Exploiting The Digital Revolution: Developing Capacity And Integrating Data Across The Disciplines Of Science (original) (raw)
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Building digital workforce capacity and skills for data-intensive science
OECD science, technology and industry policy papers, 2020
This report looks at the human resource requirements for data intensive science. The main focus is on research conducted in the public sector and the related challenges and training needs. Digitalisation is, to some extent, being driven by science and at the same time it is affecting all aspects of scientific practice. Open Science, including access to data, is being widely promoted and there is increasing investment in cyber-infrastructures and digital platforms but the skills that are required by researchers and research support professionals to fully exploit these tools are not being given adequate attention. The COVID-19 pandemic, which struck as this report was being finalised, has served to emphasise the critical importance of data intensive science and the need to take a strategic approach to strengthen the digital capacity and skills of the scientific enterprise as whole. This report includes policy recommendations for various actors and good practice examples to support these recommendations. Defining needs Provision of training Community building Career paths rewards Broader enablers National/regional governments Research agencies Professional science associations Research institutes and infrastructures Universities Key: Large ticks denote areas where actors can exert significant change; small ticks indicate where actors can have a smaller influence. NOTE: This table shows a generalised view of where different actors can have the most significant impact; an individual organisation's actual opportunity areas may differ in practice.
Charting the digital transformation of science
2020
This paper presents the results of the 2018 OECD International Survey of Scientific Authors (ISSA2), a global online survey designed and implemented to measure the key features of the digital transformation of science. The paper explores the potential impacts of digitalisation based on a combination of different indicators on research impact and responses from nearly 12 000 authors across the world. The evidence shows that although digital activity is pervasive, the transformation is uneven across fields and sectors, and is influenced by factors such as norms, experience, skills and data availability. Overall, scientists appear to be optimistic about the potential of digitalisation, especially in relation to the efficiency of research and collaboration across national borders. This paper is also the first analysis to leverage a new OECD approach to data collection in priority science policy topics for which evidence might be scarce or insufficiently timely.
2013
Established and emerging European research infrastructures are holding or will in the near future hold immense quantities of data. Power lies not only in storing and managing these data, but especially also in making them available and accessible to a wider audience, across national borders, scientific communities and disciplines, and by integrating datasets so that more complex scientific questions can be solved. This has challenges, many of which are shared between different scientific communities. To exchange existing expertise and address obstacles, the BioMedBridges, CRISP, DASISH and ENVRI projects-covering the biomedical sciences, physics, social science and humanities, and environmental sciences-have come together to identify cross-cutting topics, discuss current approaches and develop recommendations for future actions needed to solve them.
Create, curate, re-use: the expanding life course of digital research data
2007
Scientific communication used to be based on the article or the monograph. Now datasets and databases are becoming as important in some cases. Aside from their value in communication, data are also the raw stuff of the scientific record, and the basis for verifiability. So scientists need to curate the data they create, and make them available for re-use. What are the implications and effects of these changes, and what should scientists and scholars be doing about them? Curation Curation is not a new term, being well established particularly in art and museum practice. However, it is relatively new in relation to data. We are now generally well aware that there are issues relating to the long term preservation of digital data (known as digital preservation), but digital curation is more than this: maintaining and adding value to a trustworthy body of digital information over the life-cycle of scholarly and scientific materials, for current and future use. Implicit in this is that data are thoughtfully created, carefully managed and curated, and re-used in a disciplined way, where and when appropriate. Also implicit is that curation is a whole life process, with potentially evolving digital objects. Curation is clearly domain-dependent, with significant issues relating to size, numbers of objects, complexity of objects, interventions needed, ethical and legal implications, policies, practices, standards and incentives. The Digital Curation Centre (see http://www.dcc.ac.uk) takes a broad view of digital curation. Whilst not exclusively data-oriented, we predominantly focus on data resources for science and scholarship. We are concerned with: • The sustainability of the resource. • The creation or appraisal, selection, acquisition and ingest of the resource, • Growth, development of and changes to the resource, • Making the resource available ("publishing" it), • Access management and other controls on the resource, and the ethical and legal basis of these controls, • The ability to use, combine, re-combine, inter-operate, process, annotate, discuss and review the resource through time (some of which processes will in turn contribute to the development of the resource), • Linkage, context and metadata relating to the resource, • Maintaining authenticity, integrity, provenance and computational lineage information relating to the resource,
