what is novelty of research article

Educational resources and simple solutions for your research journey

Novelty in Research: What It Is and How to Know Your Work is Original

One of the key prerequisites for researcher success, irrespective of their field of study, is identifying the novelty in research. They hope to make new discoveries that build on the work of others and produce fresh perspectives on existing knowledge in their field. To achieve this, researchers invest considerable time and effort in reading relevant literature, conducting experiments, and staying up to date on the latest developments in their own and related fields. Most journals seek to publish research that is novel, significant, and interesting to its readers. Establishing novelty in research is also critical when applying for funding, which makes it essential to prove this early in the research process. But what is meant by novelty in research and how can one judge the novelty of their research study? This article will help you answer these questions in the simplest manner.

Table of Contents

What is meant by novelty in research?

The word ‘novelty’ comes from the Latin word ‘novus,’ which simply means new. Apart from new, the term is also associated with things, ideas or products for instance, that are original or unusual. Novelty in research refers to the introduction of a new idea or a unique perspective that adds to the existing knowledge in a particular field of study. It involves bringing something fresh and original to the table that has not been done before or exploring an existing topic in a new and innovative way. Novelty in research expands the boundaries of a particular research discipline and provides new insights into previously unexplored areas. It is also one of the first things academic journals look for when evaluating a manuscript submitted for publishing. This makes it essential for researchers to ensure novelty in research in order to create new knowledge and make a significant contribution to their field of study.

How can you ensure novelty in research?

Academics are often immersed in their research and so focused on excellence that it can be difficult to examine your work as an author and judge its novelty in research objectively. But this challenge can be overcome with time and practice by adding research reading to your daily schedule. Assessing novelty in research means evaluating how new and original the ideas or findings presented in a study are, in comparison to existing knowledge in the field. Here are some ways to judge the novelty of research:

• Conduct a literature review: A literature review is an essential component of any research project, and it helps to establish the context for the study by identifying what is already known about the topic. By reviewing the existing literature, researchers can identify gaps in the knowledge and formulate new questions or hypotheses to investigate, ensuring novelty in research.
• Compare with previous studies: Researchers can assess the novelty of their work by comparing their findings to those of previous studies in the same or related fields. If the results differ significantly from what has been previously reported, it can be an indication that the study is novel and potentially significant.
• Read target journal publications: Subscribe to your target journal and other reputed journals in your field of study and keep up with the articles it publishes. Since most high-impact journals typically ensure novelty in research when publishing papers, this will help you keep track of the developments and progress being made in your subject area.
• Assess contribution to the field: One way to assess novelty in research is to evaluate how much it contributes to your specific field. Research that makes a significant contribution to advancing knowledge or addressing important questions is often considered more valuable than those that simply replicate elements from previously published research.
• Consider an alternative methodology: Even if the topic or area of study has been studied, one can bring in novelty in research by exploring various methodologies or by tweaking the research question to provide new insights and perspectives. Researchers can highlight aspects of the study that have not been done before, introduce these in the proposed research design, and illuminate how this will ensure novelty in research.
• Get support from your peers: Engage with your mentors/supervisors, professors, peers, and other experts in the field to get their feedback on introducing novelties in their research. It’s a good idea to join and actively participate in scientific research and scholarly groups or networks where users provide updates on new technological innovations and development.
• Make research reading a habit: An overwhelming number of research papers are published every day, making it difficult for researchers to keep up with new, relevant developments in the world of research. This is where online tools for researchers can help you simplify this process while saving on time and effort. Smart AI-driven apps like R Discovery can understand your areas of interest and curate a reading feed with personalized article recommendation, alerts on newly published articles, summaries to help you quickly evaluate articles, and many other useful features for researchers. By taking the search out of research, it gives you back time that you can then spend to stay updated and ensure novelty in research.

In an ideal world, all research done would be completely original. Yet with rapid advances in technology and research, there are bound to be overlaps with previously published papers. The key here is to find a new way of looking at old problems, trying new methodologies and angles, and coming up with interesting insights that can add to or alter current knowledge in your field of research. Smart online tools have made it easier to read and keep up with the latest in research and we’re sure the tips above will help you better assess your project and judge the novelty of your research study.

R Discovery is a literature search and research reading platform that accelerates your research discovery journey by keeping you updated on the latest, most relevant scholarly content. With 250M+ research articles sourced from trusted aggregators like CrossRef, Unpaywall, PubMed, PubMed Central, Open Alex and top publishing houses like Springer Nature, JAMA, IOP, Taylor & Francis, NEJM, BMJ, Karger, SAGE, Emerald Publishing and more, R Discovery puts a world of research at your fingertips.  

Try R Discovery Prime FREE for 1 week or upgrade at just US$72 a year to access premium features that let you listen to research on the go, read in your language, collaborate with peers, auto sync with reference managers, and much more. Choose a simpler, smarter way to find and read research – Download the app and start your free 7-day trial today !  

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How should novelty be valued in science?

Barak a cohen.

1 Edison Family Center for Genome Sciences and Systems Biology and Department of Genetics, Washington University School of Medicine, Saint Louis, United States

Scientists are under increasing pressure to do "novel" research. Here I explore whether there are risks to overemphasizing novelty when deciding what constitutes good science. I review studies from the philosophy of science to help understand how important an explicit emphasis on novelty might be for scientific progress. I also review studies from the sociology of science to anticipate how emphasizing novelty might impact the structure and function of the scientific community. I conclude that placing too much value on novelty could have counterproductive effects on both the rate of progress in science and the organization of the scientific community. I finish by recommending that our current emphasis on novelty be replaced by a renewed emphasis on predictive power as a characteristic of good science.

DOI: http://dx.doi.org/10.7554/eLife.28699.001

Introduction

“(T)he primary novelty of this work is the ability to make a prediction about drug sensitivity. Reviewers felt that the predictive ability would be very hard to generalize, however, reducing the impact of this novel feature. This concern about novelty … was the driving factor in this decision.”

-excerpt from a rejection letter received by the author

A mere 48 years separates the discovery of the double-helix structure of DNA ( Watson and Crick, 1953 ) from the announcements that the human genome had been sequenced ( Lander et al., 2001 ; Venter et al., 2001 ). The pace and regularity with which important discoveries have been made in molecular biology is remarkable. Molecular biologists have had an uncanny knack of homing in on the small irregularities that lead to large breakthroughs. It was irregularly colored ears of corn that revealed the existence of mobile genetic elements known as transposons ( McClintock, 1950 ). Many of the most important regulators of human development first surfaced as mutations that slightly alter the rows of bristles on the undersides of fruit fly larvae ( Nüsslein-Volhard and Wieschaus, 1980 ). Scientists studying tiny roundworms that age in odd ways helped uncover micro RNAs ( Lee et al., 1993 ; Wightman et al., 1993 ), which are now thought to regulate a large fraction of human genes. Again and again molecular biologists have seized on these sorts of minutiae to gain enormous insight into the inner workings of cells. Looking back over the last 60 years one feels a great sense of pride in being part of a tradition that is undoubtedly one of the most productive in the history of science.

Given the winning formula molecular biologists appear to have hit on, it is interesting that there are large changes occurring in our community. As the size of the molecular biology community continues to grow, competition for limited funding has become much more intense. With the completion of the human genome has come immense pressure to “translate” basic research findings into new treatments for disease. In the United States our institutional leaders at the National Institutes of Health (NIH) openly worry about data showing that the rate of discovery in the biomedical sciences no longer reflects the size of their investments ( Cook et al., 2015 ; Fortin and Currie, 2013 ; Gallo et al., 2014 ; Lauer et al., 2015 ; Doyle et al., 2015 ). Undoubtedly these pressures influence the trajectories of research programs. What we do not know yet is how these pressures impact the overall productivity of our community.

One manifestation of these changes is an increasing emphasis on “novelty” in science. Our scientific establishment – through our funding agencies, review panels and editorial boards – are clearly putting a higher and higher premium on research that is deemed novel. Research programs that lack a “high degree” of novelty struggle for support and “incremental” findings are relegated to publication in second- and third-tier journals. NIH grant proposals now have an “Innovation” section where investigators must explicitly list the attributes of their research that make it novel. While funding agencies seek novelty in their grant portfolios, they are also increasingly looking for "feasibility" as resources become scarce, and this appears to put novel research programs at a disadvantage ( Alberts et al., 2014 ). As investigators struggle to walk a nearly impossible line between feasibility and novelty, the definition of novelty itself becomes blurred. Novelty can now mean anything from demonstrating a well-established phenomenon in a new system to testing a hypothesis with no precedent in the literature. Even though we cannot strictly define what is and is not novel, the message is still clear; novelty equates with good research.

