R code for analysing ordinal dataset of 234 Hong Kong preservice teachers' beliefs about proof and proving, using confirmatory factor analysis

Many variables in biological research - from body size to life history timing to environmental characteristics - are measured continuously (e.g., body mass in kilograms) but analyzed as categories (e.g., large versus small), which can lower statistical power and change interpretation. We conducted a mini-review of 72 recent publications in six popular ecology, evolution, and behavior journals to quantify the prevalence of categorization. We then summarized commonly categorized metrics and simulated a dataset to demonstrate the drawbacks of categorization using common variables and realistic examples. We show that categorizing continuous variables is common (31% of publications reviewed). We also underscore that predictor variables can and should be collected and analyzed continuously. Finally, we provide recommendations on how to keep variables continuous throughout the entire scientific process. Together, these pieces comprise an actionable guide to increasing statistical power and fac..., , , # Overcoming the pitfalls of categorizing continuous variables in ecology and evolutionary biology

https://doi.org/10.5061/dryad.5x69p8d9r

We simulated data to quantify the detrimental impact of categorizing continuous variables using various statistical breakpoints and sample sizes (details below). To give the example biological relevance, we created a dataset that illustrates the complexity of life history theory and climate change impacts, and contains a predictor variable that is frequently categorized (Table 2) - reproductive timing in one year and its effect on body size in the following year. A reasonable research question would be: How does timing of reproduction in year t influence body mass at the start of the breeding season in year t+1? For illustrative purposes, let’s say we collected data from individually banded penguins in Antarctica. Based on the mechanistic relationships between seasonally available sea ice and food availabi...

The Imperial Valley Dark Fiber Project acquired Distributed Acoustic Sensing (DAS) seismic data on a ~28 km segment of dark fiber between the cities of Calipatria and Imperial in the Imperial Valley, Southern California. Dark fiber refers to unused optical fiber cables in telecommunications networks and is repurposed in this project for DAS applications. The objective, which is further detailed in the attached journal article from Ajo-Franklin et al., is to demonstrate dark fiber DAS as a tool for basin-scale geothermal exploration and monitoring. The included DAS data were recorded during two days at the beginning the project. Data is stored in the .h5 (HDF5) file format, readable using various software tools, including the 'h5read' and 'h5info' functions in Matlab. Provided here are examples of methods to read and use the data with the 'h5py' package in Python.

Biodiversity monitoring has entered an era of ‘big data’, exemplified by a near-continuous collection of sounds, images, chemical and other signals from organisms in diverse ecosystems. Such data streams have the potential to help identify new threats, assess the effectiveness of conservation interventions, as well as generate new ecological insights. However, appropriate analytical methods are often still missing, particularly with respect to characterizing cyclical temporal patterns. Here, we present a framework for characterizing and analysing ecological responses that represent nonstationary, complex temporal patterns and demonstrate the value of using Fourier transforms to decorrelate continuous data points. In our example, we use a framework based on three approaches (spectral analysis, magnitude squared coherence, and principal component analysis) to characterize differences in tropical forest soundscapes within and across sites and seasons in Gabon. By reconstructing the underly..., We used acoustic data collected from eight sites in the Ogooué-Ivindo province of Gabon to demonstrate how time-series approaches can be leveraged to compare cyclical trends within and between groups of sites. All soundscape sampling occurred in closed, Gabonese rainforest with minimal habitat disturbance for at least twenty years. First, we sampled the soundscape in the rainy season at four sites within Ivindo National Park, between February 19th and March 2nd 2021 (referred to as the Ivindo sites). Second, we sampled the soundscape in the dry season at four sites near Massaha between July 17th and July 23rd 2021 (hereafter referred to as the Massaha sites, about 15km from the Ivindo sites). At the time of sampling, the Massaha sites were located within a logging concession but no logging activity had commenced and there was an ongoing petition for the area to be re-designated as a community conservation area. Additionally, we used one site from the Lope National Park. At each site, we..., , # Time series methods for the analysis of soundscapes and other cyclical ecological data

https://doi.org/10.5061/dryad.xpnvx0kn6

Example dataset used for demonstrating the methods in "Times Series Methods for the Analysis of Soundscapes and Other Cyclical Ecological Data" Methods in Ecology and Evolution. This data represents soundscape data for eight tropical forest sites in Gabon collected between February and July 2021. See methods for more information. The soundscape is characterized using the soundscape index Power Minus Noise (PMN) for 256 frequency bins between 0-11 kHz for each minute of the day. PMN is a proxy for acoustic activity and provides a relatively simple index to demonstrate our methodological approach.

