An important goal for this group, and the programs engaged in butterfly monitoring, is sharing the data with a wide audience. This includes both providing access to the data for researchers and other data users, and creating visualizations that convey what resources are available and what we can learn from them. We support broad use of these data, from performing exploratory ecological analyses to just finding out about what species are most common in our own backyards.
Part of sharing data is creating digestible content to translate monitoring data for a wide range of audiences. Specifically, we work to facilitate data visualizations for volunteers, the general public, management agencies, and the scientific community. Although we don't require programs to allow data visualizations and downloads to be members of our network, we work with each program to develop their own data sharing policies and encourage them to make their data available to the extent they feel comfortable. Most programs support high levels of data access, and the tools developed for data access and visualization are shared among programs.
One challenge is that different types of programs require different visualization solutions. Overall, maps and trends over time are our highest priority for creating accessible content. There are many potential ways to visualize different types of data. Some data types we create visualizations for are:
- data availability: what data exist, where and when
- observed butterfly diversity
- the probability of observing a particular species at a given time of year and location
- relative abundances of particular species
- flight timing of specific butterfly species
Currently, data requests are filled by members of our team, and involve manual data curation. We are currently seeking funding to allow for development of APIs that provide more direct access to monitoring data (in accordance with each program's data sharing policy) and real-time visualizations. We are striving to improve our data systems by integrating FAIR Principles, making these data Findable, Accessible, Interoperable, and Reusable.