Perhaps this emphasis on novelty is not really new at all, but only a codifying of something we already value implicitly. Even so, we should consider the effects that an explicit emphasis on novelty might have on the properties of scientific research that have made molecular biology so successful. These properties include our system of peer review, our scientific standards of proof and falsification, and the organization of the scientific community. Increasing the value we place on novelty will likely affect each of these factors.

Lessons from the philosophy of science

For working scientists Karl Popper is almost certainly the most influential philosopher of science. Most of us at least pay lip service to Popper’s philosophy when we recite the mantra that hypotheses can never be proved, only disproved. For many scientists the distinction between what is disprovable and what is not demarcates the line between what is and is not science, an idea taken directly from Popper’s writings. According to Popper, scientists propose new hypotheses about how the world works, and any hypotheses that are subsequently falsified by empirical observation are relegated to the scrap heap ( Popper, 1963 ). This framework of hypothesis generation and refutation is widely accepted by scientists.

What is less well appreciated is how utterly Popper rejected the notion of confirmation. Popper was adamant that the survival of a hypothesis in the face of empirical challenge says nothing about its validity, only that that the hypothesis has yet to be falsified. However, Popper’s strict adherence to this idea became difficult to defend and, to be practical, most scientists do allow that empirical evidence can either support or falsify a hypothesis.

What if anything can we infer about the value of novelty from Popper's ideas on hypotheses and falsification? Because Popper believed that hypotheses can never be proved, he stressed that hypotheses must be subjected to repeated testing, even after they have survived several empirical challenges. In this sense he valued follow-through over novelty. However, because Popper believed that “good tests kill flawed theories”, new tests must be more than trivial variations of previous experiments. The philosopher Imre Lakatos argued that good research programs are "progressive" ( Lakatos, 1970 ), and that scientists should constantly seek to expand their hypotheses into new areas of observation. Today, however, review panels are likely to tag progressive research programs as lacking in novelty because the scientists who pursue these programs seek to expand old hypotheses into new realms, rather than develop new hypotheses altogether. This is misguided. Scientists following progressive research programs require ingenuity and creativity to devise the tests that expand the reach of their hypotheses beyond the obvious. According to Popper the novelty of a new hypothesis is beside the point, unless and until the hypothesis it is meant to replace is falsified.

It appears then that nothing in the ideas of Popper or Kuhn particularly values novelty for its own sake.

Thomas Kuhn, a contemporary of Popper, was in many ways Popper’s opposite. Kuhn emphasized the importance of “paradigms”, coherent collections of claims, methodologies, and teaching practices that govern scientific inquiry. In his hugely influential book The Structure of Scientific Revolutions he explains that the purpose of a paradigm is to provide a guide for investigating the right questions ( Kuhn and Hacking, 2012 ). Here Kuhn’s philosophy sharply contrasts with Popper’s. While Popper advocated abandoning a theory the moment it was falsified, Kuhn emphasized that paradigms can tolerate a good deal of “anomalies” and still remain valid. The flexibility of paradigms allows scientists to continue working in a productive framework long after falsification would have dictated a change. If scientists had to drop their paradigms every time they encountered a problem then nothing would ever get done. Only a critical mass of anomalies requires a “paradigm shift”.

It appears then that nothing in the ideas of Popper or Kuhn particularly values novelty for its own sake. Both Popper and Kuhn emphasized the need for scientists to stick doggedly with their hypotheses, Popper because hypotheses must be challenged continually no matter how often they have been confirmed, and Kuhn because only a critical mass of anomalies can force a paradigm shift. Ironically, over time the effect of Kuhn's book has been to weaken scientists’ belief in their paradigms. Many investigators now actively search for paradigm shifts. This conflicts with Kuhn’s description of progress in which scientists cling tightly to their paradigms, giving them up only grudgingly after the weight of anomalous results renders the paradigm unsupportable. Despite their differences, novelty seeking is not a key component in the philosophies of either Popper or Kuhn.

Many scientists have a visceral reaction to philosophies that cast them as mechanically pursuing their hypotheses. Kuhn in particular was attacked for seeming to endorse a grinding and boring type of science, and he did not help his case by referring to work done in the context of a paradigm as “normal” science.

But we need not explicitly value novelty to keep science from being a dull grind. Peter Godfrey-Smith writes that Popper painted an appealing picture of scientists as “hard-headed cowboys, out on the range, with a Stradivarius tucked in their saddlebags” ( Godfrey-Smith, 2003 ). Hard-headed because they must have the determination to stick with their hypotheses, and packing a Stradivarius because they need inspiration when devising tests that expand their hypotheses into new realms. Kuhn too seemed in awe of the ability of normal science to hone in on “miniscule” findings that end up revealing deep truths about the world. Think of the little tails on the electron micrographs of the RNA:DNA hybrids that revealed the phenomenon of intron splicing ( Berget et al., 1977 ), or the examples given at the start of this article. While normal science might seem a derogatory term for what most investigators do, Kuhn saw it as requiring imagination.

Even still, as working scientists we know that much of day-to-day science involves painstaking and often repetitive work. Science succeeds because powerful social incentives help us push through the less glamorous aspects of research. Godfrey-Smith writes that the most significant reactions to the philosophies of both Popper and Kuhn emphasized the importance of social forces in science. For example, in his later writings Popper struggled with the question of exactly when an observation counts as a refutation. His solution was to shift from describing the proper methodologies of science to describing the proper social behavior of scientists. For Kuhn, paradigms highlighted the importance of the social aspects of science, including the indoctrination of students and the collective adherence to particular claims among investigators working under the same paradigm. In the next section I discuss how the increasing emphasis on novelty might influence the social structure of science.

Lessons from the sociology of science

An important question for sociologists of science – and also for scientists and funding agencies – is: What distribution of people across rival research programs is best for science? The immediate impact of emphasizing novelty might be to distribute researchers over the widest possible range of research programs, as each investigator seeks to maximize the novelty of their own research program. This might seem an efficient way of exploring the widest possible range of theories but such a distribution also raises problems. Kuhn wrote extensively of the necessity of having large groups of researchers organized around a particular set of theories. Placing too much emphasis on novelty may result in a distribution of effort that is too diffuse to enable efficient progress. But scientists consider an array of incentives besides novelty when choosing their research programs.

Robert Merton laid the foundations of the sociology of science with his discussion of reward systems in science ( Merton, 1957 ). Merton argued that recognition is the main form of reward in science. In particular the “priority rule”, which awards the most recognition to the first investigator to support a hypothesis, is an especially powerful incentive in science. To support his idea Merton showed that the history of science is chock full of disputes over priority (for example, Isaac Newton battled Gottfried Leibniz over priority for the invention of calculus ( Hall, 1980 )). One benefit of an incentive system that rewards priority is that it encourages original thought and novel lines of investigation. One might argue that this means that novelty seeking is already baked directly into the social fabric of science.

Hull viewed the success of science as a result of a delicate balance between competition and cooperation, creativity and skepticism, trust and doubt, and open-mindedness and dogmatism. Placing too much emphasis on novelty could upset this equilibrium in ways that are not optimal for scientific progress.

Some sociologists argue that the priority incentive coupled with the individual quest for credit is what produces good outcomes in the scientific community. These authors envision something like the “invisible hand” that guides free market capitalism in Adam Smith’s Wealth of Nations ( Smith, 2000 ). Scientists must balance risk versus reward when choosing between competing hypotheses to explore. The priority incentive prevents all investigators from working on the hypothesis with the highest probability of success. The argument is that credit is a pie of fixed size that can be shared either equally ( Kitcher, 1990 ) or unequally ( Strevens, 2003 ), but only by investigators who work on the winning hypothesis. When too many scientists work on the same hypothesis there is an incentive to work on novel hypotheses, even ones where the chance of success might be smaller, but where the share of credit would be larger ( Laudan, 1977 ). In this way the priority rule balances cooperation and competition between scientists, and divides individual effort between different research programs.

David Hull argued that science is particularly good at portioning effort in a way that maximizes good outcomes for the community ( Hull, 1988 ). Hull agreed with Merton that the priority rule helps to maintain a balance between cooperation and competition in science. However, he also recognized the importance of the rivalries between scientists that encourage investigators to check the validity of their competitors’ work, especially results they may want to use in their own research. This checking, along with the priority rule, helps to maintain a balance between creativity and skepticism, which Hull believed was an essential feature of science. Scientists can become overly attached to their ideas, and most are reluctant to kill their pet theories, especially theories with creative panache. To counterbalance this tendency science relies on the incentive rival scientists have to vigorously check work that may be useful to them, or results that challenge their own dogma.

Hull might have been wary about introducing an explicit incentive for novelty into the scientific community. For one thing, along with most other sociologists of science, he thought that the priority incentive already provided a powerful motivation for scientists to test novel theories. But more than others Hull viewed the success of science as a result of a delicate balance between competition and cooperation, creativity and skepticism, trust and doubt, and open-mindedness and dogmatism. Placing too much emphasis on novelty could upset this equilibrium in ways that are not optimal for scientific progress.

