30 Association of Research Libraries Research Library Issues 298 2019 The first efforts at NAL to build infrastructure and services to support data analysis and management included the i5K workspace@NAL7 and the Life Cycle Assessment Commons.8 These two projects were designed to support users in specific disciplines: insect genomics and cradle-to-grave modeling of the inputs and outputs of agricultural production processes. Later, NAL responded to calls for US public access to data as a product of federally funded research by initiating the Ag Data Commons,9 a general catalog and repository for open research data funded by USDA. Lean Start-up Methodology Having made these investments in software product infrastructure and the associated data curation services, we next turned our attention to complementary services: data science services and data management planning. With these two newest services, and particularly with its data science services, NAL’s Knowledge Services Division is beginning to apply lean start-up methodology.10 In this method, products and services undergo iterative cycles of build–measure–learn, followed by either pivoting or perseverance. With specific hypotheses in mind, one quickly creates a minimum viable product (MVP) and tests it with stakeholders. For these services, we are testing hypotheses about whether the services are needed, who can best provide these services, and how to develop the appropriate capacity and resources to deliver these services effectively. Essential to our application of this method is partnership with land-grant university libraries, information schools, and researchers at both universities and USDA ARS who are our key stakeholders. While our cycles are not yet as rapid as is recommended, we are constantly refining our methodology as we go. Defining Data Science within the Full Data Life Cycle As of this writing, data science is defined in Wikipedia as “a multi- disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.”11 Donoho offers six buckets of data science activities: (1) Data Exploration and Preparation, (2) Data
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