Thoughts on Research Excellence – Part 2: From Ideal to Practice: Supporting Excellent Research

In the previous post, I discussed how research “excellence” is often defined by proxies such as publication count and grant income. These measures have emerged historically, but are not based on a shared understanding of what excellent research actually is. I reviewed how the current operationalisation of research excellence can inadvertently lead to bad research practices. In this post, I want to offer a working definition of excellent research — and address what it takes to facilitate it.

Defining Excellent Research

When I think about the characteristics of excellent research, the following aspects come to mind:

• It has impact on the field, by providing reliable and helpful insights or methods.
• It adds to humanity’s knowledge base and understanding of the world.
• It has long-term impact on society by contributing to practical solutions.
• It uses established, validated methods that consider previous work.
• It is innovative by bringing in new ideas, while also replicating and strengthening established findings.
• It is thoroughly conducted and transparently reported to allow replication.
• It applies interdisciplinary approaches, combining perspectives and methods to get a more holistic understanding of the problem at hand.
• It is conducted in well-functioning teams.

Some of these criteria are assessed by funders and journals, others are largely ignored. This list may not be exhaustive, but it is a useful starting point for thinking about how to support excellent research. It is my list though. So who decides what criteria matter? Can each researcher or institution define excellence for themselves, or do we need some shared foundation? Is it part of “academic freedom” to come up with your own criteria of good research? These are difficult philosophical questions. 

To approach this conundrum pragmatically, let us return to the roots of science and ask what it is the motivation and purpose of science. One attempt to define a framework for thinking about this was made by sociologist Robert K. Merton. He proposed four core norms of science, distinguishing “real” science from what he called “anti-science”, which includes “science” done by authoritarian regimes like during the NS period in Germany.

According to Merton, real science is based on the strive towards (see von Schomberg, 2024):

• Communism: Scientific knowledge should be created and shared collectively (#OpenScience).
• Universalism: Quality assessment should be independent of the researcher’s identity or status.
• Disinterestedness: The primary motivation should be advancing knowledge, not personal gain.
• Organised scepticism: Claims must be critically scrutinised and supported by evidence before being accepted.

I speculate that if you showed this list to the science-affine general public, most would agree that science should be done this way — and likely assume that it is done this way. But if you have experience with the current academic system, neither is knowledge created nor shared collectively, and a researcher’s affiliation is strongly relevant for their credibility. The idea of “disinterestedness” may also seem completely at odds with how academic careers are structured. To be able to advance knowledge in the long run, you first need to advance your career. And once you are in a permanent academic position, you spend much more time on administration, management and securing funds than doing research yourself. As a result, the motivation to publish papers is often not to share meaningful insights with the community, but simply to add another item to your CV. In my career, I have rarely heard, “I found something very useful — I want to write a paper”, but I have often heard, “I need to write a paper — so I have to come up with something.” So is Merton too idealistic or outdated, or has something gone seriously wrong in how we have come to organise science? I suppose, if you have read my last blog post, you already know where I stand on that. So what can we do?

What Does It Take to Do Excellent Research?

The crucial question is: under what conditions can people actually uphold the scientific values and do excellent work? Let us assume for now that excellent research fulfils the criteria I listed above and Merton’s standards. Then it becomes clear that doing excellent research is difficult. It requires more than just funding. It relies on infrastructure, skilled and motivated people, and an environment that fosters collaboration (especially across disciplines) and openness. A constructive error culture is also important, since identifying and correcting mistakes is a central part of the scientific progress. That includes protecting junior researchers from retaliation when raising concerns, and ensuring that PIs are not overloaded with administrative duties that undermine time for critical reflection and team management.

In the following, I provide some thoughts what individuals, teams, institutions and technology can do to contribute to more excellence in academic research.

Individuals: Staying Grounded in Your Ideals

• Doing excellent research starts with knowing what drives you. Take time to reflect on why you entered academia in the first place — maybe it was curiosity, a desire to solve interesting problems, or the ambition to make the world a better place. Your true motivations can easily get lost under the constant pressure to perform. Reconnecting with them can help you stay motivated and set your own agenda instead of being steered entirely by external incentives.
  