In particular, an explicit emphasis on novelty might perturb the balance between the incentive for scientists to check their rivals’ theories and the priority rule. The priority rule provides a powerful incentive for scientists to publish their work quickly. This is good for the community because new ideas get disseminated rapidly, where they can be incorporated into other research programs. However, there is an equally powerful incentive to be correct when publishing because scientists know that other investigators who want to build on their results are likely to uncover any mistakes that make it into print. If we value novelty too much then scientists will be incentivized to publish too quickly, without imposing the rigor they might normally demand of themselves. Progress would slow to a crawl as other scientists waste time trying to build on flawed results.

Indeed, some in the scientific establishment have already warned of a “crisis in reproducibility” ( Errington et al., 2014 ; Baker, 2016 ). Not surprisingly this crisis follows an explosion in papers reporting weak claims of novelty ( Henikoff and Levis, 1991 ; Friedman and Karlsson, 1997 ). Others have argued that the reward system in modern molecular biology incentivizes statistically underpowered research designs ( Higginson and Munafò, 2016 ). To counteract this trend some of the leaders in our field now advocate funding centralized efforts to validate published studies ( Collins and Tabak, 2014 ). This suggests that priority and checking have become unbalanced in the general scientific community. Those leaders advocating for centralized checking efforts might do well to ask themselves what role their emphasis on novelty has played in precipitating this so-called crisis.

Another consequence of emphasizing novelty might be to increase the tenacity with which scientists attack their rivals’ hypotheses. Novel results are particularly likely to be attacked, in part because scientists who can lay claim to novelty enjoy so many advantages over other scientists. Rival scientists are thus incentivized to use anomalous results to discredit novel hypotheses. This is unfortunate because as Kuhn emphasized, hypotheses must be allowed to tolerate some anomalous results before they are discarded, otherwise the community cannot exploit the utility of working models. Ironically, novel research programs have a very difficult time surviving when novelty is so highly coveted.

Perhaps our obsession with novelty is a sort of communal nostalgia for the good old days, when important foundational discoveries came fast and furious.

An emphasis on novelty could also break the cohesion between scientists working within research programs. Cooperation is essential to scientific progress, and this cooperation is balanced by competition from investigators who are willing to challenge rival theories. If scientists must maximize the novelty of their research then they are more likely to pursue avenues as different as possible from their colleagues. We risk producing a community in which no single paradigm has the critical mass of supporters required to function effectively. This is a serious problem because current paradigms, imperfect though they might be, often have great utility, even though they may eventually be revised or even discarded.

Conclusions

When an area of science experiences rapid advancement over a short interval of time it may be followed by a period in which novel discoveries are harder to come by. After Mendeleyev articulated the concept of the periodic table there was an exciting period in which novel elements were rapidly discovered. As time passed it became more and more difficult to isolate the remaining elements. Perhaps molecular biology is also in a lull after a period of virtually unprecedented achievement. Almost 50 years ago Gunther Stent argued that there were no new principles left to discover in molecular biology ( Stent, 1969 ). All that scientists could look forward to would be the tedious grind of filling in details. These sorts of pronouncements have a way of being undone by events. For example, Stent’s prediction came before the discovery of splicing, reverse transcription, and micro RNAs. Even so, it may well be true that most of the foundational principles of molecular biology have already been discovered. Perhaps our obsession with novelty is a sort of communal nostalgia for the good old days, when important foundational discoveries came fast and furious.

It might also be that our desire to reward novelty stems from the frustration that research in molecular biology is not “translating” into new practical applications as fast as some might wish. The endless overpromising of novel therapeutics from our institutional leaders only makes this matter worse. Why don’t discoveries in molecular biology translate more quickly into practical applications? Is it because we are missing large chunks of basic theory? Probably not, and those who go searching for novelty and paradigm shifts are likely to be disappointed.

Instead, we face a very different set of problems. While our models are generally quite good at explaining the basic mechanisms underlying molecular biology, it is also the case that most of our models lack a quantitative formulation. Even when we know the underlying molecular mechanisms at work in a given system or process, in most cases we lack the ability to make quantitative predictions about the effects that specific perturbations will have on that system or process. We have a mountain of facts about how transcription initiates and beautiful cartoon models of this process, but we cannot predict the effects that genetic variants will have on transcription rates, whether these variants reside in cis -acting DNA sequences or in trans -acting protein factors. We know the identities of virtually all the proteins involved in apoptosis, and which of their post-translational modifications are pro- or anti-apoptotic. Yet we cannot use quantitative measures of the levels of these proteins in any cell type to make an accurate prediction of whether that cell will die or not. We understand the principles that drive peptide sequences to fold into secondary and tertiary structures, yet we cannot predict the shape any given amino acid sequence will adopt. Seen through the lens of predictive power, it is clear that the vast majority of models in molecular biology are inadequate for solving real world problems.

If we want to solve important practical problems then progressive research programs that expand and refine the predictive power of existing models are at least as important as research programs focused on novel hypotheses. One suggestion would be to replace the current emphasis on novelty with an emphasis on predictive power, particularly quantitative predictions. Research that results in models that reliably and quantitatively predict the outcomes of genetic, biochemical, or pharmacological perturbations should be valued highly, and rewarded, regardless of whether such models invoke novel phenomena.

The increasing emphasis placed on novelty brings significant dangers. As it becomes more and more important for scientists to be “the first to demonstrate” some claim, the influence of the priority rule will increase and more scientists will feel pressure to sacrifice rigor for speed of publication. We are also likely to see an increase in distasteful disputes over priority. The cohesion between competing groups may also be in jeopardy as the drive for novelty distorts the balance between competition and cooperation that has characterized the success of molecular biology over the past several decades.

Science as we practice it today is a relatively recent development. Our system of peer review, the priority rule, and the organization of scientists into cooperative social demes that compete against other groups of scientists all trace their origin to decisions made by the Royal Society in the late 1600s. For most of history humans acquired knowledge outside of what we would recognize as a scientific framework. It would be unwise to assume that science is a permanent feature of our society or that it can withstand deep structural changes and remain an efficient engine of discovery. The explicit value we now place on novelty in molecular biology is a change we should approach with caution if we are to safeguard the essential features of science that have made our field so successful.

Acknowledgements

I thank Rob Mitra, Mark Johnston, Siqi Zhao, Max Staller, Michael White, Zach Pincus, and Dana King for critical readings of the manuscripts and engaging discussions.

Competing interests

The author declares that no competing interests exist.

Author contributions

BAC, Conceptualization, Writing—original draft, Writing—review and editing.

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• eLife. 2017; 6: e28699.

Decision letter

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your manuscript "How should novelty be valued in science?" to eLife for consideration as a Feature Article. Your manuscript has been reviewed by two peer reviewers and the eLife Features Editor (Peter Rodgers). The following individuals involved in review of your submission have agreed to reveal their identity: Yitzhak Pilpel (Reviewer #1) and Angela H DePace (Reviewer #2).

The reviewers have discussed the reviews with one another and the Features Editor has drafted this decision to help you prepare a revised submission. Most of the major revisions requested are optional (we feel the article would be improved if you addressed them, but it is not essential that you do).

The paper is an impressive scholarly work. It is broad, deep and methodological. It is very well written (though perhaps could be shortened). It studies the value of novelty in science through several angles, including philosophy of science (the excellent survey and comparison of Popper's vs. Kuhn's teachings as well as other less well-known thinkers is used here very effectively to deliver the notion that both falsification as well as paradigm establishment and shifting require more than purely "novelty-science"); it considers very effectively social and cultural aspects of science (the role of fame and recognition in the process, competition etc.); it touches upon the emotional aspects of doing science, and it very effectively also touches upon science organization and policy aspects such as in funding and granting of research projects (where the call for funding, not only individualistic research is refreshing and, in a way novel, in the current atmosphere).

Major revisions:

1) The solution presented at the end (to focus on quantitative prediction as a gauge of novelty) is only one of many possible solutions, and it would be good if the author could discuss other possible solutions, although we should not insist on this.

I would argue that another solution would be including some description of the sociology of science in graduate and undergraduate education, such that the value of novelty and reproducibility/extension at the community level are more clear to people. Right now we almost exclusively lift up isolated geniuses as scientific heroes; is it no wonder that everyone chases some paradigm shift of their own? I'm sure there are other solutions as well.

2) A common complaint I hear is that the competitive nature of modern science means that authors often over-sell their findings in papers in order make them seem more novel than they really are. Again, it would be good if the author could briefly discuss this phenomenon.

