Dealing with Data: A Case Study on Information and Data Management Literacy

Our scientific body of knowledge is built upon data, which is carefully collected, analyzed, and presented in scholarly reports. We are now witnessing a dramatic shift in our relationship to data: where researchers once managed discrete, controllable building blocks of knowledge, they must now contend with a tsunami of information that paradoxically feeds the growing scientific output while simultaneously crushing researchers with its weight [1]. Numerous national and international initiatives, projects, and working groups have been established to address the data dilemma from multiple angles [2]–[6], including recent Requests for Information from the US Office of Science and Technology Policy [7] and the National Institutes of Health (NIH) [8], and a US White House announcement of spending US$200 million on “Big Data” [9]. The need for information and data management literacy extends beyond a national mandate for sharing and public access—the scientific community must embrace a culture where every scientist needs to understand how to manage, navigate, and curate huge amounts of data. Libraries have traditionally been the place to acquire information; now they have become the place to learn how to manage it. The eagle-i Consortium (see Box 1), a collaborative resource sharing network, is designed to address both the researcher's data-sharing needs and the modern library's new mandate to facilitate and accelerate the discovery of new knowledge. The launch and development of this initiative provides a vivid demonstration of the challenges that researchers, libraries, and institutions face in making their data available to others. Box 1. About eagle-i eagle-i is a US$15 million NIH-funded pilot project with the aim of facilitating biomedical research by creating a network of research resources repositories. The Network began with nine institutions chosen on the basis of their diversity and geographical location, and has recently added 16 new institutions (Table 1). The eagle-i platform consists of ontology-driven Semantic Web Entry & Editing Tool (SWEET) [25],[26], which enable resource information contained in Resource Description Framework (RDF) repositories to be published as Linked Open Data [10]. The use of an ontology that integrates domain standards for representation ensures interoperability and semantic linkage of research resources to other aspects of biomedicine. As part of the two-year pilot, each of the original participating institutions employed specialized Resource Navigators at each site to identify relevant research resources and enter data into the system, while a central Biocuration team at the Oregon Health & Science University and Harvard Medical School libraries built the ontologies and ensured the quality and consistency of the data [27]. To date, the eagle-i repositories contain records for over 47,000 resources and additional records are continually added. New institutions are invited to adopt the software and join the network [17]. As eagle-i matures, new strategies are under way to streamline the data collection process, including integration with laboratory inventory systems and with other online resources such as National Center for Biotechnology Information (NCBI) [28] and the Neuroscience Information Framework (NIF) [29]. Table 1 Participating institutions in the eagle-i Network.