This repository contains the raw data and analysis scripts supporting the associated publication which introduces a framework to help researchers select fit-for-purpose microbial cell counting methods and optimize protocols for quantification of microbial total cells and viable cells. Escherichia coli cells were enumerated using four methods (colony forming unit assay, impedance flow cytometry - Multisizer 4, impedance flow cytometry - BactoBox, and fluorescent flow cytometry - CytoFLEX LX) and repeated on multiple dates. The experimental design for a single date starts with a cell stock that is divided into 18 sample replicates (3 each for 6 different dilution factors), and each sample is assayed one or two times for a total of 30 observations. Raw data files are provided from the Multisizer 4 (.#m4) and CytoFLEX LX (.fcs 3.0). The colony forming unit assay and BactoBox readings are recorded for each date as are the derived results from the Multisizer 4 and CytoFLEX LX. Also provided are an example analysis script for the *.fcs files and the statistical analysis that was performed.

Water Quality Metrics and Filter Performance Dataset

Description

This dataset provides simulated data on various water quality parameters and their impact on the performance of water filtration systems. The dataset includes 19K+ samples, with attributes such as Total Dissolved Solids (TDS), turbidity, pH, water depth, and flow discharge. These parameters are used to estimate the filter life span (in hours) and filter efficiency (in percentage) under different conditions.

All the conditions for each feature is based on the data found on the Internet.

The dataset is ideal for exploring relationships between water quality metrics and filter performance, building predictive models, or conducting data analysis for environmental and engineering studies.

Features

• TDS (mg/l): Total Dissolved Solids in milligrams per liter (values < 500 mg/l).
• Turbidity (NTU): Measurement of water clarity in Nephelometric Turbidity Units (values < 10 NTU).
• pH: Measure of acidity/alkalinity (range: 6.0 to 8.5).
• Depth (m): Water depth in meters (range: 0.5 to 5 m).
• Flow Discharge (L/min): Water flow rate in liters per minute (range: 1 to 100 L/min).
• Filter Life Span (hours): Estimated lifespan of the filter based on input parameters (minimum value capped at 500 hours).
• Filter Efficiency (%): Estimated filtration efficiency (minimum value capped at 75%).

Applications

• Predicting filter performance based on water quality parameters.
• Analyzing the impact of water quality on filter lifespan and efficiency.
• Training machine learning models for environmental monitoring.

Note: This dataset is entirely synthetic and created for educational and research purposes. It does not represent real-world measurements but can be used to simulate scenarios for water filtration system analysis.

SSA Breast Data Quality Benchmarks (Sequencing & Sample QC, Synthetic)

  Dataset summary

This module provides a synthetic data quality benchmark dataset for breast cancer sequencing studies, focusing on:

Batch effects and inter-laboratory variation (sequencing center, platform, batch, run year). RNA quality metrics (RNA integrity number, yield, read depth, %Q30) and QC flags. DNA quality metrics (yield, coverage, %≥20x, duplication, contamination) and QC flags.

It is… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/ssa-breast-data-quality-benchmarks.

A dashboard used by government agencies to monitor key performance indicators (KPIs) and communicate progress made on strategic outcomes with the general public and other interested stakeholders.

Analyzing metabolites using mass spectrometry provides valuable insight into an individual’s health or disease status. However, various sources of experimental variation can be introduced during sample handling, preparation, and measurement, which can negatively affect the data. Quality assurance and quality control practices are essential to ensuring accurate and reproducible metabolomics data. These practices include measuring reference samples to monitor instrument stability, blank samples to evaluate the background signal, and strategies to correct for changes in instrumental performance. In this context, we introduce mzQuality, a user-friendly, open-source R-Shiny app designed to assess and correct technical variations in mass spectrometry-based metabolomics data. It processes peak-integrated data independently of vendor software and provides essential quality control features, including batch correction, outlier detection, and background signal assessment, and it visualizes trends in signal or retention time. We demonstrate its functionality using a data set of 419 samples measured across six batches, including quality control samples. mzQuality visualizes data through sample plots, PCA plots, and violin plots, which illustrate its ability to reduce the effect of experiment variation. Compound quality is further assessed by evaluating the relative standard deviation of quality control samples and the background signal from blank samples. Based on these quality metrics, compounds are classified into confidence levels. mzQuality provides an accessible solution to improve the data quality without requiring prior programming skills. Its customizable settings integrate seamlessly into research workflows, enhancing the accuracy and reproducibility of the metabolomics data. Additionally, with an R-compatible output, the data are ready for statistical analysis and biological interpretation.

