This data table provides the detailed data quality assessment scores for the Curtailment dataset. The quality assessment was carried out on the 31st of March. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at a minimum, bi-annual assessments of our data quality - for datasets that are refreshed more frequently than this, please note that the quality assessment may be based on an earlier version of the dataset. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks. We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.

This data table provides the detailed data quality assessment scores for the Technical Limits dataset. The quality assessment was carried out on the 31st of March. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at a minimum, bi-annual assessments of our data quality - for datasets that are refreshed more frequently than this, please note that the quality assessment may be based on an earlier version of the dataset. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks. We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.

This dataset contains data collected during a study "Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems" conducted by Martin Lnenicka (University of Hradec Králové, Czech Republic), Anastasija Nikiforova (University of Tartu, Estonia), Mariusz Luterek (University of Warsaw, Warsaw, Poland), Petar Milic (University of Pristina - Kosovska Mitrovica, Serbia), Daniel Rudmark (Swedish National Road and Transport Research Institute, Sweden), Sebastian Neumaier (St. Pölten University of Applied Sciences, Austria), Karlo Kević (University of Zagreb, Croatia), Anneke Zuiderwijk (Delft University of Technology, Delft, the Netherlands), Manuel Pedro Rodríguez Bolívar (University of Granada, Granada, Spain).

As there is a lack of understanding of the elements that constitute different types of value-adding public data ecosystems and how these elements form and shape the development of these ecosystems over time, which can lead to misguided efforts to develop future public data ecosystems, the aim of the study is: (1) to explore how public data ecosystems have developed over time and (2) to identify the value-adding elements and formative characteristics of public data ecosystems. Using an exploratory retrospective analysis and a deductive approach, we systematically review 148 studies published between 1994 and 2023. Based on the results, this study presents a typology of public data ecosystems and develops a conceptual model of elements and formative characteristics that contribute most to value-adding public data ecosystems, and develops a conceptual model of the evolutionary generation of public data ecosystems represented by six generations called Evolutionary Model of Public Data Ecosystems (EMPDE). Finally, three avenues for a future research agenda are proposed.

This dataset is being made public both to act as supplementary data for "Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems ", Telematics and Informatics*, and its Systematic Literature Review component that informs the study.

Description of the data in this data set

PublicDataEcosystem_SLR provides the structure of the protocol

Spreadsheet#1 provides the list of results after the search over three indexing databases and filtering out irrelevant studies

Spreadsheets #2 provides the protocol structure.

Spreadsheets #3 provides the filled protocol for relevant studies.

The information on each selected study was collected in four categories:(1) descriptive information,(2) approach- and research design- related information,(3) quality-related information,(4) HVD determination-related information

Descriptive Information

Article number

A study number, corresponding to the study number assigned in an Excel worksheet

Complete reference

The complete source information to refer to the study (in APA style), including the author(s) of the study, the year in which it was published, the study's title and other source information.

Year of publication

The year in which the study was published.

Journal article / conference paper / book chapter

The type of the paper, i.e., journal article, conference paper, or book chapter.

Journal / conference / book

Journal article, conference, where the paper is published.

DOI / Website

A link to the website where the study can be found.

Number of words

A number of words of the study.

Number of citations in Scopus and WoS

The number of citations of the paper in Scopus and WoS digital libraries.

Availability in Open Access

Availability of a study in the Open Access or Free / Full Access.

Keywords

Keywords of the paper as indicated by the authors (in the paper).

Relevance for our study (high / medium / low)

What is the relevance level of the paper for our study

Approach- and research design-related information

Approach- and research design-related information

Objective / Aim / Goal / Purpose & Research Questions

The research objective and established RQs.

Research method (including unit of analysis)

The methods used to collect data in the study, including the unit of analysis that refers to the country, organisation, or other specific unit that has been analysed such as the number of use-cases or policy documents, number and scope of the SLR etc.

Study’s contributions

The study’s contribution as defined by the authors

Qualitative / quantitative / mixed method

Whether the study uses a qualitative, quantitative, or mixed methods approach?

Availability of the underlying research data

Whether the paper has a reference to the public availability of the underlying research data e.g., transcriptions of interviews, collected data etc., or explains why these data are not openly shared?

