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.

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.

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.

The Sacramento / San Joaquin River Delta (SSJRD) is contaminated with legacy mercury (Hg) from historical mining and mineral processing activities throughout the watershed, as well as from contemporary atmospheric and industrial inputs. The current project was designed for the purpose of developing high-resolution spatial and temporal models for estimating concentrations of mercury species in surface waters of the SSJRD. The field component of the project brings together three high-resolution platforms for collecting water-quality data (fixed continuous monitoring stations (CMS) outfitted with in-situ sensors, spatial mapping using boat-mounted flow-through sensors, and satellite-based remote sensing) coupled with a discrete sample collection program for mercury species and ancillary water-quality metrics. The four mercury species targeted in the study include both particulate and filter-passing fractions of total mercury and methylmercury. Field data were collected during the period July 2019 through July 2021. Sampling at the four primary CMS sites included discrete sample collections during all station operations and maintenance visits (approximately every six weeks) and during four 13-hour to 15-hour tidal sampling events, during which samples were collected every 2 hours (approximately) over a full tidal cycle. This tidal sampling occurred once per season (winter, spring, summer, and fall) at each of the four CMS locations. Likewise, four seasonal boat-mapping sampling events were conducted, each over a 3-day period and coincident with Landsat 8 satellite overpasses on the 2nd day of sampling and within 2 days of a Sentinel 2 A/B satellite overpass. Each boat-mapping event included collection of discrete water samples for mercury species and other water-quality metrics at 33 sites over a three-day period, covering approximately 210 kilometers through the SSJRD. The models constructed to estimate concentrations of mercury species are organized into four types (Tiers), which are based on which high-resolution water-quality data platform is being emphasized, as follow: Tier 1 Models – those based only on in-situ sensor derived turbidity and dissolved organic matter fluorescence, which are the two metrics most relevant to the satellite-based data collection platforms; Tier 2 Models – those based only on CMS in situ sensor data; Tier 3 Models – those based only on data from boat-mounted flow-through sensors, including spectrophotometric measurements, associated with the spatial mapping events; and Tier 4 models – based on sensor data from both the CMS sites and boat-mapping events, but limited to sensor data common to both. The information presented herein falls under six categories, which are associated with the following six Child pages: a) Discrete Sample Data – represents laboratory analytical results and field measurements associated with discrete surface-water samples collected from both the CMS and boat-mapping sampling events; b) Optical Spectral Data – represents excitation-emissions matrix spectra (EEMs) and absorption data associated with discrete surface-water samples collected from both the CMS and boat mapping sampling events; c) High-resolution (15 minute) Temporal Data from CMS Locations – includes time series in-situ sensor data collected from the four primary fixed CMS sampling locations; d) High-Resolution Boat Mapping Data – data collected with boat mounted flow-through sensor arrays during the four mapping events; e) Remote Sensing Data – GeoTIFF image files of turbidity and dissolved organic matter (DOM) products derived from Sentinel 2 A/B imagery of the SSJRD from June 2019 – May 2021; and f) Model Archive Summaries – documentation of the 16 top global models (four model types x four mercury species) in terms of modeling approach, model statistics, validation, and final equations. In addition, a geospatial file (SSJRD_Sites.kmz) is provided on this Parent page, which identifies all of the study fixed station locations.

