Time series raw datasets of Temperature, Solar Radiation, Relative Humidity, Rainfall, Barometric Pressure, Wind Speed, and Wind Direction for the Barro Colorado (PA-BCI) site for the period from 2003-01-01 to 2016-12-31 were downloaded from the STRI website. The QA/QC protocol of raw datasets included the detection and removal of NAs, outliers and bad data points, imputation of missing data, and creation of the time series with equidistant time stamps. The time series for different meteorological parameters were aligned into a single database to be used for modeling. Statistical QA/QC analysis was performed using a series of R libraries in Rstudio. The changes in the dataset were flagged. The file BCI_met_drivers_2003-2016_QAQC_summary_report.docx provides the details of the QA/QC data analysis. This dataset was originally published on the NGEE Tropics Archive and is being mirrored on ESS-DIVE for long-term archival Acknowledgement: Steve Paton of STRI provided an initial QA/QC of all met drivers

Quality Assurance Services Market was valued at USD 5.3 Billion in 2024 and is projected to reach USD 12.9 Billion by 2031, growing at a CAGR of 11.2% during the forecast period 2024-2031.

Global Quality Assurance Services Market Drivers

The market drivers for the Quality Assurance Services Market can be influenced by various factors. These may include:

Increasing Complexity of Products and Services: The growing complexity of products and services across various industries necessitates robust quality assurance (QA) services to ensure compliance with standards and regulations.
Emphasis on Regulatory Compliance: Stringent regulatory requirements in industries such as healthcare, pharmaceuticals, aerospace, and automotive drive the demand for quality assurance services to meet regulatory standards and certifications.
Focus on Customer Experience: Organizations prioritize quality assurance to enhance customer satisfaction, improve product reliability, and maintain brand reputation through consistent delivery of high-quality products and services.
Globalization and Supply Chain Management: Globalization of supply chains requires rigorous quality control and assurance processes to manage product quality across international markets and ensure consistency.
Adoption of Industry 4.0 Technologies: Integration of advanced technologies such as IoT, AI, big data analytics, and automation in manufacturing and service sectors increases the need for quality assurance services to optimize processes and ensure product reliability.
Risk Management and Mitigation: Quality assurance services help mitigate risks associated with product defects, recalls, non-compliance, and potential legal liabilities, thereby protecting organizational assets and reputation.
Continuous Improvement Initiatives: Organizations adopt quality assurance as part of continuous improvement initiatives to achieve operational excellence, reduce waste, and enhance overall efficiency and productivity.
Demand for Software Testing Services: With the rise of digital transformation and software-driven solutions, there is an increasing demand for quality assurance services in software testing and validation to ensure application reliability and security.
Outsourcing Trends: Outsourcing of quality assurance services by organizations to specialized QA providers helps reduce costs, access expertise, and focus on core competencies, driving market growth.
Focus on Sustainable Practices: Increasing focus on sustainable practices and corporate social responsibility (CSR) encourages organizations to implement rigorous quality assurance measures to ensure environmental and ethical standards are met.

This data set contains QA/QC-ed (Quality Assurance and Quality Control) water level data for the PLM1 and PLM6 wells. PLM1 and PLM6 are location identifiers used by the Watershed Function SFA project for two groundwater monitoring wells along an elevation gradient located along the lower montane life zone of a hillslope near the Pumphouse location at the East River Watershed, Colorado, USA. These wells are used to monitor subsurface water and carbon inventories and fluxes, and to determine the seasonally dependent flow of groundwater under the PLM hillslope. The downslope flow of groundwater in combination with data on groundwater chemistry (see related references) can be used to estimate rates of solute export from the hillslope to the floodplain and river. QA/QC analysis of measured groundwater levels in monitoring wells PLM-1 and PLM-6 included identification and flagging of duplicated values of timestamps, gap filling of missing timestamps and water levels, removal of abnormal/bad and outliers of measured water levels. The QA/QC analysis also tested the application of different QA/QC methods and the development of regular (5-minute, 1-hour, and 1-day) time series datasets, which can serve as a benchmark for testing other QA/QC techniques, and will be applicable for ecohydrological modeling. The package includes a Readme file, one R code file used to perform QA/QC, a series of 8 data csv files (six QA/QC-ed regular time series datasets of varying intervals (5-min, 1-hr, 1-day) and two files with QA/QC flagging of original data), and three files for the reporting format adoption of this dataset (InstallationMethods, file level metadata (flmd), and data dictionary (dd) files).QA/QC-ed data herein were derived from the original/raw data publication available at Williams et al., 2020 (DOI: 10.15485/1818367). For more information about running R code file (10.15485_1866836_QAQC_PLM1_PLM6.R) to reproduce QA/QC output files, see README (QAQC_PLM_readme.docx). This dataset replaces the previously published raw data time series, and is the final groundwater data product for the PLM wells in the East River. Complete metadata information on the PLM1 and PLM6 wells are available in a related dataset on ESS-DIVE: Varadharajan C, et al (2022). https://doi.org/10.15485/1660962. These data products are part of the Watershed Function Scientific Focus Area collection effort to further scientific understanding of biogeochemical dynamics from genome to watershed scales. 2022/09/09 Update: Converted data files using ESS-DIVE’s Hydrological Monitoring Reporting Format. With the adoption of this reporting format, the addition of three new files (v1_20220909_flmd.csv, V1_20220909_dd.csv, and InstallationMethods.csv) were added. The file-level metadata file (v1_20220909_flmd.csv) contains information specific to the files contained within the dataset. The data dictionary file (v1_20220909_dd.csv) contains definitions of column headers and other terms across the dataset. The installation methods file (InstallationMethods.csv) contains a description of methods associated with installation and deployment at PLM1 and PLM6 wells. Additionally, eight data files were re-formatted to follow the reporting format guidance (er_plm1_waterlevel_2016-2020.csv, er_plm1_waterlevel_1-hour_2016-2020.csv, er_plm1_waterlevel_daily_2016-2020.csv, QA_PLM1_Flagging.csv, er_plm6_waterlevel_2016-2020.csv, er_plm6_waterlevel_1-hour_2016-2020.csv, er_plm6_waterlevel_daily_2016-2020.csv, QA_PLM6_Flagging.csv). The major changes to the data files include the addition of header_rows above the data containing metadata about the particular well, units, and sensor description. 2023/01/18 Update: Dataset updated to include additional QA/QC-ed water level data up until 2022-10-12 for ER-PLM1 and 2022-10-13 for ER-PLM6. Reporting format specific files (v2_20230118_flmd.csv, v2_20230118_dd.csv, v2_20230118_InstallationMethods.csv) were updated to reflect the additional data. R code file (QAQC_PLM1_PLM6.R) was added to replace the previously uploaded HTML files to enable execution of the associated code. R code file (QAQC_PLM1_PLM6.R) and ReadMe file (QAQC_PLM_readme.docx) were revised to clarify where original data was retrieved from and to remove local file paths.

