For this month’s Editor Spotlight, we talked to Dr. Ribeiro about his editorial experience, his interdisciplinary research on complex systems, and his experience with sharing data and code.
Dr. Ribeiro is an Assistant Professor of Physics at the State University of Maringá in Brazil and co-head of the research group ComplexLab. His research focuses on data analysis of complex systems and aims to address a broad spectrum of problems related to the emergence of patterns in social, economic, biological, and physical systems through the lens of physics, data science, and statistics.
Examples of recent problems investigated by Dr. Ribeiro include understanding how properties of cities change with their size, quantifying the history of art paintings using entropy and other physics-inspired approaches, identifying patterns in criminal networks, investigating the association between research productivity and scientific impact, and the developments of ordinal methods for time series and image analysis.
What motivates you to contribute as Academic Editor at PLOS ONE?
In September 2018, I was invited to join the Editorial Board of PLOS ONE as an Academic Editor. I remember feeling very excited about the invitation; in fact, I replied accepting the position in less than ten minutes. At that time, I had already published about ten articles with PLOS (my first article was published in 2011) and had served as a reviewer on dozens of other occasions (my first invitation was in 2012). I was also at the beginning of my career as an Assistant Professor of Physics at the State University of Maringá, which is still my current position.
I was (and still am) motivated by the opportunity to work with researchers from various disciplines and to help advance and disseminate scientific knowledge in different areas.
As a result of my familiarity with PLOS and its mission to provide an open-access platform for publishing and making scientific research accessible to a wider community, as well as with most of the processes involved in the scientific enterprise, I considered becoming an Academic Editor to be a natural next step. I was (and still am) motivated by the opportunity to work with researchers from various disciplines and to help advance and disseminate scientific knowledge in different areas. What I was not fully aware of at the time, was that being an editor would also allow me to further develop my skills in scientific communication, critical analysis, and decision-making. As an editor at PLOS, I am continuously exposed to a wide range of scientific research, and I find it incredibly rewarding to learn about new areas and to assist researchers in effectively communicating their findings to a broader audience. This exposure also enables me to identify emerging trends and problems of interest, as well as to see connections between seemingly disparate research areas.
Of course, not everything is perfect, and I must acknowledge the ever-increasing demand for editing and reviewing papers, as well as the challenges in securing good and timely reviewers. In my opinion, something is brewing in the scientific community, and as a researcher on complex systems, I believe that we are approaching a boiling point in scientific publishing. I hope that PLOS will continue its mission of promoting collaboration, transparency, and open science in this possible new era.
You’ve tackled an impressive array of seemingly completely unrelated topics in your research on complex systems, such as sports statistics, collective behavior in fish, and human networks. What is it that makes this field so versatile? What are the current limits that this field faces?
My background is in physics, but unlike most of my classmates from my early years of undergraduate school or some students I now encounter in physics courses I teach, I was never passionate about particles, string theory, or astronomy. Instead, I was much more fascinated by the simple idea of why some patterns emerge in nature, and I was lucky enough to start an undergraduate research project on this theme during the second year of my undergraduate course. This was in 2006, and I have been working with complex systems ever since. And as you mentioned, the topics covered by most researchers on complex systems vary a lot, which produces the false idea of unrelatedness, but when you look carefully, you will always see this search for patterns, commonalities, principles, and universalities.
As a physicist (and I am probably biased here), I always thought this identification of patterns in complex systems was very much aligned with the overall goals of most traditional research in Physics, but the strong reductionism of physics-like approaches in opposition to the complex systems’ mantra that “the whole is more than the sum of its parts” initially hampered this idea to become what we may say is now the dominant view in the research community.
I remember that publishing on topics such as sports or music in journals of the physics community required much more effort in connecting findings (sometimes in unnatural ways) with more traditional results and theories of physics. Without being too demagogic, I believe that the more open view of PLOS has somehow contributed to changing this status, and I must confess that some of the first papers I published in PLOS ONE initially got rejected by more traditional physics journals. Fortunately, today even the most traditional journals of physics have dedicated sections to complex systems research, and especially after the 2021 Nobel Prize in Physics, I do believe research on complex systems is sedimented as an official part of Physics.
(The) versatility (of the field) also makes research on complex systems naturally interdisciplinary, which requires researchers always to be open to studying new topics and collaborating with people from different backgrounds.
Moving back to your question, this versatility in approaching the most diverse topics is tightly related to the generality of concepts and methods used to tackle complex systems. To stay with a simple but crucial example, I would cite the basic idea surrounding the concept of networks, that is, a set of vertices and a set of edges among them. This is so general that you can use it to describe human and animal interactions, but also in more abstract ways, such as describing patterns in scientific careers, where vertices could represent researchers and connections among them indicate some similarity measure related to some aspect of scientific careers.
This versatility also makes research on complex systems naturally interdisciplinary, which requires researchers always to be open to studying new topics and collaborating with people from different backgrounds – which sometimes can be challenging due to differences in research culture. In addition, one of the biggest challenges I believe the field faces is the need to merge theoretical approaches (which are often based on simple models) with the advances in empirical analysis (which are becoming more and more detailed and complex) driven by the increasing availability of large-scale data at an impressive degree of detail. Another critical challenge, I would say, has to do with making society and decision-makers aware that research on complex systems is a fundamental part of the solutions to our most immediate problems, such as disease spreading, climate change, financial crises, and human conflicts.
Can you tell us your experience with data or code sharing? How has it impacted your research?
I believe that everyone considers data and code sharing to be essential aspects for the reproducibility and transparency of research. We now see many scientific journals, including PLOS ONE, require or at least incentivize authors to share data and code alongside their research articles. Sometimes, I believe there are some short-time benefits in not adhering to these practices, such as when one does expensive and time-demanding experiments and is just beginning to dig into the research question, but overall and beyond the transparency issue, I consider data and code sharing help researchers by facilitating and driving the creation of new collaborations and partnerships.
Recently, together with a Ph.D. student, we had a very positive experience with the release of a Python module implementing a set of techniques related to ordinal methods for time series and image analysis (ordpy). We have been working as well as contributing to the development of these methods for a long time, and as a result, we accumulated computational implementations of these techniques that remained restricted to our group until we were able to organize, document, and make them available. We initially thought no one would use it, but to our surprise, it has been downloaded hundreds of times per month ever since. Additionally, we have received feedback and suggestions from other researchers, and in the end, it is pretty gratifying to see others directly building upon code that was previously only available to our lab.
Disclaimer: Views expressed by contributors are solely those of individual contributors, and not necessarily those of PLOS.
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