Understanding scientific data sharing outside of the academy

Sharing and reuse of scientific data, which can enhance the transparency and reproducibility of research and lead to the creation of new knowledge from existing data, is both a growing scholarly communication practice and an expanding area of interest in information science. However, much of the literature to date has focused on the data practices of scientists working in academic environments, with less research done on understanding the practices of scientists working in other types of environments, such as government or industry. This poster presents the results of a study in which data from a worldwide survey of scientists were analyzed to determine if differences in data practices, perceptions, and access to resources for data sharing existed between scientists who reported their primary work sector as academic and those who reported a non‐academic primary work sector. Researchers\u27 perceptions of data sharing and reuse were generally positive and did not differ significantly by work sector. However, differences were found in actual reported data sharing practices, even when controlling for researchers\u27 age, geographic location, and subject discipline. Researchers outside of academia had lesser odds of reporting sharing all their data. Differences were also found in reported barriers to data sharing, as well as in reported access to and use of data sharing resources, suggesting that data sharing challenges faced by scientists working outside of academia may differ from those faced by their academic peers. Implications for the adoption of data sharing practices and technologies, as well as for knowledge sharing and creation across work sectors, are discussed, and suggestions are offered for further research

Assessment, Usability, and Sociocultural Impacts of DataONE

DataONE, funded from 2009-2019 by the U.S. National Science Foundation, is an early example of a large-scale project that built both a cyberinfrastructure and culture of data discovery, sharing, and reuse. DataONE used a Working Group model, where a diverse group of participants collaborated on targeted research and development activities to achieve broader project goals. This article summarizes the work carried out by two of DataONE’s working groups: Usability & Assessment (2009-2019) and Sociocultural Issues (2009-2014). The activities of these working groups provide a unique longitudinal look at how scientists, librarians, and other key stakeholders engaged in convergence research to identify and analyze practices around research data management through the development of boundary objects, an iterative assessment program, and reflection. Members of the working groups disseminated their findings widely in papers, presentations, and datasets, reaching international audiences through publications in 25 different journals and presentations to over 5,000 people at interdisciplinary venues. The working groups helped inform the DataONE cyberinfrastructure and influenced the evolving data management landscape. By studying working groups over time, the paper also presents lessons learned about the working group model for global large-scale projects that bring together participants from multiple disciplines and communities in convergence research

Connected Developments: The Governance of Formal Global Knowledge Networks in Sustainability Transformations

Climate change adds pressure to the international community to work cooperatively, find ways to govern technologies and expert knowledge, develop better policies, and mobilise resources, tools, and practices to deal with potential consequences and impacts. The institutional drivers underpinning current knowledge applications in globally connected spaces of sustainable development practice are increasingly complex, intertwined, and empirically understudied. In this context, this PhD thesis aims to advance our empirical understanding of why and how formal cooperation networks form, negotiate, mobilise and utilise particular technologies and expert knowledge and attempt to steer visions and pathways for change. This research combines multi-sited ethnography with social network analysis and policy analysis and investigates formal contexts of global connection. This thesis examines practices of science and technology policy through technology-driven networks in multiple locations in Europe and Southeast Asia. In particular, this thesis analyses the processes and conditions through which tools (e.g. modelling technologies), practices (e.g. climate negotiations, technology transfer activities, risk management, and environmental planning), and ways of dealing with climate-related uncertainties are implemented in a global knowledge network articulated under the UN system. The participant observation that is applied in the research is grounded in mobile contexts of project-based interactions, intergovernmental negotiations, international expert meetings, high-level advisory boards, technology assessments, implementation of technology transfer programmes, capacity-building workshops, expert discussions on anticipation and uncertainty, and the production of reports, climate policies, and procurement systems. This thesis examines how the artefacts of transfer interact in the implementation of the Technology Mechanism under the UNFCCC, drawing on cases of climate and hydrological modelling ranging from the Climate Technology Centre and Network (CTCN) to Thailand and Myanmar. It maps and analyses the global response of networked organisations, with special attention to persistent North South power dynamics imposed by global environmental governance regimes and their emergent ‘transformational claims’. This thesis delves into a critical evaluation of transformational change narratives in institutionalised knowledge systems, practices of technology transfer, and science policy spaces inside the United Nations. It contributes to a better foundational understanding of knowledge governance relating to critical social and environmental challenges, and rethinks futures of collective climate action in light of sustainability transformations theory and practice

Intelligent sensing technologies for the diagnosis, monitoring and therapy of alzheimer’s disease:A systematic review

Alzheimer’s disease is a lifelong progressive neurological disorder. It is associated with high disease management and caregiver costs. Intelligent sensing systems have the capability to provide context-aware adaptive feedback. These can assist Alzheimer’s patients with, continuous monitoring, functional support and timely therapeutic interventions for whom these are of paramount importance. This review aims to present a summary of such systems reported in the extant literature for the management of Alzheimer’s disease. Four databases were searched, and 253 English language articles were identified published between the years 2015 to 2020. Through a series of filtering mechanisms, 20 articles were found suitable to be included in this review. This study gives an overview of the depth and breadth of the efficacy as well as the limitations of these intelligent systems proposed for Alzheimer’s. Results indicate two broad categories of intelligent technologies, distributed systems and self-contained devices. Distributed systems base their outcomes mostly on long-term monitoring activity patterns of individuals whereas handheld devices give quick assessments through touch, vision and voice. The review concludes by discussing the potential of these intelligent technologies for clinical practice while highlighting future considerations for improvements in the design of these solutions for Alzheimer’s disease