This dataset includes Quality Assessed and Quality Controlled (QA/QC) meteorological data from the Billy Bar field site in the East River Watershed, Colorado in order to inform watershed hydrobiogeochemical processes. The data includes 1-hour aligned time series of Solar Radiation, Wind Speed, Wind Direction, Air Temperature, Relative Humidity, Barometric Pressure, and Precipitation. For each parameter, the QA flags are given: 1 is for extreme (potential abnormal values), and 0 is for values in the expected range. The Rmarkdown document is a stand-alone file/notebook that preserves the text, code, and code results, as well as formatting contained in the original R script. The Data profiling report is a summary and graphical presentation of the data exploration process for data analysis and model building, so that users could focus on understanding data and extracting insights. The report provides a summary of each variable and does data profiling.

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. Data used to produce the …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. Data used to produce the predicted Total Dissolved Solids map for the Cadna-owie - Hooray Aquifer in the Hydrogeological Atlas of the Great Artesian Basin (Ransley et.al., 2014). There are four layers in the Cadna-owie - Hooray Aquifer Total Dissolved Solids map data A. Location of hydrochemistry samples (Point data, Shapefile) B. Predicted Concentration (Filled contours , Shapefile) C. Predicted Concentration Contours (Contours, Shapefile) D. Prediction Standard Error (Filled contours , Shapefile) The predicted values provide a regional based estimate and may be associated with considerable error. It is recommended that the predicted values are read together with the predicted error map, which provides an estimate of the absolute standard error associated with the predicted values at any point within the map. The predicted standard error map provides an absolute standard error associated with the predicted values at any point within the map. Please note this is not a relative error map and the concentration of a parameter needs to be considered when interpreting the map. Predicted standard error values are low where the concentration is low and there is a high density of samples. Predicted standard errors values can be high where the concentration is high and there is moderate variability between nearby samples or where there is a paucity of data. Concentrations are Total Dissolved Solids mg/L. Coordinate system is Lambert conformal conic GDA 1994, with central meridian 134 degrees longitude, standard parallels at -18 and -36 degrees latitude. The Cadna-owie - Hooray Aquifer Total Dissolved Solids map is one of 14 hydrochemistry maps for the Cadna-owie - Hooray Aquifer and 24 hydrochemistry maps in the Hydrogeological Atlas of the Great Artesian Basin (Ransley et. al., 2014). This dataset and associated metadata can be obtained from www.ga.gov.au, using catalogue number 81693. References: Hitchon, B. and Brulotte, M. (1994): Culling criteria for ‘standard’ formation water analyses; Applied Geochemistry, v. 9, p. 637–645 Ransley, T., Radke, B., Feitz, A., Kellett, J., Owens, R., Bell, J. and Stewart, G., 2014. Hydrogeological Atlas of the Great Artesian Basin. Geoscience Australia. Canberra. [available from www.ga.gov.au using catalogue number 79790] Dataset History SOURCE DATA: Data was obtained from a variety of sources, as listed below: Water quality data from the Queensland groundwater database, Department of Environment and Resource Management Geological Society of Queensland water chemistry database (1970s to 1980s). Muller, PJ, Dale, NM (1985) Storage System for Groundwater Data Held by the Geological Survey of Queensland. GSQ Record 1985/47. Queensland. Geoscience Australia GAB hydrochemistry dataset 1973-1997. Published in Radke BM, Ferguson J, Cresswell RG, Ransley TR and Habermehl MA (2000) Hydrochemistry and implied hydrodynamics of the Cadna-owie - Hooray Aquifer, Great Artesian Basin, Australia. Canberra, Bureau of Rural Sciences: xiv, 229p. Feitz, A.J., Ransley, T.R., Dunsmore, R., Kuske, T.J., Hodgkinson, J., Preda, M., Spulak, R., Dixon, O. & Draper, J., 2014. Geoscience Australia and Geological Survey of Queensland Surat and Bowen Basins Groundwater Surveys Hydrochemistry Dataset (2009-2011). Geoscience Australia, Canberra Australia Water quality data from the Office of Groundwater Impact Assessment, Department of Natural Resources and Mines, Queensland Government Geoscience Australia (2010) Hydrogeochemical collection. A compilation of quality controlled groundwater data taken from well completion reports from QLD and NSW. Water quality data from the Office of Groundwater Impact Assessment, Department of Natural Resources and Mines, Queensland Government BOUNDARIES: Data covers the extent of the Cadna-owie-Hooray Aquifer and Equivalents as defined in Great Artesian Basin - Cadna-owie-Hooray Aquifer and Equivalents - Thickness and Extent dataset (Available from www.ga.gov.au using catalogue number 81678) METHOD: Groundwater chemistry data was compiled from the data sources listed above. Data was imported into ESRI ArcGIS (ArcMap 10) as data point sets and used to create a predicted values surface using an ordinary kriging method within the Geostatistical Analyst extension. A log transform was applied to the Alkalinity, TDS, Na, SO4, Mg, Ca, K, F, Cl, Cl36 data prior to kriging. No transform was applied to the 13C, 18O, 2H, pH data prior to kriging. The geostatistical model was optimized using cross validation. The search neighbourhood was extended to a 1 degree radius, comprising of 4 sectors (N, S, E and W) with a minimum and maximum of 3 and 8 neighbours, respectively, per sector. The predicted values surface was exported to a vector format (Shapefile) and clipped to the aquifer boundaries. QAQC: Prior to data analysis all hydrochemistry data was assessed for reliability by Quality Assurance/Quality Control (QA/QC) procedures. A data audit and verification were performed using various quality checking procedures including identification and verification of outliers. The ionic balance of each analysis was checked, and where the ionic charge balance differed by greater than 10%, these analyses were deemed unacceptable and were not considered for future analysis. Data that passed the initial QA/QC procedures were checked against borehole construction and stratigraphic records to determine aquifer intercepts. Data were discarded in cases where there was no recorded location information or screen interval/depth information (to cross reference with borehole stratigraphy). One exception was chemistry data obtained from the NSW Governments Triton database. Groundwater chemistry data obtained from bore records in the Triton database that was also identified as GAB bores in the NSW Governments Pinneena database were assumed to be in the Pilliga Sandstone and were allocated to the Cadna-owie Hooray equivalent aquifer, despite many not recording depth information. Groundwater chemistry data was sourced from multiple studies, government databases, and companies. Many of the studies used sub-sets of the same data. All duplicates were removed before mapping and analysis. The differences between data sources had to be reconciled to ensure that maximum value of the data was retained and for errors in the transcription to be avoided. This precluded any automated processing system. Random checks were routinely made against the source data to ensure quality of the process. Some source data was in the form of thousands of consecutive rows and required python scripts or detailed table manipulations to correctly re-format the information and re-produce records with all the well data, its location and hydrochemical data for a particular sample date on one row in the collated Excel spreadsheet. Alkalinity measurements, in particular, were often reported differently between studies and even within the same database and required conversion to a common unit. All data before 1960 was discarded. The study uses a data collection compiled from petroleum well completion reports from QLD and NSW. This data underwent a thorough QC process to ensure that drilling mud contaminated samples were excluded, based on the procedure described by Hitchon, B. & Brulotte, M. (1994). Less than 5% of the samples compiled passed the QC procedure, but these provide invaluable insight into the chemistry of very deep parts of the aquifers (typically 1 - 2km deep). Where multiple samples have been taken at the same well, an average of the analyses was used in the kriging but outliers were removed. Outliers were identified by looking for large differences between predicted and measured samples. Excessively high values compared to predicted values and typical measurements at the same bore were discarded. Dataset Citation Geoscience Australia (2015) GABATLAS - Cadna-owie - Hooray Aquifer Total Dissolved Solids map: Data. Bioregional Assessment Source Dataset. Viewed 11 April 2016, http://data.bioregionalassessments.gov.au/dataset/5044a067-35d1-4d6d-98a6-17974aa9226a.

