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Meet Guo-Qiang

Hi, everyone! My name is Guo-Qiang Zhang, and I am from China. Right after I finished my residency training in Pediatrics, I joined Prof Bright I. Nwaru’s group and started my doctoral studies at Krefting Research Centre in University of Gothenburg (Sweden). My doctoral project is to look at the effects of sex hormones on women’s health (especially asthma), utilizing epidemiological methods as well as evidence synthesis tools (e.g., systematic review, umbrella review). If I can tell you one “fun fact” about myself, I am a huge fan of Japanese anime. I started watching anime since my secondary school, and now I am still enjoying it a lot during my doctoral studies. My favorite animator is Hayao Miyazaki, who has directed plenty of fascinating anime, such as Howl’s Moving Castle and My Neighbor Totoro.

In my first year of doctoral studies (in 2019), I had the opportunity to participate in the course “Reproducibility in Medical Research” led by Prof Nwaru. It was my first time to hear about Open Science and research reproducibility. As a “fresh” full-time doctoral student full of passion for medical research, I felt overwhelmed by waves of frustration when I came to know the reproducibility crisis. The three-year experience from clinical rotations in the hospital let me spontaneously relate it to health care. I was imagining that some of the irreproducible research findings might have been carried into clinical practice, to the patients in the hospital, to the people that we care about. After spending some time with my frustration, I came to realize that in fact I can do something. In my first project, my colleagues and I conducted an umbrella review on a highly controversial topic on the impact of menopausal hormone therapy on women’s health. We put extensive efforts into making the review process as transparent as possible: we developed beforehand protocols for data extraction and statistical analysis, documented key steps of the review process, verified data in the published literature, and made all datasets and R scripts publicly available. We also noticed that investigators may reach different conclusions even upon the same results. To enhance inferential reproducibility, we provided justifications for the statistical methods applied in our review, and proposed suggestions for the interpretation of our results. When looking back at the journey, I feel grateful for the support I have received from my supervisor and colleagues, and for the experiences I have obtained from implementing reproducibility in my project.

I am passionate about Open Science and research reproducibility, and feel excited to join the Frictionless Data Reproducible Research Fellows Programme, where I work closely with a team of fellows from diverse backgrounds towards the same ultimate goal – Open Knowledge. I would like to thank Lilly Winfree for offering me the excellent opportunity as well as her constant mentoring and guidance. I am thrilled at the upcoming nine-month Fellows Programme, and hope to learn Frictionless Data tools and apply them in conducting transparent, reproducible, high-quality medical research.