Description of the data and file structure

Column names:

• Site: Site code (factor; 8 levels)
• DatetimeFinal: Date time information (datatime object)
• TotPMN: Values for characterizing the soundscape, Power minus noi...

Two broadband seismometers were installed on the 4100 level and recorded for the duration of EGS Collab Experiment #2. Inspired by published data from similar instruments installed in the Aspo Hard Rock Lab, these long-period instruments aimed to measure the tilting of the drift in response to the injection of fluid into the testbed. One instrument was installed underneath the wellheads in Site A (aka the "battery" alcove) and the other was installed along the east wall of the drift, south of Site B. Due to the feet of gravel (ballast) laid along the floor of the drift, we were unable to anchor the sensors directly to the rock. As a result, the coupling of the sensors to the experiment rock volume is likely poor. In addition, there are a number of noise sources that complicate the interpretation of the data. For example, sensor BBB is installed adjacent (within 3 ft) to the rail line that runs towards the Ross shaft. Trains (motors) run along this line almost daily and produce a large signal in these data. Careful extraction of periods of interest, as well as filtering for specific signals, is necessary. The sensors are Nanometrics Trillium Compact Posthole seismometers, sensitive down to 120 seconds period. They were installed as close to the drift wall and as deep as we could manually excavate (only about 1 ft or so). The holes were leveled with sand and the sensors were placed on a paver before backfilling with sand. The hole was then covered by a bucket filled with insulation to improve the sensor's isolation from daily temperature variations, which are minor but present due to drift ventilation from the surface. Data were recorded on Nanometrics Centaur digitizers at 100 Hz. The full response information is available in the StationXML file provided here, or by querying the sensors through the IRIS DMC (see links below). These instruments were provided free of charge through the IRIS PASSCAL instrument center. The network code is XP and the station codes are BBA and BBB. The waveform data can be queried through the IRIS FDSN server using any method the user likes. One convenient option is to use the Obspy python package: https://docs.obspy.org/packages/obspy.clients.fdsn.html

We are using the Yelp Review Dataset as the streaming data source for the DataCI example. We have processed the Yelp review dataset into a daily-based dataset by its date. In this dataset, we will only use the data from 2020-09-01 to 2020-11-30 to simulate the streaming data scenario. We are downloading two versions of the training and validation datasets:

yelp_review_train@2020-10: from 2020-09-01 to 2020-10-15

yelp_review_val@2020-10: from 2020-10-16 to 2020-10-31

yelp_review_train@2020-11: from 2020-10-01 to 2020-11-15

yelp_review_val@2020-11: from 2020-11-16 to 2020-11-30

This dataset contains data and code associated with the study "Quantifying accuracy and precision from continuous response data in studies of spatial perception and crossmodal recalibration" by Patrick Bruns, Caroline Thun, and Brigitte Röder. example_code.R contains analysis code that can be used to to calculate error-based and regression-based localization performance metrics from single-subject response data with a working example in R. It requires as inputs a numeric vector containing the stimulus location (true value) in each trial and a numeric vector containing the corresponding localization response (perceived value) in each trial. example_data.csv contains the data used in the working example of the analysis code. localization.csv contains extracted localization performance metrics from 188 subjects which were analyzed in the study to assess the agreement between error-based and regression-based measures of accuracy and precision. The subjects had all naively performed an azimuthal sound localization task (see related identifiers for the underlying raw data). recalibration.csv contains extracted localization performance metrics from a subsample of 57 subjects in whom data from a second sound localization test, performed after exposure to audiovisual stimuli in which the visual stimulus was consistently presented 13.5° to the right of the sound source, were available. The file contains baseline performance (pre) and changes in performance after audiovisual exposure relative to baseline (delta) in each of the localization performance metrics. Localization performance metrics were either derived from the single-trial localization errors (error-based approach) or from a linear regression of localization responses on the actual target locations (regression-based approach).The following localization performance metrics were included in the study: bias: overall bias of localization responses to the left (negative values) or to the right (positive values), equivalent to constant error (CE) in error-based approaches and intercept in regression-based approaches absolute constant error (aCE): absolute value of bias (or CE), indicates the amount of bias irrespective of direction mean absolute contant error (maCE): mean of the aCE per target location, reflects over- or underestimation of peripheral target locations variable error (VE): mean of the standard deviations (SD) of the single-trial localization errors at each target location pooled variable error (pVE): SD of the single-trial localization errors pooled across trials from all target locations absolute error (AE): mean of the absolute values of the single-trial localization errors, sensitive to both bias and variability of the localization responses slope: slope of the regression model function, indicates an overestimation (values > 1) or underestimation (values < 1) of peripheral target locations R2: coefficient of determination of the regression model, indicates the goodness of the fit of the localization responses to the regression line

