Students will learn how to integrate a broad range of digital tools (automated data acquisition, databases, data visualisation, ELNs, API scripting) into experimental design for chemistry and implement these skills in a real-world setting. The course is designed to:

1. Give students practical abilities to use code in the context of experimental chemistry;
2. Enable students to integrate digital methods into the experimental design for research chemistry;
3. Make students aware of the breadth of digital tools available for use in experimental chemistry;
4. Give students an understanding of what good and FAIR practice looks like in terms of code design, data storage, and electronic lab book use;
5. Increase student employability by producing chemists with an understanding of digital methods as used in chemistry.

(LO1) Implement digital techniques that simplify, accelerate, and improve chemistry research

(LO2) Use scripting and programming to acquire, record, and visualise data

(LO3) Turn machine data into insightful datasets

(LO4) Implement data analysis pipelines

(LO5) Demonstrate good practice in data and code management

(LO6) Integrate the above into experimental design for chemistry

(S1) Research Skills

(S2) Organisational skills

(S3) Problem solving skills

The module is split into two largely consecutive segments:

Lecture Workshops: 6 x 3 hr
Concepts are introduced through approx. 20-30 minute lectures, implemented in experimental workshops with demonstrator support, and reinforced through at-home exercises. Contents covered, along with approximate timeline and technical specifics:
Workshop 1 - Serial port data acquisition and ELN basics (Arduino, pyserial, pandas)
Workshop 2 - Data visualisation + code management (matplotlib, github)
Workshop 3 - Databases
Workshop 4 - Metadata, FAIR data, and Cheminformatics
Workshop 5 - GUI design and Visualisation II.
Workshop 6 - ELN scripting

Experimental Project: 6 x 3 hr weekly drop-in sessions.
Students design an Arduino-based experiment based on the workshops to explore a topic from chemistry with a strong emphasis on lab safety and sustainability.
The project can be carried out either individually or in small groups and must contain elem ents of material covered in all workshops.
Awareness of the experiment project and project pitch is built into the structure of the lecture workshops, so that students have sufficient time to design the projects and pitch.
Students initially present a brief pitch for a research project to the module team and are questioned on the project plan.
After the project each student submits a final research report of approximately 20 pages in length.
There is a final end-of-project presentation day with each individual giving a poster.

*Lecture Workshops: 18 hr
*Project: 18 hr

1. Learning what data for chemistry as gathered by an instrument looks like
2. Programming principles for the acquisition, cleaning, and visualisation of data
3. Good programming practice and code management
4. Filesystem and database design
5. Metadata and FAIR data principles
6. Electronic Lab Notebooks and scripting
7. Design and planning of an extended experimental project using digital chemistry techniques
8. Basic cheminformatics and data pipeline design
9. Analysis of data and the construction of logical arguments based on the interpretation of potentially large datasets.