Module manager: Prof. Bruce Turnbull
Email: W.B.Turnbull@leeds.ac.uk
Taught: Semesters 1 & 2 (Sep to Jun) View Timetable
Year running 2026/27
A bachelor degree with a 2:1 (hons) in engineering, environmental science, physical science or mathematics discipline
This module is not approved as an Elective
The aim of this module is to equip students with core digital, analytical, and transferable professional skills required for research in modern chemistry, with a particular emphasis on artificial intelligence, automation, and data science. The module prepares students for subsequent research projects and supports their longer-term academic and professional career development.
The module will provide students with:
1. An understanding of the research process and research culture in digital chemistry, from project conception to dissemination of results, through lectures and seminars covering topics such as computational and experimental design, data management, ethics and responsible use of AI, risk assessment, literature searching, and scientific writing;
2. Technical skills relevant to digital chemistry research, including data analysis, computational tools, and digitally enabled experimental techniques, developed through practical sessions and computer-based exercises;
3. Professional and transferable skills in scientific communication and career development, developed through written and oral exercises and employability-focused activities.
Subject specific learning outcomes:
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
1. Explain the research process by which computational, data-driven, and/or experimental studies are planned, integrated, and developed into a coherent body of publishable scientific work;
2. Use common data analysis and computational software relevant to digital chemistry and critically evaluate the quality, reliability, and limitations of the data produced;
3. Demonstrate practical research skills relevant to their intended research project, including appropriate awareness of health, safety, ethical, and responsible research practices.
Skills learning outcomes:
On successful completion of the module students will have demonstrated the following skills learning outcomes:
a. Explain and construct well-reasoned arguments relating to research culture, academic integrity, and ethical issues, including those arising from the use of digital tools, data, and AI in chemical research (Academic: academic integrity, ethics);
b. Effectively search, evaluate, and reference scientific literature and digital resources relevant to their field (Academic: information searching, referencing);
c. Communicate scientific ideas and concepts, including data-driven and digitally enabled research, to a general audience through clear written and oral communication (Work-ready: communication, core literacies; Academic: presentation skills, academic writing, academic language);
d. Recognise, evaluate, and reflect on transferable and subject-specific skills relevant to their future academic and career pathways, and identify and set personal development objectives (Academic: reflection; Work-ready: personal/self/career management);
e. Work efficiently and professionally as part of a team, demonstrating effective collaboration, communication, and interpersonal skills (Work-ready: teamwork/collaboration, interpersonal skills).
1. Lectures and seminars on the digital chemistry research process, including computational and experimental design, grant proposals, ethics and responsible use of AI, risk assessment, data management, literature searching, and scientific writing;
2. Practical sessions and computer-based exercises to develop technical skills in data analysis, computational tools, and digitally enabled experimental techniques;
3. Communication and employability workshops, together with written and oral exercises, to develop professional skills in science communication and career development.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Supervision | 4 | 1 | 4 |
| Lecture | 8 | 1 | 8 |
| Practical | 14 | 3 | 42 |
| Seminar | 16 | 2 | 32 |
| Private study hours | 214 | ||
| Total Contact hours | 86 | ||
| Total hours (100hr per 10 credits) | 300 | ||
Formative assessments on which individual feedback will be provided involving a news and views article, oral presentation, use of data analysis software, literature searching and understanding experimental processes.
Check the module area in Minerva for your reading list
Last updated: 29/05/2026
Errors, omissions, failed links etc should be notified to the Catalogue Team