Research Data Preservation Using Process Engines and Machine-Actionable Data Management Plans

Bakos, Asztrik; Miksa, Tomasz; Rauber, Andreas

doi:10.1007/978-3-030-00066-0_6

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…The tools also support the opposite scenario: a dataset already exists in a repository and information about it needs to be added to the maDMP. The prototypes were developed for Dataverse (Hido1994 andAlhirthani [2020]), Invenio (Tsepelakis [2020]) and content management system Alfresco (Bakos et al [2018]). We also performed analysis on how maDMPs can help in automating repository recommendation (Oblasser et al [2020]).…”

Section: Proof Of Concept Toolsmentioning

confidence: 99%

Application Profile for Machine-Actionable Data Management Plans

Miksa

Walk

Neish

et al. 2021

CODATA

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This paper presents the application profile for machine-actionable data management plans that allows information from traditional data management plans to be expressed in a machine-actionable way. We describe the methodology and research conducted to define the application profile. We also discuss design decisions made during its development and present systems which have adopted it. The application profile was developed in an open and consensus-driven manner within the DMP Common Standards Working Group of the Research Data Alliance and is its official recommendation.

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Section: Proof Of Concept Toolsmentioning

confidence: 99%

Application Profile for Machine-Actionable Data Management Plans

Miksa

Walk

Neish

et al. 2021

CODATA

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“…In this exercise participants were divided into groups and asked to review a series of use cases displaying workflows that could potentially be automated under a maDMP ecosystem [18]. Thus, guiding researchers through the various stages of creating a DMP.…”

Section: ) First Excercisementioning

confidence: 99%

Framing the scope of the common data model for machine-actionable Data Management Plans

Miksa

Cardoso

Borbinha

2018

2018 IEEE International Conference on Big Data (Big Data)

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Currently, research requires processing data at a large scale. Data is not anymore a collection of static documents, but often a continuous stream of information flowing into information systems. Researchers need to manage their data efficiently not only to keep it safe, but also to ensure that it can be later correctly interpreted and reused. Existing solutions are not sufficient. Traditional Data Management Plans are manually created text documents that describe how research data will be handled. Yet, researchers must implement all actions by themselves. Machine-actionable Data Management Plans are a new approach that allows systems to act on behalf of researchers and other stakeholders involved in data management, to help them manage data in an efficient and scalable way. This paper summarises the results of work performed by the Research Data Alliance working group on Data Management Plan Common Standards to realise this vision. The paper describes results of consultations and proof of concept tools that help in: identifying needs for information of stakeholders involved in data management; defining the scope of the common data model for Machine-actionable Data Management Plans to allow for exchange of information between systems; identifying necessary services and components of infrastructure that support automation of data management tasks.

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DMPFrame: A Conceptual Metadata Framework for Data Management Plans

Singh,

Madalli

2023

Journal of Library Metadata

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Research Data Preservation Using Process Engines and Machine-Actionable Data Management Plans

Cited by 3 publications

References 6 publications

Application Profile for Machine-Actionable Data Management Plans

Application Profile for Machine-Actionable Data Management Plans

Framing the scope of the common data model for machine-actionable Data Management Plans

DMPFrame: A Conceptual Metadata Framework for Data Management Plans

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