The Global Lake Ecological Observatory Network (GLEON) is an international network of people, data, and observatories whose mission is to conduct innovative science by sharing and interpreting data to understand, predict and communicate the role and response of lakes in a changing global environment. As a learning organization, GLEON has experimented with alternative approaches to helping scientists obtain the resources needed to conduct science, including large and diverse data sets from disparate data sources. Early attempts at homogenizing and centralizing data streaming from lake sensor networks led to successes in hardware development and data processing technologies, but the larger goal of a centralized repository for GLEON data was a failure. GLEON had neither the funding nor the expertise to build and sustain a centralized lake data system. Moreover, the lack of enthusiasm for a central repository cast doubt over the soundness of such a strategy for an international ecological research organization. As a result, GLEON reevaluated its approach by posing the question: What are the data resources needed by our community, and what are the unique attributes of GLEON that can help deliver those resources?
Results/Conclusions
GLEON learned that data are first class objects in science. For GLEON members, the data they bring to the collaborative science space are a primary asset. Just as importantly, the data become more usable when they are paired with the deep knowledge and expertise of those who collect and manage them. In short, GLEON has adopted a position that recognizes FAIR (findable, accessible, interoperable, reusable) data as having an important social component. Rather than asking the community just for data, GLEON scientists ask the community to participate in research as well. Although largely a manual process, the outcome has been building a strong community while building science products. The inherent limits to scalability of manual approaches are well-described. However, transforming the data management, curation, and archiving practices of the international community toward FAIR principles begins by building trust around valuation and use of primary assets, such as data. This philosophy and associated practices underlie a team science training program, the GLEON Graduate Student Fellowship Program, in which fellows learn concurrently the collaborative skills needed for team science and the skills needed for finding, accessing, and interpreting network data in keeping with the values and principles of the community.