3) In addition to the relationship between novelty and philosophical and sociological factors it would be good to discuss how competition for funding and jobs seems to be reducing novelty – as outlined, for example, in the following passage from Alberts et al. 2014. Rescuing US Biomedical Research from its Systemic Flaws. PNAS 111:5773-5777:

"Competition in pursuit of experimental objectives has always been a part of the scientific enterprise, and it can have positive effects. However, hypercompetition for the resources and positions that are required to conduct science suppresses the creativity, cooperation, risk-taking, and original thinking required to make fundamental discoveries.

Now that the percentage of NIH grant applications that can be funded has fallen from around 30% into the low teens, biomedical scientists are spending far too much of their time writing and revising grant applications and far too little thinking about science and conducting experiments. The low success rates have induced conservative, short-term thinking in applicants, reviewers, and funders. The system now favors those who can guarantee results rather than those with potentially path-breaking ideas that, by definition, cannot promise success. Young investigators are discouraged from departing too far from their postdoctoral work, when they should instead be posing new questions and inventing new approaches. Seasoned investigators are inclined to stick to their tried-and-true formulas for success rather than explore new fields.

One manifestation of this shift to short-term thinking is the inflated value that is now accorded to studies that claim a close link to medical practice […]".

It would be good to discuss these matters (in just a paragraph or two) in part 1 or part 4 of the article, but this is not essential.

4) I would consider swapping the order of sections 2 and 3. Section 3 is the stronger of the two, in my opinion, and describes one ideal version of how the scientific community functions that many of us are familiar with, at least in the abstract. It thus may serve as more of a common starting point. (Although it may be worth noting that some aspects of this ideal might not serve us well either. For example it is highly individualistic and competitive in its framing; the same goals of novelty seeking and cross-checking might be achieved by other more collaborative social structures). The segue to section 2 can then be that novelty-seeking is a requirement of the social structure described in the previous section, as is independently validating or extending results in new areas. Both of these activities can be accommodated in the philosophical frameworks presented, but there is a clear second-tier status assigned to validating or extending results in some of them. Thus the dominant influence of Kuhn's work can be seen to be somewhat destructive in the overall goals of science. (Everyone constantly seeking poorly-defined paradigm shifts isn't necessarily productive).

Author response

As directed in the decision letter I have addressed some, but not all, of the major points as the letter indicated that addressing these points was optional.

This point is addressed in the ninth paragraph of the section “Lessons from the sociology of science”. I cite to papers documenting the exponential rise in claims to novelty.

I now address this point in the Introduction (fourth paragraph) and cite the Alberts et al. (2014) paper.

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Open Access

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Research Article

Scientific collaboration, research funding, and novelty in scientific knowledge

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation Spatial Dynamics Lab, School of Architecture, Planning & Environmental Policy, University College Dublin, Dublin, Ireland

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliations Spatial Dynamics Lab, School of Architecture, Planning & Environmental Policy, University College Dublin, Dublin, Ireland, School of Applied Artificial Intelligence, Handong Global University, Pohang, South Korea

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Spatial Dynamics Lab, School of Architecture, Planning & Environmental Policy, University College Dublin, Dublin, Ireland, Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland

• Hyunha Shin, 
• Keungoui Kim, 
• Dieter F. Kogler

• Published: July 25, 2022
• https://doi.org/10.1371/journal.pone.0271678
• Reader Comments

Disruptive advancements in science and technology often rely on new ideas and findings, which in turn brings us to focus on the value of novelty in scholarly activities. Using Web of Science publication data from European regions for the period between 2008 and 2017, this study examines, first, the impact of scientific collaboration on novelty of research. Here, five levels of collaboration are considered for each article–country, three levels of regions, and institutions, and novelty is measured with keywords information. Second, we investigate both the effect and moderating effect of research funding on novelty. Our findings show that there is a negative and significant relationship between scientific collaboration and novelty. Furthermore, funded papers show lower novelty than the unfunded, but funding does have a significant moderating effect on the relationship between collaboration and novelty. This study contributes by linking diverse levels of collaboration and funding sources to article’s novelty and thus extending the scope of bibliometric research of publications.

Citation: Shin H, Kim K, Kogler DF (2022) Scientific collaboration, research funding, and novelty in scientific knowledge. PLoS ONE 17(7): e0271678. https://doi.org/10.1371/journal.pone.0271678

Editor: Kamal Kishore Pant, Indian Institute of Technology Delhi, INDIA

Received: January 12, 2022; Accepted: July 5, 2022; Published: July 25, 2022

Copyright: © 2022 Shin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Restrictions apply to the publication dataset used in this paper. The Web of Science data is owned by Clarivate Analytics. To obtain the bibliometric data in the same manner as authors (i.e., by purchasing them), readers can contact Clarivate Analytics at " https://clarivate.com/webofsciencegroup/solutions/web-of-science/contact-us/ " in order to gain access to the following Web of Science bibliographic databases: ‘1980–2017 – Annual Science Citation Index Expanded and Proceedings-Science Combined’.

Funding: The authors (HS & DFK) would like to acknowledge funding from the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 715631, ERC TechEvo). Further, the authors (KK & DFK) would also like to acknowledge funding from the Science Foundation Ireland (SFI; https://www.sfi.ie/ ) under the SFI Science Policy Research Programme (grant agreement No 17/SPR/5324, SciTechSpace). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Scholars have long emphasized the role of scientific research in technological innovation and economic growth. Scientific knowledge provides background for fundamental understanding and subsequent practical applications for technological advancements [ 1 – 3 ]. In other words, scientific knowledge landscapes a foundation for ultimate technological and economic growth. In fact, a number of patents cite non-patent literature such as scientific publications as their prior art, and the number has grown dramatically over time [ 4 ]. Further, research with higher quality are more likely to be cited by patents, and, bilaterally, patents that cite scientific papers are likely to have higher impact and commercial value [ 5 ]. In this respect, accomplishing high-quality research outputs, mostly resulting in publications, must be the foremost goal in scholarly activities.

As one of common efforts to attempt high-quality scientific research, the number of international collaborations in science has increased substantially over the last three decades, and collaborative team science has become a universal trend [ 6 – 9 ]. Especially, the need of creative and interdisciplinary ideas to solve complex problems has driven the growth [ 10 , 11 ]. In fact, previous literatures have shown that co-authored research papers tend to have higher impact when measured by the number of citations a paper gets [ 8 , 12 – 14 ]. Yet, it is only recently that researchers have started to pay attention to city-level or institution-level scientific collaborations due to the availability of collecting and processing big publication data [ 15 ].

In addition to scientific collaborations, governments and organizations are well aware of the value of investments in science. Numerous initiatives and policies of funding programs have been established by public and private funding agencies to improve quality of research activities by enhancing research competitiveness, supporting interdisciplinary collaborations, and intensifying researcher networks to motivate knowledge exchange and creation [ 16 – 19 ]. Performance of funding has often been associated with the evaluation of efficiency of research systems, which is usually assessed by comparing spending on research and development (R&D) and publication impact [ 12 , 20 , 21 ]. However, the role of funding in bibliometrics remains largely unanswered since only few attempts were made to explore the effects of funding on published articles.

So far, when assessing scientific achievements of research papers, majority of work have utilized citation impact, since it implies the realization of peer-recognition of a paper and impact on science community [ 22 , 23 ]. Citations, however, are regarded as a measure of impact, but not of research quality [ 21 ]. Besides impact, novelty or creativity is also considered valuable in scholarly outputs [ 11 , 24 ], because ground-breaking ideas and opportunities are the ones to disrupt science and technology [ 9 ]. It should be noted that impact and novelty should be distinguished, and it is not always that novel papers get high citations [ 10 , 25 ]. However, little is known about novelty of publications because of its difficulty to define and measure [ 26 ].

In this study, we focus on publication novelty as a scientific advancement with regard to collaboration and funding. First, we investigate the relationship between collaborations and novelty in publications. Novelty in scientific publications often incorporates new ideas and opportunities from interdisciplinarity across heterogenous research groups to solve practical problems [ 27 – 29 ]. Here, since interdisciplinarity, which results from scientific collaboration, is believed to bring out new research outputs, it is reasonable to expect some correlations between the collaboration and novelty. To the best of our knowledge, however, only few studies have examined the relationship between scientific collaborations and novelty. Here, we examine collaborations at country, three Nomenclature of Territorial Units for Statistics (NUTS) level regions, and institution level. We also measure novelty of articles with keywords information. Then, we aim to evaluate the role of funding in research novelty. To do so, we include a funding dummy variable as a moderator. The moderating effect is further examined with grouping our data by the number and quality of funding agencies. In short, this article aims to add to a line of bibliometric research of publications not only by examining different levels of collaboration but also by linking article’s novelty back to funding sources. Publication data is collected from Web of Science (WoS) Core Collection, and we limit our analysis on journal articles and proceedings papers in major scientific fields, Life Sciences & Biomedicine, Physical Sciences, and Technology, from European regions for the period of 2008–2017.

