Mapping the Current Landscape of Research Library Engagement with Emerging Technologies in Research and Learning: Final Report

The generation, dissemination, and analysis of digital information is a significant driver, and consequence, of technological change. As data and information stewards in physical and virtual space, research libraries are thoroughly entangled in the challenges presented by the Fourth Industrial Revolution:1 a societal shift powered not by steam or electricity, but by data, and characterized by a fusion of the physical and digital worlds.2 Organizing, structuring, preserving, and providing access to growing volumes of the digital data generated and required by research and industry will become a critically important function. As partners with the community of researchers and scholars, research libraries are also recognizing and adapting to the consequences of technological change in the practices of scholarship and scholarly communication. Technologies that have emerged or become ubiquitous within the last decade have accelerated information production and have catalyzed profound changes in the ways scholars, students, and the general public create and engage with information. The production of an unprecedented volume and diversity of digital artifacts, the proliferation of machine learning (ML) technologies,3 and the emergence of data as the “world’s most valuable resource,”4 among other trends, present compelling opportunities for research libraries to contribute in new and significant ways to the research and learning enterprise. Librarians are all too familiar with predictions of the research library’s demise in an era when researchers have so much information at their fingertips. A growing body of evidence provides a resounding counterpoint: that the skills, experience, and values of librarians, and the persistence of libraries as an institution, will become more important than ever as researchers contend with the data deluge and the ephemerality and fragility of much digital content. This report identifies strategic opportunities for research libraries to adopt and engage with emerging technologies,5 with a roughly fiveyear time horizon. It considers the ways in which research library values and professional expertise inform and shape this engagement, the ways library and library worker roles will be reconceptualized, and the implication of a range of technologies on how the library fulfills its mission. The report builds on a literature review covering the last five years of published scholarship, primarily North American information science literature, and interviews with a dozen library field experts, completed in fall 2019. It begins with a discussion of four cross-cutting opportunities that permeate many or all aspects of research library services. Next, specific opportunities are identified in each of five core research library service areas: facilitating information discovery, stewarding the scholarly and cultural record, advancing digital scholarship, furthering student learning and success, and creating learning and collaboration spaces. Each section identifies key technologies shaping user behaviors and library services, and highlights exemplary initiatives. Underlying much of the discussion in this report is the idea that “digital transformation is increasingly about change management”6 —that adoption of or engagement with emerging technologies must be part of a broader strategy for organizational change, for “moving emerging work from the periphery to the core,”7 and a broader shift in conceptualizing the research library and its services. Above all, libraries are benefitting from the ways in which emerging technologies offer opportunities to center users and move from a centralized and often siloed service model to embedded, collaborative engagement with the research and learning enterprise

Realizing the potential of astrostatistics and astroinformatics

This Astro2020 State of the Profession Consideration White Paper highlights the growth of astrostatistics and astroinformatics in astronomy, identifies key issues hampering the maturation of these new subfields, and makes recommendations for structural improvements at different levels that, if acted upon, will make significant positive impacts across astronomy

Skills and Knowledge for Data-Intensive Environmental Research.

The scale and magnitude of complex and pressing environmental issues lend urgency to the need for integrative and reproducible analysis and synthesis, facilitated by data-intensive research approaches. However, the recent pace of technological change has been such that appropriate skills to accomplish data-intensive research are lacking among environmental scientists, who more than ever need greater access to training and mentorship in computational skills. Here, we provide a roadmap for raising data competencies of current and next-generation environmental researchers by describing the concepts and skills needed for effectively engaging with the heterogeneous, distributed, and rapidly growing volumes of available data. We articulate five key skills: (1) data management and processing, (2) analysis, (3) software skills for science, (4) visualization, and (5) communication methods for collaboration and dissemination. We provide an overview of the current suite of training initiatives available to environmental scientists and models for closing the skill-transfer gap

A Grand Challenges-Based Research Agenda for Scholarly Communication and Information Science [MIT Grand Challenge PubPub Participation Platform]

