Tackling disparities in research funding: an opinionated guide for funders

Today is International Day of Women and Girls in Science, and some of the work I feel most proud about during my time at ARUK was about highlighting existing gender disparities in the field of dementia research, improving our internal funding processes to minimise biases, and increase support for those who need it the most.

It was slow-moving, and we always had the feeling that we were not doing nearly enough (because who is?). But we pressed-on, because we understood from the get-go the importance of the work.

Below I will outline some learnings for research funders from this work, and from talking with many other funders over the years about EDI work. But first, I want to use this opportunity to give a massive shoutout to two networks doing great work for women and girls in science: the Women in Neuroscience UK and the Black Women in Science Network . If you are looking to support organisations in the space, please do keep them in mind.

And now, an opinionated list of 5 learnings for funders about how to tackle gender disparities in scientific funding:

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Funders, especially charitable funders, tend to have grand visions of worlds free from diseases. But we know there is a direct through-line from historical under-representation of women and diverse researchers to the health disparities that exist today, and will continue to exist for the foreseeable future. That is the case you need to make to justify the vital importance of tackling disparities in your field of research, and the changes that your funding processes are likely to need. Funding diverse researchers is about fairness, yes, but it is also about your strategy and your vision failing if you do not engage in this work.

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I get it. Your own data presents a much more compelling case to internal stakeholders to justify the changes that are needed to minimise biases in research funding processes. And don’t get me wrong, you do need to collect, measure and analyse a variety of EDI data to ensure that your processes are fit for purpose. But at the end of the day, it is very likely that your data will tell the same story as NIHR’s, UKRI's, CRUK’s, Wellcome’s, ARUK’s… That is: that women tend to be less likely to obtain funding, particularly for senior programmes of work, and that they face disproportionate barriers when progressing in their careers. So, by all means get the process to collect EDI data going (the Data Protection Impact Assessment still haunts me to this day) , but do not wait to start doing things!

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Papers. Citation metrics. Grants.

That’s the trinity that so many people still to this day use to judge the quality of someone’s work. And yet, there’s mounting evidence that shows that peer-review, the process that helps decide who gets published or who gets a grant, is flawed, and women, people of colour, and researchers from less prestigious organisations tend to get the short end of the stick. We also know that biases influence who gets cited. And that women engage in more “unseen” work for the benefit of the broader community – from mentoring to engaging with the public – which takes away time from their research. This, by the way, is the type of work that funders repeatedly say they value, but ultimately tends to not really count to decide who gets more funding.

There is no easy fix for this problem, but there are a lot of things that can be done. From de-emphasising metrics without the right level of nuance, to applying principles of responsible research assessment, to implementing narrative CVs in order to shine a light on the broader contributions to the scientific community that go unseen in traditional CVs.

If you want to learn more, I highly encourage following/reading some of the people, institutions and initiatives that have massively shaped my thinking on research evaluation: Elizabeth Gadd , Sarah de Rijcke , Sean Sapcariu , the Research On Research Institute (RoRI) , and the San Francisco Declaration on Research Assessment (DORA).

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No one is going to be able to convince me that reviewers need to know the identity of the researchers whose science they are reviewing. The organisation, or lab in which they will conduct the work? Maybe. But their identity? Absolutely not.

Some of the most enjoyable grants I read while at ARUK were those during a double-blinding pilot we did. Part of the guidance for researchers was to avoid evident self-citations, and to explain the expertise of collaborators, instead of just name-dropping. Those grants were beautiful to read, centred around the science and the practicalities of the proposal.

Double-blind does, however, require a significant amount of resource, and needs to be carefully planned, implemented and managed. Still, its implementation is something I think is absolutely worth it.

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Give it the resource it requires. But if you don’t at the moment, take a step back, don’t claim it as a priority because it feels like it would be wrong not to, and wait until you are in a position to dedicate it the time and thought it deserves. Otherwise, cynicism may grow, and the effectiveness of your future work could be compromised.