IDWR maintains a groundwater level database containing data primarily collected by IDWR, but also includes data gathered by the USGS, USBR, and other public and private entities. Please reach out to these other entities to obtain their full complete record, as not all values are present in this database (IDWR can provide a full list of data contributors upon request). IDWR staff manually measure the "depth to water" in wells throughout Idaho. Pressure transducers in many wells provide near-continuous water level measurements. IDWR strives to create complete and accurate data and may revise these data when indicated.

“Groundwater Level Data: All Historic Data” includes all well data managed in IDWR’s internal database, regardless of current well status. For example, historic data from discontinued, abandoned, or inactive wells are contained in this dataset. IDWR’s water level data are also hosted in the Groundwater Data Portal (https://idwr-groundwater-data.idaho.gov/), which displays only actively monitored wells.

The three files included in this download are 1) discrete (manual) depth to water measurements 2) continuous* (pressure transducer) depth to water measurements, and 3) the associated well metadata.

*The continuous measurements data have been condensed to display only the shallowest daily pressure transducer measurements. Complete datasets are available upon request.

Company Datasets for valuable business insights!

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We provide fresh and ready-to-use company data, eliminating the need for complex scraping and parsing. Our data includes crucial details such as:

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TMV matlab files and and example datasetA set of linked matlab files, example dataset, and instructions (readme file) for applying the Template Modes of Variation (TMV) method for analysing phenotypic or genetic variation in thermal performance curves and other continuous reaction norms.TMV.zip

Malaysia Imports: Other Lifting, Handling, Loading or Unloading Machinery (For Example, Lifts, Other Continuous-Action Elevators and Conveyors, For Goods or Materials:Other, Belt Type data was reported at 18.033 MYR mn in Jun 2024. This records a decrease from the previous number of 19.018 MYR mn for May 2024. Malaysia Imports: Other Lifting, Handling, Loading or Unloading Machinery (For Example, Lifts, Other Continuous-Action Elevators and Conveyors, For Goods or Materials:Other, Belt Type data is updated monthly, averaging 2.096 MYR mn from Jan 2000 (Median) to Jun 2024, with 294 observations. The data reached an all-time high of 71.146 MYR mn in Jan 2019 and a record low of 0.005 MYR mn in May 2006. Malaysia Imports: Other Lifting, Handling, Loading or Unloading Machinery (For Example, Lifts, Other Continuous-Action Elevators and Conveyors, For Goods or Materials:Other, Belt Type data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.DOS: Imports: by Commodity: HS 6: 71 to 98: Value.

Title: Current velocity data from a continuous survey using a towed ADCP in Wilmington River Estuary, Georgia, USA

Author/Data Collector: Chunyan Li

Point of Contact, PI, Originator: Chunyan Li (cli@lsu.edu)

Description:

These are velocity profile data from vessel towed ADCP obtained in the Wilmington River Estuary during a survey conducted on Sep. 29, 2004, for ~ 11.5 hours. The instrument was an RDI 600 KHz Workhorse ADCP.

The ADCP was mounted on a sled towed by the boat. The vertical bins were 0.5 m. The surveys were conducted at an average cruise speed of about 2.5–3 m/s except at the turns when the vessel had to slow down and during CTD casts when the vessel had to stop for a few minutes. A Seabird Electronic SBE 19 plus CTD was used to measure the vertical profiles of water temperature, salinity, fluorescence, light attenuation, and dissolved oxygen during the survey. Note that only ADCP data are included in this dataset.

