Peer Review of RO-12

• Author: Rosa Filgueira
• Title: DARE Platform: Enabling Easy Data-Intensive Workflow Composition and Deployment
• Submission: https://doi.org/10.5281/zenodo.3357806
• Submitted as: RO2019 abstract
• Decision: accept talk (accepted for oral presentation)

Review 1

• Reviewer: Frederik Coppens https://orcid.org/0000-0001-6565-5145

Quality of Writing

Is the text easy to follow? Are core concepts defined or referenced? Is it clear what is the author’s contribution?

(delete as appropriate)

• good

The paper is structured clearly. The reasoning can be followed also by non-domain experts, although in some places a bit more context on humanities specific concepts might provide an improvement.

Research Object / Zenodo

URL for a Research Object or Zenodo record provided?   Guidelines followed?   Open format (e.g. HTML)?   Sufficient metadata, e.g. links to software?   Some form of Data Package provided?   Add text below if you need to clarify your score.

• basic (e.g. Zenodo with PDF and minimal metadata)

not sure what entails ‘detailed metadata’.

Overall evaluation

Please provide a brief review, including a justification for your scores. Both score and review text are required.

• accept

This paper identifies three challenges for the humanities field to adopt the RO model.

Building on the strengths of the RO model (aggregation, semantic description, linked data) the author exemplifies the specific limitations that require addressing to allow for uptake in the humanities community (interactivity, non-automatibility, thick provenance).

This provides a good starting point to discuss between the humanities and RO communities to further develop the model to accommodate the needs of the humanities.

Review 2

• Reviewer: Denis Parfenov

Quality of Writing

Is the text easy to follow? Are core concepts defined or referenced? Is it clear what is the author’s contribution?

• good

In order for RDM technologies to be used, they have to fit into scientists’ daily workflows.

The proposal for enabling easy data-intensive workflow composition and deployments is looking at the ways of employing cloud systems to translate scientific methods to concrete scientific workflows which can be portable and reproducible on different computing environments without making any (or little changes).

The author proposes to use combination of openly-licensed and proprietary technologies, such as dispel4py (open-source) and CWL scientific workflows (open-source), docker containers (open-source), Kubernetes infrastructure orchestration (open-source), Jupyter notebooks (open-source), and Cloud platforms, such as DARE Platform (proprietary).

The paper does not specify how risks of reliance on openly-licensed technologies (sustainability) and proprietary storage solutions should be mitigated on the long run. However, the specifies clearly the intent to enable migration to the new platforms without human interaction and with encoded methods semantics unchanged.

Research Object / Zenodo

URL for a Research Object or Zenodo record provided?   Guidelines followed?   Open format (e.g. HTML)?   Sufficient metadata, e.g. links to software?   Some form of Data Package provided?   Add text below if you need to clarify your score.

• none (e.g. only abstract in easychair/github)

I can see PDF on easychair only

Overall evaluation

Please provide a brief review, including a justification for your scores. Both score and review text are required.

• accept

Taking into consideration the eminent necessity for RDM to be incorporated into researchers workflow, leveraging EU funded cloud services and computational power with openly licensed technologies makes practical sense and should be explored further.