Period under investigation

Period (or moment) in which the study was conducted (e.g., January 2021-March 2022)

Use of theory / theoretical concepts / approaches? If yes, specify them

Does the study mention any theory / theoretical concepts / approaches? If yes, what theory / concepts / approaches? If any theory is mentioned, how is theory used in the study? (e.g., mentioned to explain a certain phenomenon, used as a framework for analysis, tested theory, theory mentioned in the future research section).

Quality-related information

Quality concerns

Whether there are any quality concerns (e.g., limited information about the research methods used)?

Public Data Ecosystem-related information

Public data ecosystem definition

How is the public data ecosystem defined in the paper and any other equivalent term, mostly infrastructure. If an alternative term is used, how is the public data ecosystem called in the paper?

Public data ecosystem evolution / development

Does the paper define the evolution of the public data ecosystem? If yes, how is it defined and what factors affect it?

What constitutes a public data ecosystem?

What constitutes a public data ecosystem (components & relationships) - their "FORM / OUTPUT" presented in the paper (general description with more detailed answers to further additional questions).

Components and relationships

What components does the public data ecosystem consist of and what are the relationships between these components? Alternative names for components - element, construct, concept, item, helix, dimension etc. (detailed description).

Stakeholders

What stakeholders (e.g., governments, citizens, businesses, Non-Governmental Organisations (NGOs) etc.) does the public data ecosystem involve?

Actors and their roles

What actors does the public data ecosystem involve? What are their roles?

Data (data types, data dynamism, data categories etc.)

What data do the public data ecosystem cover (is intended / designed for)? Refer to all data-related aspects, including but not limited to data types, data dynamism (static data, dynamic, real-time data, stream), prevailing data categories / domains / topics etc.

Processes / activities / dimensions, data lifecycle phases

What processes, activities, dimensions and data lifecycle phases (e.g., locate, acquire, download, reuse, transform, etc.) does the public data ecosystem involve or refer to?

Level (if relevant)

What is the level of the public data ecosystem covered in the paper? (e.g., city, municipal, regional, national (=country), supranational, international).

Other elements or relationships (if any)

What other elements or relationships does the public data ecosystem consist of?

Additional comments

Additional comments (e.g., what other topics affected the public data ecosystems and their elements, what is expected to affect the public data ecosystems in the future, what were important topics by which the period was characterised etc.).

New papers

Does the study refer to any other potentially relevant papers?

Additional references to potentially relevant papers that were found in the analysed paper (snowballing).

Format of the file.xls, .csv (for the first spreadsheet only), .docx

Licenses or restrictionsCC-BY

For more info, see README.txt

This data table provides the detailed data quality assessment scores for the Long Term Development Statement dataset. The quality assessment was carried out on 31st March. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality; to demonstrate our progress we conduct annual assessments of our data quality in line with the dataset refresh rate. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks. We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.

It represents the data quality dimensions concerning different types of data.

This data table provides the detailed data quality assessment scores for the Historic Faults dataset. The quality assessment was carried out on the 31st March. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at a minimum, bi-annual assessments of our data quality - for datasets that are refreshed more frequently than this, please note that the quality assessment may be based on an earlier version of the dataset. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks. We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.

It represents the data quality dimensions concerning different types of data.

Reflecting the essence of high-quality development., this paper constructs a dual-dimensional measurement index system for High-Quality Industrial Development Level (HQIDL) in the Yangtze River Delta (YRD) urban agglomeration, grounded in the five development concepts and three development dimensions.