The data are averaged at about 30-second intervals, excluding bad data. The data presented here are in ASCII with the generic format provided by the RDI’s software WinRiver II output. There are a total of two data files. There are:

ADCP_Sep29_2004_WM_000_ASC.TXT

ADCP_Sep29_2004_WM_001_ASC.TXT

Here is an example of the data –

50 50 42 50 1 20 1

4 9 29 11 32 26 57 468 60 2.361 -0.824 118.753 24.276

49.04 93.15 -0.17 -0.46 0.00 6.00 0.00 3.08 6.63 6.64 6.68 6.59

30.53 28.91 26.93 14.22 30.45

32.00327167 -81.01664167 31.50 92.41 30.4

-22.0 -7.2 -3.2 -11.2 10.0 -10.8 10.0 1.53 5.53

50 cm BT dB 0.43 0.073

1.53 52.35 193.13 -11.9 -51.0 -0.3 3.5 92.1 94.8 94.6 95.6 100 -2.02

2.03 49.89 189.47 -8.2 -49.2 -0.1 -2.1 97.7 99.9 100.4 100.4 100 -2.28

2.53 52.68 187.22 -6.6 -52.3 0.7 4.0 99.0 101.3 101.7 101.2 98 -2.84

3.03 48.92 190.92 -9.3 -48.0 -0.2 5.1 99.2 101.3 101.9 101.6 100 -2.21

3.53 51.77 185.37 -4.8 -51.5 0.3 5.7 98.6 101.4 102.0 101.2 100 -2.99

4.03 48.03 186.14 -5.1 -47.8 1.0 1.2 98.5 101.1 101.9 101.0 100 -2.64

4.53 50.69 190.02 -8.8 -49.9 0.5 3.6 98.5 101.2 101.9 101.2 100 -2.32

5.03 43.70 186.57 -5.0 -43.4 0.7 4.4 98.3 101.2 101.9 101.2 100 -2.34

5.53 41.22 186.13 -4.4 -41.0 1.5 -0.3 98.5 101.6 102.0 101.4 83 -2.35

6.03 -32768 -32768 -32768 -32768 -32768 -32768 255 255 255 255 0 2147483647

The ADCP data were used in Li et al. (2008).

Acknowledgements

I would like to thank Captain Harry Carter who assisted me by driving the boat for the whole day. He also assisted me with CTD casts, deployment, and retrieval of other CTDs. It was a great day working out with him.

References

Li, C., C. Chen, D. Guadagnoli, and I. Y. Georgiou (2008). Geometry-induced residual eddies in estuaries with curved channels: Observations and modeling studies, Journal of Geophysical Research, Vol. 113, C01005, doi:10.1029/2006JC004031.

This part of the data release includes graphical representation (figures) of data from sediment cores collected in 2009 offshore of Palos Verdes, California. This file graphically presents combined data for each core (one core per page). Data on each figure are continuous core photograph, CT scan (where available), graphic diagram core description (graphic legend included at right; visual grain size scale of clay, silt, very fine sand [vf], fine sand [f], medium sand [med], coarse sand [c], and very coarse sand [vc]), multi-sensor core logger (MSCL) p-wave velocity (meters per second) and gamma-ray density (grams per cc), radiocarbon age (calibrated years before present) with analytical error (years), and pie charts that present grain-size data as percent sand (white), silt (light gray), and clay (dark gray). This is one of seven files included in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, offshore Los Angeles and the Palos Verdes Peninsula, adjacent to the Palos Verdes Fault. Gravity cores were collected by the USGS in 2009 (cruise ID S-I2-09-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=SI209SC), and vibracores were collected with the Monterey Bay Aquarium Research Institute's remotely operated vehicle (ROV) Doc Ricketts in 2010 (cruise ID W-1-10-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=W110SC). One spreadsheet (PalosVerdesCores_Info.xlsx) contains core name, location, and length. One spreadsheet (PalosVerdesCores_MSCLdata.xlsx) contains Multi-Sensor Core Logger P-wave velocity, gamma-ray density, and magnetic susceptibility whole-core logs. One zipped folder of .bmp files (PalosVerdesCores_Photos.zip) contains continuous core photographs of the archive half of each core. One spreadsheet (PalosVerdesCores_GrainSize.xlsx) contains laser particle grain size sample information and analytical results. One spreadsheet (PalosVerdesCores_Radiocarbon.xlsx) contains radiocarbon sample information, results, and calibrated ages. One zipped folder of DICOM files (PalosVerdesCores_CT.zip) contains raw computed tomography (CT) image files. One .pdf file (PalosVerdesCores_Figures.pdf) contains combined displays of data for each core, including graphic diagram descriptive logs. This particular metadata file describes the information contained in the file PalosVerdesCores_Figures.pdf. All cores are archived by the U.S. Geological Survey Pacific Coastal and Marine Science Center.

