It’s still January, and still close enough to the new year, so it’s almost obligatory to dust off the crystal balls and have a squint into the future, and who am I to break tradition? So I'm going to do some pondering about the future of research practice.
(As for why I'm doing this? I started a new job back in October last year, which is all about research practice, and I've been doing a lot of thinking.)
Fig 1. The many and varied concepts involved in research practice, both now and about 10 years in the future.
Figure 1 is a lot of words and a lot of pictures to
illustrate the words and concepts that support and surround research practice. I’m going to address each of those things
on a case by case basis.
Firstly, and most importantly, we cannot have research
without researchers. It’s the people who make research happen, whether singly
or in groups. Modern research is increasingly group-focussed, where researchers
come together to achieve large aims and extensive projects, and hence groups
can come from almost everywhere, including in-institution, cross-institution,
international collaboration, cross- and inter-disciplinary. It’s often said
that the real innovative research happens at the boundaries where disciplines
meet, and we need inter-disciplinary teams to take advantage of that.
As a former scientific project manager, I can tell you that
teams need a lot of support, both from within the team, but also from the institution
they are based in. That’s why I’m so pleased to be part of the University of
Oxford’s Research Culture programme, which acknowledges that “Beyond resources and facilities, an environment
that enables the highest quality research must also be sustained by a positive
working culture.”
This researcher support is backed up by training, at all levels and career stages, and the promotion of best practice by all members of the research team, and supporting institution. This is where principles become practice, and where we want to ensure that those who are doing research on a daily basis are enabled to do research effectively and easily, while maintaining exceptional standards of ethics and integrity.
If
we want to make people do the right thing, we need to make it easy for them to
do the right thing, and support them in doing it.
But how do we define what the right thing is? That is
something that a single institution can’t do in isolation, and instead requires
international discussion and agreement. There are a great deal of committees
and working groups, each addressing key issues in the research environment, and
coming up with agreements and concordats for best practice in research. These
include standards on ethics and integrity, inclusion and diversity,
technological and scientific standards, and promotion (and tenure).
It’s not just the universities and research institutions
that have to consider these things – we need to work together with funders and
academic publishers to capture the full range of incentives (and disincentives)
that researchers face in the course of their work.
Of course, different issues will become more pressing at different times for the researcher. At the beginnings of a project, ensuring you have the right resources (e.g. lab space, access to archives, high performance computing resources etc.) and the right approvals (e.g. for working with humans, or health and safety for chemicals, etc.) is crucial, and researchers often need help and support in getting these pieces in play.
It’s not enough to simply do research and collect results –
if we are to progress from this research, we need to be able to communicate
what has been learnt to other researchers. The traditional way is though
academic publication and conference presentation, but research can have a far
wider impact on the world, including via policy and industrial spin-offs. The
past trends towards open access and open research have been good ones, and I am
very hopeful that such openness will become the norm in the future, as this is
good for reproducibility and verifiability of research.
Transparency in research requires more than making the
resulting journal articles open access. It also needs all the components of the
research to be managed and archived, whether that’s data, software code,
protocols, workflows, physical samples, etc. This all requires infrastructure
to support it, and that infrastructure has to be maintained and kept for
potentially long periods. It is unrealistic to expect a researcher working on a
short term contract to be able to manage and archive multiple terrabytes of
data – this is where the institution and funders need to step in to provide
that long-lived infrastructure. Many institutions already have such things in
place, as part of their libraries and archives, but these will need to be
extended to look after those assets which are “born digital”.
The amount of data humanity is creating is increasing every
day, which provides us with the ability to understand things about ourselves
and our world at an unprecedented level of detail. This “data deluge” does have
its downside, in that managing it is a challenge, and is only going to get more
so. I want to highlight the FAIR principles and the CARE principles for Indigenous Data Governance here (figure 2), as these are excellent ways of thinking about data and other
born digital objects.
Fig 2. Be FAIR (Findable, Interoperable, Reusable) and CARE
(Collective Benefit, Authority to Control, Responsibility, Ethics).
The increase of data-driven discovery, and the use of new tools such as AI and machine learning have also taken research topics that were considered difficult (how to differentiate between different objects in photos) to something that can be used (and misused) by a wide variety of actors (e.g. governments using face ID to track citizens, or hate groups using algorithms to target specific races or genders). We need to face up to these new technologies and seriously consider the impact they will have, not only from a socio-political standpoint, but also from an environmental one. Big data models use a lot of computing and a lot of energy, with a correspondingly large carbon footprint.
Last, but not least, it is helpful if we can evaluate the
outputs of the research, and understand what impact it has made, both within
the field of study and externally. This is a historically notoriously difficult
thing to determine, and can only be accurately done many, many years after the
fact. Instead we turn to proxy measurements and metrics, which are easier to
calculate, but don’t actually measure what we want them to. A much-discussed
metric is citation counts, working on the principle that the more citations an
article has, the more useful it has been to the community. Unfortunately, it’s
easy to see that this premise is flawed, simply by looking at the citations that retracted papers get, even after retraction.
Fundamentally, I think the thing that will change the most
about research practice in the next few years is going to be the increase in
data-driven discovery, and the use of new AI tools and services. These have a
huge amount of potential, though I really do think that we need to be aware of
the hype, and also of the potential harm (socially and environmentally) these
systems can cause.
We also need to remember that without our researchers,
research cannot happen. Humans are curious, innovative and creative, and we
need to support that in our researchers, and in our general lives. Remembering
that people are not just numbers is crucial too – yes, there will always be a
push for efficiency and increased speed in doing things, but this should not
come at the cost of our humanity, our dignity, or our creativity.
There are many challenges ahead of us, but I do believe that with support and collaboration, we can face them all.