These data were collected as part of the Great Lakes Restoration Initiative (GLRI) project template 678-1 entitled "Evaluate immediate and long-term BMP effectiveness of GLRI restoration efforts at urban beaches on Southern and Western Lake Michigan". This project is evaluating the effectiveness of projects that are closely associated with restoration of local habitat and contact recreational activities at two GLRI funded sites in Southern Lake Michigan and one non-GLRI site in Western Lake Michigan. Evaluation of GLRI projects will assess whether goals of recipients are on track and identify any developing unforeseen consequences. Including a third, non-GLRI project site in the evaluation allows comparison between restoration efforts in GLRI and non-GLRI funded projects. Projections and potential complications associated with climate change impacts on restoration resiliency are also being assessed. Two of the three sites to receive evaluation represent some of the most highly contaminated beaches in the United States and include restoration BMPs which could benefit urban beaches and nearshore areas throughout the Great Lakes. The urban beaches chosen for evaluation are at various stages of the restoration process and located in Indiana (Jeorse Park Beach), Illinois (63rd Street Beach), and Wisconsin (North Beach). Evaluation of effectiveness of restoration efforts and resiliency to climate change at urban beaches will provide vital information on the success of restoration efforts and identify potential pitfalls that will help maximize success of future GLRI beach and nearshore restoration projects. Data used for evaluation include continuous monitoring and synoptic mapping of nearshore currents, bathymetry, and water quality to examine nearshore transport under a variety of conditions. In addition, biological evaluations rely upon daily indicator bacteria monitoring, microbial community and shorebird surveys, recreational usage, and other ancillary water quality data. The pre- and post-restoration datasets comprised of these physical, chemical, biological, geological, and social data will allow restoration success to be evaluated using a science-based approach with quantifiable measures of progress. These data will also allow the evaluation of the resiliency of these restoration efforts under various climate change scenarios using existing climate change predictions and models. This data release is comprised of three-dimensional point measurements of basic water-quality parameters in coastal Lake Michigan at 63rd Street Beach near Chicago, Illinois, on September 22, 2016. Water-quality parameters include temperature, specific conductance, pH, dissolved oxygen, turbidity, total chlorophyll, and phycocyanin concentration. These data were collected using a YSI EcoMapper autonomous underwater vehicle (AUV) equipped with a YSI 6600 V2-4 bulkhead housing a YSI 6560FR fast response temperature/conductivity probe, YSI 6589FR fast response pH sensor, YSI 6150 ROX optical dissolved oxygen sensor, YSI 6136 turbidity sensor, YSI 6025 chlorophyll sensor, and YSI 6131 BGA-PC phycocyanin (blue-green algae) sensor. All parameters were sampled at 1-second intervals as the AUV completed the pre-programmed survey pattern of the nearshore zone. The AUV was programmed to continually undulate between the water surface and 4 feet above the bottom (dive angle of 15 degrees) as it moved at 2 knots between programmed waypoints along it survey mission path. The resulting dataset allows for analysis of the three-dimensional distributions of water-quality parameters in Lake Michigan at 63rd Street Beach.

The Gridded Population of the World, Version 4 (GPWv4): Data Quality Indicators consist of three data layers created to provide additional information about the quality of input population data and to provide context for the population count and density grids. The water mask distinguishes between pixels that are completely water and/or ice (Total Water Pixels) and pixels that also contain land (Partial Water Pixels). The data context grid categorizes pixels with estimated zero population based on information provided in the census documents, such as areas that are part of a national park, areas that have no households, etc. The Mean Administrative Unit Area grid measures the mean unit size in square kilometers. The mean unit size grid provides a quantitative surface that indicates the size of the input unit(s) from which population count and density grids are derived.

Dimensions of data quality.

This data table provides the detailed data quality assessment scores for the Embedded Capacity Register dataset. The quality assessment was carried out on 30th April. Please note, this assessment only covers 1MW and above data. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at minimum, bi-annual assessments of our data quality - for datasets that are refreshed more frequently than this, please not that the quality assessment may be based on an earlier version of the dataset. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks. We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.

This archive provides additional data for the article "Understanding Test Convention Consistency

as a Dimension of Test Quality" by Martin P. Robillard, Mathieu Nassif, and Muhammad Sohail,

published in ACM Transactions on Software Engineering and Methodology.

This dataset provides the detailed data quality assessment scores for the Voltage dataset. The quality assessment was carried out on the 31st March. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at a minimum, bi-annual assessments of our data quality - for datasets that are refreshed more frequently than this, please not that the quality assessment may be based on an earlier version of the dataset. To access our full suite of aggregated quality assessments and learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks. We welcome feedback and questions from our stakeholders regarding our approach to data quality. Our Open Data team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the dataset schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the datasets with the results when available.

The Gridded Population of the World, Version 4 (GPWv4): Data Quality Indicators, Revision 11 consists of three data layers created to provide context for the population count and density rasters, and explicit information on the spatial precision of the input boundary data. The Data Context raster explains pixels with a "0" population estimate in the population count and density rasters based on information included in the census documents, such as areas that are part of a national park, areas that have no households, etc. The Water Mask raster distinguishes between pixels that are completely water and/or ice (Total Water Pixels), pixels that contain water and land (Partial Water Pixels), pixels that are completely land (Total Land Pixels), and pixels that are completely ocean water (Ocean Pixels). The Mean Administrative Unit Area raster represents the mean input Unit size in square kilometers and provides a quantitative surface that indicates the size of the input Unit(s) from which population count and density rasters are created. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research commUnities, the 30 arc-second data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions.