The Multiple Kernel Anomaly Detection (MKAD) algorithm is designed for anomaly detection over a set of files. It combines multiple kernels into a single optimization function using the One Class Support Vector Machine (OCSVM) framework. Any kernel function can be combined in the algorithm as long as it meets the Mercer conditions, however for the purposes of this code the data preformatting and kernel type is specific to the Flight Operations Quality Assurance (FOQA) data and has been integrated into the coding steps. For this domain, discrete binary switch sequences are used in the discrete kernel, and discretized continuous parameter features are used to form the continuous kernel. The OCSVM uses a training set of nominal examples (in this case flights) and evaluates test examples for anomaly detection to determine whether they are anomalous or not. After completing this analysis the algorithm reports the anomalous examples and determines whether there is a contribution from either or both continuous and discrete elements.

Modern data analysis typically involves the fitting of a statistical model to data, which includes estimating the model parameters and their precision (standard errors) and testing hypotheses based on the parameter estimates. Linear mixed models (LMMs) fitted through likelihood methods have been the foundation for data analysis for well over a quarter of a century. These models allow the researcher to simultaneously consider fixed (e.g., treatment) and random (e.g., block and location) effects on the response variables and account for the correlation of observations, when it is assumed that the response variable has a normal distribution. Analysis of variance (ANOVA), which was developed about a century ago, can be considered a special case of the use of an LMM. A wide diversity of experimental and treatment designs, as well as correlations of the response variable, can be handled using these types of models. Many response variables are not normally distributed, of course, such as discrete variables that may or may not be expressed as a percentage (e.g., counts of insects or diseased plants) and continuous variables with asymmetrical distributions (e.g., survival time). As expansions of LMMs, generalized linear mixed models (GLMMs) can be used to analyze the data arising from several non-normal statistical distributions, including the discrete binomial, Poisson, and negative binomial, as well as the continuous gamma and beta. A GLMM allows the data analyst to better match the model to the data rather than to force the data to match a specific model. The increase in computer memory and processing speed, together with the development of user-friendly software and the progress in statistical theory and methodology, has made it practical for non-statisticians to use GLMMs since the late 2000s. The switch from LMMs to GLMMs is deceptive, however, as there are several major issues that must be thought about or judged when using a GLMM, which are mostly resolved for routine analyses with LMMs. These include the consideration of conditional versus marginal distributions and means, overdispersion (for discrete data), the model-fitting method [e.g., maximum likelihood (integral approximation), restricted pseudo-likelihood, and quasi-likelihood], and the choice of link function to relate the mean to the fixed and random effects. The issues are explained conceptually with different model formulations and subsequently with an example involving the percentage of diseased plants in a field study with wheat, as well as with simulated data, starting with a LMM and transitioning to a GLMM. A brief synopsis of the published GLMM-based analyses in the plant agricultural literature is presented to give readers a sense of the range of applications of this approach to data analysis.

Sediment load in streams and rivers is known as both a carrier and a potential source of contaminants, while sediment deposition in surface waterbodies can change stream flow, stage and morphology, and thereby has broad impacts on surface water hydrology, aquatic life, and recreation activity. For vast amounts of watersheds around the world, sparse daily measured sediment data may exist, but continuous and multi-year daily measured sediment data are largely unavailable because of time-consuming and budget constraint for measurements. A gap-filling tool is developed to predict the continuous and multi-year daily sediment loads based on sparse, limited, and discontinuous measurements using the flow-weighted method (Ouyang, 2022, https://www.fs.usda.gov/research/treesearch/66113). An example with detailed instructions associated with data and R script on how to implement the tool is available.

Supplementary Material 4: A supplementary file with examples of SPSS script for all models that have been fitted in this paper.

