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.

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.

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.

Reporting units of sample results [where 1 picoCurie (pCi) = 1 trillionth (1E-12) Curie (Ci)]: • Other samples are reported in pCi/g. Data Quality Disclaimer: This database is for informational use and is not a controlled quality database. Efforts have been made to ensure accuracy of data in the database; however, errors and omissions may occur. Examples of potential errors include: • Data entry errors. • Lab results not reported for entry into the database. • Missing results due to equipment failure or unable to retrieve samples due to lost or environmental hazards. • Translation errors – the data has been migrated to newer data platforms numerous times, and each time there have been errors and data losses. Error Results are the calculated uncertainty for the sample measurement results and are reported as (+/-). Environmental Sample Records are from the year 1998 until present. Prior to 1998 results were stored in hardcopy, in a non-database format. Requests for results from samples taken prior to 1998 or results subject to quality assurance are available from archived records and can be made through the DEEP Freedom of Information Act (FOIA) administrator at deep.foia@ct.gov. Information on FOIA requests can be found on the DEEP website. FOIA Administrator Office of the Commissioner Department of Energy and Environmental Protection 79 Elm Street, 3rd Floor Hartford, CT 06106

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.

As part of the Glassmeyer et al., (2023) review for the journal GeoHealth, the data from 84 journal articles was summarized. One of the metrics captured in the summary was the different types of quality assurance/ quality control parameters mentioned in each paper (see Data Template tab). The types of QA/QC parameters were: field blank, laboratory reagent blank, laboratory fortified blank (LFB- aka laboratory spike), laboratory fortified matrix sample (LFM- aka matrix spike) and duplicate sample. Also logged was if no QA/QC was mentioned, and if it was a review paper that summarized multiple studies (and therefore had no independent QA/QC). This dataset is that QA/QC summary.

Logistic regression classification models were fit to manually classified quality control (QC) LC-MS/MS datasets to develop a model that can predict whether a dataset is in or out of control. Model parameters were optimized by minimizing a loss function that accounts for the tradeoff between false positive and false negative errors. In addition to the 1152 training/testing datasets, we are including 2662 additional datasets, all of the same QC sample (whole cell lysate of Shewanella oneidensis). Datasets originate from 6 Thermo instrument platforms: Exactive, LTQ, VelosPro, Orbitrap, Q-Exactive, and Velos Orbitrap.

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.

Airport Data Quality Monitoring Market Outlook

As per our latest research, the global airport data quality monitoring market size reached USD 1.26 billion in 2024. The market is demonstrating robust momentum, propelled by the rising need for accurate, real-time data in airport operations and regulatory compliance. The market is expected to grow at a CAGR of 9.2% from 2025 to 2033, reaching a forecasted value of USD 2.85 billion by 2033. This growth is primarily driven by the increasing adoption of digital technologies, the proliferation of connected devices, and the rising emphasis on passenger safety and operational efficiency across airports worldwide.

One of the primary growth factors for the airport data quality monitoring market is the exponential increase in air traffic and passenger volumes globally. As airports handle more flights and passengers, the complexity of managing operations, security, baggage, and air traffic control increases significantly. Accurate and timely data becomes essential to ensure seamless coordination between different airport functions. This demand has led to the integration of advanced data quality monitoring solutions that can capture, process, and analyze vast amounts of data in real-time, minimizing errors and enhancing decision-making. Furthermore, regulatory authorities are enforcing stricter compliance norms regarding data accuracy and reporting, pushing airports and airlines to invest in robust monitoring systems.

Another significant factor fueling market growth is the ongoing digital transformation in the aviation sector. Airports are increasingly deploying IoT sensors, AI-driven analytics, and cloud-based platforms to modernize their infrastructure. These technologies generate a massive influx of data, necessitating reliable data quality monitoring tools to filter, validate, and manage this information effectively. Enhanced data quality not only improves operational efficiency but also strengthens security measures, optimizes resource allocation, and elevates the overall passenger experience. The trend towards smart airports, driven by automation and data-centric strategies, is expected to further accelerate the adoption of airport data quality monitoring solutions over the forecast period.

The growing focus on passenger safety and regulatory compliance is also shaping the evolution of the airport data quality monitoring market. Regulatory bodies such as the International Civil Aviation Organization (ICAO) and the Federal Aviation Administration (FAA) have established stringent guidelines for data management, reporting, and transparency. Non-compliance can lead to severe penalties and operational disruptions. As a result, airports and airlines are investing heavily in advanced data quality monitoring systems to ensure adherence to these standards. Additionally, the rise in cyber threats and data breaches underscores the need for robust monitoring mechanisms that can detect anomalies and safeguard sensitive information.