Abstract

This dataset was derived by the Bioregional Assessment Programme. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

The resource contains hyrochemical data which includes field sampling data (e.g. water temperature, pH, EC, DO, alkalinity) and sampling laboratory data (e..g. major ions, trace metals and isotopes). This dataset was extracted directly from the DNRM 'water analysis' table and underwent quality assurance procedure which checked the ionic balance and kept only those samples which balanced to +/- 10%.

Purpose

It has been created to analyse the hydrochemical trends in the aquifers and coal seams of QLD hydrogeological formations

Dataset History

QLD DNRM bore point data was joined to water analysis data and a spreadsheet of the matching features exported. This spreadsheet was used to provide QA/QC on each water sample by checking the ionic balance and retaining only those samples which retained a +/- 10% balance. Validated data was then returned to a shapefile point dataset and the original spreadsheet retained.

Dataset Citation

Bioregional Assessment Programme (2014) QLD DNRM Hydrochemistry with QA/QC. Bioregional Assessment Derived Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/4472af17-1470-4e7a-ae1d-9fc6443d9e1d.

Dataset Ancestors

• Derived From QLD Department of Natural Resources and Mining Groundwater Database Extract 20131111

Quality Controlled Local Climatological Data (QCLCD) contains summaries from major airport weather stations that include a daily account of temperature extremes, degree days, precipitation amounts and winds. Also included are the hourly precipitation amounts and abbreviated 3-hourly weather observations. The source data is global hourly (DSI 3505) which includes a number of quality control checks. The local climatological data annual file is produced from the National Weather Service (NWS) first and second order stations. The monthly summaries include maximum, minimum, and average temperature, temperature departure from normal, dew point temperature, average station pressure, ceiling, visibility, weather type, wet bulb temperature, relative humidity, degree days (heating and cooling), daily precipitation, average wind speed, fastest wind speed/direction, sky cover, and occurrences of sunshine, snowfall and snow depth. The annual summary with comparative data contains monthly and annual averages of the above basic climatological data in the meteorological data for the current year section, a table of the normals, means, and extremes of these same data, and sequential table of monthly and annual values of average temperature, total precipitation, total snowfall, and total degree days. Also included is a station location table showing in detail a history of, and relative information about, changes in the locations and exposure of instruments.