This example displays the quality report and quality summary information for 15 sensor measurements and 3 arbitrary quality analyses. The quality report contains the individual quality flag outcomes for each sensor measurement, i.e., rows 1–15. The quality summary includes the corresponding quality metrics and the final quality flag information, i.e., the bottom row.Overview of the information contained in the quality summary and quality report.

Superstore Sales - The Data Quality Challenge Edition (25K Records)

This dataset is an expanded version of the popular "Sample - Superstore Sales" dataset, commonly used for introductory data analysis and visualization. It contains detailed transactional data for a US-based retail company, covering orders, products, and customer information.

This version is specifically designed for practicing Data Quality (DQ) and Data Wrangling skills, featuring a unique set of real-world "dirty data" problems (like those encountered in tools like SPSS Modeler, Tableau Prep, or Alteryx) that must be cleaned before any analysis or machine learning can begin.

This dataset combines the original Superstore data with 15,000 plausibly generated synthetic records, totaling 25,000 rows of transactional data. It includes 21 columns detailing: - Order Information: Order ID, Order Date, Ship Date, Ship Mode. - Customer Information: Customer ID, Customer Name, Segment. - Geographic Information: Country, City, State, Postal Code, Region. - Product Information: Product ID, Category, Sub-Category, Product Name. - Financial Metrics: Sales, Quantity, Discount, and Profit.

🚨 Introduced Data Quality Challenges (The Dirty Data)

This dataset is intentionally corrupted to provide a robust practice environment for data cleaning. Challenges include: Missing/Inconsistent Values: Deliberate gaps in Profit and Discount, and multiple inconsistent entries (-- or blank) in the Region column.

• Data Type Mismatches: Order Date and Ship Date are stored as text strings, and the Profit column is polluted with comma-formatted strings (e.g., "1,234.56"), forcing the entire column to be read as an object (string) type.

• Categorical Inconsistencies: The Category field contains variations and typos like "Tech", "technologies", "Furni", and "OfficeSupply" that require standardization.

• Outliers and Invalid Data: Extreme outliers have been added to the Sales and Profit fields, alongside a subset of transactions with an invalid Sales value of 0.

• Duplicate Records: Over 200 rows are duplicated (with slight financial variations) to test your deduplication logic.

❓ Suggested Analysis and Modeling Tasks

This dataset is ideal for:

Data Wrangling/Cleaning (Primary Focus): Fix all the intentional data quality issues before proceeding.

Exploratory Data Analysis (EDA): Analyze sales distribution by region, segment, and category.

Regression: Predict the Profit based on Sales, Discount, and product features.

Classification: Build an RFM Model (Recency, Frequency, Monetary) and create a target variable (HighValueCustomer = 1 if total sales are* $>$ $1000$*) to be predicted by logistical regression or decision trees.

Time Series Analysis: Aggregate sales by month/year to perform forecasting.

Acknowledgements

This dataset is an expanded and corrupted derivative of the original Sample Superstore dataset, credited to Tableau and widely shared for educational purposes. All synthetic records were generated to follow the plausible distribution of the original data.

