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Science is increasingly done in large teams, making it more likely that papers will be written by several authors from different institutes, disciplines, and cultural backgrounds. A small number of “Ten simple rules” papers have been written on collaboration and on writing but not on combining the two. Collaborative writing with multiple authors has additional challenges, including varied levels of engagement of coauthors, provision of fair credit through authorship or acknowledgements, acceptance of a diversity of work styles, and the need for clear communication. Miscommunication, a lack of leadership, and inappropriate tools or writing approaches can lead to frustration, delay of publication, or even the termination of a project. To provide insight into collaborative writing, we use our experience from the Global Lake Ecological Observatory Network (GLEON) to frame 10 simple rules for collaboratively writing a multi-authored paper. We consider a collaborative multi-authored paper to have three or more people from at least two different institutions. A multi-authored paper can be a result of a single discrete research project or the outcome of a larger research program that includes other papers based on common data or methods. The writing of a multi-authored paper is embedded within a broader context of planning and collaboration among team members. Our recommended rules include elements of both the planning and writing of a paper, and they can be iterative, although we have listed them in numerical order. It will help to revisit the rules frequently throughout the writing process. With the 10 rules outlined below, we aim to provide a foundation for writing multi-authored papers and conducting exciting and influential science.

Overfitting is one of the critical problems in developing models by machine learning. With machine learning becoming an essential technology in computational biology, we must include training about overfitting in all courses that introduce this technology to students and practitioners. We here propose a hands-on training for overfitting that is suitable for introductory level courses and can be carried out on its own or embedded within any data science course. We use workflow-based design of machine learning pipelines, experimentation-based teaching, and hands-on approach that focuses on concepts rather than underlying mathematics. We here detail the data analysis workflows we use in training and motivate them from the viewpoint of teaching goals. Our proposed approach relies on Orange, an open-source data science toolbox that combines data visualization and machine learning, and that is tailored for education in machine learning and explorative data analysis., Author summary Every teacher strives for an a-ha moment, a sudden revelation by the student who gained a fundamental insight she will always remember. In the past years, authors of this paper have been tailoring their courses in machine learning to include material that could lead students to such discoveries. We aim to expose machine learning to practitioners–not only computer scientists but also molecular biologists and students of biomedicine, that is, the end-users of bioinformatics’ computational approaches. In this article, we lay out a course that aims to teach about overfitting, one of the key concepts in machine learning that needs to be understood, mastered, and avoided in data science applications. We propose a hands-on approach that uses an open-source workflow-based data science toolbox that combines data visualization and machine learning. In the proposed training about overfitting, we first deceive the students, then expose the problem, and finally challenge them to find the solution. In the paper, we present three lessons in overfitting and associated data analysis workflows and motivate the use of introduced computation methods by relating them to concepts conveyed by instructors.

© 2021 Moore et al., Bioinformatics training is required at every stage of a scientist’s research career. Continual bioinformatics training allows exposure to an ever-changing and growing repertoire of techniques and databases, and so biologists, computational scientists, and healthcare practitioners are all seeking learning opportunities in the use of computational resources and tools designed for data storage, retrieval, and analysis. There are abundant opportunities for accessing bioinformatics training for scientists in high-income countries (HICs), with well-equipped facilities and participants and trainers requiring minimal travel and financial costs alongside a range of general advice for developing short bioinformatics training courses [1–3]. However, regionally targeted bioinformatics training in low- and middle-income countries (LMICs) often requires more extensive local and external support, organization, and travel. Due to the limited expertise in bioinformatics in LMICs in general, most bioinformatics training requires a fair amount of collaboration with experts beyond the local community, country, or region. A common model of training, used as the basis of this article, includes a local host collaborating with local, regional, and international experts gathering to train local or regional participants. Recently, there has been a growth of capacity strengthening initiatives in LMICs, such as the Pan African Bioinformatics Network for Human Heredity and Health in Africa (H3ABioNet) Initiative [4–6], the Capacity Building for Bioinformatics in Latin America (CABANA) Project [7], the Asia Pacific BioInformatics Network (APBioNet) [8], and the Wellcome Connecting Science Courses and Conferences program [9]. One of the important strands of these initiatives is a drive to organize and deliver valuable bioinformatics training, but organizing and delivering short bioinformatics training workshops in an LMIC present a unique set of challenges. This paper attempts to build upon the sage advice for organizing bioinformatics workshops with specific guidance for organizing and delivering them in LMICs. It describes the processes to follow in organizing courses taking into consideration the low-resource setting. We should also note that LMICs are not a monolithic group and that setting, context, temporality, and specific location matters. LMICs are a complex regional grouping [10] and should be treated as such; however, we will present some common lessons that we hope will help organizers and trainers of bioinformatics training events in LMICs to navigate the often different, challenging, and rewarding experience., The authors who contributed to this manuscript are funded as follows: BM receives salary support from Wellcome Trust grants [WT108749/Z/15/Z, WT108749/Z/15/A], PC, VR, NM, AG’s salaries are funded in whole, or in part, by the NIH Common Fund H3ABioNet grant [U24HG006941], MC, SLFV, AR, PG, PCL’s salaries were partly funded by the UKRI-BBSRC ‘Capacity building for bioinformatics in Latin America’ (CABANA) grant, on behalf of the Global Challenges Research Fund [BB/P027849/1], JDLR is funded by ISCiii AES [ref. PI18/00591] at the CSIC/USAL (Spain) and by CYTED, RIABIO (Red Iberoamericana 521RT0118), AM’s salary is funded by [WT206194/Z/17/Z], GO is funded by the CABANA grant and SM is funded by the EMBL-EBI.

