DIGITAL SOCIETY BLOG

Wissen über unsere vernetzte Welt

DIGITAL SOCIETY BLOG

Wissen über unsere vernetzte Welt

1*WwVOOvsZl2-HrwJ6nzT6aA

Das Große Forschungsupdate

02 Juli 2020

Die COVID-19-Pandemie stellt eine Herausforderung für die Gesellschaft und ihre Institutionen dar. Die Wissenschaft ist von der Krise besonders betroffen, da von ihr erwartet wird, dass sie mit Fachwissen zur Lösung des Problems beiträgt. So schwerwiegend die Krise für die globale Gemeinschaft auch sein wird, es ist eine aufregende Zeit für die Wissenschaft und einen Wissenschaftssoziologen wie mich. Denn während die Wissenschaft damit beschäftigt ist, ein Problem zu lösen, verändert sie sich unweigerlich. Manchmal können Veränderungen so dramatisch sein, dass es schwierig ist, Katastrophe und Chance zu unterscheiden. Was ich mich frage, ist: Wie wirkt sich die Krise auf die Art der Schaffung und Verbreitung von Wissen aus? Wie wird das neue Normal aussehen?


My job is to look at research. In my research team at the Alexander von Humboldt Institute for Internet and Society (HIIG) in Berlin we work on a number of projects, all of which are inspired by the question of how new tools and practices are changing the way academic knowledge emerges, how it is organized and how it transpires. Just as many of my colleagues, the crisis caught me more or less unprepared. From one day to another I had to change plans for research projects completely, cancel events, and switch my team to remote work. And yet: many of my colleagues have been hit much harder by the crisis, because they need lab equipment or simply because they have to look after their kids. There is no doubt: the crisis is challenging scientific work. Yet, for someone like me, who researches research as a job, this time is also fascinating. We are part of a gigantic and involuntary experiment, without hypotheses, and in which scholarly knowledge is a decisive variable. I believe that the crisis will change the way we do research in the long run. And I am reasonably optimistic that it could mean a necessary update for science as a whole.

Complexity Avoidance Is a Problem

To make my point clear, I need to take a step back. A recurring topic in my research is scholarly impact. If I had to summarize my learnings of the last years in one sentence, it would be this one: Impact means coping with complexity. It is the core business of researchers to reduce complexity, both in the ways in which they come to understand a problem and how they deliver answers and solutions. Research, in my understanding, is always about finding the right problem, making the best possible sense of it, and — nowadays more than before — applying knowledge to shape reality. I find that a core problem which impedes impact is complexity avoidance, when a problem is not confronted in all its facets with the appropriate means at hand. Avoidance of Complexity can be observed especially with regard to digitisation.

One aspect my colleagues and I research is “open science”, which we define as the best possible use of digital tools to make science more accessible, transparent, and inclusive. The underlying question in all of our projects is always how the digital can challenge and benefit scientific value creation. In this spirit, we conducted several studies in the last ten years, for example on academic data sharing, the replication behaviour of researchers or the emergence of digital research infrastructures. A common thread through all of our studies is that, in many respects, researchers are not yet able to embrace the digital. For example:

  • Research data is not made available in a reusable manner because researchers consider them raw material for articles, a communication format that is established but can only carry a certain type of information. Data, as well as code, videos or audio, can carry new kinds of information but are not yet considered an academic output. The diversity of possible knowledge flows is constrained.
  • Replication studies are rarely conducted because they require a lot of effort (e.g., because the underlying data and methods of published research is not available) and are hardly publishable (e.g., because what’s the news value of a verification or refutation of a result?) Yet they are important as an additional form of quality assurance in times of increasing publication rates and capacity limits of the traditional peer review model. Efficient possibilities for quality assurance are not exploited to the full.
  • Research infrastructures, which today are essentially software services, are designed without considering user needs. The way especially public infrastructures are built is usually everything else but agile: An idea of 2014, receives funding in 2015, and is realized in 2019 — according to the idea of 2014. This is completely at odds with the way software is built. The result is services for knowledge organization that nobody needs.

In many ways, the aforementioned examples of complexity avoidance in research are an expression of path dependence, a continuation of an analogue logic of scholarly work, superimposed into the digital world. Of course, there are many other instances of complexity avoidance that are not necessarily linked to digitization. For example: The fact that academic research (at least in Germany and many other European countries) is organized in a disciplinary manner, when many of the problems academics are facing today are interdisciplinary. Or the tendency to confuse attention as a proxy for relevance when measuring societal impact (e.g., Altmetrics, which basically count the attention that academic outputs receive on social media). In my eyes, these instances of complexity avoidance are detrimental to scientific progress.

Corona as a Complex Problem for Research

Now we are faced with the COVID-19 crisis, a complex problem that we as researchers cannot avoid. And while researchers are busy solving the problem, the problem itself changes scholarly practice. I believe, in many ways, for the better.