Extraction, categorisation and recommendations of research software quality indicators from seven canonical source. This work began at the BioHackathon 2024 (BH24) as Project #5. Over 300 indicators from seven sources were extracted. In follow on conference calls after the BH24 refining the indicators took place - for example, deciding on which ones should be kept, maybe should be considered and which ones discarded. Discarding indicators was informed by duplicate indicators and those that advocated a particular philosophy that might not have being universally recognised as necessary for quality research software. We also highlighted the difficulty level in implemented the indicators - i.e. in how much effort was required (easy, possible, hard) in ascertaining whether software, a service or project governance satisfied a particular indicator. This is made available to allow others to use this as a starting point for their own project considerations of which software quality indicators to include and/or take into account. There are current gaps around green software indicators and those in the Reusable part of the FAIR (Findable, Accessible, Interoperable and Reusable) acronym. This exercise did not define new indicators, it set out to categorise existing indicators from various canonical sources (both in the research software space and in the wider software engineering space). You can see the slide about progress at the BH24 and further work has been undertake as part of the ELIXIR Tools Platform WP3 (Software Best Practices group + it was open to those who attended the BH24 Project #5) which is part of the ELIXIR Scientific Programme of Work 2024-2028. Some individuals who took part were funded by the EOSC EVERSE and ELIXIR STEERS projects. All indicators have been categorised apart from those in the 'X - uncategorised' super group. Definitions have been double checked against the canonical sources.

