FAIR Dataset Quality Information
This is the document for community guidelines of consistently curating and representing dataset quality information, in line with the FAIR principles.
This document provides resources for developing community guidelines for consistently curating and representing dataset quality information and captures the outcomes. The guidelines aim to help curate dataset quality information that is findable and accessible, machine- and human-readable, interoperable, and reusable.
The target community is any entity that produces, publishes, manages, or uses digital Earth Science datasets or products. However, the guidelines will be general enough to be applicable to digital datasets of other disciplines.
Guidelines Document Draft for Community Review
A complete draft of the guidelines document (v00r05-20210417) is out for community review. The current document can be accessed at https://doi.org/10.31219/osf.io/xsu4p. A Google Form facilitates anonymous comment collection can be accessed here, which will be available until Friday June 4, 2021. Alternatively, you can use this template to capture all your comments and suggestions and send it to Ge Peng at [], Carlo Lacagnina at [], or Ivana Ivánová at [].
Community feedback is important in helping us improve the quality of the document. Please contact us if you have any questions.
- Case statement for developing community guidelines for consistently curating and representing dataset quality information (Peng et al. 2020a)
- Call to Action for Global Access to and Harmonization of Quality Information of Individual Earth Science Datasets (Peng et al. 2020b)
Summary Report of the Pre-ESIP Workshop
This workshop summary report (Peng et al. 2020c) 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.
Multi-dimensions of Data and Information Quality:
- Quality Attributes for Data Consumers (Wang and Strong 1996)
- Multi-dimensions of Earth Science Data and Information Quality (Ramapriyan et al. 2017)
- Overview of Data Quality Perspectives and Maturity Models (Peng 2018; Peng et al. 2019a: Recording)
Existing Fitness for Purpose Assessment approaches Through the Full Life Cycle of Earth Science Datasets:
- Measurement systems:
- GAIA-CLIM Measurement Maturity Matrix (Thorne et al 2015)
- Production systems:
- Scientific quality:
- NASA Technical Readiness Levels for Operations (Mankins 2009)
- NOAA STAR Data Product Algorithm Maturity Matrix (Zhou, Divakarla & Liu 2016; Zhou et al. 2019)
- Perspectives of Data Uncertainty (Moroni et al. 2019)
- OGC UncertML (Williams et al. 2009)
- Operational Readiness Levels For Disaster Operations (ESIP Disasters Cluster)
- Product quality:
- NOAA CDR Product Maturity Matrix (Bates and Privette 2012)
- Stewardship quality:
- NCEI/CICS-NC Scientific Data Stewardship Maturity Matrix (Peng et al. 2015)
- CEOS WGISS Data Management and Stewardship Maturity Matrix (WGISS DSIG 2017)
- WMO Stewardship Maturity Matrix for Climate Data (SMM-CD Working Group 2019)
- GEOSS Data Management Principles and Data Sharing Principles (GEO DMP TF 2015; GEO DSWG 2014)
- Service quality:
- Level of Services Models: NSIDC (Duerr et al. 2009) and NASA Earth Science Data System
- NCEI Tiered Scientific Data Stewardship Services (Peng et al. 2016)
- GCOS ECV Data and Information Access Matrix
- Global Ocean Observing System (GOOS) framework
- NCEI/ESIP-DSC Data Use and Services Maturity Matrix (Serv-MM Working Group 2018)
- Data Use and Impact (Downs 2019)
Dataset-level metadata quality:
- Completeness: NCEI Collection-Level Metadata Rubric Tool (An Assessment Example)
- FAIR metadata checklist – NCEAS MetaDIG (Habermann 2019 )
- Metadata checklist of LTER network data management system (O’Brien et al. 2016)
- Data set provenance for science (Hills et al. 2015)
- Stewardship quality metadata (Peng et al 2019b)
- AtMoDat (ATmospheric MOdel DATa) Maturity Indicator Metadata (Neumann et al. 2020)
Portfolio Management and Repository Certifications
- NGDA Data Lifecycle Maturity Model (LMM) (NGDA 2015; Peltz-Lewis et al. 2014)
- CoreTrustSeal Trustworthy Data Repository Requirements (Edmunds et al. 2016; 2019)
- USGS Trusted Data Repository Checklist (Faundeen 2017)
- The TRUST Principles for Digital Repositories (Lin et al. 2020)
FAIR Data Principles
- FAIR Data Principles (Wilkinson et al. 2016)
- RDA FAIR Data Maturity Model (RDA FAIR Data Maturity Model WG 2020)
- EOSC FAIR Metrics (Genova et al. 2020)