Regionally, North America and Europe continue to dominate the market, supported by their advanced airport infrastructure and early adoption of digital technologies. However, the Asia Pacific region is emerging as a lucrative market, driven by rapid airport expansions, increasing passenger traffic, and supportive government initiatives. Middle East & Africa and Latin America are also witnessing steady growth, fueled by investments in airport modernization and a growing emphasis on safety and security. Overall, the global landscape for airport data quality monitoring is evolving rapidly, with significant opportunities for innovation and expansion across all regions.

Component Analysis

The component segment of the airport data quality monitoring market is categorized into software, hardware, and services. Each of these components plays a pivotal role in ensuring the integrity and reliability of data within airport environments. Software solutions are at the forefront, providing platforms for data integration, valid

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.

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.

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.

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.

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.

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

†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.

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: Southeast: Minas Gerais data was reported at 122.360 % in 2022. This records an increase from the previous number of 121.040 % for 2021. Coliforms: Southeast: Minas Gerais data is updated yearly, averaging 92.480 % from Dec 2012 (Median) to 2022, with 11 observations. The data reached an all-time high of 122.360 % in 2022 and a record low of 66.510 % in 2013. Coliforms: Southeast: Minas Gerais 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.

Our dataset offers a unique blend of attributes from YouTube and Google Maps, empowering users with comprehensive insights into online content and geographical reach. Let's delve into what makes our data stand out:

Unique Attributes: - From YouTube: Detailed video information including title, description, upload date, video ID, and channel URL. Video metrics such as views, likes, comments, and duration are also provided. - Creator Info: Access author details like name and channel URL. - Channel Information: Gain insights into channel title, description, location, join date, and visual branding elements like logo and banner URLs. - Channel Metrics: Understand a channel's performance with metrics like total views, subscribers, and video count. - Google Maps Integration: Explore business ratings from Google My Business and location data from Google Maps.

Data Sourcing: - Our data is meticulously sourced from publicly available information on YouTube and Google Maps, ensuring accuracy and reliability.

Primary Use-Cases: - Marketing: Analyze video performance metrics to optimize content strategies. - Research: Explore trends in creator behavior and audience engagement. - Location-Based Insights: Utilize Google Maps data for market research, competitor analysis, and location-based targeting.

Fit within Broader Offering: - This dataset complements our broader data offering by providing rich insights into online content consumption and geographical presence. It enhances decision-making processes across various industries, including marketing, advertising, research, and business intelligence.

Usage Examples: - Marketers can identify popular video topics and optimize advertising campaigns accordingly. - Researchers can analyze audience engagement patterns to understand viewer preferences. - Businesses can assess their Google My Business ratings and geographical distribution for strategic planning.

With scalable solutions and high-quality data, our dataset offers unparalleled depth for extracting actionable insights and driving informed decisions in the digital landscape.

This data describes water quality parameters and catchment characteristics for 88 catchments draining into German drinking water reservoirs. This data was used in more detail in Musolff et al. (2017). The data comprises: Catchment - number of the catchment Year - year for which the data was averaged SUVA254 - Specific ultraviolet absorbance at a wavelenght of 254 nm of the filtered water sample [L m-1 mg-1] NO3 - nitrate concentration of the filtered water sample [µmol L-1] Fe - dissolved iron concentration [µmol L-1] DOC - dissolved organic carbon concentration [mmol L-1] PO4 - soluble reactive phosphorus concentrations of the filteres water sample [µmol L-1] Forest - share of the catchment covered by forest following CLC (2016), static metric [%] TWI90 - topographic wetness index following Beven and Kirkby (1979) using a 10 m digital elevation model, static metric [-] The hydrochemical data was averaged using Box-Cox-transformation (Box and Cox, 1964) for the samples of each year, arithmetic mean and backtransformation. On average 11 samples per years have been averaged. The data is stored as a CSV file.

References Beven, K. J., & Kirkby, M. J. (1979). A physically based, variable contributing area model of basin hydrology. Hydrological Sciences Journal, 24(1), 43-69. Box, G. E. P., & Cox, D. R. (1964). An Analysis of Transformations. Journal of the Royal Statistical Society Series B-Statistical Methodology, 26(2), 211-252. CLC. (2016). CORINE Land Cover 2012 v18.5. . https://land.copernicus.eu/pan-european/corine-land-cover.