The collaboration, funding, and novelty nexus

Novelty in scholarly publications.

Along with the access to publication data, substantial bibliometric research focusing on the impact of research has been conducted [ 22 ]. Systematic analysis of citations serves as a good proxy for scientific performance, since it implies the realization of peer-recognition of a paper and impact on science community [ 23 ]. Thus, majority of work has assessed the scientific achievement of research papers as: highly cited papers are the outcomes of high qualified research. Citations, however, are regarded as a measure of impact, not of research quality [ 21 ].

As an alternative to citation impact, novelty or creativity is also valued in scholarly activities, because ground-breaking ideas and opportunities are the ones to disrupt science and technology. In other words, scientific advancements are led by rare but important scientific discoveries which involve different perspectives on defining problems and combining diverse methods and models. Since research is a process of solving problems that involves diverse combinations of components such as problem defining, methods, etc., there can always be new combinations of those preexisting components. This often encourages further research and brings new creation of knowledge that can be described as novelty in publications [ 9 , 11 , 24 , 26 ]. Novelty and citation impact of a paper should be distinguished because, first, it is not always that novel papers get high citations. Instead, higher variance in citations is observed among novel papers. While they have a higher opportunity for obtaining popularity, they also confront a higher uncertainty in impact. Also, novel studies may take longer time to gain recognition, which may undervalue the studies when relying on the measurement by short term citation windows [ 24 ]. In terms of the relationship between novelty and citation impact, studies have shown diverged results: positive [ 11 , 30 ] or inverted U-shape [ 26 ]. Furthermore, according to Lee , Walsh , and Wang [ 10 ], the two measures depict different patterns in terms of collaborative works. For instance, the study claimed that there is an inverted U-shape relation between team size and novelty, while there is a continuous increasing relation between team size and impact. In addition, novelty is likely to be driven by knowledge variety, while impact is affected by team size.

Novelty in scientific publications is involved with new ideas and contributions that often come across disciplines [ 23 ]. In other words, exchanging and sharing knowledge from heterogenous research groups to solve practical problems is expected to increase novelty [ 27 – 29 ]. Specifically, scientific breakthroughs or inventions are achieved by new recombination of knowledge by extending the variety of knowledge pool and linking distant sources of knowledge. It is likely that novel outcomes are typically the result of recombining elements from more distant knowledge domains than within similar knowledge domains [ 2 , 31 ]. These recombinant processes are regarded as the main mechanism for being creative or novel, which is demonstrated in publishing breakthrough-class papers [ 32 ].

Building on the idea of recombination of knowledge, scholars have attempted to describe or measure the novelty of publications by paper’s unusual combinations of references or keywords. First of all, Uzzi et al . [ 11 ] introduced the measurement of novelty by drawing on unusual or unexpected pairwise combinations of journals from references. This “atypical” combinations represent relatively new knowledge because they are rare in the combination domain. Given the bibliography data from WoS, frequencies of each journal pair were recorded to figure out whether it is atypical or conventional by comparing the observed frequency to the distribution of journal pairs that would have occurred by chance. This method was adopted by other studies [ 10 , 23 ] to measure novelty of a paper. Moreover, Wang , Veugelers , and Stephan [ 24 ] defined a cosine similarity index to measure the ease of journal combinations that never have been made in the preceding three years. In short, atypical combination of references or the appearance of new reference combinations based on bibliography data was utilized to capture novelty.

However, some critics pointed out the use of references for measuring novelty, because most of the cited references are selected to contextualize the issues rather than to solve practical problems [ 29 ], and there are some risks that it might miss or overestimate the novelty depending on the use of references [ 28 ]. Plus, Bornmann et al . [ 32 ] tested whether the measurement of novelty by reference data converges to the assessment of novelty with F1000Prime data–a post publication peer review–and failed to show the validity.

Subsequently, new methods of measuring novelty based on keywords of a research paper emerged as an alternative: novelty measured by keyword combinations. Bornmann et al . [ 32 ] introduced the “Score K” defined as “the proportion of new keywords whereby newness is judged against the available keywords in one subject category from the same publication year” (p.5). In addition, Carayol , Agenor , and Oscar [ 30 ] applied the methodology of Lee , Walsh , and Wang [ 10 ] with the replacement of pairwise journal reference combinations with the pairwise keyword combinations. Lastly, Yan , Tian , and Zhang [ 26 ] used both of the two dimensions: new pairings of keywords in related research area [ 30 ] and the appearance of new keywords [ 32 ]. The study concluded that both measurements–new pairings and new appearance–capture similar phenomena.

Scientific collaboration and its impact

According to Cugmas , Mali , and Žiberna [ 33 ], interactions among scientists and their scientific collaborations are important in the process of knowledge sharing and developing new ideas which are the prerequisites for scientific innovation. By pooling diverse perspectives through collaborations, it can contribute to the richness of using terminologies, research approaches, and methodologies in a science. Scientific collaborations can be defined and categorized in various ways depending on the units of actors, individual or organizational, and the type of information entailed in collaborating processes. Generally, scientific collaboration is operationalized through co-authorships in publications. While there may be several definitions and classifications of scientific collaborations, two components are common to all of them: a pursuit of a common goal and sharing knowledge.

Due to growing specialization in science labor, decrease in communication and travel costs, and the need to access interdisciplinary ideas and various database [ 10 , 11 ], the number of internationally co-authored papers has grown significantly over the last three decades [ 8 , 15 ]. Internationally co-authored papers take up to about 25% of WoS articles [ 7 ], and the increase in published outputs from Western Europe and United States is primarily driven by the growth of international collaborations [ 6 ]. Further, international collaboration between elite research teams became one of research trends, and the team size is getting bigger [ 9 ]. On one hand, there are still many countries in which domestic collaborations grow faster than international collaborations. In these countries, it is the interurban collaborations within a same country that reinforce research outputs [ 34 ]. Consequently, some studies tried to take account of intercity co-authorships both within and between countries [ 35 – 38 ]. However, most of the studies that deal with city level collaborations focused only on large or top publishing cities, and still, it needs more investigation since collaborations at city and institutional level have been analyzed not much due to data collecting and processing availability [ 15 ].

A great deal of studies has analyzed the effect of collaborations on scientific impact. Those studies have found that internationally co-authored research papers are likely to have higher impact in terms of citation counts [ 12 – 14 , 21 ]. For example, Narin , Stevens , and Whitlow [ 39 ] found that papers with international co-authorship gained citations twice the rate of those from a single country, and Wagner , Whetsell , and Leydesdorff [ 8 ] and Glänzel and Schubert [ 40 ] also confirmed the positive relationship between co-authorship and impact in science disciplines and in all research disciplines, respectively. Wagner et al . [ 21 ] showed strong evidence of positive effect of openness, which represents percentage of internationally co-authored articles of a country and mobility of researchers, on citation impact using Scopus data and claimed that there are national benefits from participating in international scientific cooperation.

Similar results are found at city and institutional level studies. For instance, Csomós , Vida , and Lengyel [ 15 ] showed a positive correlation between a Jaccard index to measure relative strength of intercity collaborations and a binary variable of highly cited papers. Moreover, Abbasi and Jaafari [ 41 ] explored the correlation between institutional collaboration types and their impact on research. The types were classified into intra- or inter-departmental collaborations and intra- or inter-institutional collaborations. While all types of collaborations had positive and significant effect on impact, the results represented that inter- departmental and institutional collaborations show higher correlation with average number of citations than intra- collaborations. However, publications with co-authoring from different departments and institutions inside the same country had higher impact than those across countries.

In the meantime, collaborations across heterogeneous groups foster knowledge dissemination within a wider range of information, thus increasing the possibility to bring out new research outputs [ 27 – 29 ]. As interdisciplinarity which results from collaboration is believed to enhance novelty, it is reasonable to expect some correlations between the two elements. However, only few trials have been made to evaluate the collaboration effect on novelty of publications. Wagner , Whetsell , and Mukherjee [ 23 ] analyzed the relationship between the number of countries per article and novelty and found that international collaboration indicates low novelty, and Lee , Walsh , and Wang [ 10 ] showed an inverted-U shape relationship between team size and novelty. Additionally, Wu , Wang , and Evans [ 9 ] revealed that smaller teams tend to create disruptive research by exploring novel ideas from less-popular works, while big teams tend to rely on recent successes and ongoing stream of funding. The findings from these studies on collaboration and novelty are contrary to the results from other studies that show citation impact rises with team size.