Groundwater samples were collected and analyzed from 1,015 wells as part of the National Water-Quality Assessment Project of the U.S. Geological Survey National Water-Quality Program and the water-quality data and quality-control data are included in this data release. The samples were collected from three types of well networks: principal aquifer study networks, which are used to assess the quality of groundwater used for public water supply; land-use study networks, which are used to assess land-use effects on shallow groundwater quality; and major aquifer study networks, which are used to assess the quality of groundwater used for domestic supply. Groundwater samples were analyzed for a large number of water-quality indicators and constituents, including nutrients, major ions, trace elements, volatile organic compounds (VOCs), pesticides, radionuclides, and microbial indicators. Data from samples collected between 2012 and 2019 are associated with networks described in a collection of data series reports and associated data releases (Arnold and others, 2016a,b, 2017a,b, 2018a,b, 2020a,b; Kingsbury and others, 2020 and 2021). This data release includes data from networks sampled in 2019 through 2023. For some networks, certain constituent group data were not completely reviewed and released by the analyzing laboratory for all network sites in time for publication of this data release. For networks with incomplete data, no data were published for the incomplete constituent group(s). Datasets excluded from this data release because of incomplete results will be included in the earliest data release published after the dataset is complete. NOTE: While previous versions are available from the author, all the records in previous versions can be found in version 4.0. First posted - December 12, 2021 (available from author) Revised - January 27, 2023 (version 2.0: available from author) Revised - November 2, 2023 (version 3.0: available from author) Revised - April 18, 2025 (version 4.0) The compressed file (NWQP_GW_QW_DataRelease_v4.zip) contains 24 files: 23 files of groundwater-quality, quality-control data, and general information in ASCII text tab-delimited format, and one corresponding metadata file in xml format that includes descriptions of all the tables and attributes. A shapefile containing study areas for each of the sampled groundwater networks also is provided in folder NWQP_GW_QW_Network_Boundaries_v4 of this data release and is described in the metadata (Network_Boundaries_v4.zip). The 23 data files are as follows: Description_of_Data_Fields_v4.txt: Information for all constituents and ancillary information found in Tables 3 through 21. Network_Reference_List_v4.txt: References used for the description of the networks sampled by the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) Project. Table_1_site_list_v4.txt: Information about wells that have environmental data. Table_2_parameters_v4.txt: Constituent primary uses and sources; laboratory analytical schedules and sampling period; USGS parameter codes (pcodes); comparison thresholds; and reporting levels. Table_3_qw_indicators_v4.txt: Water-quality indicators in groundwater samples collected by the USGS NAWQA Project. Table_4_nutrients_v4.txt: Nutrients and dissolved organic carbon in groundwater samples collected by the USGS NAWQA Project. Table_5_major_ions_v4.txt: Major and minor ions in groundwater samples collected by the USGS NAWQA Project. Table_6_trace_elements_v4.txt: Trace elements in groundwater samples collected by the USGS NAWQA Project. Table_7_vocs_v4.txt: Volatile organic compounds (VOCs) in groundwater samples collected by the USGS NAWQA Project. Table_8_pesticides_v4.txt: Pesticides in groundwater samples collected by the USGS NAWQA Project. Table_9_radchem_v4.txt: Radionuclides in groundwater samples collected by the USGS NAWQA Project. Table_10_micro_v4.txt: Microbiological indicators in groundwater samples collected by the USGS NAWQA Project. Table_11_qw_ind_QC_v4.txt: Water-quality indicators in groundwater replicate samples collected by the USGS NAWQA Project. Table_12_nuts_QC_v4.txt: Nutrients and dissolved organic carbon in groundwater blank and replicate samples collected by the USGS NAWQA Project. Table_13_majors_QC_v4.txt: Major and minor ions in groundwater blank and replicate samples collected by the USGS NAWQA Project. Table_14_trace_element_QC_v4.txt: Trace elements in groundwater blank and replicate samples collected by the USGS NAWQA Project. Table_15_vocs_QC_v4.txt: Volatile organic compounds (VOCs) in groundwater blank, replicate, and spike samples collected by the USGS NAWQA Project. Table_16_pesticides_QC_v4.txt: Pesticide compounds in groundwater blank, replicate, and spike samples collected by the USGS NAWQA Project. Table_17_radchem_QC_v4.txt: Radionuclides in groundwater replicate samples collected by the USGS NAWQA Project. Table_18_micro_QC_v4.txt: Microbiological indicators in groundwater blank, replicate, and spike samples collected by the USGS NAWQA Project. Table_19_TE_SpikeStats_v4.txt: Statistics for trace elements in groundwater spike samples collected by the USGS NAWQA Project. Table_20_VOCLabSpikeStats_v4.txt: Statistics for volatile organic compounds (VOCs) in groundwater spike samples collected by the USGS NAWQA Project. Table_21_PestFieldSpikeStats_v4.txt: Statistics for pesticide compounds in groundwater spike samples collected by the USGS NAWQA Project. References Arnold, T.L., Bexfield, L.M., Musgrove, MaryLynn, Lindsey, B.D., Stackelberg, P.E., Barlow, J.R., DeSimone, L.A., Kulongoski, J.T., Kingsbury, J.A., Ayotte, J.D., Fleming, B.J., and Belitz, Kenneth, 2017a, Groundwater-quality data from the National Water-Quality Assessment Project, January through December 2014 and select quality-control data from May 2012 through December 2014: U.S. Geological Survey Data Series 1063, 83 p., https://doi.org/10.3133/ds1063. Arnold, T.L., Bexfield, L.M., Musgrove, MaryLynn, Lindsey, B.D., Stackelberg, P.E., Barlow, J.R., DeSimone, L.A., Kulongoski, J.T., Kingsbury, J.A., Ayotte, J.D., Fleming, B.J., and Belitz, Kenneth, 2017b, Datasets from Groundwater quality data from the National Water Quality Assessment Project, January through December 2014 and select quality-control data from May 2012 through December 2014: U.S. Geological Survey data release, https://doi.org/10.5066/F7W0942N. Arnold, T.L., Bexfield, L.M., Musgrove, M., Erickson, M.L., Kingsbury, J.A., Degnan, J.R., Tesoriero, A.J., Kulongoski, J.T., and Belitz, K., 2020a, Groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January through December 2016, and previously unpublished data from 2013 to 2015: U.S. Geological Survey Data Series 1124, 135 p., https://doi.org/10.3133/ds1124. Arnold, T.L., Bexfield, L.M., Musgrove, M., Lindsey, B.D., Stackelberg, P.E., Lindsey, B.D., Barlow, J.R., Kulongoski, J.T., and Belitz, K., 2018b, Datasets from Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, January through December 2015 and Previously Unpublished Data from 2013-2014, U.S. Geological Survey data release, https://doi.org/10.5066/F7XK8DHK. Arnold, T.L., Bexfield, L.M., Musgrove, M., Stackelberg, P.E., Lindsey, B.D., Kingsbury, J.A., Kulongoski, J.T., and Belitz, K., 2018a, Groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January through December 2015, and previously unpublished data from 2013 to 2014: U.S. Geological Survey Data Series 1087, 68 p., https://doi.org/10.3133/ds1087. Arnold, T.L., DeSimone, L.A., Bexfield, L.M., Lindsey, B.D., Barlow, J.R., Kulongoski, J.T., Musgrove, MaryLynn, Kingsbury, J.A., and Belitz, Kenneth, 2016a, Groundwater quality data from the National Water-Quality Assessment Project, May 2012 through December 2013 (ver. 1.1, November 2016): U.S. Geological Survey Data Series 997, 56 p., https://doi.org/10.3133/ds997. Arnold, T.L., DeSimone, L.A., Bexfield, L.M., Lindsey, B.D., Barlow, J.R., Kulongoski, J.T., Musgrove, MaryLynn, Kingsbury, J.A., and Belitz, Kenneth, 2016b, Groundwater quality data from the National Water Quality Assessment Project, May 2012 through December 2014 and select quality-control data from May 2012 through December 2013: U.S. Geological Survey data release, https://doi.org/10.5066/F7HQ3X18. Arnold, T.L., Sharpe, J.B., Bexfield, L.M., Musgrove, M., Erickson, M.L., Kingsbury, J.A., Degnan, J.R., Tesoriero, A.J., Kulongoski, J.T., and Belitz, K., 2020b, Datasets from groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January through December 2016, and previously unpublished data from 2013 to 2015: U.S. Geological Survey data release, https://doi.org/10.5066/P9W4RR74. Kingsbury, J.A., Sharpe, J.B., Bexfield, L.M., Arnold, T.L., Musgrove, M., Erickson, M.L., Degnan, J.R., Kulongoski, J.T., Lindsey, B.D., and Belitz, K., 2020, Datasets from Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, January 2017 through December 2019 (ver. 1.1, January 2021): U.S. Geological Survey data release, https://doi.org/10.5066/P9XATXV1. Kingsbury, J.A., Bexfield, L.M., Arnold, T.L., Musgrove, M., Erickson, M.L., Degnan, J.R., Tesoriero, A.J., Lindsey B.D., and Belitz, K., 2021, Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, January 2017 through December 2019: U.S. Geological Survey Data Series 1136, 97 p., https://doi.org/10.3133/ds1136.