Teaching well is a craft like any other, and success often comes from an accumulation of small improvements rather than from any single large change. This paper describes 10 practices you can use when teaching programming (and other subjects). All are easy to adopt and have proven their value in institutional classrooms, intensive workshops, and other settings.

Hosting an artist in a scientific lab is likely a new experience for many scientists in the natural and engineering sciences, and perhaps also for many artists, yet it can be a very beneficial experience for both parties [1]. “Art and science are in a tension that is most fruitful when these disciplines observe and penetrate each other and experience how much of the other they themselves still contain” [2]. During our science and art collaborations in the last years, we have learned what connects and what separates our disciplines, how different yet common our worlds of working and thinking are, and how stimulating such collaborations can be. Although scientists and artists belong to two different cultural worlds, many share research as a congruent method to explore and understand the world around us. Often, scientific and artistic work spaces are indistinguishable as they are full of equipment, materials, tools, and computers to run experiments and analyze data [3,4]. Science and art are fundamentally connected through their focus on creativity [5]. Also, both scientists and artists deliberately venture into the public realm in the spirit of Hannah Arendt: “Humanity is never won in loneliness and never by handing one’s work over to the public. Only if you take your life and person[ality] into the venture of the public realm, will you reach [humanity]” [6]. At the most fundamental level, science and art both try to understand the world around us and to guide society to recognize and solve problems. Artistic and scientific research may also have much more in common than one expects at first sight: They both involve years of schools and personal development, they both involve trial and error, and the sharing of results with different communities. However, transdisciplinary cooperation requires openness, a willingness to take risks, the ability for self-reflection, respect, and esteem for the other culture as well as a lot of appreciative listening from both parties [7,8]. Our paper thus intends to serve as a practical guide for both, artists-in-residence and the hosting scientific lab to easier cross borders, to better collaborate, to better learn from each other, and to sustainably bridge the different cultures of science and the arts. Our discussion starts at the point where a decision for such an interaction has already taken place. Still wondering if this is for you? There is much to gain for both sides. For the scientists, for example, this interaction can be a source of new ideas and questions, offering new points of view. Some of us also felt that this interaction offered training in explaining research in clear, simple language, and provided opportunities for interfacing with the science-curious public in a curated context. For the artists, this can be about learning new tools, methods, and approaches and about the specific topics on which a lab works. Some of the following 10 “rules” apply more to the artist, some more to the hosting science lab, and some to both (Fig 1). Open in a separate window Fig 1 Summary of the 10 rules, arrayed from intellectual/philosophical to practical/logistical and divided into rules more applicable to the hosting science lab and rules relevant for both the hosting lab and the artist. Rule 1: Be open The basic mindset and attitude required for both scientists and artists is openness. Openness to new input, willingness to share ideas, and a sharing attitude. It is not at all required to have an artistic “inclination” on the part of the hosting scientist, but nothing can replace an open attitude. Be open to accept that research can be both: scientific research and artistic research (for a definition of artistic research, see [9]).