It integrates environmental and information sciences to define entities and natural processes with language common to both humans and computers.
Examples of these initiatives are National Science Foundation Datanet projects, DataONE, Data Conservancy, and Artificial Intelligence for Environment & Sustainability.
On the data collection end, this can be addressed by better data-sharing practices, such as by linking datasets when publishing papers or studies.
Integrate: Synthesizing datasets together can be difficult and labor-intensive, largely due to the methodological differences in data collection.
Ecosystem studies, by definition, encompass interactions across the entire life sciences spectrum, from microscopic biochemical reactions to large-scale geological phenomena.
Since ecosystem-level questions require a broad perspective, data-related ecosystem projects would likely incorporate data from several databases.
The current push for smart cities, and sensor network integration into infrastructure, has positioned as a major source of data for ecological studies.
For example, analyzing mobility patterns can identify areas that may lend themselves well to building parks and green spaces.
On the macro-scale, it can be used to identify societal trends or environmental factors that lend themselves to spillover, locations of infection, and practices that cause disease transmission.