Nature Masterclass: Analyzing and Interpreting Your Research Data with AI [Tuesday Session]
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Information about the Thursday session offering
Artificial intelligence can support researchers at many stages of experimentation, from collecting data to developing models, recognising patterns and troubleshooting problems. Used well, it can save time and open up new ways of working. However, reliable analysis still depends on sound research design, well-managed data and a clear analytical plan. AI cannot compensate for fundamental flaws in a study or turn poor-quality data into reliable evidence.
The challenge for researchers is knowing where AI adds genuine value and how to use it responsibly. How can they verify its outputs and manage risks such as bias, data privacy and security, and conclusions that go beyond the evidence?
In this webinar, you’ll learn from distinguished panellists with expertise in AI research, editorial practice and policy. They will examine the role of AI across data preparation, analyses, interpretation and visualisation. They will offer practical guidance and explain where human judgement and subject expertise remain essential. Whether you are new to AI or already using it in your work, the webinar will help you make more informed decisions about its role in your research.
You will have the opportunity to submit your own questions when you register. Please keep them relevant to analysing and interpreting research data with assistance from AI. Covered topics will help you to:
- Understand how AI can support different stages of data analysis, from planning and preparation to coding, troubleshooting and interpreting scientific results.
- Evaluate where AI adds value while recognising when subject expertise, robust research methods and human oversight remain essential.
- Manage research data responsibly by validating AI outputs, protecting sensitive information, documenting AI use and following institutional, funder and publisher guidance.
- Apply AI in ways to strengthen, rather than replace, your own analytical thinking and research expertise.
Sponsored by:
Speakers
Ellie Gendle
Ellie joined Springer Nature in 2023 as Head of Performance in the Research Integrity Group, before becoming Head of Journals Policy the following year. She leads the strategic direction of journal editorial policies, overseeing their development, implementation and ongoing compliance. Her work includes shaping Springer Nature policies in response to emerging challenges in scholarly publishing, including responsible use of AI in research and writing.
Alexia-Ileana Zaromytidou
Alexia-Ileana completed her PhD at the London Research Institute, Cancer Research UK (now the Francis Crick Institute), followed by postdoctoral research in the lab of Joan Massagué at Memorial Sloan Kettering Cancer Center in New York. In 2019, she became the launch Chief Editor of Nature Cancer, where she handles a broad range of manuscripts, including those involving current AI technologies, and brings insight into how emerging methods are applied and evaluated in high-impact publishing.
Helder Nakaya
Alongside his roles at the University of São Paulo and Hospital Israelita Albert Einstein in Brazil, Helder performs additional teaching roles at Emory University and the University of Oxford. His research focuses on systems immunology, using large-scale biological data, network analysis and machine learning to understand immune responses, particularly in infectious diseases and vaccination, including work on predicting vaccine-induced immunity for diseases such as yellow fever.
Veda Storey
Veda’s research interests are in data management, modelling and design science research. She is particularly interested in the assessment of the impact of new and emerging technologies, including AI systems, on business and society. She plays an active role in editorial leadership and mentoring early-career researchers.