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. Data used to produce the …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. Data used to produce the predicted Total Dissolved Solids map for the Hutton Aquifer and equivalents in the Hydrogeological Atlas of the Great Artesian Basin (Ransley et.al., 2014). There are four layers in the Hutton Aquifer and equivalents Total Dissolved Solids map data A. Location of hydrochemistry samples (Point data, Shapefile) B. Predicted Concentration (Filled contours , Shapefile) C. Predicted Concentration Contours (Contours, Shapefile) D. Prediction Standard Error (Filled contours , Shapefile) The predicted values provide a regional based estimate and may be associated with considerable error. It is recommended that the predicted values are read together with the predicted error map, which provides an estimate of the absolute standard error associated with the predicted values at any point within the map. The predicted standard error map provides an absolute standard error associated with the predicted values at any point within the map. Please note this is not a relative error map and the concentration of a parameter needs to be considered when interpreting the map. Predicted standard error values are low where the concentration is low and there is a high density of samples. Predicted standard errors values can be high where the concentration is high and there is moderate variability between nearby samples or where there is a paucity of data. Concentrations are Total Dissolved Solids mg/L. Coordinate system is Lambert conformal conic GDA 1994, with central meridian 134 degrees longitude, standard parallels at -18 and -36 degrees latitude. The Hutton Aquifer and equivalents Total Dissolved Solids map is one of four hydrochemistry maps for the Hutton Aquifer and equivalents and 24 hydrochemistry maps in the Hydrogeological Atlas of the Great Artesian Basin (Ransley et.al., 2014). This dataset and associated metadata can be obtained from www.ga.gov.au, using catalogue number 81709. References: Hitchon, B. and Brulotte, M. (1994): Culling criteria for ‘standard’ formation water analyses; Applied Geochemistry, v. 9, p. 637–645 Ransley, T., Radke, B., Feitz, A., Kellett, J., Owens, R., Bell, J. and Stewart, G., 2014. Hydrogeological Atlas of the Great Artesian Basin. Geoscience Australia. Canberra. [available from www.ga.gov.au using catalogue number 79790] Dataset History SOURCE DATA: Data was obtained from a variety of sources, as listed below: Water quality data from the Queensland groundwater database, Department of Environment and Resource Management Geological Society of Queensland water chemistry database (1970s to 1980s). Muller, PJ, Dale, NM (1985) Storage System for Groundwater Data Held by the Geological Survey of Queensland. GSQ Record 1985/47. Queensland. Geoscience Australia GAB hydrochemistry dataset 1973-1997. Published in Radke BM, Ferguson J, Cresswell RG, Ransley TR and Habermehl MA (2000) Hydrochemistry and implied hydrodynamics of the Cadna-owie - Hooray Aquifer, Great Artesian Basin, Australia. Canberra, Bureau of Rural Sciences: xiv, 229p. Feitz, A.J., Ransley, T.R., Dunsmore, R., Kuske, T.J., Hodgkinson, J., Preda, M., Spulak, R., Dixon, O. & Draper, J., 2014. Geoscience Australia and Geological Survey of Queensland Surat and Bowen Basins Groundwater Surveys Hydrochemistry Dataset (2009-2011). Geoscience Australia, Canberra Australia Water quality data from the Office of Groundwater Impact Assessment, Department of Natural Resources and Mines, Queensland Government Geoscience Australia (2010) Hydrogeochemical collection. A compilation of quality controlled groundwater data taken from well completion reports from QLD and NSW. Water quality data from the Office of Groundwater Impact Assessment, Department of Natural Resources and Mines, Queensland Government BOUNDARIES: Data covers the extent of the Hutton Aquifer and equivalents as defined in Great Artesian Basin - Hutton Aquifer and equivalents - Thickness and Extent dataset (Available from www.ga.gov.au using catalogue number 81682). METHOD: Groundwater chemistry data was compiled from the data sources listed above. Data was imported into ESRI ArcGIS (ArcMap 10) as data point sets and used to create a predicted values surface using an ordinary kriging method within the Geostatistical Analyst extension. A log transform was applied to the Alkalinity, TDS, Na, SO4, Mg, Ca, K, F, Cl, Cl36 data prior to kriging. No transform was applied to the 13C, 18O, 2H, pH data prior to kriging. The geostatistical model was optimized using cross validation. The search neighbourhood was extended to a 1 degree radius, comprising of 4 sectors (N, S, E and W) with a minimum and maximum of 3 and 8 neighbours, respectively, per sector. The predicted values surface was exported to a vector format (Shapefile) and clipped to the aquifer boundaries and clipped further where there was no data within 100 km. QAQC: Prior to data analysis all hydrochemistry data was assessed for reliability by Quality Assurance/Quality Control (QA/QC) procedures. A data audit and verification were performed using various quality checking procedures including identification and verification of outliers. The ionic balance of each analysis was checked, and where the ionic charge balance differed by greater than 10%, these analyses were deemed unacceptable and were not considered for future analysis. Data that passed the initial QA/QC procedures were checked against borehole construction and stratigraphic records to determine aquifer intercepts. Data were discarded in cases where there was no recorded location information or screen interval/depth information (to cross reference with borehole stratigraphy). Groundwater chemistry data was sourced from multiple studies, government databases, and companies. Many of the studies used sub-sets of the same data. All duplicates were removed before mapping and analysis. The differences between data sources had to be reconciled to ensure that maximum value of the data was retained and for errors in the transcription to be avoided. This precluded any automated processing system. Random checks were routinely made against the source data to ensure quality of the process. Some source data was in the form of thousands of consecutive rows and required python scripts or detailed table manipulations to correctly re-format the information and re-produce records with all the well data, its location and hydrochemical data for a particular sample date on one row in the collated Excel spreadsheet. Alkalinity measurements, in particular, were often reported differently between studies and even within the same database and required conversion to a common unit. All data before 1960 was discarded. The study uses a data collection compiled from petroleum well completion reports from QLD and NSW. This data underwent a thorough QC process to ensure that drilling mud contaminated samples were excluded, based on the procedure described by Hitchon, B. & Brulotte, M. (1994). Less than 5% of the samples compiled passed the QC procedure, but these provide invaluable insight into the chemistry of very deep parts of the aquifers (typically 1 - 2km deep). Where multiple samples have been taken at the same well, an average of the analyses was used in the kriging but outliers were removed. Outliers were identified by looking for large differences between predicted and measured samples. Excessively high values compared to predicted values and typical measurements at the same bore were discarded. Dataset Citation Geoscience Australia (2015) Hutton Aquifer and equivalents Total Dissolved Solids map: Data. Bioregional Assessment Source Dataset. Viewed 11 April 2016, http://data.bioregionalassessments.gov.au/dataset/f5f16389-d97e-46b3-bd43-83255acf257d.