Let me illustrate this with an example: For a long time now, science policy-makers, funders, and even researchers themselves have advocated for Open Access, which, broadly speaking, means that scholarly outputs (generally articles) are freely available online. According to numbers by the European Commission’s Open Science Monitor, 64% of the articles that were published in 2018 were paywalled, 15 % were published in green open access (i.e., self-archived articles in repositories), and 19% gold open access (i.e., peer-reviewed articles that are published open access, usually after paying a so-called Article Processing Charge). In other words: The normal state of publishing is closed access.

When it comes to COVID-19 related research, the situation is reversed. For our blog journal Elephant in the Lab, a few colleagues and I track the COVID-19 related research in near time. As a datasource we use Scopus, which covers mainly peer reviewed research (gold OA), and the repositories bioRxiv and medRxiv, which covers mainly preprints and working papers (green OA). We find that the majority of the articles on COVID-19 in Scopus are published open access and that green open access figures are on the rise. In this situation, where it is vital that research is fast and available to all, open access is the new normal. And not only that: green open access is becoming increasingly important. This is remarkable in that it makes scholarly publishers practically superfluous for this form of publication. A persistent path dependence of science, namely the dependence on a few large publishing houses, is partially dissolving.

We can also observe that digital research infrastructures are being built overnight, that research data is being exchanged in near real time and that researchers are collaborating internationally and across disciplines. There are also countless examples of meaningful public engagement, ranging from scientific policy advice to podcasts (e.g., Germany’s favorite podcast at the moment is “Corona-Virus Update” with the virologist Christian Drosten). Science is heard. In a recently published Corona special edition of an annual population survey in Germany, 73 percent of respondents agreed that they trust science (compared to 46 percent in 2019). For me, openness to society is an important and often forgotten dimension of Open Science. All in all, when it comes to research on COVID-19, Open Science is suddenly the gold standard. Science is coping with complexity, and not only when it comes to finding a vaccine.

Prospective Falsificationism

Of course, change does not happen smoothly. With the transition to the new normal come a lot of new challenges. For example: If green open access prevails as an important form of publishing, what could new mechanisms for quality assurance be? (Side note: I think overlay journals would be a great solution.) If researchers play a more active role in society, how can we make sure that they do not overstep boundaries and lose trust among the population (for example, because they let themselves be politically instrumentalized or have political ambitions themselves)? Currently, female researchers publish comparatively less than men, presumably because they have more parenting responsibilities than men. How can we establish a more equal and inclusive academic culture in the long term? These are, in my view, only a few of the crucial challenges for research post Corona.

And this is the morbid beauty of a crisis like this. It makes visible what works, what doesn’t work and what could work differently in academic research. It shows pathways for how research could go further, post Corona. We are, in a way, forced to imagine the future with the best possible knowledge we have. This, in turn, could give rise to new epistemic rationale. I call it prospective falsificationism, inspired by the great science philosopher Karl Popper’s falsificationism. According to him, nothing is ever the final truth and must always be questioned. What if we apply this principle not to the existing, but to what might be? We, as a society that included researchers, would need to think about a utopian state and then researchers would try to falsify this utopia in order to support society to arrive at the best possible state. As I see it, the crisis is already forcing researchers to do this, to reason upon uncertainty. And this is how I understand my job at the moment, to observe the situation and to make sense of it so that hopefully we, as a research community, can learn from it.

Nobody wanted this crisis and it involves many dangers for democracy, as the Director General of the International Committee of the Red Cross, Yves Daccors, rightly pointed out in a call organised by the Network of Centers. Yet when I look at science, a societal institution that I think I understand quite well, I see moments of openness, of social innovation, and of solidarity. I hope that means that academic research will emerge stronger from the crisis and that it receives a long overdue update. Because academia is indispensable for an enlightened, deliberative democracy.

Thank you to my colleague Rebecca Kahn for the valuable comments

Dieser Beitrag spiegelt die Meinung der Autorinnen und Autoren und weder notwendigerweise noch ausschließlich die Meinung des Institutes wider. Für mehr Informationen zu den Inhalten dieser Beiträge und den assoziierten Forschungsprojekten kontaktieren Sie bitte info@hiig.de

Benedikt Fecher, Dr.

Forschungsprogrammleiter: Wissen & Gesellschaft

Bleiben Sie in Kontakt

und melden Sie sich für unseren monatlichen Newsletter mit den neusten Blogartikeln an.

JOURNALS DES HIIG

Zukünftige Veranstaltungen

Weitere Artikel

Sicherheit im Cyberspace: Workshop-Einblicke

Die Analyse der Sicherheit im Cyberspace kann eine Vielzahl von Themen umfassen. Der untersuchte Workshop berührte einige von ihnen und lieferte wertvolle Einsichten für die Gesellschaft und letztlich relevante Ideen...

Virtual Fellowship

The Internet and Society Fellowship is internationally focused and offers a unique opportunity for innovative thinkers who wish to engage in the exchange of research and to set up new…

Same same but (not so) different

Anonymisierung wird als Lösung für Datenschutzprobleme angepriesen, während machine learning als gefährlich gilt – dabei haben die beiden mehr gemeinsam, als es scheint. Wir wollen herausfinden, was, und vor allem...