The role of funding in publications

Numerous policies and initiatives of funding programs have been established by national governments, organizations, and private agencies in order to enhance competitiveness and facilitate interdisciplinary coordination of research activities [ 19 , 41 , 42 ]. As an example, a pan-European funding program, the Framework Programs, by the European Commission is designed to pool researchers and resources so that all countries and regions in the EU can participate in international collaborations and move forward to frontier knowledge [ 18 ]. The research funding is recognized to motivate new exchange of knowledge and intensify contact networks among researchers, constituting instruments for deepening collaborations and knowledge creation, where scientific and technological capabilities are often concentrated in typical geographic boundaries [ 16 , 17 ].

Measuring the performance of funding has often been associated with the evaluation of efficiency and effectiveness of a country’s research system which is assessed by spending on R&D and publication impact. However, results of analyses on the relationship between R&D funding and nation’s citation impact are not consistent. While positive relationship is observed in some studies [ 19 , 21 ], others show a negative or only a slight effect of funding [ 12 , 20 ]. In addition, few attempts have been made to assess the effect of funding on individual research papers since publications are the primary output of academic activities, but they also show inconclusive results. While Li , Azoulay , and Sampat [ 5 ] represents positive correlations between grants award from National Institutes of Health (NIH) and possibility of being cited by commercial patents, Yan , Tian , and Zhang [ 26 ] depicts insignificant effect of funding on an article’s citation impact. Yet little is known about whether and in what direction the acquisition of funding has impact on scientific output [ 42 ].

Moreover, as seen above, measuring the effect of funding is often coupled with research impact. Several scholars, however, are concerned about this impact agenda in research funding [ 43 ]. Research with less potential, which is likely to be new, would be alienated in research funding decisions or under-funded because of the impact agenda, while research with potential impact would be prioritized, even if they are equally valuable. In other words, the so-called ‘blue-skies’ research may not be competitive in funding environment. Placing greater value on the popular research topics with practical relevance at the expense of others might narrow the range of research and ultimately challenge new discoveries that have no obvious application at the time. In terms of novelty of research, however, the role of funding has been mostly unanswered.

Materials and methods

We retrieved publication data from the WoS database. It is the most representative and widely used database for bibliometric analyses and covers a wide range of information on published research [ 22 ]. For our analysis, first, we limited our sample to journal articles and proceedings papers in major scientific fields–Life Sciences & Biomedicine, Physical Sciences, and Technology. Second, we extracted papers that were written by authors with affiliation located in European regions. To do so, upon the affiliation address information provided, we obtained the NUTS codes by additional geo-coding process and filtered publications from European regions accordingly. Moreover, time periods were restricted to 2008–2017 to cope with the balance of variable observations. From the WoS dataset, we noticed that funding information starts to increase dramatically from 2008, while the frequencies of most of our variables show consistency over time. As shown in Fig 1 , only few papers have funding details until 2007. This should be because of changes in requirement for researchers to report funding information in acknowledgements. Regardless of any reason, our sample includes data from 2008 to maintain the consistency of frequencies of observations. Lastly, we excluded research papers with zero backward citations and any missing values. The final dataset contains 3,077,225 observations.

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https://doi.org/10.1371/journal.pone.0271678.g001

Measurement of novelty.

Lastly, we take natural logarithm transformation of novelty measure as a dependent variable to alleviate high-skewness and roughly normalize the distribution of the variable.

Independent variables.

The first aim of this study is to figure out the effect of scientific collaborations on novelty. In our study, geographical diversity is considered in collaboration which is supposed to be beneficial by extending knowledge pool but at the same time is influenced largely by coordination costs. Through collaboration, researchers from diverse geographical background are endowed access to sophisticated and specialized knowledge from various local sources. On the other hand, research collaborations with large heterogeneity are difficult to manage. Further, geographical proximity is considered a salient factor to draw collective fine outcomes [ 44 ]. To analyze whether geographically diverse collaborations increase research novelty, this paper measures research collaborations as the count of different countries–following Wagner , Whetsell , and Mukherjee [ 23 ] ‘s work–and NUTS1-3 regions. Further, according to Larivière et al . [ 45 ], international collaborations and interinstitutional collaborations may not have same gains. Thus, we also measure the count of different institutions per article from the affiliation information. In short, we measure all macro-meso-micro level of geographical collaborations: for each paper i , each number of five collaboration level is calculated. For example, a paper with two authors from Gaziantep University, Turkey and Assistance Publique—Hôpitaux de Paris, France has two counts for all levels of collaboration.

Secondly, to analyze whether funding works as a tool to enhance research novelty, we identified papers’ funding information and created a dummy variable: if a paper i was assigned at least one grant, then the dummy indicates 1; otherwise, 0. First, we investigate whether funded works or non-funded ‘blue-skies’ works have higher novelty. Then, to assess the underlying mechanism of funding, we also include funding as a moderator between collaboration and novelty to see if the funding affects the direction and/or strength of the relationship between the two variables. Furthermore, we conduct additional analyses with more detailed categorical variables of funding–number of funding agencies and class of funding agencies–to compare their effects.

Control variables.

According to previous studies, many factors such as team size or field diversity have been shown to influence publication outputs including citation impact and novelty. Following the studies, we control for the number of authors [ 23 , 24 , 26 , 28 , 41 ], subjects [ 23 , 26 ], and references [ 10 , 23 , 24 , 28 ]. We also add a country border dummy to see if the effects of collaborations between regions or institutions are different when they are within the same country or not.

Estimation models.

Besides, some of the prior studies used field fixed effects since novelty can vary significantly across disciplines. Those studies, however, include non-scientific research fields such as Social Sciences or Arts & Humanities [ 10 , 11 , 23 , 30 ]. On the other hand, this paper assumes that there would be little disciplinary effects between the three fields we use since Wagner , Whetsell , and Mukherjee [ 23 ] categorized these fields into a “Science” field among Social Sciences and Arts & Humanities. Additional estimation models for different fields will be run as robustness checks.

Descriptive analysis

In advance to estimating our model, we first check our dataset’s reliability by exploring geographical distributions of our focal variables. Figs 2 – 4 illustrate our data on variables of scientific collaboration, research funding, and publication novelty in Life Science & Biomedicine, Physical Sciences, and Technology field from 2008 to 2017 in Europe. First of all, Fig 2 depicts inter- national and regional co-authorships between European regions. As the color gets darker, it means that the number of scientific collaborations is higher than the ones in lighter color. It is evident that the collaborative activities are concentrated in Western Europe countries, as already shown in previous studies, e.g., Adams [ 6 ], especially in Germany, United Kingdom, France, Italy, and Spain. At regional level, western and northern regions rank in top co-publishers. Lists of top co-publishing countries and NUTS2 regions are shown in S1 Table . Next, Fig 3 demonstrates the number of funded publications for each country and region, which is in a similar pattern to Fig 2 .

Source: Authors’ illustration.

https://doi.org/10.1371/journal.pone.0271678.g002

https://doi.org/10.1371/journal.pone.0271678.g003

https://doi.org/10.1371/journal.pone.0271678.g004

On the other hand, Fig 4 shows the average novelty of publications, which displays a quite different pattern from Figs 2 and 3 . As the color gets closer to grey, it represents lower novelty and as the color gets closer to red, it means higher novelty. We observe some spots of high publication novelty in regions from Eastern Europe along with western regions. While top regions in the number of co-publications and funded publications shown in Figs 2 and 3 correspond to top publishing regions known as Western and Northeastern Europe [ 16 ], some regions with high average novelty shown in Fig 4 do not match with those well-known regions. In other words, we realize that large number of publications nor the collaborations does not stand for high research novelty.

In this study, we have measured the novelty by the first appearance of new keywords, thus possible explanations to this figure are: first, institutions in peripheral regions are likely to come up with some novel keywords, while big and popular institutions in well-known regions would stick to their specific research era and keywords. Wu , Wang , and Evans [ 9 ] has found that large teams focus on popular and high impact works, while small teams build on less popular ideas which may appear as novel keywords. Thinking that core regions are likely to participate in big research teams, peripheral regions in small collaborative teams are the ones who work on novel ideas. Furthermore, novel ideas have high risks that those can either result in influential outcomes or just fade away [ 30 ]. Only the successful works among them later become the source of big-team’s high impact research. Thus, it is reasonable for collaboration, funding, and novelty variables to vary in their patterns when plotted on geographical maps.