In April 2011 a new set of clinical quality indicators was introduced to replace the previous four hour waiting time standard, and measure the quality of care delivered in A&E departments in England. Further details on the background and management of the quality indicators are available from the Department of Health (DH) website. This is the publication of data on the Accident and Emergency (A&E) clinical quality indicators, drawn from A&E data within provisional Hospital Episode Statistics (HES). These data relate to A&E attendances in March 2012 and draw on 1.53 million detailed records of attendances at major A&E departments, single speciality A&E departments (e.g. dental A&Es), minor injury units and walk-in centres in England. This report sets out data coverage, data quality and performance information for the following five A&E indicators: Left department before being seen for treatment rate Re-attendance rate Time to initial assessment Time to treatment Total time in A&E Publishing these data will help share information on the quality of care of A&E services to stimulate the discussion and debate between patients, clinicians, providers and commissioners, which is needed in a culture of continuous improvement. These A&E HES data are published as experimental statistics to note the shortfalls in the quality and coverage of records submitted via the A&E commissioning data set. The data used in these reports are sourced from Provisional A&E HES data, and as such these data may differ to information extracted directly from Secondary Uses Service (SUS) data, or data extracted directly from local patient administration systems. Provisional HES data may be revised throughout the year (for example, activity data for April 2011 may differ depending on whether they are extracted in August 2011, or later in the year). Indicator data published for earlier months have not been revised using updated HES data extracted in subsequent months. The data presented here represent the output of the existing A&E Commissioning Dataset (CDS V6 Type 010). It must be recognised that these data will not exactly match the data definitions for the A&E clinical quality indicators set out in the guidance document A&E clinical quality indicators: Implementation guidance and data definitions (external link). The DH is currently working with Information Standards Board to amend the existing CDS Type 10 Accident and Emergency to collect the data required to monitor the A&E indicators. A&E HES data are collected and published by the NHS Information Centre for Health and Social Care. The data in this report are secondary analyses of HES data produced by the Urgent & Emergency Care team, Department of Health. A&E HES data are published as experimental statistics to note the known shortfalls in the quality of some A&E HES data elements. The published information sets out where data quality for the indicators may be improved by, for example, reducing the number of unknown values (e.g. unknown times to initial assessment) and default values (e.g. the number of attendances that are automatically given a time to initial assessment of midnight 00:00). The quality and coverage of A&E HES data have considerably improved over the years, and the Department and the NHS Information Centre are working with NHS Performance and Information directors to further improve the data.

Synchrophasor Data Quality Assurance Market Outlook

According to our latest research, the global Synchrophasor Data Quality Assurance market size reached USD 765 million in 2024, reflecting strong momentum in the power grid modernization sector. The market is projected to expand at a robust CAGR of 12.1% from 2025 to 2033, reaching an estimated USD 2.14 billion by 2033. This growth is primarily driven by the increasing need for real-time grid monitoring, the proliferation of renewable energy sources, and the stringent regulatory mandates for grid reliability and security. As utilities and grid operators worldwide prioritize grid resilience and operational efficiency, the adoption of advanced synchrophasor data quality assurance solutions is accelerating.

One of the primary growth factors for the Synchrophasor Data Quality Assurance market is the global shift towards smart grid infrastructure and the integration of distributed energy resources. As power grids become more complex and interconnected, the volume and velocity of synchrophasor data generated by Phasor Measurement Units (PMUs) are increasing exponentially. This surge in data necessitates robust data quality assurance mechanisms to ensure accurate, reliable, and timely information for critical grid operations. Furthermore, the adoption of renewable energy sources such as wind and solar has introduced greater variability and uncertainty into grid operations, making high-quality synchrophasor data essential for real-time monitoring, state estimation, and fault detection.

Another significant driver is the growing regulatory emphasis on grid reliability and cybersecurity. Regulatory agencies across North America, Europe, and Asia Pacific are mandating utilities to implement advanced monitoring and reporting systems to enhance grid resilience against physical and cyber threats. Synchrophasor data quality assurance solutions play a pivotal role in meeting these regulatory requirements by providing comprehensive data validation, cleansing, and anomaly detection capabilities. Additionally, the increasing frequency of extreme weather events and grid disturbances has heightened the need for continuous, high-fidelity data streams to support rapid situational awareness and decision-making.

Technological advancements in data analytics, artificial intelligence, and machine learning are further propelling market growth. Modern synchrophasor data quality assurance platforms leverage these technologies to automate data validation processes, detect subtle anomalies, and provide actionable insights for grid operators. The convergence of big data analytics with synchrophasor technology is enabling utilities to move beyond traditional monitoring towards predictive maintenance and proactive grid management. This technological evolution is not only enhancing operational efficiency but also reducing downtime and maintenance costs, thereby driving the adoption of data quality assurance solutions across the energy sector.