Identifying Grand Challenges A global and multidisciplinary community of stakeholders came together in March 2018 to identify, scope, and prioritize a common vision for specific grand research challenges related to the fields of information science and scholarly communications. The participants included domain researchers in academia, practitioners, and those who are aiming to democratize scholarship. An explicit goal of the summit was to identify research needs related to barriers in the development of scalable, interoperable, socially beneficial, and equitable systems for scholarly information; and to explore the development of non-market approaches to governing the scholarly knowledge ecosystem. To spur discussion and exploration, grand challenge provocations were suggested by participants and framed into one of three sections: scholarly discovery, digital curation and preservation, and open scholarship. A few people participated in three segments, but most only attended discussions around a single topic. To create the guest list of desired participants within our three workshop target areas we invited a distribution of expertise providing diversity across several facets. In addition to having expertise in the specific focus area, we aimed for the participants in each track to be diverse across sectors, disciplines, and regions of the world. Each track had approximately 20-25 people from different parts of the world—including the United States, European Union, South Africa, and India. Domain researchers brought perspectives from a range of scientific disciplines, while practitioners brought perspectives from different roles (drawn from commercial, non-profit, and governmental sectors). Notwithstanding, we were constrained by our social networks, and by the location of the workshop in Cambridge, Massachusetts— and most of the participants were affiliated with US and European institutions. During our discussions, it quickly became clear that the grand challenges themselves cannot be neatly categorized into discovery, curation and preservation, and open scholarship—or even, for that matter, limited to library science and information sciences. Several cross-cutting themes emerged, such as a strong need to include underrepresented voices and communities outside of mainstream publishing and academic institutions, a need to identify incentives that will motivate people to make changes in their own approaches and processes toward a more open and trusted framework, and a need to identify collaborators and partners from multiple disciplines in order to build strong programs. The discussions were full of energy, insights, and enthusiasm for inclusive participation—and concluded with a desire for a global call to action to spark changes that will enable more equitable and open scholarship. Some important and productive tensions surfaced in our discussions, particularly around the best paths forward on the challenges we identified. On many core topics, however, there was widespread agreement among participants, especially on the urgent need to address the exclusion of knowledge production and access of so many people around the globe, and the troubling overrepresentation in the scholarly record of white, male, English-language voices. Ultimately, all agreed that we have an obligation to better enrich and greatly expand this space so that our communities can be catalysts for change. Towards a more inclusive, open, equitable, and sustainable scholarly knowledge ecosystem: Vision; Broadest impacts; Recommendations for broad impact. Research landscape: Challenges, threats, and barriers; Challenges to participation in the research community; Restrictions on forms of knowledge; Threats to integrity and trust; Threats to the durability of knowledge; Threats to individual agency; Incentives to sustain a scholarly knowledge ecosystem that is inclusive, equity, trustworthy, and sustainable; Grand Challenges research areas; Recommendations for research areas and programs. Targeted research questions, research challenges: Legal economic, policy, and organizational design for enduring, equitable, open scholarship; Measuring, predicting, and adapting to use and utility across scholarly communities; Designing and governing algorithms in the scholarly knowledge ecosystem to support accountability, credibility, and agency; Integrating oral and tacit knowledge into the scholarly knowledge ecosystem. Integrating research, practice, and policy: The need for leadership to coordinate research, policy, and practice initiatives; Role of libraries and archives as advocates and collaborators; Incorporating values of openness, sustainability, and equity into scholarly infrastructure and practice; Funders, catalysts, and coordinators; Recommendations for integrating research, practice, and policy

Evolution of artificial intelligence research in Technological Forecasting and Social Change: Research topics, trends, and future directions

Artificial intelligence (AI) is a set of rapidly expanding disruptive technologies that are radically transforming various aspects related to people, business, society, and the environment. With the proliferation of digital computing devices and the emergence of big data, AI is increasingly offering significant opportunities for society and business organizations. The growing interest of scholars and practitioners in AI has resulted in the diversity of research topics explored in bulks of scholarly literature published in leading research outlets. This study aims to map the intellectual structure and evolution of the conceptual structure of overall AI research published in Technological Forecasting and Social Change (TF&SC). This study uses machine learning-based structural topic modeling (STM) to extract, report, and visualize the latent topics from the AI research literature. Further, the disciplinary patterns in the intellectual structure of AI research are examined with the additional objective of assessing the disciplinary impact of AI. The results of the topic modeling reveal eight key topics, out of which the topics concerning healthcare, circular economy and sustainable supply chain, adoption of AI by consumers, and AI for decision-making are showing a rising trend over the years. AI research has a significant influence on disciplines such as business, management, and accounting, social science, engineering, computer science, and mathematics. The study provides an insightful agenda for the future based on evidence-based research directions that would benefit future AI scholars to identify contemporary research issues and develop impactful research to solve complex societal problems

Collaborative yet independent: Information practices in the physical sciences

In many ways, the physical sciences are at the forefront of using digital tools and methods to work with information and data. However, the fields and disciplines that make up the physical sciences are by no means uniform, and physical scientists find, use, and disseminate information in a variety of ways. This report examines information practices in the physical sciences across seven cases, and demonstrates the richly varied ways in which physical scientists work, collaborate, and share information and data. This report details seven case studies in the physical sciences. For each case, qualitative interviews and focus groups were used to understand the domain. Quantitative data gathered from a survey of participants highlights different information strategies employed across the cases, and identifies important software used for research. Finally, conclusions from across the cases are drawn, and recommendations are made. This report is the third in a series commissioned by the Research Information Network (RIN), each looking at information practices in a specific domain (life sciences, humanities, and physical sciences). The aim is to understand how researchers within a range of disciplines find and use information, and in particular how that has changed with the introduction of new technologies

MOVING: A User-Centric Platform for Online Literacy Training and Learning

Part of the Progress in IS book series (PROIS)In this paper, we present an overview of the MOVING platform, a user-driven approach that enables young researchers, decision makers, and public administrators to use machine learning and data mining tools to search, organize, and manage large-scale information sources on the web such as scientific publications, videos of research talks, and social media. In order to provide a concise overview of the platform, we focus on its front end, which is the MOVING web application. By presenting the main components of the web application, we illustrate what functionalities and capabilities the platform offer its end-users, rather than delving into the data analysis and machine learning technologies that make these functionalities possible