Supporting Science, Policy, and Sustainable Development
DATE: 22nd – 24th February 2022
Venue: Colophon Room, CBC Building, Kirstenbosch, Cape Town
Deadline for submissions: 10th December 2021
Background
SANBI is the home of the South African Node of the Global Biodiversity Information Facility (GBIF) and is funded by the Department of Science and Innovation. Its vision is to contribute to South Africa’s sustainable development by facilitating access to biodiversity and related information on the internet.
This course ties into SANBI-GBIF’s strategy to develop biodiversity informatics skills nationally. Through this course, it is intended to focus on Data Management and Cleaning of primary biodiversity data, to ensure that data and the information derived from this is relevant and can ultimately be used for improved decision making.
Through this course, it is intended to introduce participants to the GBIF landscape and Knowledge Management Platform or research infrastructure. Participants will also be introduced to key software packages to manage, clean and visualize data such as the R package and QGIS. Participants will also learn about R basics, issues with biodiversity data and the need for data cleaning, getting a standardized taxonomy, resolving coordinate issues and biodiversity georeferencing.
Workshop Information
The course will be conducted by Dr Vernon Visser from the Centre for Statistics in Ecology, Environment and Conservation (SEEC) at the University of Cape Town. Ms Hannelie Snyman (SANBI) and Ms Fatima Parker-Allie (SANBI-GBIF) will form part of the training team. The venue will be the Colophon Room in the CBC Building at Kirstenbosch from Tuesday the 22nd of February to Thursday the 24th of February 2022. The course will be conducted in English. For logistical reasons and COVID considerations, the workshop numbers are limited. Interested members are requested to please complete and return the application form attached by the 10th December 2021.
All participants will have to bring a laptop to work on during the course.
Target Audience and Minimum requirements for participants
The target audience for this course would include stakeholders from research organisations, museums, herbaria, provincial organisations, government officials dealing with biodiversity data, biodiversity information practitioners, academics, and students.
Minimum Requirements:
- Relevant bachelor’s degree in natural or earth science such as biology, botany, zoology, forestry, geography, or a related field of biodiversity informatics, or Geographic Information Systems (GIS). Participants have found some prior experience in using R is helpful for this course, although this is not a requirement.
- Criteria for the selection of participants will include:
a. Relevance of work/study to the course
b. Commitment to apply and disseminate skills - An effort will be made to ensure there is a racial, gender and national institutional representation among the participants
Application pack
The application pack must include:
• A completed application form (attached)
• A motivation (one page or less)
• A letter of support from your supervisor or institution
• A copy of your CV
Please send the attached completed application form, brief CV and letter of support from your supervisor by 10th December 2021.
Cost
No registration fee will be charged for the course. Refreshments and lunch will be provided. Participants are required to arrange their own transport and accommodation. Participants will need to have the use of a laptop throughout the training. SANBI-GBIF will consider applications for financial assistance for a limited number of participants. If assistance is available, expenses covered include economy airfare, accommodation, and ground transport. Bookings will be made by the SANBI-GBIF office. If you require funding, please indicate this on the application document (see below) and include a letter of motivation and CV with the application documents.
DOWNLOAD THE APPLICATION FORM HERE
SANBI-GBIF_Data Management & Cleaning by Dane McDonald on Scribd