The Blackwell Business dimensions in total quality series is a book series. It includes 2 books, written by 2 different authors.

Dimensions of data quality.

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.

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

The Open Data Maturity (ODM) assessment is carried out yearly and provides a benchmark of European countries development in the field of open data. It is based on the following dimensions:

• Policy: focusing on countries’ open data policies and strategies;
• Impact: looking into the activities to monitor and measure open data reuse and its impact;
• Portal: assessing portal functions and features that enable users to access open data via the national portal and support interaction within the open data community;
• Quality: focusing on mechanisms that ensure the quality of the (meta)data..

This assessment helps the countries to better understand their level of maturity, to capture their progress over time and to find areas for improvement. Additionally, the study provides an overview of best practices implemented across Europe that could be transferred to other national and local contexts.

The 35 participant countries in the 2022 edition are the 27 EU Member States, 3 European Trade Association (EFTA) countries (Norway, Switzerland, Iceland), 4 candidate countries (Albania, Montenegro, Serbia, Ukraine) and Bosnia and Herzegovina.

The scores of the ODM assessment for each participating country and the questionnaire used in the survey are provided as a re-usable dataset. The complete report and the methodology can be found under documentation.

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.

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.

This paper tests whether the dimensions involved in preferential choice tasks are evaluated independently from one another. Common decision heuristics satisfy dimensional independence, and multi-strategy models that assume that decision makers use a repertoire of these heuristics predict that they are unable to represent and respond to dimensional dependencies in the decision environment. In contrast, some single-strategy models are able to violate dimensional independence, and subsequently adapt to environments that feature interacting dimensions. Across five experiments, this paper documents systematic violations of the assumption of dimensional independence. This suggests that decision makers are able to modify their behavior to respond to dimensional dependencies in their environment, and in turn those models that are unable to do this do not provide a full account of human strategy selection and behavior change. This paper ends with a discussion of ways in which some existing models can be modified to incorporate violations of dimensional independence.

This network project brings together economists, psychologists, computer and complexity scientists from three leading centres for behavioural social science at Nottingham, Warwick and UEA. This group will lead a research programme with two broad objectives: to develop and test cross-disciplinary models of human behaviour and behaviour change; to draw out their implications for the formulation and evaluation of public policy. Foundational research will focus on three inter-related themes: understanding individual behaviour and behaviour change; understanding social and interactive behaviour; rethinking the foundations of policy analysis. The project will explore implications of the basic science for policy via a series of applied projects connecting naturally with the three themes. These will include: the determinants of consumer credit behaviour; the formation of social values; strategies for evaluation of policies affecting health and safety. The research will integrate theoretical perspectives from multiple disciplines and utilise a wide range of complementary methodologies including: theoretical modeling of individuals, groups and complex systems; conceptual analysis; lab and field experiments; analysis of large data sets. The Network will promote high quality cross-disciplinary research and serve as a policy forum for understanding behaviour and behaviour change.

The Australian Antarctic Data Centre's Larsemann Hills topographic GIS dataset was mapped from aerial photography. Refer to the metadata record 'Larsemann Hills - Mapping from aerial photography captured February 1998', Entry ID gis135. Since then GIS data with the locations and attributes of a range of features has been created from various sources, often for the purpose of environmental management. The features include station buildings, refuges, camp sites, management zones, helicopter landing areas, anchorages, beaches, a grave, monuments and Physics equipment. The data are included in the GIS data available for download from a Related URL below. The data conforms to the SCAR Feature Catalogue which includes data quality information. See a Related URL below. Data described by this metadata record has Dataset_id = 6. Each feature has a Qinfo number which, when entered at the 'Search datasets and quality' tab, provides data quality information for the feature, including the origin of the data.

Additional file 1. The process of simulation data in this study at https://youtu.be/5BLJtiif2M4.

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.

Demographics and attitudes/opinions of QDM. Visit https://dataone.org/datasets/sha256%3A7f31869f30c8448ea123dd2ad9ac6154ed6fddc66905e434af03d103ee9cbe47 for complete metadata about this dataset.

The Ancillary Data component of the Indicators of Coastal Water Quality Collection includes a 5 arc-minute (approximately 9 x 9 km at the equator) sequence grid, grid cell centroids that relate to the grid cells in the tabular "Indicators of Coastal Water Quality: Change in Chlorophyll-a Concentration 1998-2007" data set, and a country buffer data set that is divided by exclusive economic zones (EEZ). The data are produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities.

The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities. The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.

The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population. The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.

The Australian Antarctic Data Centre's Mawson Station GIS data were originally mapped from March 1996 aerial photography. Refer to the metadata record 'Mawson Station GIS Dataset'. Since then various features have been added to this 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, other features have been 'eyed in' as more accurate data were not available. The eyeing in has been done based on advice from Australian Antarctic Division staff and using as a guide sources such as an aerial photograph, an Engineering plan, a map or a sketch. GPS data or measurements using a measuring tape may also have been used.

The data are included in the data available for download from a Related URL below. The data conform to the SCAR Feature Catalogue which includes data quality information. See a Related URL below. Data described by this metadata record has Dataset_id = 119. Each feature has a Qinfo number which, when entered at the 'Search datasets and quality' tab, provides data quality information for the feature.

