If there is anything that we have learned over the past year, it’s how important scientific data are for preparing, planning, and responding to extreme climatic, environmental, and human health events. To do this, scientific data must be made accessible to the people who need it, and appropriately translated, prepared, and relevant for their needs.
To this end, I’ve been engaged in efforts to help overcome challenges for scientific data. One product was an open-access scientific article published last month entitled “Ten rules to increase the societal value of Earth Science data“. The article was written in collaboration with Middle Path, FourBridges (a consulting firm), South African Weather Service, Resources for the Future and the VALUABLES Consortium, and NASA Jet Propulsion Laboratory.
The article offers 10 rules for data providers and their partners to implement in order to improve how Earth Science data meet the needs of their end users. The rules are loosely organized according to project management principles. Initial rules focus on defining problems, planning for data use, creating effective teams, and examining a diverse selection of solutions. The next set of rules are best applied throughout a project, and include concepts like evaluation, interoperability, trust, adoption, and documentation. Finally, the last rule addresses the challenge of determining when to close a project.
The figure above depicts the conceptual framework that guided the discussion in the paper. The large blue triangle represents an EO value chain that encompasses data, information, knowledge, and wisdom. The figure also depicts the role of actors (people icon), processes for transforming data along the value chain (green triangles), and examples of sources of data and results from the processes (gears icon).
Three main approaches are often used to increase the societal benefit of EO data:
(A) Seeking to recruit new users (data exist but the value chain does not),
(B) Improving a current value chain (value chain exists); and
(C) Identifying and/or developing data and data products based on user needs (users exists, but the value chain may or may not exist)