Coliforms: Southeast: Rio de Janeiro data was reported at 98.810 % in 2022. This records a decrease from the previous number of 105.830 % for 2021. Coliforms: Southeast: Rio de Janeiro data is updated yearly, averaging 95.040 % from Dec 2012 (Median) to 2022, with 11 observations. The data reached an all-time high of 105.830 % in 2021 and a record low of 83.060 % in 2013. Coliforms: Southeast: Rio de Janeiro data remains active status in CEIC and is reported by Ministry of Cities. The data is categorized under Brazil Premium Database’s Environmental, Social and Governance Sector – Table BR.EVB014: Quality Indicators: Sample Quantity Compliance Index.

The All CMS Data Feeds dataset is an expansive resource offering access to 118 unique report feeds, providing in-depth insights into various aspects of the U.S. healthcare system. With over 25.8 billion rows of data meticulously collected since 2007, this dataset is invaluable for healthcare professionals, analysts, researchers, and businesses seeking to understand and analyze healthcare trends, performance metrics, and demographic shifts over time. The dataset is updated monthly, ensuring that users always have access to the most current and relevant data available.

Dataset Overview:

118 Report Feeds: - The dataset includes a wide array of report feeds, each providing unique insights into different dimensions of healthcare. These topics range from Medicare and Medicaid service metrics, patient demographics, provider information, financial data, and much more. The breadth of information ensures that users can find relevant data for nearly any healthcare-related analysis. - As CMS releases new report feeds, they are automatically added to this dataset, keeping it current and expanding its utility for users.

25.8 Billion Rows of Data:

• With over 25.8 billion rows of data, this dataset provides a comprehensive view of the U.S. healthcare system. This extensive volume of data allows for granular analysis, enabling users to uncover insights that might be missed in smaller datasets. The data is also meticulously cleaned and aligned, ensuring accuracy and ease of use.

Historical Data Since 2007: - The dataset spans from 2007 to the present, offering a rich historical perspective that is essential for tracking long-term trends and changes in healthcare delivery, policy impacts, and patient outcomes. This historical data is particularly valuable for conducting longitudinal studies and evaluating the effects of various healthcare interventions over time.

