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

Partescano, Elena; Jack, Maria Eugenia Molina; Vinci, Matteo; Cociancich, Alexia; Altenburger, Alessandro; Giorgetti, Alessandra; Galgani, François

doi:10.1016/j.marpolbul.2021.112965

Cited by 8 publications

(6 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The PostGIS spatial extension has allowed for the inclusion of several additional pieces of information regarding maritime spatial delimitations (e.g., marine MSFD regions and subregions). The development of a web service in Java code using Spring and Hibernate technologies has made it possible to implement a useful input data validation service through checks that verify syntactic and semantic correctness [21]. Through the mapping of the entities and relationships of the database in an object language, it was also possible to automate the insertion and extraction of data effectively and efficiently.…”

Section: Marine Litter Data Workflowmentioning

confidence: 99%

“…In relation to marine litter, standard formats and common vocabulary services have been developed based on existing best practices and guidelines for marine litter monitoring and research (e.g., [17]), structuring a detailed and qualified data management system focused on coastline macrolitter, seafloor macrolitter from trawling and floating, and sediment microlitter [21]. Some of these data and metadata were used to calculate the first beach litter baselines based on harmonized and FAIR datasets (2012-2016).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Mediterranean Focus Overview of EU Marine Litter Data Management Performed in the Framework of the European Marine Observation and Data Network Chemistry

Molina Jack,

Altenburger,

Caer

et al. 2023

Sustainability

Self Cite

View full text Add to dashboard Cite

The Mediterranean Sea is an almost completely closed basin connecting several countries. Its configuration leads to its peculiarity and richness, but the intensive activities within the basin and along the coast aggravate the ecological conditions. The existing regulatory European Framework for environmental protection has already been in place through a series of legal instruments for almost 20 years. In this context, open science could play a fundamental role. The existing data must become findable, accessible, interoperable, and reusable (FAIR) to provide stakeholders and decision-makers with the instruments to understand how to improve the available information and support decisions based on the best set of existing information. Since 2009, the European Marine Observation and Data Network, EMODnet, has provided access to high-quality marine information supporting research and stakeholders’ mission and objectives. Data related to pollution are collated, validated, and published using standard protocols, formats, and vocabularies, thus becoming FAIR. For marine litter, a detailed and qualified data management system for macro- and microlitter in diverse compartments was structured. Some of these data and metadata were already used to calculate the first coastline litter baselines based on harmonized and FAIR datasets (2012–2016). The availability of these data related to the Mediterranean area is relevant, but additional work is required.

show abstract

Section: Marine Litter Data Workflowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Mediterranean Focus Overview of EU Marine Litter Data Management Performed in the Framework of the European Marine Observation and Data Network Chemistry

Molina Jack,

Altenburger,

Caer

et al. 2023

Sustainability

Self Cite

View full text Add to dashboard Cite

show abstract

“…Documentation and metadata equate to transparency in the research process and facilitates the four Rs. Standards for capturing data-level documentation should be part of the data management plan, built into each stage of the research data lifecycle, and aligned with the FAIR Principles (Partescano, 2021;Wilkinson, et al, 2016). Project-level documentation, including standard operating procedures and record-keeping best practices, is also needed (Murphy, 2019;Schreier, Wilson and Resnik, 2006).…”

Section: Definitions Of Core Componentsmentioning

confidence: 99%

Research data integrity: A cornerstone of rigorous and reproducible research

Condon

Simpson

Emanuel

2022

View full text Add to dashboard Cite

Research data integrity provides a strong foundation for high quality research outcomes, and it is an essential part of the research data lifecycle due to its critical role in research rigor, reproducibility, replication, and data reuse (the four Rs). Understanding research data integrity is therefore imperative in collaborative interdisciplinary research and collaborative cross-sector research where different norms, procedures, and terminology regarding data exist. Research data integrity is closely associated with data management, data quality, and data security. Producing data that are reliable, trustworthy, valid, and secure throughout the research process requires purposefully planning for research data integrity and careful consideration of research data lifecycle actions like data acquisition, analysis, and preservation. In addition, purposeful planning enables researchers to conduct rigorous research and generate outcomes that are reproducible, replicable, and reusable. To advance this conversation, we developed two tools: a concept model that visually represents the relationship between data management, data quality, and data security as components of research data integrity, and a schema for implementing these components in practice. We contend that disentangling research data integrity and its components, developing a standardized way of describing their interplay, and intentionally addressing them in the research data lifecycle reduces threats to research data integrity. In this paper, we break down the complexity of research data integrity to make it more understandable and propose a practical process by which research data integrity can be achieved in a way that is useful for data producers, providers, users, and educators. We position our concept model and schema within the larger dialog around research integrity and data literacy and illuminate the role that research data integrity and its components (data management, data quality, and data security) play in the four Rs. In this paper, we present a concept model and schema for use as tools for instruction/training and practical implementation. Using these tools, we examine the role of research data integrity in rigorous and reproducible research and offer insight into ensuring research data integrity throughout the research process.

show abstract

“…These databases may hold more and differently formatted information, so direct comparison with these sources is not always possible, although it is possible to download harmonized datasets where data are formatted following Guidelines regarding vocabulary and values accepted in EMODnet marine litter data hub (EMODnet, 2022b,c). Also, the interoperability between repositories appears to be working well and FAIR principles were considered and are being applied to marine litter in Europe in the context of EMODnet chemistry, improving released data sets quality (Partescano et al, 2021).…”

Section: Marine Litter Data Sharing Around the Worldmentioning

confidence: 99%

Where are Brazil's marine litter scientific data?

2022

View full text Add to dashboard Cite

The environmental sciences work with datasets every day. Recently, data sharing has become a more familiar activity for academic researchers. Records of marine litter are scarce and generally difficult to find worldwide, especially in databases. This work reviews and analyzes data repositories to identify the existence of datasets related to marine litter in Brazil. Only one global repository specializing in marine litter was found, and it is in the early stages of operation. Only two datasets about marine litter in Brazil were found in the generalist repository Figshare that do not follow all the FAIR principles (Findable, Accessible, Interoperable, and Reusable) for data sharing. A few initiatives are being developed aiming to collect and share marine litter data, but only one of them (Our Blue Hands) is already in place and uses a standardized, replicable method, and aims to share the data by design. Our work identified interoperability as the main point to be tackled within our context. In the UN Decade of Ocean Science for Sustainable Development (2021–2030), it is essential that repositories are created, improved, and encouraged to address the specific needs of marine litter data-sharing and researchers' behavioral shift to start sharing the data already collected. Data sharing not only allows for the integrated vision of the academic community but can also contribute to public policies, helping decision-makers and encouraging a more sustainable science regarding financial and natural resource use.

show abstract

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

Cited by 8 publications

References 13 publications

A Mediterranean Focus Overview of EU Marine Litter Data Management Performed in the Framework of the European Marine Observation and Data Network Chemistry

A Mediterranean Focus Overview of EU Marine Litter Data Management Performed in the Framework of the European Marine Observation and Data Network Chemistry

Research data integrity: A cornerstone of rigorous and reproducible research

Where are Brazil's marine litter scientific data?

Contact Info

Product

Resources

About