The Avian Knowledge Network and some of the lessons learned from the birding community

1. The Avian Knowledge Network and some of the lessons learned from the birding community Denis Lepage Senior Scientist

2. About Bird Studies Canada • National not-for-profit, established in 1960 • Conservation, science and population monitoring • Work with a network of nearly 20,000 volunteers to gather data through a variety of programs

3. Canadian Bird MonitoringDatasets • eBird Canada: 3,0 million • Project FeederWatch: 1,8 million • Long Point Migration: 1,6 million • Ontario Breeding Bird Atlas: 1,4 million • Maritimes Breeding Bird Atlas: 0,5 million • BC Breeding Bird Atlas: 0,2 million • Marsh Monitoring Program: 0,2 million • Ontario Nest Record Scheme: 0,1 million • etc.

4. Avian Knowledge Network

5. Goals of the AKN Organize heterogeneous datasets on birds through a network of AKN nodes Provide long-term archive and redundancy Make those datasets accessible in a common format Facilitate analysis and visualizations

6. Avian Knowledge Network • Approximately 100 million records currently in AKN format (14 million in Canada) • Data standard called Bird Monitoring Data Exchange (BMDE, an extension of DarwinCore) • Additional extensions created for specific needs (banding, nesting data)

7. Bird Monitoring Schema Flat structure, and primarily for occurrence data, like DarwinCore Adds information about data collection events (protocol, effort, etc.) Allows for inference about absence and relative abundance Focuses on being able to answer specific types of questions that are relevant to our end users

8. 1. Acquiring data

9. Feedback is key to success!

10. 2. Organizing and using data Ontario Atlas Maritimesb Atlas Ontario Atlas Maritimesb Atlas Ontario Atlas Maritimes Atlas BC Atlas eBird BC Atlas eBird BC Atlas eBird Qc Atlas Project FeederWatch Qc Atlas Project FeederWatch Qc Atlas Project FeederWatch Mb Atlas Etc. Mb Atlas Etc. Mb Atlas Etc.

11. 2. Organizing and using data eBird MMP Atlas BMDE Database Data Portal GBIF, AKN, DataOne Analysis Presentation

12. 3. Sharing data There are many barriers to overcome to have organizations understand the value of data sharing Custodians see data as a way to enhance: recognition, control, revenues There are costs associated with sharing data They may want to ensure that they are the ones to publish results

13. http://www.naturecounts.ca

17. Avian Knowledge NetworkData Access Levels • Level 1: archive only • Level 2: summaries only • Level 3: raw data available by request • Level 4: raw data available by request, plus occurrence data shared with global networks (GBIF, etc.) • Level 5: open access

18. 4. Data persistence • Data loss is potentially a massive issue • Global data networks good redundancy, but may not offer full data richness • Other approaches, such as those developed by NCEAS (EML), need to be considered • There are lots of data out there!

19. 5. Data presentations • Focus on reusable tools, such as web services • Based on a common data structure, which makes tools easily usable across projects • Ideally based on open-source solutions and common standards

20. Data presentations