Monthly Updates:

• To ensure that users have access to the most current information, the dataset is updated monthly. These updates include new reports as well as revisions to existing data, making the dataset a continuously evolving resource that stays relevant and accurate.

Data Sourced from CMS:

• The data in this dataset is sourced directly from the Centers for Medicare & Medicaid Services (CMS). After collection, the data is meticulously cleaned and its attributes are aligned, ensuring consistency, accuracy, and ease of use for any application. Furthermore, any new updates or releases from CMS are automatically integrated into the dataset, keeping it comprehensive and current.

Use Cases:

Market Analysis:

• The dataset is ideal for market analysts who need to understand the dynamics of the healthcare industry. The extensive historical data allows for detailed segmentation and analysis, helping users identify trends, market shifts, and growth opportunities. The comprehensive nature of the data enables users to perform in-depth analyses of specific market segments, making it a valuable tool for strategic decision-making.

Healthcare Research:

• Researchers will find the All CMS Data Feeds dataset to be a robust foundation for academic and commercial research. The historical data, combined with the breadth of coverage across various healthcare metrics, supports rigorous, in-depth analysis. Researchers can explore the effects of healthcare policies, study patient outcomes, analyze provider performance, and more, all within a single, comprehensive dataset.

Performance Tracking:

• Healthcare providers and organizations can use the dataset to track performance metrics over time. By comparing data across different periods, organizations can identify areas for improvement, monitor the effectiveness of initiatives, and ensure compliance with regulatory standards. The dataset provides the detailed, reliable data needed to track and analyze key performance indicators.

Compliance and Regulatory Reporting:

• The dataset is also an essential tool for compliance officers and those involved in regulatory reporting. With detailed data on provider performance, patient outcomes, and healthcare utilization, the dataset helps organizations meet regulatory requirements, prepare for audits, and ensure adherence to best practices. The accuracy and comprehensiveness of the data make it a trusted resource for regulatory compliance.

Data Quality and Reliability:

The All CMS Data Feeds dataset is designed with a strong emphasis on data quality and reliability. Each row of data is meticulously cleaned and aligned, ensuring that it is both accurate and consistent. This attention to detail makes the dataset a trusted resource for high-stakes applications, where data quality is critical.

Integration and Usability:

Ease of Integration:

• The dataset is provided in a CSV format, which is widely compatible with most data analysis tools and platforms. This ensures that users can easily integrate the data into their existing wo...

†Proportion obtained by removing parallel pairs with one empty and one non-empty values. ‡Proportion obtained by treating pairs with one empty and one non-empty values as symmetrical examples.

ESG Data Quality Management for Banks Market Outlook

According to our latest research, the global ESG Data Quality Management for Banks market size reached USD 1.37 billion in 2024, reflecting a robust and accelerating demand for high-integrity ESG data in the banking sector. The market is expected to grow at a CAGR of 17.2% from 2025 to 2033, reaching an estimated USD 5.12 billion by 2033. This growth is primarily driven by stringent regulatory requirements, increasing stakeholder pressure for transparency, and the need for reliable ESG metrics to inform risk management and investment decisions.

One of the core growth drivers for the ESG Data Quality Management for Banks market is the intensifying regulatory landscape. Governments and regulatory bodies across the globe are mandating stricter ESG disclosure norms, compelling banks to invest in sophisticated data management solutions to ensure compliance. The European Union’s Sustainable Finance Disclosure Regulation (SFDR) and the US Securities and Exchange Commission’s (SEC) proposed climate-related disclosure rules are prime examples of such regulatory frameworks. These regulations not only require banks to collect, verify, and report ESG data but also emphasize the quality and reliability of this information. As a result, banks are increasingly adopting advanced ESG data quality management platforms to streamline data collection, validation, and reporting processes, thereby mitigating compliance risks and enhancing their reputation among stakeholders.

Another significant growth factor is the rising importance of ESG factors in risk management and investment analysis. Banks are recognizing that ESG risks, such as climate change, social unrest, and governance failures, can have profound financial implications. To effectively identify, assess, and mitigate these risks, banks require high-quality ESG data that is accurate, timely, and auditable. The integration of ESG data quality management solutions enables banks to develop more robust risk models, improve credit assessments, and make informed lending and investment decisions. Furthermore, investors and clients are increasingly demanding transparency regarding banks’ ESG performance, further driving the adoption of data quality management tools that can provide granular, verifiable, and actionable ESG insights.