Abstract

The Global Data Regulation Diagnostic provides a comprehensive assessment of the quality of the data governance environment. Diagnostic results show that countries have put in greater effort in adopting enabler regulatory practices than in safeguard regulatory practices. However, for public intent data, enablers for private intent data, safeguards for personal and nonpersonal data, cybersecurity and cybercrime, as well as cross-border data flows. Across all these dimensions, no income group demonstrates advanced regulatory frameworks across all dimensions, indicating significant room for the regulatory development of both enablers and safeguards remains at an intermediate stage: 47 percent of enabler good practices and 41 percent of good safeguard practices are adopted across countries. Under the enabler and safeguard pillars, the diagnostic covers dimensions of e-commerce/e-transactions, enablers further improvement on data governance environment.

The Global Data Regulation Diagnostic is the first comprehensive assessment of laws and regulations on data governance. It covers enabler and safeguard regulatory practices in 80 countries providing indicators to assess and compare their performance. This Global Data Regulation Diagnostic develops objective and standardized indicators to measure the regulatory environment for the data economy across countries. The indicators aim to serve as a diagnostic tool so countries can assess and compare their performance vis-á-vis other countries. Understanding the gap with global regulatory good practices is a necessary first step for governments when identifying and prioritizing reforms.

Geographic coverage

80 countries

Analysis unit

Country

Kind of data

Observation data/ratings [obs]

Sampling procedure

The diagnostic is based on a detailed assessment of domestic laws, regulations, and administrative requirements in 80 countries selected to ensure a balanced coverage across income groups, regions, and different levels of digital technology development. Data are further verified through a detailed desk research of legal texts, reflecting the regulatory status of each country as of June 1, 2020.

Mode of data collection

Mail Questionnaire [mail]

Research instrument

The questionnaire comprises 37 questions designed to determine if a country has adopted good regulatory practice on data governance. The responses are then scored and assigned a normative interpretation. Related questions fall into seven clusters so that when the scores are averaged, each cluster provides an overall sense of how it performs in its corresponding regulatory and legal dimensions. These seven dimensions are: (1) E-commerce/e-transaction; (2) Enablers for public intent data; (3) Enablers for private intent data; (4) Safeguards for personal data; (5) Safeguards for nonpersonal data; (6) Cybersecurity and cybercrime; (7) Cross-border data transfers.

Response rate

100%

The issue of data quality has emerged as a critical concern, as low-quality data can impede data sharing, diminish intrinsic value, and result in economic losses. Current research on data quality assessment primarily focuses on four dimensions: intrinsic, contextual, presentational, and accessibility quality, with intrinsic and presentational quality mainly centered on data content, and contextual quality reflecting data usage scenarios. However, existing approaches lack consideration for the behavior of data within specific application scenarios, which encompasses the degree of participation and support of data within a given scenario, offering valuable insights for optimizing resource deployment and business processes. In response, this paper proposes a data contribution assessment method based on maximal sequential patterns of behavior paradigms (DecentralDC). DecentralDC is composed of three steps: (1) mining the maximal sequential patterns of sharing and exchange behavior paradigms; (2) determining the weights of these paradigms; (3) calculating the contribution of sharing and exchange databases combined with data volume. To validate our approach, two sharing and exchange scenarios of different scales are established. The experimental results in two scenarios validate the effectiveness of our method and demonstrate a significant reduction in cumulative regret and regret rate in data pricing due to the introduction of data contribution. Specifically, compared to the most competitive baseline, the improvements of mean average precision in two scenarios are 6% and 8%. The code and simulation scenarios have been open-sourced and are available at https://github.com/seukgcode/DecentralDC.

These data were compiled through the Institutional Dimensions of Restoring Everglades Water Quality research project. One of the manuscripts generated by this project focused on the social capital dynamics in the Everglades Agricultural Area. These data represent different social capital aspects reflected by the responses of interview subjects. The purpose of analyzing social capital was to explore why and how farmers cooperated given that the state law, the Everglades Forever Act, which required the adoption of best management practices, relied on shared compliance for farmers to improve water quality. The study sought to undrestand how different aspects of social capital (broadly pro-social norms of reciprocity and trust) either encouraged or discouraged farmers to adopt BMPs effectively.

Data Notes

• Class size audits are conducted by CESE (Centre for Education Statistics and Evaluation) in March each year. Audits were not conducted in 1998, 1999, 2000 and 2001.

• Data for 2020 should be treated with caution. The collection took place in March when schools were impacted by COVID-19, so fewer data checks were carried out.

• Students attending schools for specific purposes (SSPs), students in support classes in regular schools and distance education students are excluded from average class size calculations.

• The average class size for each grade is calculated by taking the number of students in all classes that a student from that grade is in (including composite/multi age classes) divided by the total number of classes that includes a student from that grade. This can result in a lower Kindergarten to Year 6 average class size than any individual year level.

• From 2017, school size is based on primary enrolment rather than school classification.

• Schools change size, so data in Table 2 is not necessarily comparable to previous iterations in earlier fact sheets.

Data Source

Education Statistics and Measurement, Centre for Education Statistics and Evaluation.

Data quality statement

The Class Size Audit Data Quality Statement addresses the quality of the Class Size Audit dataset using the dimensions outlined in the NSW Department of Education's data quality management framework: institutional environment, relevance, timeliness, accuracy, coherence, interpretability and accessibility. It provides an overview of the dataset's quality and highlights any known data quality issues.

The Australian Antarctic Data Centre's Macquarie Island Station GIS Dataset was originally produced from low level aerial photography of the station and from ground surveys. Refer to the metadata record 'Macquarie Island Station GIS Dataset'.

Since then various features have been added to this 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 a Related URL below. The data conform to the SCAR Feature Catalogue which includes data quality information. See a Related URL below. Data described by this metadata record has Dataset_id = 31. Each feature has a Qinfo number which, when entered at the 'Search datasets and quality' tab, provides data quality information for the feature.