15  Further Reading

Here are some suggested readings. None of these are required, but they were useful or interesting to me and may be for you. If you have suggestions, I’m always looking for readings to add to this list.

15.1 Mental Health and Wellbeing in Academia

• Nature Editorial. (2019). The mental health of PhD researchers demands urgent attention. Nature, 575, 257–258.
• Woolston, C. (2019). PhDs: the tortuous truth. Nature, 575, 403–406.
• Levecque, K., Anseel, F., De Beuckelaer, A., Van der Heyden, J., & Gisle, L. (2017). Work organization and mental health problems in PhD students. Research Policy, 46(4), 868–879.
• Satinsky, E. N., Kimura, T., Kiang, M. V., et al. (2021). Systematic review and meta-analysis of depression, anxiety, and suicidal ideation among Ph.D. students. Scientific Reports, 11, 14370.

15.2 Building Your Scientific Career

• Gu, J. & Bourne, P. E. (2007). Ten simple rules for graduate students. PLOS Computational Biology, 3(11), e229.
• Bartlett, M. J., Arslan, F. N., Bankston, A., & Sarabipour, S. (2021). Ten simple rules to improve academic work-life balance. PLOS Computational Biology, 17(7), e1009124.
• Bourne, P. E. & Barbour, V. (2011). Ten simple rules for building and maintaining a scientific reputation. PLOS Computational Biology, 7(6), e1002108.
• Bik, H. M., Dove, A. D. M., Goldstein, M. C., et al. (2015). Ten simple rules for effective online outreach. PLOS Computational Biology, 11(4), e1003906.
• Cheplygina, V., Hermans, F., Albers, C., Bielczyk, N., & Smeets, I. (2020). Ten simple rules for getting started on Twitter as a scientist. PLOS Computational Biology, 16(2), e1007513.

15.3 Lab Culture and Mentorship

• Masters, K. S. & Kreeger, P. K. (2017). Ten simple rules for developing a mentor-mentee expectations document. PLOS Computational Biology, 13(9), e1005709.
• Sanzari, C. M., Evans, S. J. W., Engel, A., & McBride, J. (2022). Ten simple rules for establishing a mentorship programme. PLOS Computational Biology, 18(5), e1010015.
• National Academies of Sciences, Engineering, and Medicine. (2019). The Science of Effective Mentorship in STEMM. Washington, DC: The National Academies Press.
• Stanford VPGE. (2016). Guidelines for Faculty-Student Advising at Stanford.
• Chaudhary, V. B. & Berhe, A. A. (2020). Ten simple rules for building an antiracist lab. PLOS Computational Biology, 16(10), e1008210.

15.4 Communities in Science

• National Academies report on sexual harassment in STEM.
• Nature’s “Racism in science” collection.
• The SAFE Labs Handbook (eLife).
• Perez-Lopez, E., Gavrilova, L., Disla, J., et al. (2022). Ten simple rules for creating and sustaining antiracist graduate programs. PLOS Computational Biology, 18(10), e1010516.
• Hall, S. M., Kochin, D., Carne, C., et al. (2024). Ten simple rules for pushing boundaries of inclusion at academic events. PLOS Computational Biology, 20(3), e1011797.

15.5 Scientific Writing

• Mensh, B. & Kording, K. P. (2017). Ten simple rules for structuring papers. PLOS Computational Biology, 13(9), e1005619.
• Zhang, W. (2014). Ten simple rules for writing research papers. PLOS Computational Biology, 10(1), e1003453.
• Bourne, P. E. (2005). Ten simple rules for getting published. PLOS Computational Biology, 1(5), e57.

15.6 Presentations and Posters

• Bourne, P. E. (2007). Ten simple rules for making good oral presentations. PLOS Computational Biology, 3(4), e77.
• Naegle, K. M. (2021). Ten simple rules for effective presentation slides. PLOS Computational Biology, 17(12), e1009554.
• Lortie, C. J. (2017). Ten simple rules for short and swift presentations. PLOS Computational Biology, 13(3), e1005373.
• Erren, T. C. & Bourne, P. E. (2007). Ten simple rules for a good poster presentation. PLOS Computational Biology, 3(5), e102.
• Morrison, M. A. (2019). Better Scientific Poster: #betterposter. Open Science Framework.

15.7 Peer Review

• Bourne, P. E. & Korngreen, A. (2006). Ten simple rules for reviewers. PLOS Computational Biology, 2(9), e110.
• Stiller-Reeve, M. (2018). How to write a thorough peer review. Nature.
• Mathioudakis, A. G., Wagner, D., & Dumas, O. (2022). How to peer review: practical advice for early career researchers. Breathe, 18(4), 220160.