Technological advancements also play a pivotal role in the growth trajectory of the ESG Data Quality Management for Banks market. With the advent of artificial intelligence, machine learning, and big data analytics, banks can now automate the collection, cleansing, and analysis of large volumes of ESG data from diverse sources. These technologies enhance data accuracy, reduce manual intervention, and provide real-time insights, enabling banks to respond swiftly to evolving ESG risks and opportunities. Additionally, the proliferation of cloud-based ESG data management platforms offers scalability, flexibility, and cost-effectiveness, making it easier for banks of all sizes to implement and scale their ESG data quality initiatives.

From a regional perspective, Europe currently leads the ESG Data Quality Management for Banks market, driven by its progressive regulatory environment and strong emphasis on sustainable finance. North America follows closely, with increasing regulatory scrutiny and growing investor demand for ESG transparency propelling market growth. The Asia Pacific region is poised for the fastest growth, fueled by rapid digitalization in the banking sector and emerging ESG regulations in key markets such as China, Japan, and Australia. Latin America and the Middle East & Africa, while still nascent, are witnessing rising awareness of ESG issues and gradually strengthening regulatory frameworks, which are expected to contribute to market expansion over the forecast period.

Component Analysis

The Component segment of the ESG Data Quality Management for Banks market is primarily bifurcated into Software and

Brazil Turbidity data was reported at 101.360 % in 2022. This records an increase from the previous number of 98.880 % for 2021. Brazil Turbidity data is updated yearly, averaging 101.360 % from Dec 2012 (Median) to 2022, with 11 observations. The data reached an all-time high of 109.650 % in 2019 and a record low of 65.110 % in 2015. Brazil Turbidity data remains active status in CEIC and is reported by Ministry of Cities. The data is categorized under Brazil Premium Database’s Environmental, Social and Governance Sector – Table BR.EVB014: Quality Indicators: Sample Quantity Compliance Index.

Turbidity: South data was reported at 116.690 % in 2022. This records an increase from the previous number of 115.350 % for 2021. Turbidity: South data is updated yearly, averaging 115.350 % from Dec 2012 (Median) to 2022, with 11 observations. The data reached an all-time high of 141.730 % in 2020 and a record low of 110.970 % in 2013. Turbidity: South data remains active status in CEIC and is reported by Ministry of Cities. The data is categorized under Brazil Premium Database’s Environmental, Social and Governance Sector – Table BR.EVB014: Quality Indicators: Sample Quantity Compliance Index.

Coliforms: South: Rio Grande do Sul data was reported at 103.790 % in 2022. This records a decrease from the previous number of 110.240 % for 2021. Coliforms: South: Rio Grande do Sul data is updated yearly, averaging 103.790 % from Dec 2012 (Median) to 2022, with 11 observations. The data reached an all-time high of 110.240 % in 2021 and a record low of 99.450 % in 2019. Coliforms: South: Rio Grande do Sul data remains active status in CEIC and is reported by Ministry of Cities. The data is categorized under Brazil Premium Database’s Environmental, Social and Governance Sector – Table BR.EVB014: Quality Indicators: Sample Quantity Compliance Index.