The U.S. Geological Survey (USGS), in cooperation with DuPage County Stormwater Management Department, maintains a watershed data management (WDM) database of hourly meteorological and hydrologic data for use in a near real-time streamflow simulation system. These data are stored daily in the WDM database by USGS staff and are initially provisional and subject to change due to periodic equipment malfunctions and/or weather related issues. This system is used for the management and operation of reservoirs and other flood-control structures in the West Branch DuPage River (WBDR) watershed in DuPage County, Illinois. The WDM database is updated with quality-assured and quality-controlled (QA/QC) meteorological and hydrologic data for each water year (WY) and is named as WBDRXX.wdm where XX represents the last two digits of the WY. A WY is the 12-month period, October 1 through September 30, in which it ends. The WDM database is a binary, direct-access electronic file (Flynn and others, 1995). It was developed by the USGS to be used with hydrologic and water-quality models and analyses. Data within the WDM database are stored in datasets and each dataset is assigned a number called a dataset number (DSN). The WDM database can be accessed with the ANNIE computer program (Flynn and others, 1995), with the Generation and Analysis of Model Simulation Scenarios (GenScn), an interactive computer program (Kittle and others, 1998), or with Better Assessment Science Integrating point and Nonpoint Sources (BASINS), a multipurpose environmental analysis system (U. S. Environmental Protection Agency, 2015). This is version 1.1 of this data release with the corrected meteorological data for the period January 1, 2007, through September 30, 2022. Errors have been found in each of ARGNXX.WDM prior to WY 2023. WBDR13.wdm contains erroneous meteorological data and related flag values thereby. WBDR13.WDM is replaced with WBDR22.WDM. This WDM file contains corrected meteorological data from ARGN23.WDM (Bera, 2024a) for the period from January 1, 2007, through September 30, 2022, along with other data mentioned in the WBDR13.WDM. While WBDR13.WDM is available from the author, all the records in WBDR13.WDM can be found in this version as well. The WDM file WBDR22.WDM contains meteorological and hydrologic data collected in and near DuPage County, Illinois. The precipitation data are collected from a tipping-bucket rain-gage network located in and near DuPage County. The hydrologic data (stage and discharge) are collected at USGS streamflow-gaging stations in and around DuPage County. The WDM database WBDR22.WDM is quality-assured and quality-controlled (QA/QC) to ensure the datasets are complete and accurate and contains data from January 1, 2007, through September 30, 2022. The Open File Report Bera (2017) describes the data organization, sources, and QA/QC in detail. The complete list of datasets in WBDR22.WDM database is given in table 2, and a list of dataset attributes is given in Appendix 1 (Bera (2017). To open WBDR22.WDM file user needs to install Sara Timeseries utility described in the section "Related External Resources". First posted - October 12, 2017 (available from author) References Cited: Bera, M., 2024a, Meteorological Database, Argonne National Laboratory, Illinois: U.S. Geological Survey data release, https://doi.org/10.5066/P146RBHK. _ 2024b, Watershed Data Management (WDM) Database (WBDR22.WDM) for West Branch DuPage River Streamflow Simulation, DuPage County, Illinois, January 1, 2007, through September 30, 2022: U.S. Geological Survey data release, https://doi.org/10.5066/P1LDIASU. Bera, Maitreyee, 2017, Watershed Data Management (WDM) database for West Branch DuPage River streamflow simulation, DuPage County, Illinois, January 1, 2007, through September 30, 2013: U.S. Geological Survey Open-File Report 2017–1099, 39 p., https://doi.org/10.3133/ofr20171099. Flynn, K.M., Hummel, P.R., Lumb, A.M., and Kittle, J.L., Jr., 1995, User’s manual for ANNIE, version 2, a computer program for interactive hydrologic data management: U.S. Geological Survey Water-Resources Investigations Report 95–4085, 211 p. Kittle, J.L., Jr., Lumb, A.M., Hummel, P.R., Duda, P.B., and Gray, M.H., 1998, A tool for the generation and analysis of model simulation scenarios for watersheds (GenScn): U.S. Geological Survey Water-Resources Investigations Report 98–4134, 152 p.

Abstract The dataset was derived by the Bioregional Assessment Programme from "QLD DNRM Hydrochemistry with QA/QC" and "NSW Office of Water Groundwater Quality extract 28_nov_2013" data provided by the Qld DNRM and NSW Office of Water. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. The dataset contains the outputs of a multivariate …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from "QLD DNRM Hydrochemistry with QA/QC" and "NSW Office of Water Groundwater Quality extract 28_nov_2013" data provided by the Qld DNRM and NSW Office of Water. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. The dataset contains the outputs of a multivariate statistical analysis conducted on groundwater chemistry data for different river basins or sub-regions within the CLM bioregion. The analysis was conducted using Statgraphics software. Only samples that passed the QA/QC checks (e.g. charge balances within +-5%) were included in the analysis. Dataset History The original datasets were clipped to the CLM bioregion. After an initial data quality check, only those samples that met the criteria (e.g. charge balance between + and - 5%) were included in the multivariate statistical analysis. Multivariate statistical analysis was conducted on the remaining dataset (i.e. those samples that did not meet the QA/QC criteria removed), resulting in different groundwater chemistry groups. The methodology is described in more detail by Raiber et al., (2012). M Raiber, PA White, CJ Daughney, C Tschritter, P Davidson (2012). Three-dimensional geological modelling and multivariate statistical analysis of water chemistry data to analyse and visualise aquifer structure and groundwater composition in the Wairau Plain, Marlborough District, New Zealand, Journal of Hydrology 436, 13-34 Dataset Citation Bioregional Assessment Programme (2014) CLM - Groundwater Chemistry outputs from multivariate statistics. Bioregional Assessment Derived Dataset. Viewed 28 September 2017, http://data.bioregionalassessments.gov.au/dataset/4c128b86-1089-4ba9-85f8-76bbd65db396. Dataset Ancestors Derived From NSW Office of Water - Groundwater quality extract Derived From QLD DNRM Hydrochemistry with QA/QC Derived From QLD Department of Natural Resources and Mining Groundwater Database Extract 20131111

