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

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

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 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'.

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.

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.

The Macquarie Island Station Area GIS Dataset is a topographic and facilities data base covering Australia's Macquarie Island Station and its immediate environs. The database includes all man made and natural features within the operational area of the station proper. Attributes are held for many facilities including, buildings, site services, communications, fuel storage, aeronautical and management zones. The spatial data have been compiled from low level aerial photography, ground surveys and engineering plans. Detail attribution of hydraulic site services includes make, size and engineering plan number.

The dataset conforms to the SCAR Feature Catalogue which includes data quality information.

The data is included in the 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 = 25. Each feature has a Qinfo number which, when entered at the 'Search datasets & quality' tab, provides data quality information for the feature.

Changes have occurred at the station since this dataset was produced. For example some buildings and other structures have been removed and some added. As a result the data available for download from a Related URL below is updated with new data having different Dataset_id(s).

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.

The City Prosperity Indices comprise six major components (Productivity, Infrastructure Development, Quality of Life, Equity and Social Inclusion, Environmental Sustainability, Urban Governance and Legislation) and each components has it own key ingredients and indicators which enable to calculate the city prosperity index of a city.

Sample characteristics, N = 245.

Central Composite Design (CCD) with 3 controllable variables (x) and 19 responses (Y) related to the H13 hardened steel turning with Wiper CC 650 tools. The 19 response surfaces were defined to focus on five important process dimensions: quality, costs, productivity, economical feasibility and sustainability. The CCD developed was based by Campos (2015) and in industrial dataset.

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

City Prosperity Index (CPI) database, 2016, containing data on 25 indicators across the 6 dimensions of the CPI: productivity, infrastructure development, quality of life, equity and social inclusion, environmental sustainability and governance and legislation.

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.

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.

The retrospective database is a compilation of historical water-quality and ancillary data collected before NAWQA Study Units initiated sampling in 1993. This coverage contains the point locations of monitoring locations where historical water-quality data was collected. Water-quality data were obtained by study-unit personnel from the U.S. Geological Survey (USGS) National Water Information System (NWIS), from records of State water-resource agencies, and from STORET, the U.S. Environmental Protection Agency national database. Ancillary data describing characteristics of sampled sites were compiled by NAWQA Study Units or obtained from national-scale digital maps.

Mueller and others (1995) used this data to determine preexisting water-quality conditions in the first 20 NAWQA Study Units that began in 1991. Also, Nolan and Ruddy (1996) used the data to describe areas of the United States at risk of nitrate contamination of ground water.

Supplemental_Information:

The retrospective database includes over 22,000 surface-water samples. The surface-water data are for samples collected during 1980-90 at sites that had a minimum of 25 monthly samples. Year of sampling is included in the retrospective database because it was reported most often by the various Study Units. Year of sampling also is convenient because some Study Units reported median constituent concentrations. If sampling date ranges for median values fell within a single year, then year of sampling was retained in the national data set for that sample.

Because sampling, preservation, and analytical techniques associated with these historical data changed during the period of record and are different for different agencies, reported nutrient concentrations were aggregated into the following groups: (1) ammonia as N, (2) nitrate as N, (3) total nitrogen, (4) orthophosphate as P, and (5) total phosphorus. For example, ammonia includes both ammonium ions and un-ionized ammonia. More information on methods used to aggregate constituent data is available in the report by Mueller and others (1995).

Much of the ancillary data, such as well and aquifer descriptions and land-use classification for surface-water drainage basins, were provided by NAWQA Study Units. Data evaluated at the national scale include land use, soil hydrologic group, nitrogen input to the land surface, and the ratios of pasture or woodland to cropland.

Land-use classification of surface-water sites is based on Anderson Level I categories (Anderson and others, 1976). Land use at surface-water sites was classified by NAWQA Study Unit personnel based on the Anderson Level I categories. Many surface-water sites were affected by mixed land uses, such as Forest and Agricultural, or Agricultural and Urban. Surface-water sites with very large drainage areas (greater than 10,000 square miles) were considered to be affected by multiple land uses, and were designated as Integrated land use. More detailed descriptions of the land-use categories in the retrospective database are given by Mueller and others (1995).

Soil hydrologic group was determined from digital maps compiled by the U.S. Soil Conservation Service (1993). The categorical values (A, B, C, and D) from the digital maps were converted to numbers to permit aggregation (Mueller and others, 1995). Surface-water sites were assigned the area-weighted mean for soil mapping units in the upstream drainage basin. Many surface-water sites did not have digitized basin boundaries available, so hydrologic group could not be evaluated.

Fertilizer and manure applications were estimated from national databases of fertilizer sales (U.S. Environmental Protection Agency, 1990) and animal populations (U.S. Bureau of the Census, 1989). Nitrogen input by atmospheric deposition was derived from data provided by the National Atmospheric Deposition Program/National Trends Network (1992).

Population data were obtained from the U.S. Bureau of the Census (1991). Total population in the upstream drainage was compiled for the surface-water data set.

Within the database, concentrations less than detection are reported as negative values of the detection limit. Missing values are indicated by a decimal point. (During processing of the tabular data, these decimal points were replaced will NULL values; See Data_Quality_Information section.

Historical data can be of limited use in national assessments because of inconsistencies between and within agencies in database structure and format and in sample collection, preservation, and analytical procedures. For example, changes in sample collection and analytical procedures can cause shifts in constituent concentrations that are unrelated to possible changes in environmental factors. See Mueller and others (1995) for assumptions and limitations associated with the retrospective database.

[Summary provided by the EPA.]

Chi-square test result of the dimensional error count by institution.

Additional file 4. Excel file to calculate Delta and GC as well as contents for this study.