Cropping systems in the northern Great Plains must possess a resilient soil resource to be sustainable. Detecting the effects of management on soil properties in this region is challenging, frequently requiring the use of long-term experiments. A study was conducted to quantify the interactive effects of tillage, crop sequence, and cropping intensity on soil properties for two long-term cropping system experiments in the northern Great Plains. The experiments were established in 1984 and 1993 on the Area IV Soil Conservation Districts Cooperative Research Farm near Mandan, North Dakota USA. Soil physical, chemical, and biological properties considered as indicators of soil quality were evaluated in spring 2001 in both experiments. Samples were collected from the 0-30 cm depth in increments of 0-7.5, 7.5-15, and 15-30 cm using a step-down probe. As a contrast to treatments in the 1984 experiment, samples were collected from a nearby moderately grazed pasture with the same soil type. Soil samples were evaluated for soil bulk density, electrical conductivity, soil pH, soil nitrate-nitrogen, soil organic carbon, total soil nitrogen, particulate organic matter carbon and nitrogen, potentially mineralizable nitrogen, and microbial biomass carbon and nitrogen. Supplemental soil assessments of water-stable aggregation and infiltration rate were conducted in the 1984 experiment, while stover biomass production in the 1993 experiment complemented soils data. Laboratory methods followed accepted protocols. Particulate organic matter was measured with two methods. For the 1984 experiment, material retained on a 0.053 mm sieve was collected and analyzed by dry combustion for carbon and nitrogen content, while a weight loss-on-ignition method was used for 0.053–0.5 and 0.5–2.0 mm size fractions for the 1993 experiment. Data may be used to better understand soil property responses to crop rotation and tillage practices under rainfed conditions within a semiarid continental climate. Applicable USDA soil types include Temvik, Wilton, Grassna, Linton, Mandan, and Williams.

FragPipe is recognized as one of the fastest computational platforms in proteomics, making it a practical solution for the rapid quality control of high-throughput sample analyses. Starting with version 23.0, FragPipe introduced the “Generate Summary Report” feature, offering .pdf reports with essential quality control metrics to address the challenge of intuitively assessing large-scale proteomics data. While traditional spreadsheet formats (e.g., tsv files) are accessible, the complexity of the data often limits user-friendly interpretation. To further enhance accessibility, PSManalyst, a Shiny-based R application, was developed to process FragPipe output files (psm.tsv, protein.tsv, and combined_protein.tsv) and provide interactive, code-free data visualization. Users can filter peptide-spectrum matches (PSMs) by quality scores, visualize protease cleavage fingerprints as heatmaps and SeqLogos, and access a range of quality control metrics and representations such as peptide length distributions, ion densities, mass errors, and wordclouds for overrepresented peptides. The tool facilitates seamless switching between PSM and protein data visualization, offering insights into protein abundance discrepancies, samplewise similarity metrics, protein coverage, and contaminants evaluation. PSManalyst leverages several R libraries (lsa, vegan, ggfortify, ggseqlogo, wordcloud2, tidyverse, ggpointdensity, and plotly) and runs on Windows, MacOS, and Linux, requiring only a local R setup and an IDE. The app is available at (https://github.com/41ison/PSManalyst.

The Environmental Protection Agency (EPA) provides air pollution data about ozone and particulate matter (PM2.5) to CDC for the Tracking Network. The EPA maintains a database called the Air Quality System (AQS) which contains data from approximately 4,000 monitoring stations around the country, mainly in urban areas. Data from the AQS is considered the "gold standard" for determining outdoor air pollution. However, AQS data are limited because the monitoring stations are usually in urban areas or cities and because they only take air samples for some air pollutants every three days or during times of the year when air pollution is very high. CDC and EPA have worked together to develop a statistical model (Downscaler) to make modeled predictions available for environmental public health tracking purposes in areas of the country that do not have monitors and to fill in the time gaps when monitors may not be recording data. This data does not include "Percent of population in counties exceeding NAAQS (vs. population in counties that either meet the standard or do not monitor PM2.5)". Please visit the Tracking homepage for this information.View additional information for indicator definitions and documentation by selecting Content Area "Air Quality" and the respective indicator at the following website: http://ephtracking.cdc.gov/showIndicatorsData.action

Mixed methods study into the reasons physical therapists use feedback reports on the quality of care measured by quality indicators.This dataset constitutes data of three evaluation surveys, held in 2009, 2010 and 2011. Participating physical therapists in the project Qualiphy (Kwaliefy), that was meant to measure the quality of physical therapy in primary care in the Netherlands through quality indicators, were asked to evaluate the project, for example with respect to feasibility, usability of the results/feedback reports and assistance during the project. The objective of the evaluation survey was to examine whether (parts of ) the project needed to be improved and to assess whether the feedback reports were being used by participating physical therapists to improve the quality of care.This dataset does not include the raw data of the Qualiphy project, please contact the rights holder for further information. The questionnaires are in Dutch, the codebook, project description and data file are in English.