Implementation of a Data Management Quality Management Framework at the Marine Institute, Ireland

Leadbetter, Adam; Carr, Ramona; Flynn, Sarah; Meaney, Will; Moran, Siobhan; Bogan, Yvonne; Brophy, Laura S.; Lyons, Kieran; Stokes, David L.; Thomas, Robert

doi:10.1007/s12145-019-00432-w

Cited by 6 publications

(6 citation statements)

References 4 publications

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“…This takes the Data Stewardship Maturity Framework of Peng et al (2015) as its starting point and will assess the values encoded for various elements in the Data Catalogue's data model to produce a rating for a given dataset. As discussed by Flynn et al (2019), this approach can also be specialised in order to provide an assessment of the suitability of a dataset for a given application, in the case of their study for Marine Spatial Planning.…”

Section: Discussionmentioning

confidence: 99%

“…7 The metadata publication process from internal data catalogue to external GeoNetwork instance. The Data Steward and Data Coordinator Roles are described in Leadbetter et al (2019) Table 4 How the data cataloguing platform described in this paper addresses the requirements of the FAIR principles of Data Management…”

Section: Methodsmentioning

confidence: 99%

“…The data catalogue should also be able to handle both observed and modelled data, and for modelled data to provide information on the input parameters to the model and the methods employed by the model. Flynn et al (2019) conclude that a data cataloguing system for MSP can allow the availability and suitability of data for the MSP process to be assessed at regular review cycles. Friddell et al, 2014 demonstrate that in other cross-disciplinary topics, in their case polar research, modularity is required in order to represent datasets, projects or programmes and other polar data resources within the catalogue system.…”

Section: Introductionmentioning

confidence: 99%

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A modular approach to cataloguing marine science data

et al. 2020

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The ability to access and search metadata for marine science data is both a key requirement for answering fundamental principles of data management (making data Findable, Accessible, Interoperable and Reusable) and also in meeting domain-specific, community defined standards and legislative requirements placed on data publishers. This paper describes a modular data model to answer the functional requirements developed from these drivers and illustrates how this data model can be operationalised. The ability of this solution to meet the FAIR principles is then assessed.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A modular approach to cataloguing marine science data

et al. 2020

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“…Given the challenges and opportunities that arose while implementing the EQC framework of the CDS C3S, exploration of the existing literature and coordination with other Earth data services, and in particular the other Copernicus services, is a key task to build a state-of-art EQC framework (i.e., benchmarking). It is important to investigate the standards and best practices implemented in similar services operating around the world (e.g., Leadbetter et al 2020, RfII 2020. This helps to assess the applicability of the different approaches to the EQC function.…”

Section: Lessons Learnedmentioning

confidence: 99%

Quality Management Framework for Climate Datasets

Lacagnina

Doblas-Reyes

Larnicol

et al. 2022

CODATA

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A relatively young operational climate service is the Copernicus Climate Change Service (C3S, Thépaut et al. 2018), one of the six operational thematic services established by the European Commission within the Copernicus Earth Observation Programme (EC 2020). The C3S, implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF), aims to be an authoritative source of climate information for a wide variety of downstream users, ranging from policy makers to industrial sectors. The backbone of C3S is a cloud-based Climate Data Store (CDS), designed to be a single point of access to a catalogue of climate datasets of different categories, including in-situ and satellite observations, seasonal forecasts, reanalysis, and climate projections. In a progressive commitment to establish relations of trust between data providers and downstream users, as well as providing sufficient guidance for users to address their specific needs, C3S has made significant investments in the development of an Evaluation and Quality Control (EQC) function. By being transparent and characterising data quality attributes in a traceable and reproducible way, C3S is setting the basis for the inclusion of reliable climate data into policies and actions. 4 Lacagnina et al.

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“…The overall aim of the Data Management Quality Management Framework (QMF), developed at the Marine Institute in Ireland (Leadbetter et al 2019), is to encourage and support continual improvement of the quality of the delivered data, products and services by assuring the quality of the processes and procedures used in the generation of data or data products.…”

Section: Msp Data Portalsmentioning

confidence: 99%

Lessons from a Marine Spatial Planning data management process for Ireland

Flynn

Meaney

Leadbetter

et al. 2020

International Journal of Digital Earth

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This paper presents a framework containing ten components to deliver a data management process for the storage and management of data used for Marine Spatial Planning (MSP) in Ireland. The work includes a data process flow and a recommended solution architecture. The architecture includes a central data catalogue and a spatial storage system. The components of the process are presented to maximise the reuse potential of any dataset within an MSP context. The terms 'Suitability' and 'Readiness' in the MSP context are offered as both formal and considered assessments of data, as is the applicability of a data stewardship maturity matrix. How data contained in such a storage system can be published externally to potential consumers of these data is also explored. The process presents a means of managing data and metadata to ensure data lineage is optimised by carrying information about the origin of and the processing applied to the data; to evaluate the quality and relevance of geospatial datasets for use in MSP decisions in Ireland. The process was piloted in the National Marine Planning Framework for Ireland in the development of draft map products; feedback from the public consultation is ongoing and not presented.

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Implementation of a Data Management Quality Management Framework at the Marine Institute, Ireland

Cited by 6 publications

References 4 publications

A modular approach to cataloguing marine science data

A modular approach to cataloguing marine science data

Quality Management Framework for Climate Datasets

Lessons from a Marine Spatial Planning data management process for Ireland

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