From a regional perspective, North America currently leads the Synchrophasor Data Quality Assurance market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The United States, in particular, has been at the forefront of synchrophasor technology deployment, supported by significant investments from the Department of Energy and other government agencies. Europe is witnessing rapid growth, driven by the increasing integration of renewables and cross-border interconnections, while Asia Pacific is emerging as a high-growth region due to ongoing grid modernization initiatives in countries such as China, India, and Japan. Latin America and the Middle East & Africa are also gradually adopting synchrophasor data quality assurance solutions, albeit at a slower pace, as they embark on their respective grid modernization journeys.

Component Analysis

The Synchrophasor Data Quality Assurance market is segmented by component into Software, Hardware, and Services. The software segment dominate

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...

Coliforms: Northeast: Rio Grande do Norte data was reported at 54.720 % in 2022. This records a decrease from the previous number of 55.350 % for 2021. Coliforms: Northeast: Rio Grande do Norte data is updated yearly, averaging 55.350 % from Dec 2012 (Median) to 2022, with 11 observations. The data reached an all-time high of 96.610 % in 2012 and a record low of 38.500 % in 2017. Coliforms: Northeast: Rio Grande do Norte 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.

Supporting data for the reproduction of the results reported in Corbetta, E., Bocklitz, T., Machine learning based estimation of experimental artifacts and image quality in fluorescence microscopy (2024) [1].

MM-IQA_Images_png and MM-IQA_Images_tif

Folder containing all the supporting datasets of the publication.

• images_manual_inspection: semisynthetic dataset used for the manual inspection of the quality metrics.
• images_lda_training: semisynthetic dataset used to train the Linear Discriminant Analysis (LDA) model.
• images_lda_prediction: datasets predicted by the LDA model.
  • images_experimental: every subfolder is a dataset composed of measurements of a different sample. Images are from publicly available datasets from [2] and [3].
  • images_known_semisynthetic: knwon semisynthetic dataset used for prediction, assessment and interpretation of the trained model.

Tif files are the original data used for the study.

MM-IQA_Source_data

Folder containing all the supporting metadata of the publication.

• manual_inspection: quality metrics computed for the semisynthetic dataset used for the manual inspection.
  • manual_inspection_bg: indices for the selection of the background region in each sample.
  • manual_inspection_free_parameters: parameters used for the generation of the simulated artifacts.
  • manual_inspection_metrics: quality metrics computed for the dataset, used for the manual inspection.
  • manual_inspection_samples: free parameters associated to each image of the dataset for manual inspection.
• LDA_training: semisynthetic dataset used to train the Linear Discriminant Analysis (LDA) model.
  • lda_metrics_synthetic+semisynthetic_uniform_max: quality metrics computed for the training dataset, with maximum normalization of the images. (Not used in the manuscript)
  • lda_metrics_synthetic+semisynthetic_uniform_rescale01: quality metrics computed for the training dataset, with image values rescaled between 0 and 1.
  • parameters_all_degradations: parameters used for the generation of the simulated artifacts.
• LDA_prediction: datasets predicted by the LDA model.
  • experimental: quality metrics computed for measurements of different samples. Images are from publicly available datasets from [2] and [3].
  • known_semisynthetic: metadata for the knwon semi-synthetic dataset used for prediction, assessment and interpretation of the trained model:
    • known_semisynthetic_free_parameters: parameters used for the generation of the simulated artifacts.
    • known_semisynthetic_metrics_rescale01: quality metrics computed for the training dataset, with image values rescaled between 0 and 1.
    • known_semisynthetic_maxnorm_lda_results: lda prediction results, when metrics are maximum normalized to the training dataset.
    • known_semisynthetic_znorm_lda_results: lda prediction results, when metrics are z-score normalized to the training dataset.

