Build essential skills in managing, integrating, and analysing environmental data. This category covers data standards, quality control procedures, analytical techniques, and open data principles that underpin effective biodiversity informatics and water resource management.

Target Audience: Data managers, GIS specialists, researchers, IT professionals, and anyone responsible for handling biodiversity or environmental datasets.

Key Learning Outcomes:

  • Apply biodiversity data standards (Darwin Core, TDWG)
  • Implement data quality control and validation procedures
  • Conduct environmental data analysis using appropriate statistical methods
  • Understand and apply open data licensing principles

Biodiversity Data Standards

Understand the international standards that enable biodiversity data sharing and interoperability. This course introduces Darwin Core, TDWG standards, and the data structures used by GBIF, FBIS, and other biodiversity information systems.

What You Will Learn:

  • The importance of data standards for biodiversity informatics
  • Darwin Core: core terms, extensions, and controlled vocabularies
  • TDWG (Biodiversity Information Standards) community and resources
  • Data publishing formats: Darwin Core Archive, CSV, and APIs
  • Taxonomic backbones and name matching services
  • Georeferencing standards and uncertainty documentation
  • Metadata: documenting datasets for discovery and reuse

Duration: 6–8 hours
Prerequisites: Basic understanding of biodiversity data
Language: English (Portuguese translation available)

Data Integration & Quality Control

Learn practical techniques for integrating data from multiple sources and ensuring data quality. This course addresses common challenges in combining biodiversity and hydrological datasets, including format conversion, duplicate detection, validation rules, and error correction.

What You Will Learn:

  • Data integration workflows: ETL (Extract, Transform, Load) processes
  • Handling diverse data formats: spreadsheets, databases, text files, and APIs
  • Duplicate detection and record matching algorithms
  • Validation rules and automated quality checks
  • Taxonomic verification and name standardisation
  • Spatial data validation: coordinate checking and georeferencing correction
  • Documentation and audit trails for data processing

Duration: 8–10 hours
Prerequisites: Biodiversity Data Standards
Language: English (Portuguese translation available)

Environmental Data Analysis

Develop analytical skills for exploring and interpreting environmental datasets. This course covers statistical methods, visualisation techniques, and software tools commonly used in freshwater ecology and water resource assessment, with practical exercises using INWARDS and FBIS data.

What You Will Learn:

  • Exploratory data analysis: summary statistics, distributions, and outliers
  • Time series analysis for hydrological and water quality data
  • Multivariate methods: ordination (PCA, NMDS) and clustering
  • Species-environment relationships: correlation and regression
  • Trend detection and change point analysis
  • Data visualisation best practices: charts, maps, and dashboards
  • Software tools: R, Python, Excel, and QGIS for environmental analysis

Duration: 10–12 hours
Prerequisites: Basic statistics knowledge; Data Integration & Quality Control (recommended)
Language: English (Portuguese translation available)

Open Data & Licensing (CC0, Creative Commons)

Explore the principles and practices of open data in biodiversity and environmental science. This course covers licensing frameworks, data sharing policies, and the practical steps for publishing open datasets that can be freely accessed and reused by the global community.

What You Will Learn:

  • The open data movement and its benefits for science and conservation
  • Understanding copyright and intellectual property in data
  • Creative Commons licenses: CC0, CC-BY, CC-BY-SA, and their appropriate use
  • JRS Biodiversity Foundation open data policy requirements
  • Preparing datasets for open publication: documentation and metadata
  • Data repositories and publication platforms: GBIF, Zenodo, GitHub
  • Citation and attribution: giving and receiving credit for data
  • Ethical considerations: sensitive species data and indigenous knowledge

Duration: 4–5 hours
Prerequisites: None
Language: English (Portuguese translation available)