EDI offers interactive online training on a number of topics, in a variety of formats. We provide individual and multiple lessons that focus on a single topic, but with varying levels of detail. We also conduct comprehensive online data publishing training, as a day-long event or a series of shorter webinars which are distributed over several days. Additionally, we offer consultations in design and implementation of a data publication workflow that best meets your needs. Please contact us at info@environmentaldatainitiative.org if you would like to request online training.
Catalog of lectures
1. About EDI: Benefits, challenges and steps of data publishing
- The Environmental Data Initiative: Core activities and services for data curation.
- Our Data Repository: Trustworthy and endorsed.
- Benefits & challenges of data publishing.
- Overview of the data publishing workflow.
2. The data publishing workflow for tabular ecological data
- Organizing data into publishable units: What is a dataset? What are raw versus processed data?
- Creating clean data for archiving: What are “clean” or publishable data? The concept of a single table.
- Describing data with metadata: The importance of metadata content and scope. Filling out the EDI metadata template.
- Creating metadata in the Ecological Metadata Language: Using the EML Assemblyline.
- The EDI data repository: Architecture, services and uploading data.
- Citing data: Digital Object Identifier (DOI). Creating a catalog of your data on a local website. How to reference your data packages?
3. On-boarding of new Information Managers
- Tools for creating EML, discussion lists
4. How to convert data into into a harmonized format.
- Convert community observation data into ecocomDP format
- Convert meteorological and hydrological data into the climDB 2.0 format
5. We teach The Carpentries curricula on fundamental programming and data management skills for tabular data
- “Ecology” workshop using R: Data cleaning, management, analysis, and visualization.
- “Ecology” workshop using Python: Data cleaning, management, analysis, and visualization.