Data quality and FAIR principles applied to marine litter data in Europe

2021 ◽  
Vol 173 ◽  
pp. 112965
Author(s):  
Elena Partescano ◽  
Maria Eugenia Molina Jack ◽  
Matteo Vinci ◽  
Alexia Cociancich ◽  
Alessandro Altenburger ◽  
...  
Keyword(s):  
Data Quality ◽  
Marine Litter ◽  
Fair Principles

scholarly journals Laying the Groundwork for Developing International Community Guidelines to Effectively Share and Reuse Digital Data Quality Information – Case Statement, Workshop Summary Report, and Path Forward

2020 ◽  
Author(s):  
Ge Peng ◽  
Carlo Lacagnina ◽  
Robert R. Downs ◽  
Ivana Ivanova ◽  
David F. Moroni ◽  
...  
Keyword(s):  
Data Quality ◽  
International Community ◽  
Digital Data ◽  
Quality Information ◽  
Summary Report ◽  
Workshop Summary ◽  
Fair Principles ◽  
Case Statement

This document provides background for and summarizes main takeaways of a workshop held virtually to kick off the development of community guidelines for consistently curating and representing dataset quality information in a way that is in line with the FAIR principles.

scholarly journals Beyond the FAIRness of COVID-19 Data: What about Quality?

2021 ◽  
Author(s):  
Fabrizio Pecoraro ◽  
Daniela Luzi
Keyword(s):  
Data Quality ◽  
National Level ◽  
Epidemic Spread ◽  
National Institutions ◽  
Gender And Age ◽  
Share Data ◽  
Fair Principles

Different datasets have been deployed at national level to share data on COVID-19 already at the beginning of the epidemic spread in early 2020. They distribute daily updated information aggregated at local, gender and age levels. To facilitate the reuse of such data, FAIR principles should be applied to optimally find, access, understand and exchange them, to define intra- and inter-country analyses for different purposes, such as statistical. However, another aspect to be considered when analyzing these datasets is data quality. In this paper we link these two perspectives to analyze to what extent datasets published by national institutions to monitor diffusion of COVID-19 are reusable for scientific purposes, such as tracing the spread of the virus.

scholarly journals Measuring FAIR Principles to Inform Fitness for Use

2018 ◽  
Vol 13 (1) ◽  
pp. 35-46
Author(s):  
Carolyn Hank ◽  
Bradley Wade Bishop
Keyword(s):  
Best Practices ◽  
Data Quality ◽  
Data Management ◽  
Open Science ◽  
Conceptual Level ◽  
Data Types ◽  
Good Data ◽  
Secondary Purpose ◽  
Fair Principles ◽  
Data Quality Metrics

For open science to flourish, data and any related digital outputs should be discoverable and re-usable by a variety of potential consumers. The recent FAIR Data Principles produced by the Future of Research Communication and e-Scholarship (FORCE11) collective provide a compilation of considerations for making data findable, accessible, interoperable, and re-usable. The principles serve as guideposts to ‘good’ data management and stewardship for data and/or metadata. On a conceptual level, the principles codify best practices that managers and stewards would find agreement with, exist in other data quality metrics, and already implement. This paper reports on a secondary purpose of the principles: to inform assessment of data’s FAIR-ness or, put another way, data’s fitness for use. Assessment of FAIR-ness likely requires more stratification across data types and among various consumer communities, as how data are found, accessed, interoperated, and re-used differs depending on types and purposes. This paper’s purpose is to present a method for qualitatively measuring the FAIR Data Principles through operationalizing findability, accessibility, interoperability, and re- usability from a re-user’s perspective. The findings may inform assessments that could also be used to develop situationally-relevant fitness for use frameworks.

Towards Developing Community Guidelines for Sharing and Reusing Quality Information of Earth Science Datasets

2021 ◽  
Author(s):  
Carlo Lacagnina ◽  
Ge Peng ◽  
Robert R. Downs ◽  
Hampapuram Ramapriyan ◽  
Ivana Ivanova ◽  
...  
Keyword(s):  
Data Quality ◽  
Earth Science ◽  
Sufficient Evidence ◽  
Quality Information ◽  
Federal Policies ◽  
Data Life Cycle ◽  
Community Effort ◽  
Fair Principles ◽  
Available Information

<p>The knowledge of data quality and the quality of the associated information, including metadata, is critical for data use and reuse. Assessment of data and metadata quality is key for ensuring credible available information, establishing a foundation of trust between the data provider and various downstream users, and demonstrating compliance with requirements established by funders and federal policies.</p><p>Data quality information should be consistently curated, traceable, and adequately documented to provide sufficient evidence to guide users to address their specific needs. The quality information is especially important for data used to support decisions and policies, and for enabling data to be truly findable, accessible, interoperable, and reusable (FAIR).</p><p>Clear documentation of the quality assessment protocols used can promote the reuse of quality assurance practices and thus support the generation of more easily-comparable datasets and quality metrics. To enable interoperability across systems and tools, the data quality information should be machine-actionable. Guidance on the curation of dataset quality information can help to improve the practices of various stakeholders who contribute to the collection, curation, and dissemination of data.</p><p>This presentation outlines a global community effort to develop international guidelines to curate data quality information that is consistent with the FAIR principles throughout the entire data life cycle and inheritable by any derivative product.</p>

A Review of Failure Handling Mechanisms for Data Quality Measures

2012 ◽  
Author(s):  
Nurul A. Emran ◽  
Noraswaliza Abdullah ◽  
Nuzaimah Mustafa
Keyword(s):  
Data Quality ◽  
Quality Measures ◽  
Failure Handling

The Role of Technological Change in the Long-run Global Economy Forecasting

2013 ◽  
pp. 97-116 ◽  
Author(s):  
A. Apokin
Keyword(s):  
Technological Change ◽  
Data Quality ◽  
Long Range ◽  
Global Economy ◽  
World Economy ◽  
Qualitative Approaches ◽  
Long Run ◽  
The World ◽  
National Economies

The author compares several quantitative and qualitative approaches to forecasting to find appropriate methods to incorporate technological change in long-range forecasts of the world economy. A?number of long-run forecasts (with horizons over 10 years) for the world economy and national economies is reviewed to outline advantages and drawbacks for different ways to account for technological change. Various approaches based on their sensitivity to data quality and robustness to model misspecifications are compared and recommendations are offered on the choice of appropriate technique in long-run forecasts of the world economy in the presence of technological change.

Bankruptcy Model Construction and its Limitation in Input Data Quality

2019 ◽  
Vol 10 (2) ◽  
pp. 117-125
Author(s):  
Dana Kubíčková ◽  
◽  
Vladimír Nulíček ◽  
Keyword(s):  
Data Quality ◽  
Normal Distribution ◽  
Input Data ◽  
Model Construction ◽  
The Third ◽  
Third Stage ◽  
Discriminant Analyses ◽  
One Year ◽  
The University ◽  
Multivariate Discriminant

The aim of the research project solved at the University of Finance and administration is to construct a new bankruptcy model. The intention is to use data of the firms that have to cease their activities due to bankruptcy. The most common method for bankruptcy model construction is multivariate discriminant analyses (MDA). It allows to derive the indicators most sensitive to the future companies’ failure as a parts of the bankruptcy model. One of the assumptions for using the MDA method and reassuring the reliable results is the normal distribution and independence of the input data. The results of verification of this assumption as the third stage of the project are presented in this article. We have revealed that this assumption is met only in a few selected indicators. Better results were achieved in the indicators in the set of prosperous companies and one year prior the failure. The selected indicators intended for the bankruptcy model construction thus cannot be considered as suitable for using the MDA method.

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