ObjectiveTo systematically survey existing definitions, concepts, and criteria of clinical research quality, both developed by stakeholder groups as well as in the medical literature. This study serves as a first step in the development of a comprehensive framework for the quality of clinical research.Study design and settingWe systematically and in duplicate searched definitions, concepts and criteria of clinical research quality on websites of stakeholders in clinical research until no further insights emerged and in MEDLINE up to February 2015. Stakeholders included governmental bodies, regulatory agencies, the pharmaceutical industry, academic and commercial contract research organizations, initiatives, research ethics committees, patient organizations and funding agencies from 13 countries. Data synthesis involved descriptive and qualitative analyses following the Framework Method on definitions, concepts, and criteria of clinical research quality. Descriptive codes were applied and grouped into clusters to identify common and stakeholder-specific quality themes.ResultsStakeholder concepts on how to assure quality throughout study conduct or articles on quality assessment tools were common, generally with no a priori definition of the term quality itself. We identified a total of 20 explicit definitions of clinical research quality including varying quality dimensions and focusing on different stages in the clinical research process. Encountered quality dimensions include ethical conduct, patient safety/rights/priorities, internal validity, precision of results, generalizability or external validity, scientific and societal relevance, transparency and accessibility of information, research infrastructure and sustainability. None of the definitions appeared to be comprehensive either in terms of quality dimensions, research stages, or stakeholder perspectives.ConclusionClinical research quality is often discussed but rarely defined. A framework defining clinical research quality across stakeholders’ individual perspectives is desirable to facilitate discussion, assessment, and improvement of quality at all stages of clinical research.

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.docx provides the structure of the protocol

PDEtypes.png provides a typology of public data ecosystems

PDE_conceptual_model.png provides a conceptual model of elements and formative characteristics that contribute most to value-adding public data ecosystems

PublicDataEcosystem_SLR.xlsx, 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 - presented in PublicDataEcosystem_SLR.xlsx - 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 restrictions***
CC-BY

For more info, see README.txt

Administrative data play an important role in performance monitoring of healthcare providers. Nonetheless, little attention has been given so far to the emergency department (ED) evaluation. In addition, most of existing research focuses on a single core ED function, such as treatment or triage, thus providing a limited picture of performance. The goal of this study is to harness the value of routinely produced records proposing a framework for multidimensional performance evaluation of EDs able to support internal decision stakeholders in managing operations. Starting with the overview of administrative data, and the definition of the desired framework’s characteristics from the perspective of decision stakeholders, a review of the academic literature on ED performance measures and indicators is conducted. A performance measurement framework is designed using 224 ED performance metrics (measures and indicators) satisfying established selection criteria. Real-world feedback on the framework is obtained through expert interviews. Metrics in the proposed ED performance measurement framework are arranged along three dimensions: performance (quality of care, time-efficiency, throughput), analysis unit (physician, disease etc.), and time-period (quarter, year, etc.). The framework has been judged as “clear and intuitive”, “useful for planning”, able to “reveal inefficiencies in care process” and “transform existing data into decision support information” by the key ED decision stakeholders of a teaching hospital. Administrative data can be a new cornerstone for health care operation management. A framework of ED-specific indicators based on administrative data enables multi-dimensional performance assessment in a timely and cost-effective manner, an essential requirement for nowadays resource-constrained hospitals. Moreover, such a framework can support different stakeholders’ decision making as it allows the creation of a customized metrics sets for performance analysis with the desired granularity.

Sample characteristics, N = 245.

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.

Results of psychometric analysis for the 23-item MOH instrument after factor analysis, N = 245.