Several quality control measures were taken during the project. These included: - Central provision of sampling equipment and sample bags to all field teams - Randomised sample identification scheme so that samples were presented to the laboratories in a sequence unrelated to the order in which they were collected (as much as practically feasible) - Prevention of contamination in the field and in the lab - Prevention of sample mix-up in the field and in the lab - Field duplicates: every 10th site, a field duplicate sample was collected to help quantify total (sampling + analytical) precision (not identified as such to the lab) - Certified Reference Materials (CRMs) TILL-1, TILL-2 (Natural Resources Canada) were run with every batch on GA's XRF & ICP-MS to help quantify analytical precision and bias - Laboratory duplicates (splits), internal project standards (MRIS, WRIS, ORIS, MRIS2, WRIS2), exchanged project standards (GEMAS-Ap, GEMAS-Gr from EuroGeoSurveys; SoNE-1 from United States Geological Survey), and international CRMs (TILL-1, TILL-3, LKSD-1, STSD-3 from Natural Resources Canada) were covertly inserted in the analytical suites for in-house and external analyses to help quantify analytical precision and bias (not identified as such to the lab) - Internal project standard (GRIS) for pH 1:5, EC 1:5 and grain size measurements (not identified as such to the lab) In addition to the above measures, the analytical labs applied their own QA/QC procedures, including running CRMs and/or internal standards, replicating digests and/or analysis, and analysis of blanks. The present report uses some of the above data to quantitatively assess the quality of the NGSA data, which allows a quality statement to be made about the NGSA data.

Analysis of ‘i08 Stations Monitoring Continuous Hydstra’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/e7449a80-0a3f-43f8-9778-7bff9f7b9c18 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

This is a point feature class of environmental monitoring stations maintained in the California Department of Water Resources’ (hereafter the Department) Hydstra continuous database management system used for collection of high frequency continuous timeseries data for groundwater, surface water, water quality and tidal station types. The QA/QC data timeseries data associated with these stations is published through the Departments Water Data Library web application. This dataset is comprised of a “Stations Table” and a related “Period of Record Table”. Stations table is the primary feature class and contains basic information about each station including Station Name, Latitude, Longitude and Description. The Period of Record Table is a related feature class that contains a list of parameters (i.e. stage, flow, depth to groundwater, water temperature, turbidity, pH, etc.) collected at each station along with the start date and end date (period of record) for each parameter and the number of data points collected.

--- Original source retains full ownership of the source dataset ---

Analysis of ‘i08 Stations Monitoring Continuous Hydstra’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/37ae7a39-525d-4afc-a675-ed5b5ba7a880 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

This is a point feature class of environmental monitoring stations maintained in the California Department of Water Resources’ (hereafter the Department) Hydstra continuous database management system used for collection of high frequency continuous timeseries data for groundwater, surface water, water quality and tidal station types. The QA/QC data timeseries data associated with these stations is published through the Departments Water Data Library web application. This dataset is comprised of a “Stations Table” and a related “Period of Record Table”. Stations table is the primary feature class and contains basic information about each station including Station Name, Latitude, Longitude and Description. The Period of Record Table is a related feature class that contains a list of parameters (i.e. stage, flow, depth to groundwater, water temperature, turbidity, pH, etc.) collected at each station along with the start date and end date (period of record) for each parameter and the number of data points collected.

