Key Research Skills BIOL5126
- Academic Session: 2023-24
- School: School of Biodiversity One Health Vet Med
- Credits: 40
- Level: Level 5 (SCQF level 11)
- Typically Offered: Semester 1
- Available to Visiting Students: Yes
This course will teach all students a common baseline for writing of scientific essays, proposals and papers, oral presentation skills; introduction to the statistical analysis environment R, which is rapidly taking over as the most versatile programme for biological applications; Advanced Generallized Linear Mixed models (working in the R environment), which is critical for analysis of complex datasets; and Experimental design and power analysis, both in terms of being able to plan their own experiments but also being able to critically evaluate the validity of conclusions drawn from data analysed in published papers.
This course will be taught over the first 9 weeks of the Semester, with 2 hours lecture, 2 hours tutorial, and 5 hours laboratory per week. Contact hours are high to enhance supervised learning of core skills. Students will also give an oral presentation.
In-class and home assignments (coursework) will comprise 60% of the mark and will be divided equally between the Scientific Communications and R components. The remaining 40% of the mark will be based on a scientific report (3000 words) in the form of a publishable journal article that will integrate skills across all topics. Specifically, students will be provided with a dataset and a brief description of the motivation for why the data were collected. They will need to analyse the data using the skills learned in the introduction to R, Experimental design, and Advanced Statistics components and write up the report as a full scientific paper appropriate for submission to a peer-reviewed journal.
The aims of this course are to ensure that all students enrolled in the MSc/PGdip and MRES programmes receive advanced and evidence-based training in the key skills essential for any modern ecology/evolution-based research career and for the courses that they will take later in the programme. All sessions will involve practical hands-on training, as well as lectures introducing the concepts. Sessions are divided broadly into: 1) Scientific Communication and 2) R & Statistics (including Introduction to the Programming Environment R, Introduction to General Linear Models, Advanced Statistics, and Experimental Design & Power Analysis)
Intended Learning Outcomes of Course
By the end of this course students will be able to:
■ Carry out an appropriate and thorough search of the primary literature.
■ Critique scientific evidence
■ Produce well-structured and critical evidence based essays, grant proposals and scientific reports that set the context of the objectives based on a critical review of the primary literature, and clearly describe methodology (including quantitative analyses), present results in an easily understandable format, and discuss results in the context of the broader body of literature in the relevant scientific field
■ Download and install R, along with packages and libraries relevant to the analysis of biological data, import data, use objects, and plot data, acquiring technical help as required from literature and online sources
■ Critically discuss appropriate uses of some of the key features of R including random number generation, data manipulation, input output, and basic descriptive statistics.
■ Use R to implement a wide range of generalised linear mixed models, and discuss critically the justification for choice of models for particular scientific questions
■ Organize data in a form appropriate for further analysis
■ Use the evidence base to formulate null and alternate hypotheses associated with particular statistical tests
■ Critically interpret the output from these analyses, test identified hypotheses and discuss the results in the context of the primary literature
■ Recognize and critically assess the underlying models associated with these statistical analyses
■ Identify and interpret statistical interactions and random effects in the context of real data
■ Conduct a full range of diagnostic tests to ensure the data complies with assumptions of the methodology
■ Take an critical evidence-based approach to designing effective experiments (and other data collection exercises)
■ Critically evaluate other scientists' experimental designs
■ Critically discuss the key concepts in experimental design with reference to the literature
■ Integrate knowledge and skills learned in the analysis of experimental data and scientific writing to write a report using real data to generate a specific hypothesis to be tested in the context of a critique of the existing background in the primary literature, describe the specific methods used to analyse the data, describe and interpret the results based on the evidence base and write a critical discussion that sets the results in the context of the primary literature
Minimum Requirement for Award of Credits