ARL, CNI, EDUCAUSE Release “Facilitating Information Discovery and Use,” Third Installment of Emerging Technologies Report

Mapping the Current Landscape of Research Library Engagement with Emerging Technologies in Research and Learning:Facilitating Information Discovery and UseBy Sarah LippincottEdited by Mary Lee Kennedy, Clifford Lynch, and Scout CalvertApril 14, 2020 2 Mapping the Current Landscape of Research Library Engagement: Facilitating Information Discovery and UseTable of ContentsLandscape Overview 3Strategic Opportunities 5Invest in user-centered discovery tools 5Reveal hidden digital collections through enhanced description 15Expose library collections and services beyond library systems 23Key Takeaways 25Endnotes 27This is the third installment of a forthcoming report, Mapping the Current Landscape of Research Library Engagement with Emerging Technologies in Research and Learning, that will be published in its entirety by late spring 2020.The following installments are being published as they become available at https://doi.org/10.29242/report.emergingtech2020.landscape:Executive Summary [published March 26, 2020]Introduction, Methodology, and Cross-Cutting Opportunities[published April 2, 2020]Facilitating Information Discovery and Use [published April 14, 2020]Stewarding the Scholarly and Cultural RecordAdvancing Digital ScholarshipFurthering Learning and Student SuccessBuilding and Managing Learning and Collaboration Spaces 3 Mapping the Current Landscape of Research Library Engagement: Facilitating Information Discovery and UseLandscape OverviewThe library’s role as connector between researchers and information has evolved over hundreds of years. Historically, libraries amassed and disseminated broad and deep collections of print and digital resources to their local communities. To many constituents, this remains the primary perceived function of libraries today. Libraries continue to invest significant portions of their annual budgets to license and purchase information resources, and continue to use collection size as a primary metric of quality and value.1 Academic libraries are adept at managing discrete publications: negotiating licenses and purchasing agreements, making content “discoverable via institutional systems populated with hand-crafted metadata,”2 and ensuring long-term preservation. However, this model is being rapidly disrupted and displaced by a “greatly expanded scholarly record—one that is less dependent on papers and articles, and that is increasingly expressed in terms of networks of links and associations among diverse research artifacts.”3 The expanded scholarly record has engendered three interrelated challenges for library discovery and access.1. The types of information researchers seek is changing. Researchers increasingly require access to information resources outside the traditional scope of library collections, from massive data sets, to visualizations, three-dimensional objects, and computer models. Many researchers work outside of and across traditional disciplinary boundaries and require information sources from a range of fields of study. For some researchers, metadata, rather than published content, may be the primary object of study.2. What researchers intend to do with that information is changing. Researchers increasingly expect to mine, process, and analyze content. With knowledge production rapidly outpacing human processing capacity, researchers will increasingly rely on machines to parse and interpret information. For example, experiments in unsupervised text mining of the scientific 4 Mapping the Current Landscape of Research Library Engagement: Facilitating Information Discovery and Useliterature have demonstrated that the data in the existing published scientific literature contains a wealth of unrecognized discoveries.4 Only by analyzing this content at scale can scholars identify the overlooked patterns and connections embedded in the scholarly record.3. How researchers go about looking for that information is changing. Researchers increasingly expect search and discovery interfaces that support a range of inputs and outputs. For example, new math-aware search engines allow users to enter mathematical equations as search terms and return results based on similarities in either the structure or meaning of the equation.5 The Dig That Lick project searches its large-scale corpus of jazz recordings for pattern similarities based on a user’s input on a virtual keyboard.6 In addition to accepting non-textual inputs, researchers increasingly expect searches to return personalized, context-aware results. As search practices vary widely by discipline, scholars desire discovery tools that align with their field’s research methods and expectations.Together, these changes in scholarly expectations signal a future in which the library catalog and other local discovery systems will diminish in value, in favor of web-scale discovery. The library’s role in discovery is undoubtedly shifting, a trend accelerated by emerging technologies such as machine learning (ML). One expert