--- Original source retains full ownership of the source dataset ---

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. Data used to produce the predicted Total Dissolved Solids map for the Hutton Aquifer and equivalents in the Hydrogeological Atlas of the Great Artesian Basin (Ransley et.al., 2014). There are four layers in the Hutton Aquifer and equivalents Total Dissolved Solids map data A. Location of hydrochemistry samples (Point data, Shapefile) B. Predicted Concentration (Filled contours , Shapefile) C. Predicted Concentration Contours (Contours, Shapefile) D. Prediction Standard Error (Filled contours , Shapefile) The predicted values provide a regional based estimate and may be associated with considerable error. It is recommended that the predicted values are read together with the predicted error map, which provides an estimate of the absolute standard error associated with the predicted values at any point within the map. The predicted standard error map provides an absolute standard error associated with the predicted values at any point within the map. Please note this is not a relative error map and the concentration of a parameter needs to be considered when interpreting the map. Predicted standard error values are low where the concentration is low and there is a high density of samples. Predicted standard errors values can be high where the concentration is high and there is moderate variability between nearby samples or where there is a paucity of data. Concentrations are Total Dissolved Solids mg/L. Coordinate system is Lambert conformal conic GDA 1994, with central meridian 134 degrees longitude, standard parallels at -18 and -36 degrees latitude. The Hutton Aquifer and equivalents Total Dissolved Solids map is one of four hydrochemistry maps for the Hutton Aquifer and equivalents and 24 hydrochemistry maps in the Hydrogeological Atlas of the Great Artesian Basin (Ransley et.al., 2014). This dataset and associated metadata can be obtained from www.ga.gov.au, using catalogue number 81709. References: Hitchon, B. and Brulotte, M. (1994): Culling criteria for ‘standard’ formation water analyses; Applied Geochemistry, v. 9, p. 637–645 Ransley, T., Radke, B., Feitz, A., Kellett, J., Owens, R., Bell, J. and Stewart, G., 2014. Hydrogeological Atlas of the Great Artesian Basin. Geoscience Australia. Canberra. [available from www.ga.gov.au using catalogue number 79790] Dataset History SOURCE DATA: Data was obtained from a variety of sources, as listed below: Water quality data from the Queensland groundwater database, Department of Environment and Resource Management Geological Society of Queensland water chemistry database (1970s to 1980s). Muller, PJ, Dale, NM (1985) Storage System for Groundwater Data Held by the Geological Survey of Queensland. GSQ Record 1985/47. Queensland. Geoscience Australia GAB hydrochemistry dataset 1973-1997. Published in Radke BM, Ferguson J, Cresswell RG, Ransley TR and Habermehl MA (2000) Hydrochemistry and implied hydrodynamics of the Cadna-owie - Hooray Aquifer, Great Artesian Basin, Australia. Canberra, Bureau of Rural Sciences: xiv, 229p. Feitz, A.J., Ransley, T.R., Dunsmore, R., Kuske, T.J., Hodgkinson, J., Preda, M., Spulak, R., Dixon, O. & Draper, J., 2014. Geoscience Australia and Geological Survey of Queensland Surat and Bowen Basins Groundwater Surveys Hydrochemistry Dataset (2009-2011). Geoscience Australia, Canberra Australia Water quality data from the Office of Groundwater Impact Assessment, Department of Natural Resources and Mines, Queensland Government Geoscience Australia (2010) Hydrogeochemical collection. A compilation of quality controlled groundwater data taken from well completion reports from QLD and NSW. Water quality data from the Office of Groundwater Impact Assessment, Department of Natural Resources and Mines, Queensland Government BOUNDARIES: Data covers the extent of the Hutton Aquifer and equivalents as defined in Great Artesian Basin - Hutton Aquifer and equivalents - Thickness and Extent dataset (Available from www.ga.gov.au using catalogue number 81682). METHOD: Groundwater chemistry data was compiled from the data sources listed above. Data was imported into ESRI ArcGIS (ArcMap 10) as data point sets and used to create a predicted values surface using an ordinary kriging method within the Geostatistical Analyst extension. A log transform was applied to the Alkalinity, TDS, Na, SO4, Mg, Ca, K, F, Cl, Cl36 data prior to kriging. No transform was applied to the 13C, 18O, 2H, pH data prior to kriging. The geostatistical model was optimized using cross validation. The search neighbourhood was extended to a 1 degree radius, comprising of 4 sectors (N, S, E and W) with a minimum and maximum of 3 and 8 neighbours, respectively, per sector. The predicted values surface was exported to a vector format (Shapefile) and clipped to the aquifer boundaries and clipped further where there was no data within 100 km. QAQC: Prior to data analysis all hydrochemistry data was assessed for reliability by Quality Assurance/Quality Control (QA/QC) procedures. A data audit and verification were performed using various quality checking procedures including identification and verification of outliers. The ionic balance of each analysis was checked, and where the ionic charge balance differed by greater than 10%, these analyses were deemed unacceptable and were not considered for future analysis. Data that passed the initial QA/QC procedures were checked against borehole construction and stratigraphic records to determine aquifer intercepts. Data were discarded in cases where there was no recorded location information or screen interval/depth information (to cross reference with borehole stratigraphy). Groundwater chemistry data was sourced from multiple studies, government databases, and companies. Many of the studies used sub-sets of the same data. All duplicates were removed before mapping and analysis. The differences between data sources had to be reconciled to ensure that maximum value of the data was retained and for errors in the transcription to be avoided. This precluded any automated processing system. Random checks were routinely made against the source data to ensure quality of the process. Some source data was in the form of thousands of consecutive rows and required python scripts or detailed table manipulations to correctly re-format the information and re-produce records with all the well data, its location and hydrochemical data for a particular sample date on one row in the collated Excel spreadsheet. Alkalinity measurements, in particular, were often reported differently between studies and even within the same database and required conversion to a common unit. All data before 1960 was discarded. The study uses a data collection compiled from petroleum well completion reports from QLD and NSW. This data underwent a thorough QC process to ensure that drilling mud contaminated samples were excluded, based on the procedure described by Hitchon, B. & Brulotte, M. (1994). Less than 5% of the samples compiled passed the QC procedure, but these provide invaluable insight into the chemistry of very deep parts of the aquifers (typically 1 - 2km deep). Where multiple samples have been taken at the same well, an average of the analyses was used in the kriging but outliers were removed. Outliers were identified by looking for large differences between predicted and measured samples. Excessively high values compared to predicted values and typical measurements at the same bore were discarded.

The global market for Simultaneous Thermal Analyzers (STA) is experiencing robust growth, driven by increasing demand across diverse sectors like pharmaceuticals, polymers, and materials science. The market, valued at approximately $250 million in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033. This growth is fueled by several key factors. Advancements in STA technology, leading to improved accuracy, sensitivity, and automation, are attracting wider adoption. Stringent quality control regulations in industries such as pharmaceuticals and the burgeoning need for advanced materials characterization are further stimulating market expansion. The rising popularity of STA in research and development activities, particularly in academic institutions and government laboratories, also significantly contributes to market growth. Furthermore, the development of sophisticated software solutions for data analysis and interpretation enhances the usability and appeal of STA instruments. Segment-wise, portable STA systems are gaining traction due to their convenience and flexibility, while the QA/QC applications segment dominates the market share, driven by the need for rigorous quality control procedures in manufacturing. The applications of STA across various industries, from studying pharmaceutical processes and polymer analysis to medical research, highlight its versatility and critical role in modern scientific advancements. While challenges like the high initial investment cost of STA instruments could act as a restraint, the long-term benefits of improved efficiency and product quality outweigh this consideration for many end-users. The competitive landscape is shaped by a mix of established players and emerging companies, continuously striving for innovation and market share. This dynamic competitive environment ensures continued progress and fosters technological advancements in the STA market.

