Gallantries Grant - Intellectual Output 4 - Data analysis and modelling for evidence and hypothesis generation and knowledge discoverypurlPURL: https://gxy.io/GTN:P00015
We recommend you follow the tutorials in the order presented on this page. They have been selected to fit together and build up your knowledge step by step. If a lesson has both slides and a tutorial, we recommend you start with the slides, then proceed with the tutorial.
This Learning Pathway collects the results of Intellectual Output 4 in the Gallantries Project
- SC4.1) Statistical analysis. This will build on the basic statistics covered in IO1 to give a much better statistical comprehension often needed in more advanced analyses like modeling.
- SC4.2) Interactive data visualisation. For most cases, existing visualisations are sufficient, but knowing which visualisation is appropriate and why can be a key point often missed. Additionally sometimes analyses will require custom visualisation such as for geographic information system data.
- SC4.3) Hypothesis generation. When a researcher is handed a large pile of data, figuring out which questions to ask, and what the expected answer is, is the first step of good science.
- SC4.4) Advanced data modelling. Given a hypothesis for some data, a researcher should know how to model changes across some unknown variables, predicting into the future or filling in potential missing gaps in data.
Year 1: Biodiversity data handling and visualisation
learners will understand how to handle biodiversity data and analyse it, as well as elements of visualisation, identifying the optimal visualisation for a dataset. [SC1.1,SC1.4, SC2.1, SC2.3, SC4.1-3]
|Compute and analyze biodiversity metrics with PAMPA toolsuite
|Biodiversity data exploration
|Cleaning GBIF data for the use in Ecology
Year 2: Metabarcoding and environmental DNA data analysis
analysis of environmental DNA samples requires integrative analysis of highly diversified samples, and new techniques to scale with the data [SC1.4, SC1.5, SC2.1, SC3.1, SC4.1-4]
Metabarcoding/eDNA through Obitools
Genetic composition EBV class Community composition EBV class EBV dataset EBV workflow eDNA Metabarcoding
Year 3: Species distribution modeling
As an application of data modeling, we will use species migration and biodiversity to teach learners how to build models for complex data and visualise the results. [SC1.1, SC2.4, SC4.1-4]
|Species distribution modeling
This material is reviewed by our Editorial Board:Yvan Le Bras Bérénice Batut
This material was funded by:Gallantries