Extending 200 kilometers (km) along the Atlantic Coast of Central Florida, Indian River Lagoon (IRL) is one of the most biologically diverse estuarine systems in the continental United States. The lagoon is characterized by shallow, brackish waters and a width that varies between 0.5 and 9.0 km; there is significant human development along both shores. Scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center used continuous resistivity profiling (CRP, a towed electronic array) measurements, electrical resistivity tomography (ERT), and basic physical water column properties (for example, depth and temperature) to investigate submarine groundwater discharge at two locations, Eau Gallie North and Riverwalk Park, along the western shore of IRL. Eau Gallie North is near the central section of IRL and Riverwalk Park is approximately 20 km north of the Eau Gallie site. At each CRP study site, an 11-electrode marine resistivity array was towed over seven north–south shore parallel transects (EA–EG and RA–RG, respectively), situated between 75–1000 meters offshore, and approximately 1.5 km in length. Each transect was mapped three times in an alternating north–south direction to account for data collected by the concurrently-operating radon mapping system (Everhart and others, 2018). Repeat streaming resistivity surveys were collected bimonthly along these same tracklines, between March and November 2017, to determine seasonal and temporal variability. Since resistivity is a function of both geology and salinity, it is assumed that temporal shifts will reflect salinity changes, as the underlying geology will be presumed to remain constant. ERT study areas consisted of land- and shallow water-based surveys, where [DC] electrical current was injected into the ground via two current electrodes and received by nine potential electrodes. Electrode positions for both sites were recorded along six transects (T01-T06) and are provided in this data release as supplemental information (please see the ERT location map files included in, ERT_survey_maps.zip).

Meteorological and hydrological data monitored at stations in the Limpopo Basin in southern Africa. Most data are recorded every 15-minutes. The transboundary Limpopo River Basin crosses Botswana, Mozambique, South Africa, and Zimbabwe. At over 400,000 km2, the Limpopo River Basin is home to 18 million people living in both rural and urban areas. Industries in the Basin include businesses in the urban areas and water-intensive uses such as agriculture and mining; industrial water use is growing rapidly (LBPTC, 2010). In addition to the human residents, the Basin contains some of the most biodiverse natural areas on the planet (Kahinda et al., 2016). The rainfall in the Basin is heterogeneous with some sub-basins receiving less than 400 mm on average and other downstream sub-basins in Mozambique receiving over 750 mm annually (LBPTC, 2010). Even meteorological stations located in close proximity demonstrate substantial spatial variation within sub-basins. The Basin has experienced severe droughts in the last decade (LBPTC, 2010). In addition to the variation in the amount of rainfall, the timing, especially the start of the growing season, has varied significantly (Edokpayi et al., 2018). However, there remain many questions about the reliability of rainfall data and other water measurements due in part to the infrequent calibration and validation of field site measurements. The limited confidence in these data, combined with the substantial variation through time and space necessitates an integrated approach to improve data collection, validation, and overall Basin water resource management in the Basin. The goal of this project is to build resilience through the support of Basin stakeholders, including The Limpopo Watercourse Commission (LIMCOM), to improve governance around water resources management and water security in the Basin. A systems approach, such as integrated water resources management (IWRM) is needed to address such complex, large, and interrelated components of water resources. IRWM is recommended by the United States Agency for International Development (USAID) Water and Development Strategy Implementation Guide (2014). This context will be combined with data collection and validation, data sharing, and continuous evaluation of the interrelations that affect water resources. For example, surface water quality will affect ecosystem biodiversity and those who depend on those ecosystems for drinking water or fish as a food protein source. Another example is groundwater; Petrie et al. (2014) found that groundwater in the Limpopo Basin holds great potential for increased water access; however, groundwater measurement data are currently insufficient to calculate sustainable total withdrawal rates. With improved data collection, validation, and sharing of these data, managers will have a basis for scientifically-based decisions and may be able to utilize groundwater resources more effectively to increase (e.g., agricultural) productivity. These same resource managers need monitoring and surveillance tools to measure water consumption by users within the basin, how demand and use changes through time, and to understand if historic and current sparse monitoring is accurate. Since rural users withdraw water in informal schemes, measurements of water use currently do not exist. This project will support water resources monitoring, and the development of methods for water quality and quantity measurement based on in situ sensors and satellite measurements. These measurements will enable characterization of water resource dynamics at the whole Basin scale and form the foundation for hydrologic modeling that can help estimate hard-to-measure parameters and also provide holistic assessments of Basin scale stocks and flows. To support data sharing, the project will use cloud-based, automated data collection and web-based data sharing. All sensors and data will be shared. Increased monitoring and