Gallantries Grant - Intellectual Output 4 - Data analysis and modelling for evidence and hypothesis generation and knowledge discovery
purlPURL: https://gxy.io/GTN:P00015Comment: What is a Learning Pathway?
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
Success Criteria:
- 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]
Time estimation: 6 hours
Learning Objectives
- Upload data from DATRAS portal of ICES
- Pre-process population data with Galaxy
- Learning how to use an ecological analysis workflow from raw data to graphical representations
- Learning how to construct a Generalized Linear (Mixed) Model from a usual ecological question
- Learning how to interpret a Generalized Linear (Mixed) Model
- Obtain and filter/manipulate occurrence data
- Compute and visualize phenology of a species through the years
- Compute temporal abundance trends
- Explore Biodiversity data with taxonomic, temporal and geographical informations
- Have an idea about quality content of the data regarding statistical tests like normality or homoscedasticity and coverage like temporal or geographical coverage
- Get occurrence data on a species
- Visualize the data to understand them
- Clean GBIF dataset for further analyses
| Lesson | Slides | Hands-on | Recordings |
|---|---|---|---|
| Compute and analyze biodiversity metrics with PAMPA toolsuite | |||
| Regional GAM | |||
| 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]
Time estimation: 1 hour
Learning Objectives
- Deal with paired-end data to create consensus sequences
- Clean, filter and anlayse data to obtain strong results
| Lesson | Slides | Hands-on | Recordings |
|---|---|---|---|
| Metabarcoding/eDNA through Obitools |
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]
Time estimation: 1 hour
Learning Objectives
- Find and download occurrences data from GBIF
- Find and download environmental data
- Process both occurrences and environmental data
- Partition occurrence data
- Model a theoretical ecological niche and predict species distribution in a future climate scenario by using SDM
| Lesson | Slides | Hands-on | Recordings |
|---|---|---|---|
| Species distribution modeling |
Editorial Board
This material is reviewed by our Editorial Board:
Yvan Le Bras
Bérénice Batut
Funding
These individuals or organisations provided funding support for the development of this resource