- A self-assessment tool to measure the FAIR-ness of an organization (Bruin et al. 2020)
Organizational Challenges and Approaches
- NASA’s ESDSWG Data Quality Working Group Recommendations (Wei et al. 2019 - recording)
- Gaps in Essential Climate Variables Assessment (Nightingale et al. 2019)
- FAIR and Data Management for a Multidisciplinary Research Center (Westra and Zhang 2019)
Data Quality Management Framework
- High Quality Global Data Management Framework for Climate Data (HQ-GDMFC) (WMO 2019)
- Implementation of a Data Management Quality Management Framework at the Marine Institute, Ireland (Leadbetter et al. 2019)
- The Data Quality Challenge. Recommendations for Sustainable Research in the Digital Turn (Rat für Informationsinfrastrukturen 2020)
- Conceptual Enterprise Framework for Managing Scientific Data Stewardship (Peng et al. 2018); (Peng, Privette, & Maycock 2019)
- Data producers, publishers, providers, and service providers for improved data sharing and reuse;
- Data quality management professionals for improved data quality and usability;
- Entities or organizations that manage and steward Earth science datasets during any stage of their full life cycle for improved enterprise data management and stewardship;
- End users who integrate various datasets and associated quality information for improved interoperability and reusability.
- Data are representations of observations, objects, or other entities and can refer to anything that is collected, observed, generated or derived, and used as a basis for reasoning, discussion, or calculation. Data can be either structured or unstructured, and can be represented in quantitative, qualitative, or physical forms.
- Scientific or research data is defined as: the recorded factual material commonly accepted in the scientific community as necessary to validate research findings.
- Digital data, distinguished from physical records, such as paper weather reports, are represented in discrete numerical form that can be used by a computer or electronic device.
- Data product refers to “a product that facilitates an end goal through the use of data,” usually with a well-thought out algorithm or approach (Patil 2012). Data products tend to be structured and can be raw measurements or scientific products derived from raw measurements or other products. Products can also be statistical or numerical model outputs, including analyses, reanalyses, predictions, or projections. Earth Science data products may be further categorized based on their processing levels.
- Dataset is an identifiable collection of physical records, and it can be processed, curated or published by a single agent. It may refer to a digital rendition of factual materials, or a product of a given version of an algorithm/model. A dataset may contain one or many physical samples or data files in an identical format, having the same geophysical variable(s) and product specification(s), such as the geospatial location or spatial grid. The general notion of datasets found in the literature currently is characterized by an interrelated family of more specific concepts: grouping, content, relatedness, and purpose (Renear et al 2010). Dataset and data product may be used interchangeably.
- Dataset quality includes quality of both data and associated information, examples of which are metadata, software, algorithms, and practices or procedures applied to the dataset throughout its entire life cycle. Dataset quality is a multi-dimensional construct perception and/or a judgment of data's fitness or trustworthiness to serve intended research uses in a given context.
- Dataset quality information includes quality of both data quality descriptive information such as those captured in documents, e.g., papers or reports, and quality metadata that is captured in a metadata record, throughout the entire life cycle of a dataset.
- Information is considered as data being processed, organized, structured, communicated or presented so as to be meaningful to the recipient in a given context.
- Knowledge is an abstract concept, defined as a familiarity, awareness, or understanding of someone or something, gained through education, experience, or association. It can refer to a theoretical or practical understanding of a subject.
- Maturity model refers to a maturity reference or assessment model with desired evolution in discrete stages from a certain aspect or perspective of dataset quality.