Managing Open and FAIR Research Projects

https://doi.org/10.5438/9bqx-mt29
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Implementing FAIR Workflows: A Proof of Concept Study in the Field of Consciousness is a 3-year project funded by the Templeton World Charity Foundation. In this project, DataCite works with a number of partners on providing an exemplar workflow that researchers can use to implement FAIR practices throughout their research lifecycle. In this blog series, the different project participants share perspectives on FAIR practices and recommendations.


In the latest post, Xiaoli Chen, project lead at DataCite, interviews Tanya Brown, Scientific Project Manager of ARC-Cogitate, a large-scale neuroscience consortium project, at the Max Planck Institute for Empirical Aesthetics, a core FAIR Workflows project partner, about managing collaborative Neuroscience projects and what’s involved to make them FAIR.

Hi Tanya, would you please introduce yourself and your role in both projects?

My role is to be the central hub and knowledge holder of all the different aspects of the project. As the scientific project manager, I maintain a comprehensive understanding of the scientific objectives and coordinate the resources and strategic plans to achieve the project goals. For Cogitate, there is the central adversarial collaboration that includes more than 40 international team members from over a dozen different institutions, along with ancillary projects that are focused on creating robust Open and FAIR tools. There is an intricate interplay across the various efforts that necessitates a bird’s-eye view to effectively coordinate across all our team members and their respective activities, and map out plans in a comprehensive, informed, and linked way. In doing so, Cogitate and its related side projects progress in a synergistic way and the final outcomes are complementary and maximally impactful.

Compared with research projects that don’t have explicit Open and FAIR requirements, how different is it to work on these two projects?

I adopted Open Science early in my career, having only worked with Open Science projects so far (i.e., The Virtual Brain, My Virtual Dream and COGITATE), so my comments here are observational, based on what I’ve witnessed over the years. For Cogitate, every decision that we make, every plan, every action, is in some way touched by our commitment to being Open and FAIR. Considerations must be taken not only on how we do things, but also what we do, why, and when.

An example is the releasing of our code. For Cogitate, one of our grant deliverables is to make all our analysis code openly available. But this is not merely the flick of a switch. There is a stepwise process that will ultimately lead us to having truly Open and FAIR code. For this, I have developed a Code Release Strategy. Code needs to be complete, made openly available on GitHub, and assigned with the appropriate license. Code needs to be checked (which is a rather subjective endeavour, whereby we have created in-house standards and best practices). And for code that is produced as part of a consortium effort, how does one maintain credit for their code contributions and also uphold accountability and maintenance upon release? Sorting out all of this takes additional time, knowledge, and resources. Open and FAIR does not simply happen—it requires strategy, effort, and accountability. Formal project management is actually relatively uncommon in the field of Neuroscience. Most often, a project’s lead scientist performs this role. However, I firmly attest that scientists should be focused on doing science, while Project Managers take care of how to do science. With the increased demands that Open Science and FAIR practices impose on researchers, it is critical that teams are equipped with roadmaps that incorporate the nuances of getting everything done with the highest quality. This is one aspect of our work for which I bring value.

Is the job how you expected it to be? What aspects of the work have caught you off guard working to make these projects Open?

A major eye-opener is that many people claim they are doing Open Science, but there is a rather subjective and wide spectrum of what it means to be Open and FAIR, and how well they’re fairing (pardon the pun). I think I’ve learned a lot along the way about the delta between saying “I’m Open and FAIR” and actually achieving a level of Openness and FAIRness that is consequential. For instance, there are huge gaps in attributing credit and contributions for individual researchers that participate in large-scale consortium projects. Although the CRediT system offers some additional details about who did what, there remains a significant lack of the nuance necessary to truly differentiate what a particular researcher brought to the table. There may be several team members recognized for “collecting data”. However, someone who collected behavioural data has contributed very differently when compared with someone who collected iEEG data. Having the ability to capture these specific efforts could be consequential down the road, when a researcher is entering the job market. I’m committed to building more meaningful ways of recognizing each individual’s contributions. One way is through a contribution matrix, whereby each researcher ranks themselves on a 4-point scale (0 = no contribution, 4 = lead contributor) for each of the CRediT taxonomy categories. This can then be taken one step further such that each team member is given the opportunity to review and change both their own rankings and/or those of other team members, in relation to each other’s scores. An example of this will be presented in our next consortium paper.

