Nature Masterclass: Analyzing and Interpreting Your Research Data with AI [Thursday Session]
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Information about the Tuesday 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.
Oliver Graydon
Oliver joined the Nature Publishing Group (now Springer Nature) in 2006 to launch the journal. He has a PhD in optical communications from the University of Southampton’s Optoelectronics Research Centre. He is also a Fellow of Optica. In his editorial role, he oversees the editorial operations at Nature Photonics and pays close attention to how AI-based tools are being applied and evaluated in high-impact publishing.
Samraat Pawar
Samraat's research explores ecological and evolutionary dynamics using mathematical modelling and analyses of large datasets. This includes computational approaches to understanding biological systems. As part of Imperial’s quantitative ecology and AI research community, Samraat is also actively involved in training researchers in data-driven modelling in ecology and evolution.
Nicki Tiffin
Nicki's research integrates genomics, epidemiological and routine health data using bioinformatics to better understand disease in African populations. She also works on ethics and data governance, developing tools to support responsible research. Nicki applies AI to harmonise and analyse large health datasets, as well as in day-to-day research and administrative tasks. She is a Calestous Juma Science Leadership Fellow with the Bill & Melinda Gates Foundation.