Source data can be used to reproduce the results of the manuscript, using the codes shared in the public GitLab repository multi-marker-IQA.

How to use the source data

The following table describes which data can be used in the scripts provided in the public GitLab repository multi-marker-IQA.

MM-IQA_Scripts

• multi-marker-iqa-main: original GitLab repository for MM-IQA, version available at the date of manuscript publication.
• Notebooks_peer_review: additional notebooks generated during the peer-review process with the computation of metrics for natural images and correlation measures.

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

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.

Coliforms: North data was reported at 73.250 % in 2022. This records an increase from the previous number of 71.400 % for 2021. Coliforms: North data is updated yearly, averaging 73.350 % from Dec 2012 (Median) to 2022, with 11 observations. The data reached an all-time high of 113.310 % in 2014 and a record low of 2.720 % in 2015. Coliforms: North 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 AQS Data Mart is a database containing all of the information from AQS. It has every measured value the EPA has collected via the national ambient air monitoring program. It also includes the associated aggregate values calculated by EPA (8-hour, daily, annual, etc.). The AQS Data Mart is a copy of AQS made once per week and made accessible to the public through web-based applications. The intended users of the Data Mart are air quality data analysts in the regulatory, academic, and health research communities. It is intended for those who need to download large volumes of detailed technical data stored at EPA and does not provide any interactive analytical tools. It serves as the back-end database for several Agency interactive tools that could not fully function without it: AirData, AirCompare, The Remote Sensing Information Gateway, the Map Monitoring Sites KML page, etc.

AQS must maintain constant readiness to accept data and meet high data integrity requirements, thus is limited in the number of users and queries to which it can respond. The Data Mart, as a read only copy, can allow wider access.

The most commonly requested aggregation levels of data (and key metrics in each) are:

Sample Values (2.4 billion values back as far as 1957, national consistency begins in 1980, data for 500 substances routinely collected) The sample value converted to standard units of measure (generally 1-hour averages as reported to EPA, sometimes 24-hour averages) Local Standard Time (LST) and GMT timestamps Measurement method Measurement uncertainty, where known Any exceptional events affecting the data NAAQS Averages NAAQS average values (8-hour averages for ozone and CO, 24-hour averages for PM2.5) Daily Summary Values (each monitor has the following calculated each day) Observation count Observation per cent (of expected observations) Arithmetic mean of observations Max observation and time of max AQI (air quality index) where applicable Number of observations > Standard where applicable Annual Summary Values (each monitor has the following calculated each year) Observation count and per cent Valid days Required observation count Null observation count Exceptional values count Arithmetic Mean and Standard Deviation 1st - 4th maximum (highest) observations Percentiles (99, 98, 95, 90, 75, 50) Number of observations > Standard Site and Monitor Information FIPS State Code (the first 5 items on this list make up the AQS Monitor Identifier) FIPS County Code Site Number (unique within the county) Parameter Code (what is measured) POC (Parameter Occurrence Code) to distinguish from different samplers at the same site Latitude Longitude Measurement method information Owner / operator / data-submitter information Monitoring Network to which the monitor belongs Exemptions from regulatory requirements Operational dates City and CBSA where the monitor is located Quality Assurance Information Various data fields related to the 19 different QA assessments possible

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.epa_historical_air_quality.[TABLENAME]. Fork this kernel to get started.

Acknowledgements

Data provided by the US Environmental Protection Agency Air Quality System Data Mart.

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.

Lucror Analytics: Comprehensive Sustainability Data on High Yield Bond Issuers

At Lucror Analytics, we specialize in delivering meticulously curated data solutions designed for professionals and organizations across various sectors. Our data products focus on issuer and issue-level credit information, over 300 advanced ESG and sustainability metrics, and proprietary quantitative insights for high-yield bond issuers in Europe, Latin America, and Asia. Whether you are an asset manager, institutional investor, or sustainability-focused institution, our comprehensive data provides the insights needed to make informed decisions.