These data were collected as part of the Great Lakes Restoration Initiative (GLRI) project template 678-1 entitled "Evaluate immediate and long-term BMP effectiveness of GLRI restoration efforts at urban beaches on Southern and Western Lake Michigan". This project is evaluating the effectiveness of projects that are closely associated with restoration of local habitat and contact recreational activities at two GLRI funded sites in Southern Lake Michigan and one non-GLRI site in Western Lake Michigan. Evaluation of GLRI projects will assess whether goals of recipients are on track and identify any developing unforeseen consequences. Including a third, non-GLRI project site in the evaluation allows comparison between restoration efforts in GLRI and non-GLRI funded projects. Projections and potential complications associated with climate change impacts on restoration resiliency are also being assessed. Two of the three sites to receive evaluation represent some of the most highly contaminated beaches in the United States and include restoration BMPs which could benefit urban beaches and nearshore areas throughout the Great Lakes. The urban beaches chosen for evaluation are at various stages of the restoration process and located in Indiana (Jeorse Park Beach), Illinois (63rd Street Beach), and Wisconsin (North Beach). Evaluation of effectiveness of restoration efforts and resiliency to climate change at urban beaches will provide vital information on the success of restoration efforts and identify potential pitfalls that will help maximize success of future GLRI beach and nearshore restoration projects. Data used for evaluation include continuous monitoring and synoptic mapping of nearshore currents, bathymetry, and water quality to examine nearshore transport under a variety of conditions. In addition, biological evaluations rely upon daily indicator bacteria monitoring, microbial community and shorebird surveys, recreational usage, and other ancillary water quality data. The pre- and post-restoration datasets comprised of these physical, chemical, biological, geological, and social data will allow restoration success to be evaluated using a science-based approach with quantifiable measures of progress. These data will also allow the evaluation of the resiliency of these restoration efforts under various climate change scenarios using existing climate change predictions and models. This data release is comprised of two-dimensional, near-surface point measurements of basic water-quality parameters in coastal Lake Michigan at Jeorse Park Beach at Gary, Indiana, on September 21, 2016. Water-quality parameters include temperature, specific conductance, pH, dissolved oxygen, turbidity, total chlorophyll, and phycocyanin concentration. These data were collected using an EXO2 multiparameter sonde (SN 16F100255) equipped with a version 2 handheld display with a built-in Global Positioning System (GPS) receiver (SN 16N999907), temperature/conductivity probe (SN 16C104865), pH sensor (SN 15M100825), optical dissolved oxygen sensor (SN 15L101706), turbidity sensor (SN 16D100455), total algae phycocyanin smart sensor (SN 16C103752), central wiper, and depth sensor. The sonde was deployed off the starboard side of a manned survey vessel using a fixed aluminum mount at a depth of approximately 1.6 feet below the water surface. All parameters were sampled at 1-second intervals as the vessel completed the survey of the nearshore zone. The resulting dataset allows for analysis of the two-dimensional distributions of near-surface water-quality parameters in Lake Michigan at Jeorse Park Beach.

Context: In response to the exponential growth of data and increasingly regulatory requirements, such as the General Data Protection Regulation (GDPR) and Brazil’s General Data Protection Law (LGPD), Data Governance (DG) has become a strategic priority for organizations. These regulations underscore the importance of structured strategies to ensure compliance, data quality, and security.

Objective: This study presents an overview of DG maturity models and practical approaches to assess and enhance organizational capabilities. Based on the evidence gathered, it proposes a conceptual framework composed of six dimensions to support the evaluation and advancement of DG maturity.

Method: We carried out a Systematic Literature Review (SLR), focusing on DG maturity assessments, tools, and best practices from academy and industry. After the conduction of the SLR, 22 primary studies were selected. These studies were analyzed and synthesized to highlight main features of existing models, tools, and practices applied in organizational data governance.