As we have checked the reliability of our data and variables through Figs 2 – 4 , we now present descriptive results for individual paper level analysis. Fig 5 represents the scatterplots of collaborations and novelty. Since negative relationship between the two variables is observed at every collaboration type, we can expect that collaborations have negative effect on publication’s novelty.

https://doi.org/10.1371/journal.pone.0271678.g005

Table 1 depicts summary statistics of the variables and their correlation matrix. First, means of collaboration count gradually increases from country ( Country ) to region ( NUTS1 , NUTS2 , and NUTS3 ) and to organization ( Institution ) level– 1.3, 1.59, 1.71, 1.8, and 2.57, respectively. In addition, the mean of a funding dummy ( Fund . d )– 0.64 –tells us that about 64% of research papers from European regions are funded. In the correlation matrix, we can observe that collaboration counts for each collaboration type are negatively correlated with novelty ( Novelty ).

https://doi.org/10.1371/journal.pone.0271678.t001

Scientific collaboration, research funding, and novelty in publications

Given the importance of collaboration effect in science, our first research question is how novelty in scientific publication responds to the collaboration. Table 2 estimates the effects of different levels of collaborations on novelty. All of five models show that collaboration has a negative and significant effect on novelty, which means collaborative works tend to have lower novelty than the works from within a same country, region, or institution. Furthermore, the coefficients of country dummy in models (2)—(5) show positive signs, which means that inter- city and institutional collaborations within a single country have higher novelty than those across several countries. From these results, we can assume that novelty decays with geographic distances involved in scientific collaborations.

https://doi.org/10.1371/journal.pone.0271678.t002

Also, the results in Table 2 clearly show that non-funded ‘blue-skies’ works have higher novelty than funded works. However, to further assess the role of funding, we added a funding variable as a moderator to see whether the funding changes the nature of the relationship between collaboration and novelty. The moderating effect is measured by the interaction term of collaboration and funding dummy. According to the coefficients of interaction term Col⊆Funding in Table 2 , we verify that funding has a significant positive moderating effect: it alleviates the negative effect of collaboration on novelty. In other words, while remote collaborations have negative effect on novelty, however, among the collaborative works, funded works are more novel than the unfunded ones. This phenomenon is captured in margins plots shown in Fig 6 . The funded works (in orange line, funding dummy = 1) catch up the unfunded works (in blue line, funding dummy = 0) at one point and show higher novelty.

Std Dev: Standard Deviation of collaboration variable.

https://doi.org/10.1371/journal.pone.0271678.g006

In addition, we attempted to identify details of funding effect by additional analyses with categorical variables of the number of funding agencies and class of funding agencies. First, in terms of the number of funding agencies, we divide publications by whether the paper was funded by multiple agencies, single agency, or no agency (no funding) and see if there are differences in the effects. When setting no agency as a base-level, the results from Table 3 show that papers funded by multiple agencies have the lowest novelty. Nevertheless, both interaction variables are positive and significant throughout all levels of collaboration except for Col⊆Single at institutional level. Also, Col⊆Multiple coefficients are higher than Col⊆Single , showing that moderating effect is ranked in multiple, single, and no agency order.

https://doi.org/10.1371/journal.pone.0271678.t003

Next, to figure out whether the effects vary depending on the quality of funding agencies, we classified the agencies into high, mid, and low agency. To do so, we counted the frequency of funding agencies among the whole dataset and assumed that if typical agency appears many times, then the size of grant the agency is supporting would be large, which means that the agency is likely to be one of top tier funding agencies. Following, we specified top 5% and low 5% funding agencies and added a categorical variable of high, mid, low, and no agency accordingly. When setting no agency as a base-level, Table 4 again shows that funded works have lower novelty. Interaction variables Col⊆High and Col⊆Mid are positive and significant throughout all levels of collaboration except for Col⊆Mid at institutional level, while Col⊆Low variables are positive but not significant. Moreover, moderating effect is ranked in high, mid, low, and no agency order except at the country level.

https://doi.org/10.1371/journal.pone.0271678.t004

Robustness tests

In this section, we discuss the robustness of our models in Table 2 by subdividing our samples by different research fields (called as ‘subheading’ in the WoS), since results may vary depending on the disciplines. By excluding the sample without subheading information (NA, 17.60%), the final sample for robustness check includes 2,535,507 publications with the disciplines Life Science & Biomedicine (61.73%), Physical Sciences (27.20%), and Technology (11.07%) (See Table 5 ).

https://doi.org/10.1371/journal.pone.0271678.t005

We represent the results for each field at NUTS2 level in Table 6 , and full results are appended in S2 Table . Although some of the coefficients show weaker significance, e.g., the moderating effect of funding in Technology, the overall results are generally consistent with the results in Table 2 . In other words, collaborations have negative effects on novelty, and funding variables show positive moderating effects in all three disciplines, thus fulfilling the robustness.

https://doi.org/10.1371/journal.pone.0271678.t006

Discussion and conclusion

As new ideas and contributions bring disruptive advancements in science and technology, we focused on the value of novelty in scientific publications. Revisiting the research questions, this study examined, first, the relationship between scientific collaboration and novelty of research, and second, the effect of research funding on novelty. To do so, we retrieved scientific publication data from the WoS that were published in major scientific fields from European regions between 2008–2017. Then, we counted collaborations per article at country, NUTS1-3 regions, and institution level and evaluated novelty using research paper keywords. Further, we generated funding variables based on the funding information of each paper to evaluate the role of funding. To figure out the relationship between collaboration and novelty, we conducted OLS regressions and confirmed that there is a negative and significant relationship between co-authorship and novelty. Furthermore, by adding funding variables and interaction terms of collaboration and funding, we could confirm that funded works showed lower novelty than non-funded works, however, funding does have a significant and positive moderating effect on the relationship between collaboration and novelty, especially, for papers funded by multiple and top agencies.

We could, first, confirm the negative effect of collaborations on novelty. A collaborative learning process is a kind of an agreement among several researchers and organizations through exchanging, modifying, or elaborating ideas [ 46 ]. This can enlarge readership, and thus, citation impact [ 13 ], but may result in filtering out some disruptive ideas or outliers when the collaboration size gets bigger. Wu , Wang , and Evans [ 9 ] has also argued that among high impact papers, it is likely for smaller teams to create disruptive outcomes and for larger teams to conduct developmental research. In other words, while smaller groups tend to explore and raise promising ideas from less popular findings, larger groups of researchers tend to rely on recent successes and refine or develop those based on common research designs. Furthermore, novelty may be restrained since collaborative works mostly rely on distant communications. Teams from diverse geographical areas have no choice but to use communication technologies to share and exchange knowledge. However, tacit knowledge and inherited know-hows which are critical components in scientific knowledge and innovations are not likely to be transmitted through those channels. Tacit knowledge is rather transferred through face-to-face communications or physically proximate distant, and it becomes even more immobile for higher complex knowledge [ 47 ]. Thus, creativity or innovative ideas decay among distances, which decreases the opportunity for novel outcomes [ 23 ]. Consequently, we can assume that there are distant decay effects in publication novelty when collaborating with remote countries or regions.

Secondly, we find that non-funded ‘blue-skies’ research have higher novelty than funded works. Especially, research recognized by multiple or top agencies were likely to have the lowest novelty, and this confirms the pervasive impact agenda in funding environment. However, we also find a positive moderating effect of funding, which indicates a positive function of funding as an instrument to motivate explorative research, i.e., the acquisition of funding mitigates the negative effect of distant collaborations on novelty. In other words, funding allows the collaborative researchers to overcome the difficulties or restrictions that come along with remote collaborations and to pursue novel outcomes. Especially, this positive effect is the highest when researchers are awarded by multiple funding agencies or top tier agencies. This highlights the importance of funding in science activities that it not only contributes to the scientific advancement but also enhances the opportunity of novel outcomes.

Our findings contribute, first, by examining macro-meso-micro levels of collaborations in Europe and by linking them to the value of novelty in scholarly activities. Also, we could reveal the role of funding in research novelty which has been mostly unanswered. Here, we utilized moderating variables of funding in our analyses to examine whether funding does leverage academic activities and outputs, and we further categorized the effects of funding by the number of funding agencies and the class of funding agencies, which could provide us details of funding’s effectiveness. This estimation method can be used in further studies to assess the effectiveness of research funding in science.

However, our research has some limitations that could be complemented in future research. Currently, our data is limited to 2008–2017, which should be extended in further studies. By expanding the time frame of observations to recent years, we can confirm recent trends in research activities. Moreover, it is highly likely that there have been significant challenges and responding changes in scholarly activities, e.g., collaborations and grant awarding, due to the breakout of COVID-19 pandemic. It would be very interesting to figure out some specific inclinations observed during those times compared to pre-pandemic periods. Our data is also limited to papers in scientific disciplines. As prior studies have shown, there may be different results for non-scientific research area. We also limit our geographical scope to European regions. Thus, there are still many opportunities to expand this research to a wider range of research fields and geographical areas. Lastly, we adopt a relatively simple metric to measure novelty. We believe our current measure is a fine start to evaluate novelty using keywords in publications but may not be sufficient to capture genuine scientific novelty and may be difficult to consider different aspects of novelty that come from different types of research, e.g., theoretical and computational research or experimental and applied research. In future studies, measurement of novelty should be elaborated to capture novelty more thoroughly and systematically.

Supporting information

S1 table. top countries and nuts2 regions in co-publications..