15.8 Grant Writing

• Bourne, P. E. & Chalupa, L. M. (2006). Ten simple rules for getting grants. PLOS Computational Biology, 2(2), e12.
• Guyer, R. A., Schwarze, M. L., Gosain, A., et al. (2021). Top ten strategies to enhance grant-writing success. Surgery, 170(6), 1727–1731.
• NIH general grant writing tips.

15.9 Scientific Computing and Reproducibility

• Noble, W. S. (2009). A quick guide to organizing computational biology projects. PLOS Computational Biology, 5(7), e1000424.
• Sandve, G. K., Nekrutenko, A., Taylor, J., & Hovig, E. (2013). Ten simple rules for reproducible computational research. PLOS Computational Biology, 9(10), e1003285.

15.10 AI in Research

15.10.1 Using AI Transparently

• International Committee of Medical Journal Editors. Recommendations: Use of Artificial Intelligence.
• Suchikova, Y., Tsybuliak, N., Teixeira da Silva, J. A., & Nazarovets, S. (2025). GAIDeT (Generative AI Delegation Taxonomy). Accountability in Research.
• National Academies of Sciences, Engineering, and Medicine. (2024). Human accountability and responsibility needed to protect scientific integrity in an age of AI.
• Cacciamani, G. E., et al. (2023). The CANGARU guidelines for generative AI use in academia (protocol). arXiv:2307.08974.

15.10.2 The Costs of Outsourcing Thinking

• Bastani, H., Bastani, O., Sungu, A., et al. (2025). Generative AI without guardrails can harm learning. PNAS, 122, e2422633122.
• Fan, Y., et al. (2025). Beware of metacognitive laziness: effects of generative AI on learning motivation, processes, and performance. British Journal of Educational Technology, 56(2), 489–530.
• Bainbridge, L. (1983). Ironies of automation. Automatica, 19(6), 775–779.
• Budzyń, K., et al. (2025). Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy. Lancet Gastroenterology & Hepatology, 10(10), 896–903.
• Sparrow, B., Liu, J., & Wegner, D. M. (2011). Google effects on memory: cognitive consequences of having information at our fingertips. Science, 333(6043), 776–778.
• Kazemitabaar, M., et al. (2023). Studying the effect of AI code generators on supporting novice learners in introductory programming. CHI ’23.

15.10.3 AI, Bias, and Equity

• Omiye, J. A., Lester, J. C., Spichak, S., Rotemberg, V., & Daneshjou, R. (2023). Large language models propagate race-based medicine. npj Digital Medicine, 6, 195.
• Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.

15.10.4 Reporting AI Research

• Gallifant, J., et al. (2025). TRIPOD-LLM: reporting guideline for studies using large language models in health. Nature Medicine, 31(1), 60–69.
• Huo, B., et al. (2025). CHART: reporting guideline for chatbot health advice studies. Artificial Intelligence in Medicine, 168, 103222.

15.11 Metascience and the Science of Science

15.11.1 Hiring, Prestige, and Access

• Clauset, A., Arbesman, S., & Larremore, D. B. (2015). Systematic inequality and hierarchy in faculty hiring networks. Science Advances, 1(1), e1400005.
• Wapman, K. H., Zhang, S., Clauset, A., & Larremore, D. B. (2022). Quantifying hierarchy and dynamics in US faculty hiring and retention. Nature, 610, 120–127.
• Morgan, A. C., LaBerge, N., Larremore, D. B., et al. (2022). Socioeconomic roots of academic faculty. Nature Human Behaviour, 6, 1625–1633.

15.11.2 Credit, Bias, and Inequality

• Ross, M. B., Glennon, B. M., Murciano-Goroff, R., et al. (2022). Women are credited less in science than men. Nature, 608, 135–145.
• Huang, J., Gates, A. J., Sinatra, R., & Barabasi, A.-L. (2020). Historical comparison of gender inequality in scientific careers across countries and disciplines. PNAS, 117(9), 4609–4616.
• Ginther, D. K., Schaffer, W. T., Schnell, J., et al. (2011). Race, ethnicity, and NIH research awards. Science, 333, 1015–1019.
• Tomkins, A., Zhang, M., & Heavlin, W. D. (2017). Reviewer bias in single- versus double-blind peer review. PNAS, 114(48), 12708–12713.

15.11.3 How Science Works (and Doesn’t)

• Sinatra, R., Wang, D., Deville, P., Song, C., & Barabasi, A.-L. (2016). Quantifying the evolution of individual scientific impact. Science, 354, aaf5239.
• Wu, L., Wang, D., & Evans, J. A. (2019). Large teams develop and small teams disrupt science and technology. Nature, 566, 378–382.
• Park, M., Leahey, E., & Funk, R. J. (2023). Papers and patents are becoming less disruptive over time. Nature, 613, 138–144.