Our proprietary approach integrates industry-leading ESG methodology with analyst-adjusted credit data, offering a unique, customizable solution that adapts to your requirements. We ensures data quality, relevance, and actionable insights, enabling users to stay ahead in a rapidly evolving regulatory environment.

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Comprehensive ESG Integration Our datasets feature over 300 ESG metrics per issuer, carefully analyzed and curated by experts to provide actionable insights. Our integrated approach allows businesses to incorporate sustainability factors seamlessly into their workflows, enhancing investment strategies and aligning with regulatory or ethical standards.

Analyst-Adjusted and Data-Enriched Lucror’s data is not merely aggregated—it is curated, analyzed, and, where appropriate, adjusted by our team of experts. This human layer ensures accuracy and relevance, providing users with data that reflects real-world dynamics rather than raw or unverified figures.

Focus on High-Yield Bond Issuers Our exclusive focus on high-yield bond issuers in Europe, Latin America, and Asia provides a niche yet vital dataset. We offer detailed insights into the ESG performance and sustainability profile of over 400 companies, ensuring comprehensive coverage across key HY markets.

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Public Sources: Reliable inputs such as issuer filings, bond documentation, annual and sustainability reports, ESG disclosures, and press releases are systematically incorporated.

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Our rigorous methodology allows us to provide high-quality, validated data that organizations can trust.

Primary Use Cases Lucror Analytics’ data products cater to a wide range of applications across different verticals. Some of the primary use cases include:

1. ESG Investing - Integration and Reporting With increasing demand for sustainable investing, our ESG data empowers organizations to evaluate and integrate environmental, social, and governance factors into their decisions. The metrics are particularly valuable for asset managers and institutions aligning with ESG frameworks or regulatory requirements.

2. Regulatory Compliance Lucror’s datasets are invaluable for organizations navigating the increasingly stringent regulatory landscape. With detailed ESG metrics and issuer-level credit data, businesses can ensure compliance with global and regional reporting requirements, such as the EU Taxonomy, SFDR (Sustainable Finance Disclosure Regulation), SASB, and other frameworks. Our enriched data enables companies to meet disclosure obligations, align with sustainability goals, and maintain transparency with stakeholders, reducing compliance risks and enhancing trust in their practices.

3. Risk Management Incorporating Lucror’s comprehensive datasets into risk models enables businesses to identify vulnerabilities and mitigate potential risks more effectively. This is especially critical in high-yield markets where risk factors are more pronounced and ESG data for some issuers is sparse.

Key Features of Lucror’s Sustainability Data

ESG and Sustainability Metrics Over 300 analyst-curated ESG metrics covering environmental impact, social responsibility, governance standards, and disclosure practices.

Tailored Datasets Flexibility to deliver data in customized formats and frequencies, ensuring alignment with specific business needs.

Global Coverage with a Regional Focus Comprehensive datasets tailored to key regions for high yield —Europe, Latin Am...

Detailed data analysis reports, generated using in-house R notebooks, to supplement "Comparison of NGS Quality Metrics and Concordance in the Detection of Cancer-Specific Molecular Alterations Between Formalin-Fixed Paraffin-Embedded and Fresh-Frozen Samples in Comprehensive Genomic Profiling with the TSO 500 Assay", by Loderer, Hornáková, Tobiášová, Lešková, Halašová, Danková, Plank and Grendár

This data release includes metrics from the Regional Stream Quality Assessment (RSQA) from the Southeast Region for habitat stressors related to water-quality and habitat substrate. The goals of RSQA are to characterize multiple water-quality factors that are stressors to aquatic life ‐ contaminants, nutrients, sediment, and streamflow alteration – and to develop a better understanding of the relation of these stressors to ecological conditions in streams throughout the region. In order to characterize water-quality variables and stream-habitat measurements as an aggregation of multiple measurements over a sampling period, and in support of ecological stressor modelling, metrics (summary statistics or indices) were computed from individual results by site using consistent methods over a consistent time frame. Water-quality metrics are based on discrete samples as well as long-term deployed passive samplers.