Co-pub is the number of collaborated publications; Sol-pub is the number of non-collaborated publications.

https://doi.org/10.1371/journal.pone.0271678.s001

S2 Table. Results for each discipline.

+p < .1, *p < .05, **p < .01, ***p < .001. a Collaboration variable.

https://doi.org/10.1371/journal.pone.0271678.s002

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Understanding Research Novelty and Research Gaps – Exploring the unknown

By charlesworth author services.

• 07 November, 2023

In academic research, the concepts of research novelty and research gaps stand as two pivotal pillars dictating the success and significance of a study. Understanding these concepts is fundamental to encapsulate different facets of the research process, determining the originality and necessity of a study.

What Is Research Novelty

Novelty in research can be described as the uniqueness or originality of the idea. It brings freshness in scholarly endeavours by exploring topics, questions, and/or problems that have not been extensively investigated before. The relevance of novelty lies in introducing new ideas, concepts, methodologies or insights, which offer new perspectives to the existing body of knowledge. Demonstrating research novelty not only poses to be a gateway to publishing in prestigious journals but also helps in gaining recognition. Furthermore, it prevents redundancy, ensuring researchers don’t tread already explored paths.

6 Strategies to Find Research Novelty

Researchers seeking novelty often engage in innovative experiments, employ new approaches, propose unique hypotheses or delve into unexplored territories within their field of study. Ensuring research novelty can be challenging as bringing novelty requires objective evaluation of the presented ideas or findings when compared to existing knowledge.

Some strategies to find and ensure novelty in research are as follows:

1. Conducting an Extensive Literature Review 

Analysing existing literature uncovers gaps in knowledge, guiding the formulation of new questions or hypotheses, thereby ensuring novelty.

2. Comparing with Previous Studies

Contrasting research findings with previous studies helps determine the originality and significance of the current research.

3. Staying Updated with the Latest Developments

Subscribing to reputable journals in the field helps researchers track and align with developments, maintaining novelty in their work.

4. Assessing Contribution to the Field

Evaluating how much the research contributes to advancing knowledge is a key indicator of its novelty and value.

5. Considering Alternative Methodologies

Introducing novelty can involve exploring new methodologies or tweaking research questions to offer fresh perspectives.

6. Seeking Peer Support

Engaging with mentors, peers and scholarly groups to receive feedback and guidance on introducing novelty into research efforts.

What Are Research Gaps

Research gaps denote identifying gaps or deficiencies within the current literature that necessitate further exploration. These gaps serve acts as the rationale or motivation for a study, thus highlighting its necessity.

How to Identify Research Gaps

Identifying research gaps primarily occurs during the literature review process. Researchers meticulously analyse existing works to determine what aspects remain unexplored or demand further investigation.

Here are some tips to identify research gaps.

1. Review Existing Studies

Thoroughly understand the contributions of previous studies and create a list of your unresolved questions. If your questions remain unanswered in the existing literature, it may indicate a potential research gap.

2. Explore Suggestions for Future Research

Analyse the conclusion or "suggestions for future research" sections of existing studies to identify areas where further research is needed.

3. Trace Seminal Works

Identify influential studies cited repeatedly in the literature to uncover related research.

4. Utilise Literature Reviews and Meta-Analyses

Comprehensive papers like meta-analyses, literature reviews, and systematic reviews offer an overview of existing research, trends, and changes over time in a field.

By using these techniques, researchers can identify areas where further studies are necessary. 

Difference Between Research Gap and Research Novelty

While research novelty focuses on introducing unique elements, research gaps identify the areas that demand attention due to their inadequacy in current literature. Here are some differences between research novelty and research gaps.

Understanding research novelty and research gaps are indispensable for any researcher striving to make a meaningful contribution to their field. They drive the development and progression of knowledge. 

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what is novelty in research

Novelty in research refers to the quality of being new, original, or unique. It signifies that the research findings, methods, or approach have not been previously documented or explored in the same manner. Novelty is a fundamental aspect of research and is essential for advancing knowledge, solving problems, and making meaningful contributions to a particular field or discipline.

Here are key aspects of novelty in research:

• Originality : Novelty implies that the research introduces something new and not merely reiterates existing knowledge. It can involve fresh insights, innovative methodologies, or previously unexplored research questions.
• Advancing the Field : Novel research contributes to the advancement of a particular field or area of study. It pushes the boundaries of knowledge and expands the frontiers of human understanding.
• Addressing Gaps : Researchers often identify gaps in existing literature or knowledge and aim to fill these gaps with novel research. By addressing unanswered questions or unexplored aspects, they contribute to the accumulation of new knowledge.
• Innovation : Novelty often involves innovative thinking. Researchers may employ creative or unconventional approaches to gather and analyze data, leading to unique findings and interpretations.
• Interdisciplinary Insights : Sometimes, novelty emerges when researchers apply insights and methodologies from one discipline to another, resulting in fresh perspectives and discoveries.
• Surprising Results : Research can be considered novel when it yields unexpected or surprising results that challenge preconceived notions or prevailing theories.
• Contributing to Scientific Progress : Novel research is a driving force behind scientific progress. It encourages the continual evolution of knowledge by building upon what is already known.
• Applicability : Novelty is not limited to theoretical research; it can also be applicable to practical or applied research. Discovering new solutions to real-world problems, for instance, can be considered a novel contribution.

how to write novelty in research

Writing about the novelty in your research is a critical aspect of presenting your work effectively, whether you are preparing a research paper, thesis, or a presentation. Communicating the uniqueness and originality of your research findings is essential to make your work stand out in the academic or scientific community. Here are some steps and strategies for effectively conveying the novelty in your research:

• Begin your research paper or presentation with a clear and engaging introduction that highlights the importance of the research problem.
• Explain the context of your research by discussing existing literature and the current state of knowledge in your field.
• Clearly articulate the gap or deficiency in the current body of knowledge that your research addresses. This is where you can establish the need for your study.
• Clearly outline your research objectives or hypotheses. This should demonstrate what you intend to achieve with your study.
• Provide a detailed account of your research methodology. Explain how your approach is different from or builds upon existing methods.
• If your research involves unique data sources, datasets, or experimental setups, emphasize this in your writing. Explain why these sources are novel and how they contribute to the uniqueness of your research.
• If your research employs innovative techniques, tools, or technology, describe these in detail. Explain how these methods contribute to the novelty of your research.
• Clearly present your research findings. Emphasize any unexpected or unique results that you have obtained.
• Use graphs, charts, or visuals to illustrate your findings and make them more accessible to your audience.
• Discuss the implications and significance of your research findings. Explain how they fill the identified research gap and contribute to the field.
• Provide a comparative analysis of your research with existing literature and studies. Highlight the differences and innovations that make your research unique.
• Acknowledge any limitations in your study, but also discuss how these limitations do not diminish the novelty and significance of your findings.
• In your conclusion, reiterate the novelty and originality of your research. Summarize the key contributions your work makes to the field.
• Write in a clear, concise, and precise manner. Avoid jargon or overly complex language that might obscure the uniqueness of your research.
• Share your work with peers, mentors, or colleagues to get their feedback on how effectively you’re conveying the novelty in your research.
• Ensure that you provide proper citations and references for existing literature and sources you have used in your research. This demonstrates your familiarity with the field and helps reinforce the uniqueness of your work.

It’s important to note that achieving novelty in research can be challenging. Researchers must conduct thorough literature reviews to ensure they are not duplicating existing work. Additionally, they must craft research questions, designs, and methodologies that bring a fresh perspective to the subject matter. The pursuit of novelty requires a deep understanding of the research area, critical thinking, and a willingness to explore uncharted territories.

In the academic and scientific community, novelty is highly valued and often a key criterion for the acceptance of research papers in reputable journals or conferences. It is a testament to the researcher’s ability to contribute meaningfully to the collective knowledge of their field.

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How should novelty be valued in science?

Affiliation.

• 1 Edison Family Center for Genome Sciences and Systems Biology and Department of Genetics, Washington University School of Medicine, Saint Louis, United States.
• PMID: 28742499
• PMCID: PMC5526661
• DOI: 10.7554/eLife.28699

Scientists are under increasing pressure to do "novel" research. Here I explore whether there are risks to overemphasizing novelty when deciding what constitutes good science. I review studies from the philosophy of science to help understand how important an explicit emphasis on novelty might be for scientific progress. I also review studies from the sociology of science to anticipate how emphasizing novelty might impact the structure and function of the scientific community. I conclude that placing too much value on novelty could have counterproductive effects on both the rate of progress in science and the organization of the scientific community. I finish by recommending that our current emphasis on novelty be replaced by a renewed emphasis on predictive power as a characteristic of good science.

Keywords: novelty; peer review; philosophy of science; science policy; scientific publishing; sociology of science.

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Conflict of interest statement

The author declares that no competing interests exist.

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