Research Data Management and the Canadian Academic Library: An Organizational Consideration of Data Management and Data Stewardship

Research data management (RDM) has become a professional imperative for Canada’s academic librarians. Recent policy considerations by our national research funding agencies that address the ability of Canadian universities to effectively manage the massive amounts of research data they now create has helped library and university administrators recognize this gap in the research enterprise and identify RDM as a solution. RDM is not new to libraries, though. Rather, it draws on existing and evolving organizational functions in order to improve data collection, access, use, and preservation. A successful research data management service requires the skills and knowledge found in a library’s research liaisons, collections experts, policy analysts, IT experts, archivists and preservationists. Like the library, research data management is not singular but multi-faceted. It requires collaboration, technology and policy analysis skills, and project management acumen. This paper examines research data management as a vital information, technical, and policy service in academic libraries today. It situates RDM not only as actions and services but also as a suite of responsibilities that require a high level of planning, collaboration, and judgment, thereby binding people to practice. It shows how RDM aligns with the skill sets and competencies of librarianship and illustrates how RDM spans the library’s organizational structure and intersects with campus stakeholders allied in the research enterprise

Big data for monitoring educational systems

This report considers “how advances in big data are likely to transform the context and methodology of monitoring educational systems within a long-term perspective (10-30 years) and impact the evidence based policy development in the sector”, big data are “large amounts of different types of data produced with high velocity from a high number of various types of sources.” Five independent experts were commissioned by Ecorys, responding to themes of: students' privacy, educational equity and efficiency, student tracking, assessment and skills. The experts were asked to consider the “macro perspective on governance on educational systems at all levels from primary, secondary education and tertiary – the latter covering all aspects of tertiary from further, to higher, and to VET”, prioritising primary and secondary levels of education

Music 2025 : The Music Data Dilemma: issues facing the music industry in improving data management

© Crown Copyright 2019Music 2025ʼ investigates the infrastructure issues around the management of digital data in an increasingly stream driven industry. The findings are the culmination of over 50 interviews with high profile music industry representatives across the sector and reflects key issues as well as areas of consensus and contrasting views. The findings reveal whilst there are great examples of data initiatives across the value chain, there are opportunities to improve efficiency and interoperability

Global Innovations in Measurement and Evaluation

We researched the latest developments in theory and practice in measurement and evaluation. And we found that new thinking, techniques, and technology are influencing and improving practice. This report highlights 8 developments that we think have the greatest potential to improve evaluation and programme design, and the careful collection and use of data. In it, we seek to inform and inspire—to celebrate what is possible, and encourage wider application of these ideas

A Framework for Integrating Transportation Into Smart Cities

In recent years, economic, environmental, and political forces have quickly given rise to “Smart Cities” -- an array of strategies that can transform transportation in cities. Using a multi-method approach to research and develop a framework for smart cities, this study provides a framework that can be employed to: Understand what a smart city is and how to replicate smart city successes; The role of pilot projects, metrics, and evaluations to test, implement, and replicate strategies; and Understand the role of shared micromobility, big data, and other key issues impacting communities. This research provides recommendations for policy and professional practice as it relates to integrating transportation into smart cities

Open Data, Grey Data, and Stewardship: Universities at the Privacy Frontier

As universities recognize the inherent value in the data they collect and hold, they encounter unforeseen challenges in stewarding those data in ways that balance accountability, transparency, and protection of privacy, academic freedom, and intellectual property. Two parallel developments in academic data collection are converging: (1) open access requirements, whereby researchers must provide access to their data as a condition of obtaining grant funding or publishing results in journals; and (2) the vast accumulation of 'grey data' about individuals in their daily activities of research, teaching, learning, services, and administration. The boundaries between research and grey data are blurring, making it more difficult to assess the risks and responsibilities associated with any data collection. Many sets of data, both research and grey, fall outside privacy regulations such as HIPAA, FERPA, and PII. Universities are exploiting these data for research, learning analytics, faculty evaluation, strategic decisions, and other sensitive matters. Commercial entities are besieging universities with requests for access to data or for partnerships to mine them. The privacy frontier facing research universities spans open access practices, uses and misuses of data, public records requests, cyber risk, and curating data for privacy protection. This paper explores the competing values inherent in data stewardship and makes recommendations for practice, drawing on the pioneering work of the University of California in privacy and information security, data governance, and cyber risk.Comment: Final published version, Sept 30, 201

Geospatial information infrastructures

Manual of Digital Earth / Editors: Huadong Guo, Michael F. Goodchild, Alessandro Annoni .- Springer, 2020 .- ISBN: 978-981-32-9915-3Geospatial information infrastructures (GIIs) provide the technological, semantic,organizationalandlegalstructurethatallowforthediscovery,sharing,and use of geospatial information (GI). In this chapter, we introduce the overall concept and surrounding notions such as geographic information systems (GIS) and spatial datainfrastructures(SDI).WeoutlinethehistoryofGIIsintermsoftheorganizational andtechnologicaldevelopmentsaswellasthecurrentstate-of-art,andreflectonsome of the central challenges and possible future trajectories. We focus on the tension betweenincreasedneedsforstandardizationandtheever-acceleratingtechnological changes. We conclude that GIIs evolved as a strong underpinning contribution to implementation of the Digital Earth vision. In the future, these infrastructures are challengedtobecomeflexibleandrobustenoughtoabsorbandembracetechnological transformationsandtheaccompanyingsocietalandorganizationalimplications.With this contribution, we present the reader a comprehensive overview of the field and a solid basis for reflections about future developments

Managing Research Data in Big Science

The project which led to this report was funded by JISC in 2010--2011 as part of its 'Managing Research Data' programme, to examine the way in which Big Science data is managed, and produce any recommendations which may be appropriate. Big science data is different: it comes in large volumes, and it is shared and exploited in ways which may differ from other disciplines. This project has explored these differences using as a case-study Gravitational Wave data generated by the LSC, and has produced recommendations intended to be useful variously to JISC, the funding council (STFC) and the LSC community. In Sect. 1 we define what we mean by 'big science', describe the overall data culture there, laying stress on how it necessarily or contingently differs from other disciplines. In Sect. 2 we discuss the benefits of a formal data-preservation strategy, and the cases for open data and for well-preserved data that follow from that. This leads to our recommendations that, in essence, funders should adopt rather light-touch prescriptions regarding data preservation planning: normal data management practice, in the areas under study, corresponds to notably good practice in most other areas, so that the only change we suggest is to make this planning more formal, which makes it more easily auditable, and more amenable to constructive criticism. In Sect. 3 we briefly discuss the LIGO data management plan, and pull together whatever information is available on the estimation of digital preservation costs. The report is informed, throughout, by the OAIS reference model for an open archive