Hours:
20 hours (5 credits)

Room:
Aula Riunioni del Dipartimento di Ingegneria dell'Informazione, via G. Caruso 16, Pisa – Ground Floor

Short Abstract:
An e-Infrastructure is a distributed network of service nodes, residing on multiple sites and managed by one or more organizations. e-Infrastructures allow scientists residing at distant places to collaborate. They offer a multiplicity of facilities as-a-service, supporting data sharing and usage at different levels of abstraction, e.g. data transfer, data harmonization, data processing workflows etc. e-Infrastructures are gaining an important place in the field of biodiversity conservation. Their computational capabilities help scientists to reuse models, obtain results in shorter time and share these results with other colleagues. They are also used to access several and heterogeneous biodiversity catalogues.

In this course, the D4Science e-Infrastructure will be used to conduct experiments in the field of biodiversity conservation. D4Science hosts models and contributions by several international organizations involved in the biodiversity conservation field. The course will give students an overview of the models, the practices and the methods that large international organizations like FAO and UNESCO apply by means of D4Science. At the same time, the course will introduce students to the basic concepts under e-Infrastructures, Virtual Research Environments, data sharing and experiments reproducibility and repeatability. Hands-on exercises, using on-line Web interfaces, will allow students to practically apply models to a number of datasets and will practically show how a Computer Science system can meet modern Open Science requirements.

Course Contents in brief:

• e-Infrastructures and for Virtual Research Environments
• Practice with the D4Science e-Infrastructure
• Geospatial data visualization and representation
• Statistical models for species distribution modelling
• Accessing large heterogeneous biodiversity data catalogues
• Signal processing of biodiversity-related observations
• Machine Learning applied to species observation records
• Lexical search in large taxonomic trees
• Cloud computing applied to biodiversity analyses

Schedule:

May 08-12, 2017

• Day 1. Introduction to e-Infrastructures, Virtual Research Environments and Large Biodiversity Catalogues– 9.00 – 13.00
• Day 2. Geospatial data descriptions, catalogues and visualization – 9.00 - 13.00
• Day 3. Trends analysis of species observation records and environmental data– 9.00 - 13.00
• Day 4. Data Processing: operations on large species datasets and taxonomic trees – 9.00 – 13.00
• Day 5. Data Processing: species distribution modelling using machine learning techniques and Cloud computing– 9.00 – 13.00