• Offered for Year: 2025/26

• Module Leader(s): Dr Matt Bawn

• Owning School: Natural and Environmental Sciences
• Teaching Location: Newcastle City Campus

Semesters

Your programme is made up of credits, the total differs on programme to programme.

Aims

This module aims to equip students with the foundational skills and knowledge required to work with complex biological and environmental datasets in the context of industrial bioscience. As data-driven approaches become increasingly central to biotechnology, students need to be proficient in sourcing relevant data from public repositories, understanding the structure and standards of biological data, and using computational tools to prepare and integrate these datasets for research and innovation.

Students will engage with real-world challenges in genomics, metagenomics, proteomics, and environmental monitoring, learning how to navigate key databases and apply programmatic analyses. Emphasis is placed on data curation, harmonisation across multiple sources, and understanding the legal and ethical considerations associated with data reuse.

By embedding practical skills within authentic industrial contexts, including case studies from biotechnology and environmental companies, the module fosters the ability to critically assess data sources, select appropriate datasets for specific research problems, and communicate data acquisition strategies effectively. The overall aim is to develop students’ confidence and competence in handling bioscience data to support hypothesis generation, decision-making, and innovation in industrial and applied research settings.

Outline Of Syllabus

•       Biological data ecosystems and public databases (e.g. NCBI, UniProt, Ensembl,)
•       Programmatic data access via APIs
•       Bioscience data formats (e.g. FASTA, GFF) and ontologies
•       Metadata standards and sample annotation
•       Integration and harmonisation of multi-source datasets
•       Ethical considerations in data reuse
•       Case studies from industry (e.g. Procter & Gamble, Northumbrian Water)

Teaching Methods

Teaching Activities

Teaching Rationale And Relationship

This module delivers core content through integrated computational workshops that combine short lecture segments with hands-on exercises. This blended approach ensures students immediately apply theoretical concepts such as data standards, programmatic access, and legal considerations within practical contexts, directly supporting the intended knowledge outcomes.
Computational workshops guide students through retrieving, cleaning, and integrating real-world biological and environmental datasets, aligning closely with all three intended skills outcomes. Weekly portfolio tasks reinforce learning by providing structured, incremental challenges that build technical proficiency and promote critical thinking.
To enhance real-world relevance and career insight, asynchronous industrial talks are embedded throughout the module. These recorded contributions from professionals in biotechnology and environmental sectors offer students exposure to current data challenges, tools, and applications in industry. They complement the technical content by contextualising it within real life decision making and innovation processes.
The final project-based assessment consolidates learning by requiring students to design, document, and justify a complete data strategy for an industrially relevant bioscience problem. This applied, practice led teaching model ensures students develop both conceptual understanding and technical capability, preparing them for data centric roles in research and industrial biotechnology.

Assessment Methods

The format of resits will be determined by the Board of Examiners

Other Assessment

Formative Assessments

Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.

Assessment Rationale And Relationship

The assessment strategy is designed to reflect the applied, skills-focused nature of the module and to ensure alignment with the intended learning outcomes. It consists of two components: a practical portfolio (40%) and a project report (60%), both of which prioritise real-world data challenges and reproducible workflows.
The practical portfolio supports ongoing engagement through a series of weekly tasks, where students apply techniques introduced in workshops to realistic bioscience data scenarios. Students are encouraged to submit these tasks for peer-to-peer review, enabling the exchange of formative feedback and promoting collaborative learning. This process develops critical evaluation skills and fosters a supportive learning community. At the end of the module, each student selects three tasks to submit as their final portfolio for summative assessment. These are marked through a combination of peer and academic review, ensuring both reflective practice and academic rigour.
The project report acts as a capstone assignment, requiring students to design and justify a complete data acquisition and integration strategy for an industrially relevant bioscience research problem. This summative assessment consolidates the knowledge and skills developed during the module and mirrors the type of independent, applied work expected in professional contexts.
Together, this assessment approach balances structured skill development with opportunities for reflection, feedback, and synthesis, ensuring students are well-prepared for data-driven roles in bioscience research and industry

Reading Lists

• DSC8013's Reading List

Timetable

• Timetable Website: www.ncl.ac.uk/timetable/
• DSC8013's Timetable