• To stay true to your own agenda, it is important to define your own boundaries. Not all common practices in academia are acceptable just because they are widespread. Reflecting on your values allows you to decide what you are — and are not — willing to participate in. Although good scientific practice is codified in guidelines, disciplinary norms and “academic freedom” often normalise bad practices or create grey zones. The system is meant to be self-corrective, and as you are part of the system, that means you also carry responsibility: through your research practices, your peer reviews, and reporting mechanisms.
  
• Taking responsibility for sustained research quality is not easy. In practical terms, taking responsibility means reporting and correcting your own mistakes and addressing questionable behaviours that you observe in others. Often, these are not a result of bad intentions. Therefore, you can try to do that in a constructive and non-accusative way, by framing it in an open-minded manner, distinguishing your observations from your interpretations. An example of such wording is: “I noticed that when you showed your results in a lab meeting, you had looked at five different outcome measures, but in the manuscript draft only the one outcome measure, for which you found a statistically significant effect, is mentioned. This gives me the impression you are prioritising a clean story over transparency. I understand that you want to keep the paper as simple as possible, at the same time, I do not feel comfortable intentionally leaving out such important information and results, as this may mislead the research community. Shall we brainstorm together how we can include the other measures without overcomplicating the paper?”
  
• Even when done carefully, speaking up or pushing back against problematic norms can isolate you or even put your career at risk. But it can also prompt others to rethink their own behaviour. Confidence in doing this often comes from clarity about your values and boundaries. That is where imposter beliefs can get in the way — the idea that others are more competent and know better. Identifying imposter beliefs, understanding where these come from, and actively doing reality checks – for example by talking to peers and mentors – can help staying confident and firm. The wider scientific community has great people, so go look for like-minded colleagues also outside your own institution. Especially sub-communities focused on open science can be a valuable place to find peers and mutual support.
  
• Choosing teams and supervisors who share your values is a more subtle but effective way to create alignment. During job interviews, ask questions to evaluate whether the employer can provide the conditions that you need in order to do your research in the best possible manner. Look for PIs and groups that are transparent, collaborative, and committed to quality. A good way to find out about the atmosphere in a research group is to reach out to people who work or previously have worked in the group for an informal chat.
  
• Finally, you can become a role model. Creating useful resources for your community can help building a profile as a high quality researcher. Sharing methods, workflows, and data builds trust and shows rigour. Rigorously conducted research that challenges mainstream ideas is highly valuable and can give you broad visibility as an innovative researcher. But even without news headline worthy outcomes, producing resources that are informative and helpful for the research field is appreciated, feels good and can be strongly sold in your next application.

Teams: Building Culture and Collaboration

Even though the stereotype of the ingenious and antisocial scientist is widespread, excellent science is really a team sport. The combination of interdisciplinarity and high quality standards across domains cannot be fulfilled by a single person. In reality, however, many research groups function as loosely connected individuals. The competitive academic system with its focus on individual accomplishments and a seemingly endless “researcher in training” phase does simply not support the establishment of well functioning, efficient, long term research teams. 

• To counteract, research groups can proactively try to incorporate collaborative practices in their daily work. Creating structured formats — like brainstorming sessions, code review or hackathons — can help to use the potential of collective intelligence, find errors more quickly, and use resources more effectively by reducing uncoordinated parallel work. These formats also help to stay motivated, especially those who do enjoy working in teams.
  
• A constructive research culture requires space for disagreement without hostility. The principle “be hard on the problem, soft on the person” is a helpful guide (see Harvard principles for negotiation). In academia, we need space for critical, even intense debates about methods and interpretation — but this must go hand-in-hand with respect and psychological safety. As a researcher, you should actually be pleased being challenged and contradicted, as this may bring new perspectives and opportunities to refine your theories or the communication strategies of your ideas. However, when valid intellectual challenges are framed as personal attacks — or come with them — it can be difficult to accept the critique or engage with it constructively. There are some principles for communication, such as the concept of nonviolent communication by Marshall Rosenberg, that are a good framework for understanding and fostering a constructive research culture. 
  
• Blocking time in your calendar to reflect on how you work (not just what you are working on) is not only helpful for individuals, but also for research groups. In my experience, work meetings in academia are largely technical and rarely focus on other topics. With constant deadlines and pressure, it can be challenging to make time for it. A good opportunity, however, are retreat-like events that are often organised by research groups. Use this valuable time together not only to discuss research progress, but also to reflect on shared goals, challenges, organisation, and communication structures. There are many interactive formats that facilitate such discussions and can provide invaluable insights at the same time as being fun and contributing to team spirit. If you do not have time to think or are unsure how to organise retreats effectively, you can get external help from people like me.

