource datasets available in our state. Our goal is to use data to inform both data storytelling (as in this inaugural report) and water quality indicators, including watershed report cards. The 2017 Water Quality Status Report is organized around seven major themes that our team thought both individually and collectively tell important stories about the overall health of our state’s surface waters. Each theme-specific story includes a brief background, a data analysis summary, an overview of management actions, and access to the raw data. For more information please contact the Office of Information Management and Analysis (OIMA). Data for the section “Setting Flow Targets to Support Biological Integrity in Southern California Streams” can be found on the California open data portal. Data for the section “Nutrients and Algae in Aquatic Ecosystems” can be found here.

This HydroShare resource was created as a demonstration of how a reproducible data science workflow can be created and shared using HydroShare. The hsclient Python Client package for HydroShare is used to show how the content files for the analysis can be managed and shared automatically in HydroShare. The content files include a Jupyter notebook that demonstrates a simple regression analysis to develop a model of annual maximum discharge in the Logan River in northern Utah, USA from annual maximum snow water equivalent data from a snowpack telemetry (SNOTEL) monitoring site located in the watershed. Streamflow data are retrieved from the United States Geological Survey (USGS) National Water Information System using the dataretrieval package. Snow water equivalent data are retrieved from the United States Department of Agriculture Natural Resources Conservation Service (NRCS) SNOTEL system. An additional notebook demonstrates how to use hsclient to retrieve data from HydroShare, load it into a performant data object, and then use the data for visualization and analysis. For a full description of the workflows and technologies used, see the paper linked in the Related Resources section on this page.

WATER MONITORING STATION ANALYSIS – CALENDAR YEAR 2013 to 2016 SITE NUMBER: 393937093090901 SITE NAME: Turkey Creek nr Sumner MO, Fulbright Rd COOPERATION: Swan Lake National Wildlife Refuge WATERSHED: 48.9 square miles EQUIPMENT: Sutron Satlink in metal gage housing in communication with a transducer mounted on a fencepost in the streambed. Instruments are powered by two solar panels run to a battery inside the gage housing. Stage data are collected at 15 minute intervals, and water temperature data are collected at hourly intervals. Prior to 09/29/2015, data logger was a Sutron Monitor 1. SITE CHARACTERISTICS: The site is located approximately one mile north of where Turkey joins Elk Creek just upstream of Swan Lake National Wildlife Refuge and Silver Lake. The transducer is deployed throughout the year collecting water temperature and water level data. The bottom is mucky with a significant portion of clays and fine sediments. Water levels respond rapidly to rainfall, but low flows are typical where the control is often affected by Silver Lake water levels downstream. GAGE RECORD: The record from 2013 to 2016 has numerous gaps due to equipment malfunction at this site. Site visits were made by Region 3 Hydrologists or Refuge staff to download the data, take logger readings and record surface water levels. One stage reading per site visit was used for datum corrections(Table 1)....

The Study of Data Center Water Consumption in India Report is Segmented by Source of Water Procurement (Potable Water, Non-Potable Water, Other Alternate Sources), by Data Center Type (Enterprise, Colocation, Cloud Service Providers), and by Data Center Size (Mega, Massive, Large, Medium, Small). The Market Sizes and Forecasts are Provided in Terms of Volume (Billion Liters).

The dataset and analysis scripts accompanies the scientific article, "Quantifying ecosystem states and state transitions of the Upper Mississippi River using topological data analysis." We coupled this highly dimensional dataset with multiple topological data analysis (TDA) techniques to classify ecosystem states, identify state variables, and detect state transitions over 30 years.

The following datasets are used for the Water Rights Demand Analysis project and are formatted to be used in the calculations. The State Water Resources Control Board Division of Water Rights (Division) has developed a methodology to standardize and improve the accuracy of water diversion and use data that is used to determine water availability and inform water management and regulatory decisions. The Water Rights Demand Data Analysis Methodology (Methodology https://www.waterboards.ca.gov/drought/drought_tools_methods/demandanalysis.html ) is a series of data pre-processing steps, R Scripts, and data processing modules that identify and help address data quality issues related to both the self-reported water diversion and use data from water right holders or their agents and the Division of Water Rights electronic water rights data.

This page is meant to act as an archival repository for previous Water Shortage Vulnerability analyses, which includes the Water Shortage Vulnerability Sections, Water Shortage Vulnerability for Small Water Systems, and Social Vulnerability Index by Block Groups. All data are presented in their original format. Please read the documentation found at https://water.ca.gov/Programs/Water-Use-And-Efficiency/SB-552/SB-552-Tool for more informatoin.

Dataset Overview: The "UN Global Water Data 2012-2022" dataset compiles global water access and sanitation metrics, highlighting changes over a decade.

Data Science Applications: Useful for predictive modeling, geographic analysis, and trend visualization in water access and public health sectors. Further data will require cleaning to remove null columns etc .

Column Descriptors: - DATAFLOW: Data collection framework - REF_AREA: Geographic area covered - INDICATOR: Specific metric measured - SEX: Gender breakdown, if applicable - TIME_PERIOD: Year of data collection - OBS_VALUE: The observed value or measurement - UNIT_MEASURE: Units of measurement used - Additional columns include details on data sources, observation status, and methodological notes.

Ethically Collected Data: Ensures adherence to ethical standards in data collection, respecting privacy and consent.

Acknowledgments: Thanks to UNICEF Data Warehouse for providing this dataset. Explore more at UNICEF Data Explorer.

Also thank you to the Daily News archives for the dataset image used here article.

This dataset contains all of the supporting materials to accompany Helsel, D.R., Hirsch, R.M., Ryberg, K.R., Archfield, S.A., and Gilroy, E.J., 2020, Statistical methods in water resources: U.S. Geological Survey Techniques and Methods, book 4, chapter A3, 454 p., https://doi.org/10.3133/tm4a3. [Supersedes USGS Techniques of Water-Resources Investigations, book 4, chapter A3, version 1.1.]. Supplemental material (SM) for each chapter are available to re-create all examples and figures, and to solve the exercises at the end of each chapter, with relevant datasets provided in an electronic format readable by R. The SM provide (1) datasets as .Rdata files for immediate input into R, (2) datasets as .csv files for input into R or for use with other software programs, (3) R functions that are used in the textbook but not part of a published R package, (4) R scripts to produce virtually all of the figures in the book, and (5) solutions to the exercises as .html and .Rmd files. The suff ...