Understanding Data Processing, Analysis, and Management

Given the broader context of an increase in the amount of natural history and scientific data being collected and made available, as well as a growing need to collect, process, analyze, and archive this data, this module offers learners training that equips them to confidently address these challenges.

The specific objectives will be to learn and apply the methods and tools necessary for the collection, verification, management, and analysis of scientific and naturalist data; to acquire statistical terminology and master descriptive statistics; and to acquire the knowledge and approach necessary to take statistical aspects into account when implementing sampling protocols. This will be divided into three sections as follows:

Unit 1: Fundamentals of Data Processing and Descriptive Statistics

Theoretical and practical knowledge of data entry, formatting, manipulation, verification, and visualization (using Spreadsheet/R). Descriptive statistical terminology (e.g., mean, median, variance), proficiency in descriptive statistical tools

Unit 2: Concepts in Inferential Statistics

Inferential statistics (the most commonly used hypothesis tests), power analyses (case studies).

Section 3: Tools and Measures for Describing Diversity

Biodiversity indices (e.g., Shannon and Simpson diversity indices, species richness), tools used to study biodiversity (accumulation curves, clustering analyses [NJ, UPGMA])—Case study using the R software.

Open to continuing education

EU-targeted competencies:

- E-1 Understand descriptive statistics (trend, dispersion, distribution)

- E-2 Understand standard tests, conditions, and limitations of use

- E-4 Be able to apply statistical knowledge to develop a sampling and/or experimental design

- E-5 Be able to describe data numerically and represent it graphically

- E-6 Know how to select and perform a hypothesis test appropriate for the biological question and the type of data (t-test, Fisher's exact test, chi-square test, comparison of two proportions, correlation)

- E-7 Be able to use a spreadsheet and the R software for simple statistical analyses, calculations, and data manipulation and visualization

- I-17 Be able to use computer tools to enter, analyze, and save data (spreadsheet, R)