BIOL5301 Big Data, Modelling and Meta-analysis in Biology, Conservation and the Environment

Credit
6 points
Offering
(see Timetable)
AvailabilityLocationMode
Semester 2UWA (Perth)Face to face
Content
Scientists of all types (e.g., biologists, ecologists, conservation scientists, environmental scientists and marine scientists) now generate or utilize large amounts of observational or derived data, for example GPS records, visual or electronic satellite signals, eDNA samples, organismal genomics, climate records, computer simulation outputs, literature search results, or data assembled by global data collection schemes such as those administered by the United Nations system. A wealth of data is now available, both via the peer-reviewed scientific literature and online databases, increasingly as open source resources. New knowledge and science can emerge from the consideration, harmonization, cross-linking, combining and analyzing of these existing or new data sets. For example, big data are used to probe everything from the regulation of genes and the evolution of genomes, to why estuarine algae bloom, what microbes are found on the skin of whales, to assess the distribution of fish stocks or the impact of fishing on marine ecosystems around the world, or how the genetic make-up of different plants and animals might determine their vulnerability to global change. In this unit, students will be introduced to some of the types and sources of large to very large biological, ecological, and environmental data of interest. A series of lectures supported by discussions, tutorial classes, and hands-on lab sessions, led by researchers actively engaged in the data they talk about, will introduce students to the logic, background, research questions and techniques necessary to understand and use big-data.
Outcomes
Students are able to (1) plan undertake and produce a systematic review in their field of specialisation; (2) design computational scripts to manipulate, investigate, visualise and analyse large datasets in their field of specialisation; (3) design virtual experiments with simulation and statistical models to address specific hypotheses and questions; (4) evaluate data and databases on global fisheries: harmonizing disparate data sets and adding ecological/economic/policy value to core fisheries data; and (5) assess the challenges and requirements involved in designing, developing, building and populating a large database..
Assessment
Indicative assessments in this unit are as follows: (1) assignments/reports and (2) project. Further information is available in the unit outline.

Supplementary assessment is not available in this unit.
Unit Coordinator(s)
Dr Michael Renton and Prof Dirk Zeller
Unit rules
Prerequisites:
SCIE4402 Data Management and Analysis in the Natural Sciences
Advisable prior study:
SCIE4402 Data Management and Analysis in the Natural Sciences
Contact hours
(possibly) 10 half days of contact
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