DCSO: towards an ontology for machine-actionable data management plans.

The concept of Data Management Plan (DMP) has emerged as a fundamental tool to help researchers through the systematical management of data. The Research Data Alliance DMP Common Standard (DCS) working group developed a set of universal concepts characterising a DMP so it can be represented as a machine-actionable artefact, i.e., machine-actionable Data Management Plan (maDMP). The technology-agnostic approach of the current maDMP specification: (i) does not explicitly link to related data models or ontologies, (ii) has no standardised way to describe controlled vocabularies, and (iii) is extensible but has no clear mechanism to distinguish between the core specification and its extensions.This paper reports on a community effort to create the DMP Common Standard Ontology (DCSO) as a serialisation of the DCS core concepts, with a particular focus on a detailed description of the components of the ontology. Our initial result shows that the proposed DCSO can become a suitable candidate for a reference serialisation of the DMP Common Standard.

Ten principles for machine-actionable data management plans.

Data management plans (DMPs) are documents accompanying research proposals and project outputs. DMPs are created as free-form text and describe the data and tools employed in scientific investigations. They are often seen as an administrative exercise and not as an integral part of research practice. There is now widespread recognition that the DMP can have more thematic, machine-actionable richness with added value for all stakeholders: researchers, funders, repository managers, research administrators, data librarians, and others. The research community is moving toward a shared goal of making DMPs machine-actionable to improve the experience for all involved by exchanging information across research tools and systems and embedding DMPs in existing workflows. This will enable parts of the DMP to be automatically generated and shared, thus reducing administrative burdens and improving the quality of information within a DMP. This paper presents 10 principles to put machine-actionable DMPs (maDMPs) into practice and realize their benefits. The principles contain specific actions that various stakeholders are already undertaking or should undertake in order to work together across research communities to achieve the larger aims of the principles themselves. We describe existing initiatives to highlight how much progress has already been made toward achieving the goals of maDMPs as well as a call to action for those who wish to get involved.

Identifying impact of software dependencies on replicability of biomedical workflows.

Complex data driven experiments form the basis of biomedical research. Recent findings warn that the context in which the software is run, that is the infrastructure and the third party dependencies, can have a crucial impact on the final results delivered by a computational experiment. This implies that in order to replicate the same result, not only the same data must be used, but also it must be run on an equivalent software stack. In this paper we present the VFramework that enables assessing replicability of workflows. It identifies whether any differences in software dependencies among two executions of the same workflow exist and whether they have impact on the produced results. We also conduct a case study in which we investigate the impact of software dependencies on replicability of Taverna workflows used in biomedical research of Huntington's disease. We re-execute analysed workflows in environments differing in operating system distribution and configuration. The results show that the VFramework can be used to identify the impact of software dependencies on the replicability of biomedical workflows. Furthermore, we observe that despite the fact that the workflows are executed in a controlled environment, they still depend on specific tools installed in the environment. The context model used by the VFramework improves the deficiencies of provenance traces and documents also such tools. Based on our findings we define guidelines for workflow owners that enable them to improve replicability of their workflows.