Results: The findings highlight widely used maturity models and a range of practices—such as policy formalization, staff training, and iterative quality assessment—that support organizations in addressing challenges related to data integration, security, and strategic alignment. Building on this synthesis, the study introduces a six-dimensional framework (DG2M) to guide organizations in assessing current capabilities and implementing targeted improvements.

Conclusions: This work reinforces the significance of DG maturity models and emerging best practices in structuring governance initiatives. The proposed framework serves as a practical tool to support organizations in identifying gaps and enhancing their data governance capabilities in alignment with regulatory and strategic demands.

Keywords: Data Governance, Data Maturity, Systematic Literature Review, Data Governance Framework, Data Quality

Website for the framework featuring an interactive view: https://datagovernancematurity.github.io/framework/" target="_blank" rel="noopener">https://datagovernancematurity.github.io/framework/

Available Resources

The following files are available for download and reference:

• Overview of the SLR Process: A detailed description of the methodology, including research questions, inclusion/exclusion criteria, and quality assessment protocols.
• Collected Articles and Selection Criteria: A spreadsheet documenting the collected studies and their evaluation against inclusion and exclusion criteria.
• Quality Assessment Results: A spreadsheet with the quality assessment of the selected studies, based on predefined questions to ensure methodological rigor.
• Data Extraction Results: A detailed spreadsheet capturing information extracted from the selected studies, including objectives, methods, and outcomes.
• Proposed Framework Construction: a spreadsheet that supported the development of this framework.

Water-quality constituents were measured along planned survey lines, which were generally perpendicular to the shoreline and spaced 100 meters apart, over an approximately 2.3-mile section of nearshore Lake Erie on June 10-12, 2019 (survey 1), and August 19-21, 2019 (survey 2), with a YSI EcoMapper autonomous underwater vehicle (AUV) using an undulating dive pattern (a 15-degree dive angle between the surface and 6 feet above the bottom). Water-quality data collected in this area included three-dimensional measurements of water temperature, specific conductance, pH, dissolved oxygen, chlorophyll, and phycocyanin (blue-green algae). The water-quality data were measured at 1-second intervals and were geo-referenced with the AUV onboard Global Positioning System (GPS WAAS) system. There was a total of 40 survey lines which typically started about 50 meters offshore and ended 500 meters offshore, and the survey lines covered the area around the mouth of Euclid Creek, Villa Angela Beach, Wildwood Marina, and the Easterly Wastewater Treatment Plant. Data were post-processed with a custom MATLAB script. Each water-quality measurement has an associated latitude and longitude (WGS84) and sample depth (in meters) below the surface. All water-quality sensors were calibrated per USGS standard methods prior to the start of the survey. These water-quality measurements were collected over the course of six total days, three in early summer (survey 1) and three in late summer (survey 2), to characterize circulation, mixing, and transport patterns in nearshore Lake Erie in the vicinity of Euclid Creek and Villa Angela Beach.

The Australian Antarctic Data Centre's Casey Station GIS data were originally mapped from Aerial photography (January 4 1994). Refer to the metadata record 'Casey Station GIS Dataset'. Since then various features have been added to these data as structures have been removed, moved or established. Some of these features have been surveyed. These surveys have metadata records from which the report describing the survey can be downloaded. However, the locations of other features have been obtained from a variety of sources. The data are included in the data available for download from the provided URLs. The data conforms to the SCAR Feature Catalogue which includes data quality information. See the provided URL. Data described by this metadata record has Dataset_id = 17. Each feature has a Qinfo number which, when entered at the 'Search datasets and quality' tab, provides data quality information for the feature.

This dataset includes the one-dimensional HEC-RAS water quality model simulation input and output files for two simulation discharges at the Lazy Day (LD) reach on the Big Piney River near St. Robert, Missouri. Simulations were run for environmental DNA (eDNA) sample collection dates on July 07, 2020 and July 23, 2021. For each eDNA collection date, the transport of freshwater mussel eDNA from Cumberlandia monodonta was simulated by specifying eDNA as an arbitrary constituent in the HEC-RAS water quality module and assigning a first order rate of decay. To account for the variation of the eDNA field samples at the upstream boundary condition, as well as the laboratory derived decay constants, we ran three model simulations for each eDNA collection date: a) the mean eDNA concentration at the upstream boundary with the mean decay constant (k), b) the mean eDNA concentration at the upstream boundary plus 1SE with the mean k minus 1SE, and c) the mean eDNA concentration at the upstrea ...