What’s the general takeaway of working alongside researchers? What type of support do researchers appreciate the most?

I think it’s a different way of approaching research and a different way of thinking. It’s foreign to a lot of researchers to have a specific plan from the get-go, and further, to then monitor progress along the way. A lot of researchers have an idea and then jump right in, they immediately start collecting data, analyzing it, and writing it into a paper. This is somewhat analogous to building a bridge as one is crossing it, in contrast to having somebody build a prototype of the bridge, and show you exactly how to get across this great divide, what the decision points are and where each decision (path) will lead. Furthermore, much of project planning is related to defining what you’re not going to do. It’s always helpful to define this at the beginning, because scope creep is very common in large projects, especially in science—you take one step forward and then there are 15 different ways that you can go. It’s not to say that we will never build the bridge, it is just a way of keeping a project manageable and continuing to make progress. Making a list of your must-haves versus your nice-to-haves is often a really good place to start.

What part of Open and FAIR practices resonates with researchers? What activities are instantly taken up and what do they forget whenever they are not reminded?

The researchers in these two particular projects (Cogitate and FAIR Workflows) already have an inherent acceptance of the fact that they were going to be participating in an Open and FAIR capacity. They’ve already entered into an agreement in which everything’s going to be Open. But, when they actually put that into practice, it turns out what they agreed to is much more work than they originally anticipated. It would be very helpful for researchers to have a better understanding of exactly what’s entailed—a sense of what committing to Open and FAIR means. Then, when it comes to project planning, one can take into account the knowledge, skills, resources, and time requirements to complete any given project in an Open and FAIR way. A part of the FAIR Workflows Project effort has been spent on making an important contribution to this effort, by measuring the amount of time spent on Open and FAIR activities for the project. An outcome of this will be a tangible and measurable (and therefore useful) piece of information for future researchers to consider.

In addition, we still don’t have all of the answers as to what is the correct and most effective way to do things. Even when we have the answers to this, it doesn’t necessarily mean that’s how we should be doing things—there still might be some better or even optimal way. But, what we have is an exceptional consortium model in the Cogitate project, where collaborators do not shy away from asking questions and learning how to navigate Open policies and share large-scale multisite datasets using existing infrastructure. The tremendous pre-project effort spent on building agreements across project partners was instrumental in defining both joint interests and specific terms for individual institutions based on their unique contributions to the project.

What’s missing? What do we need to work on to better support researchers’ Open and FAIR practices?

The field is moving towards being Open and FAIR, and there’s a lot of enthusiasm—which is great. Many contributors are building tools that support these concepts, and I think everyone is entering into things with the best of intentions. However, it would be misleading to think that a lot of these tools and/or methods will work for everybody or will be the correct solution in each and every instance. I think we really need to take a step back and look at the long term. We’re starting to see an explosion of tools (and an even greater amount of Open Data) being produced, but as a consequence, it’s becoming overwhelming as a newcomer or somebody who is trying to adopt some of these things to know where to look or what is the correct answer. If we don’t have a decision-making mechanism in place to really help an individual researcher find the best way to achieve what they are aiming to accomplish, we will only be wasting more resources. I think this is where Scientific Project Managers can help. 

Furthermore, one of the most important aspects that needs to be maintained in the Open and FAIR space is diversity! We need input from multidisciplinary researchers, across all career stages, and all nations and cultures. A richness of ideas and possibilities can then flourish and add value in ways that simply cannot be realized if we remain in silos.

Anything else I haven’t asked but you would like to add to the topic?

I continue to champion the importance of the Scientific Project Manager role, and the more that we see large-scale consortiums or big projects coming up in the Open and FAIR world, the more important it is for the researcher community to consider Scientific Project Managers as core team members. It should also be noted as a career option for PhDs and postdoctoral researchers, because it’s a really great way of staying involved in science and continuing to contribute when one doesn’t really fit the archetype of a battling scientist.

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This project was made possible through the support of a grant from Templeton World Charity Foundation, Inc. The opinions expressed in this publication are those of the author(s) and do not necessarily reflect the views of Templeton World Charity Foundation, Inc.

Tanya Brown
Scientific Project Manager at Max Planck Institute for Emperical Aesthetics