OLS-5 speed blog: FAIR data check in Open Access journals and publication Steps

- Frédérique

Photo by Markus Winkler on Unsplash

TU Delft OPEN Publishing, the Open Access academic publisher of TU Delft, publishes Open Access (OA) journals and open textbooks and open books. It is a part of the open science programme of TU Delft that consists of seven interrelated projects: Open Education, Open Access, Open Publishing Platform, FAIR Data, FAIR Software, Open Hardware and Citizen Science.


Although TU Delft OPEN Publishing is a part of the open science programme, I noticed in most of the Open Access (OA) journals published by TU Delft OPEN Publishing, there is a lack of integration of open science principles such as Open Data, Open peer review or authorship transparency in their publication processes. It indicates that not every step of the publication workflow is fully described and available.

In OA journals, authors must share their data when publishing. At the same time, authors, reviewers and editors should be made aware of the importance and benefits of FAIR Data in open publishing. To enable that, we as publishers should provide a toolkit to the journal users so that they can effectively implementation FAIR Data in the publishing workflow.

Ultimately, the success of the project is linked to the open collaboration of the journal editorial boards with authors and users of published articles.


Being part of the Open Science program of the TU Delft, it was important for me to develop Open Science skills while building a deeper understanding and the true meaning behind open leadership, accessibility and openness.

My main objectives were to:

  1. Lead an open project to promote open science principles
  2. Work more openly by learning new skills to build an open project within a complex environment and with stakeholders with diverse needs and opinions
  3. Improve my open communication skills
    • by sharing knowledge and interacting with different communities
    • by clearly defining my vision and ideas

I choose the OLS program for these main reasons:


The initial goal was to make open science principles such as Open Data, Open peer review or authorship and contributor transparency part of the publication processes. However, after discussions with my mentors, it was decided to work on one open science principle at a time. I chose to focus first on Open Data. This project aims to embed FAIR DAta in the OA journals’ publication processes to meet the level of Open quality expected of any TU Delft product falling under the Open Science program. This project will establish in the long term the identity of TU Delft OPEN Publishing as a trustworthy academic publisher within and beyond TU Delft.

FAIR Data: Implementation of data sharing in the OA journals publication workflow

Fruitful discussions and exchanges with my mentors and three experts resulted in the plan below.

Engaging the stakeholders

  1. Start with a Proof Of Concept (POC) with one newly launched AO journal and with one established OA journal

POC successful the new workflow with open data checking points might be better accepted by the community if it is coming from their peer (editors).

  1. Consider open data badges and
  2. Check Open results of the data badges: Pilot results, Challenges, and Opportunities
  3. Focus on and encourage Data Citation rather than DAS which is a standard section in most publications
  4. Provide a template for DAS and datasets Reviewers and examples of dataset citations & DAS

Metadata Check

Data Access Statement (DAS)

  1. DAS is often incomplete or in contradiction with the FAIR data statement
  2. Data available upon request is NOT acceptable
  3. DAS should include datasets DOI and meaningful metadata
  4. DAS template could be useful

Data Steward, Data manager, Curator roles

  1. As an expert, they could perform the DAS check of every submission
  2. in the case of TU Delft OPEN Publishing: approach TU Datastewards and 4TU.Research.Data community to hear their thoughts.


  1. Identify publication workflow stages

    3 stages were identified:

    • Submit
    • Review
    • Publish
  2. Identity in the publication workflow checkpoints for data sharing:

Open Data in publication workflow

Datasets peer-review

Datasets peer-review

Next Steps

Immediate steps

  1. Get feedback from journal editors on the draft publication workflow
  2. Get feedback from Datastewards and 4TU.Research.Data Community
  3. Use a Proof of Concept to implement FAIR Data in the publication workflow in a newly launched OA journal
  4. Seek funding to cover the work of checking the Data

Long-term steps

  1. Revise publishing workflow if necessary
  2. Assess the Proof Of Concept and if successful apply to other journals
  3. Create a guide for journals users
  4. Make visuals to highlight the benefits of FAIR Data for journals users in open publishing
  5. Adapt the Implementation of data sharing in the OA journals publication workflow to open access books and open access textbooks.

Stay connected

I would like to give back to the OLS community by staying connected with the OLS community and its members. I could in the first instance come back as an expert.

On a personal level I aim to do the followings:

  1. Influence the Open Publishing team’s ways of working to encourage sharing projects and progresses openly, to use (where applicable) Github or Hackmd for collaborative work and to use alternative open source software.
  2. Raise awareness on open science best practices, diversity and inclusion, community interactions
  3. Apply the Open leadership skills developed during OLS-5 in my daily work
  4. Continue my Open Leadership Training

Lessons Learned

At the start of the programme, I was under the impression that I knew what working open meant but I could not be far from the truth. I was also confronted with the fear of sharing a project openly and its consequences. After a few sessions with my mentors and a few assignments, I soon realised I needed to change my mindset and start from scratch to fully benefit from the program. I struggled with the description of my vision and writing down the unique value proposition of my project. It was also a challenge (and it is still a challenge) to build an open science project and to ask for open feedback from the community.

Here is a summary of the lessons learned:

  1. Working open is a philosophy with values that are useful in everyday life
  2. Defining clearly and briefly the project
  3. Asking feedback from a pool of experts opens horizons and improves the project
  4. Breaking down the project into small projects with a POC and minimum viable product is more than enough to get a project going


I would like to thank my two mentors Arielle Bennett and Julien Colomb for dedicating their time to this project and for their valuable guidance. I also would like to thank experts Patricia Herterich, Sara Elgebali and Jez Cope for their advice and for taking the time to talk to me.

Finally, I am grateful to the entire OLS team for setting up this fantastic program and a special thank you to Emmy Tsang for convincing me to join OLS-5.