This data package contains locally verified monthly meteorological observations from a NOAA National Weather Service station located at the USDA Jornada Experimental Range headquarters in southern New Mexico, USA. Monthly summary data (based on daily observations) has been collected there by USDA staff since 1914 for minimum and maximum air temperature and daily accumulated precipitation using standard U.S. climatological service instrumentation and procedures. The included data were verified and transcribed directly from the original paper data sheets and have undergone quality control and assurance procedures different than those in place at NOAA. These data therefore differ from those directly downloadable from NOAA servers. Local verification and transcription of observations from the data sheets ceased in 1998 and data are now directly entered to the NOAA system. Therefore, this dataset is complete and will no longer be added to.All observations from this weather station have also undergone NOAA QA/QC procedures and those data are available by accessing the Jornada Experimental Range, NM US GHCN station through the National Climatic Data Center portal https://www.ncdc.noaa.gov/cdo-web/datasets/GSOM/stations/GHCND:USC00294426/detail - daily and monthly data are available).

Abstract This dataset was derived by the Bioregional Assessment Programme. The parent datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset contains analyses and summaries of hydrochemistry data for the Galilee subregion, and includes an additional quality assurance of the source hydrochemistry and waterlevel data to remove anomalous and …Show full descriptionAbstract This dataset was derived by the Bioregional Assessment Programme. The parent datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset contains analyses and summaries of hydrochemistry data for the Galilee subregion, and includes an additional quality assurance of the source hydrochemistry and waterlevel data to remove anomalous and outlier values. Dataset History Several bores were removed from the 'chem master sheet' in the QLD Hydrochemistry QA QC GAL v02 (GUID: e3fb6c9b-e224-4d2e-ad11-4bcba882b0af) dataset based on their TDS values. Bores with high or unrealistic TDS that were removed are found at the bottom of the 'updated data' sheet. Outlier water level values from the JK GAL Bore Waterlevels v01 (GUID: 2f8fe7e6-021f-4070-9f63-aa996b77469d) dataset were identified and removed. Those bores are identified in the 'outliers not used' sheet Pivot tables were created to summarise data, and create various histograms for analysis and interpretation. These are found in the 'chemistry histogram', 'Pivot tables', 'summaries'. Dataset Citation Bioregional Assessment Programme (2016) Hydrochemistry analysis of the Galilee subregion. Bioregional Assessment Derived Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/fd944f9f-14f6-4e20-bb8a-61d1116412ec. Dataset Ancestors Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements 20131204 Derived From QLD DNRM Hydrochemistry with QA/QC Derived From QLD Hydrochemistry QA QC GAL v02 Derived From QLD DNRM Galilee Mine Groundwater Bores - Water Levels Derived From Galilee bore water levels v01 Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements linked to bores v3 03122014 Derived From RPS Galilee Hydrogeological Investigations - Appendix tables B to F (original) Derived From Geoscience Australia, 1 second SRTM Digital Elevation Model (DEM) Derived From Carmichael Coal Mine and Rail Project Environmental Impact Statement Derived From QLD Department of Natural Resources and Mining Groundwater Database Extract 20131111

Broadband and audio magnetotelluric (BBMT and AMT) data at 476 sites on a 2 Km grid were acquired in the Cloncurry region between July and November 2016. The survey covered an area of appriximatly 40 km x 60 km on the eastern margin of the Mount Isa Province. The Cloncurry magnetotelluric (MT) project was funded by the Geological Survey of Queensland and is a collaborative project between the Geological Survey of Queensland and Geoscience Australia. Geoscience Australia managed the project and peformed data QA/QC, data analysis, and produced two-dimensional (2D) and three dimensional (3D) inverse models for both the BBMT and AMT data. This report details the field acquisition program and the methodologies used for processing, analysing, modelling and inverting the data.

Metal, hydrocarbon, or nutrient data have not been recorded for the Arctic coastal plain 1002 area of the Arctic National Wildlife Refuge (Arctic Refuge) in areas of prospective oil and corridor development. Pre-development baseline data for contaminants are necessary to enable general characterization of water quality and contaminant residues, as well as to provide site-specific pre-development information in the event of a Congressional decision to open the Arctic coastal plain to oil and gas exploration and development. This study examines 1988-1989 samples of sediments, water, sedge, birds, invertebrates, and fishes from the 1002 area. Volume 1 of the three volumes in this report describes the study area, study sites, methods, and objectives, and provides summary statistics (geometric mean, arithmetic mean, arithmetic standard deviation, maximum, minimum, and median) for those analytes with more than 2/3 of the concentrations greater than the limit of detection. Volume 2contains the raw metal and hydrocarbon contaminant data, and the raw water quality data. Volume 3summarizes quality assurance/quality control (QA/QC) results which include mean relative percent differences (RPD's) from duplicate analyses, mean percent recoveries from spiked analyses, mean recoveries and Z scores from standard reference material analyses, and maximum concentrations from blank analyses. For a comprehensive description of all quality assurance/quality control methods, also see Volume 1. These reports provide a database on a sufficient number of aquatic, terrestrial, and lagoon samples to enable general characterization of water quality and contaminant residues, as well as to provide site specific pre-development information. The reader is strongly encouraged to use the QA/QC data in Volume 3 to assess data quality on an analyte-by-analyte basis for each sample matrix. This information will be used by Refuge management and State and Federal regulators to assess any post development changes that result from any oil and gas exploratory or production activities. The data will also be useful in evaluating special use permits, Clean Water Act Sections 402 and 404 permits, and State wastewater permits, and in recommending appropriate mitigation measures if development occurs on the 1002 area.