BIO8068 : Management and Visualisation of Data in Ecology and Conservation
- Offered for Year: 2019/20
- Module Leader(s): Dr Roy Sanderson
- Owning School: Natural and Environmental Sciences
- Teaching Location: Newcastle City Campus
|Semester 2 Credit Value:||10|
Modern ecological surveys can produce large datasets, e.g. from GPS tags on animals, camera traps, genetics data etc. These large datasets require environmental scientists trained in data analytics, in other words capable of managing the entire pipeline from raw data, data cleaning and management, modelling, visualisation, and communication to other scientists and non-specialists. Students will learn different methods of storing and handling large datasets, the theory and practice of reproducible research, robust software coding with version control, data visualisation, both static and interactive (web-based) graphical display and communication of results. The module will focus primarily on data from ecology and environmental science, but will also include examples of data processing skills needed to understand complex bioinformatics or similar data.
Outline Of Syllabus
• Why are large datasets a challenge? Problems of understanding large ecological datasets, assessing data quality and visualisation.
• The challenge of data collected by other ecologists: spreadsheets, messy data, missing meta-data. The need for standardised methods of cleaning and checking data
• Concept of data processing pipelines from raw data to published report; advantages for reproducible research
• Relational databases: storing linked tables of data to allow advanced selection of variables, query, and processing. Commercial and open-source options.
• Data visualisation – good and bad practice in graphics for environmental data.
• Interactive visualisation – embedding data and model outputs in interactive websites. Maps, graphs and tabular data
• Version control – why it is essential to have a fully annotated history of all updates to software code. Collaborative and individual version control. Web-hosted version control.
• Producing word-processed reports or presentations from directly from data without the need for copy-and-paste
|Guided Independent Study||Assessment preparation and completion||1||30:00||30:00||Practical lab report|
|Scheduled Learning And Teaching Activities||Lecture||6||1:00||6:00||N/A|
|Scheduled Learning And Teaching Activities||Practical||10||2:00||20:00||PC-based classes to provide relevant skills|
|Guided Independent Study||Directed research and reading||1||4:00||4:00||Lectures follow up|
|Guided Independent Study||Independent study||1||40:00||40:00||Preparation for continuous assessment|
Teaching Rationale And Relationship
There will be an emphasis on PC-based practical classes, rather than lectures, to ensure a high-level of practical skills are developed through hands-on use of relevant datasets and software.
The format of resits will be determined by the Board of Examiners
|Practical/lab report||2||M||100||Students will be provided with typical raw ecological datasets to clean, analyse and interpret and visualise. Approx. 2000 words|
Assessment Rationale And Relationship
This is very much a skills-based module, and so the assessment will test students’ abilities to process complex environmental data, using typical ‘messy’ datasets often encountered. They will have to demonstrate that they can analyse and interpret their data in a reproducible manner, know how to select appropriate graphical/tabular displays.