Institutions: Aligning Structures with Scientific Integrity

Institutions help shape the environment in which researchers operate. If an institution wants to foster truly excellent research, they need to emphasise their commitment to quality and create conditions that support it — structurally and operationally. 

• This starts with rethinking hiring and evaluation: create procedures for selecting candidates not just for their publication record, but for how the underlying research was done, and for their leadership and project management skills as well as their ability to support collaborative science. This also includes recognising practices like contributing valuable data and tools to the research community.
  
• PIs, such as professors and group leaders, have management positions. Institutions should require their managers to continuously develop their leadership skills. Nobody is born with the skills for how to run an effective team, develop lab-specific codes of conduct, and deal with challenging situations. And institutions should not be fooled by the argument of “academic freedom” (Wissenschaftsfreiheit). This freedom refers to choosing the scientific topic to study and how to do the research, it does not justify disregarding standards for scientific integrity and responsible management.
  
• Thorough research takes time and often comes with extra effort. What institutions can do to enable that right now is twofold. On one hand to optimise processes to reduce administrative burden on individuals. On the other hand, provide tools, infrastructure and support to implement the excellent research practices. Additionally, working towards an institutional structure with different roles would allow research work to be organised more effectively and career pressures to be relieved. Creating staff scientist positions and positions such as data stewards can crucially contribute to a research system that has embedded quality management structures in place.

Tools and Technology: Lowering Barriers, Raising Standards

Tools and technology can make high-quality science easier — if they are accessible and straightforward to integrate into daily workflows. Such tools include open-source community software, data repositories, and tools like Git for version control. 

• As an individual researcher, it is a worthwhile investment to learn how to code, structure your data properly, and implement reproducible workflows. Although it may require time upfront, it will increase confidence in your work and save time later — especially if you want to reuse parts of your pipeline across multiple projects. Well implemented research processes also ensure that research groups can function sustainably, even as members come and go. In addition to better documented research processes, technology can therefore prevent wasting resources as well.
  
• Since excellent research builds on previous work, previous work needs to be available to use. Information from paper publications is generally not detailed enough to reproduce other researcher’s methods. Open science practices have been developed to allow transparent communication of the entire research process. Technological infrastructure, such as Software Heritage, protocols.io and the osf, has been set up to make it as easy as possible to implement these practices. 
  
• Responsible development and use of AI can enhance research quality by accelerating time-consuming manual processes, identifying bugs or overlooked limitations, and generating new ideas and methods. However, doing so must be guided by thoughtful, community-led verification of AI-based tools and workflows. Institutions should provide training and usage guidelines that help researchers stay within ethical and legal boundaries while making the most of technological advances. At the same time, as a research ecosystem we also need long-term technical developments that are grounded in research ethics — to avoid reliance on proprietary tools or those designed for other purposes or interests.
  
• With regard to research methodology, replication efforts are essential for building a reliable scientific foundation that others can build on. For example, the large-scale ManyLabs replication initiative in psychology — often mentioned in the context of low reproducibility of previously published findings — also highlights which findings do meet strict replication criteria. When building on previous work, be strategic. Reach out to the original authors to gather insights that may not be included in the published papers. Conferences are a good opportunity for these informal exchanges. And when taking a new direction in your research, conducting a meta-analysis or structured literature review can help reduce the risk of basing your project on weak or inconsistent findings.

To conclude

Excellent research does not just happen through individual “brilliance” — it happens in environments where researchers can thrive and follow scientific values. It happens in collaborative teams with constructive communication habits. It happens when supportive structures are in place. It happens where technology facilitates the implementation of reliable workflows. Creating such conditions is not simple — but it is not unrealistic either. Many tools, approaches, and good examples already exist. So even though the academic system sometimes seems to be in a pretty bad state, it does not have to stay that way. We can start improving how we work within it. Every small effort counts.

And yes, in case you are wondering — the way I design my workshops and consultations is very much shaped by the principles described in this post. Do not hesitate to reach out if you are looking for support.