Mind the Gap: Big Data vs. Interoperability and Reproducibility of Science
Earth Observation Open Science and Innovation, 2018
The global landscape in the supply, creation and use of geospatial data is changing very rapidly with new satellites, sensors and mobile devices reconfiguring the traditional lines of demand and supply, and the number of actors involved. As the volume, heterogeneity and rapidity of change of the data increases many organisations worldwide are reflecting on how to manage and exploit Big Data. The opportunities are many for business, science and policy but so are the challenges at technical, methodological, organisational, legal and ethical levels. In this chapter, we situate the discussion of Big Data in the context of the increasing challenges of the scientific method in a world of contested politics, in which science can no longer be seen as "neutral". We argue for a more open and participative science starting from the shared framing of problems across multiple stakeholders. In this context, the reproducibility of science is not just about the ability to repeat an experiment but also about the transparency of the process leading to a shared outcome. Opening up science to make it truly participative will need a major paradigm shift. It also needs an underpinning information infrastructure geared towards sharing data, information and knowledge across multidisciplinary and transdisciplinary boundaries. We use the development of the Global Earth Observation System of System (GEOSS) as a case study, because it highlights well the nature of these challenges when handling multidisciplinary Big Data across more than 80 countries and 90 international organisations. As we show, there is an increasing gap between the rapidity of technological progress and the slow pace of the organisational and
How Has Your Science Data Grown? Digital Curation and the Human Factor, a Critical Literature Review
Archival Science, 2014
Focusing on North America and the United Kingdom, this critical literature review underscores the ways in which sharing, accessing, and reusing science data allow researchers and other stakeholders to address new imperatives in scientific research. Science data stakeholders should harness the principles and practices of digital curation, an overarching concept that encompasses data curation and that centers on adding value to digital data assets. This review first probes data sharing, access, and reuse in specific intellectual and institutional contexts. Next, it examines the ways in which science data sharing, access, and reuse benefits scholarship, primarily by encouraging new research questions and by allowing the reproduction of previous findings. Third, it addresses the infrastructure of science data curation, particularly the roles of cyberinfrastructure, research communities, collaboration, planning, policy, and standards and best practices. Fourth, it turns to the role of institutions—archives, research libraries, institutional repositories, and centers—in curating science data. Archival principles such as provenance, selection and appraisal, authenticity, metadata, risk management, and trust play a pivotal role in digital curation. Finally, it delineates avenues for further research such as sustainability, costing, planning and policy, training and education, researcher practices, and raising awareness.
Digital Science: Cyberinfrastructure, e-Science and Citizen Science
Progress in IS, 2018
Digital change and scientific development have mutual implications. On one hand, science and technology development has been a major factor to digital change. On the other hand, the digital era has brought major changes to scientific knowledge production. First, there is a cyberinfrastructure—not only infrastructure for computing, but a major virtual lab where all professionals in science and technology (e.g., researchers, engineers, technicians) can collaborate and exchange data, information, and knowledge. In Europe, this new infrastructure is referred to as e-science. Second, the digital era has increased coproduction beyond frontiers of traditional players, bringing other participants to scientific development. Such kind of co-work is central to both citizen science and transdisciplinary knowledge coproduction, where non-academic players engage in activities such as planning, data gathering, and impact assessment of science. In this chapter, we define digital science as a convergent phenomenon of cyberinfrastructure, e-science, citizen science and transdisciplinarity. We examine how digital science has been a disruptive factor to traditional scientific development, changing productivity, expanding frontiers and challenging traditional processes in science, such as planning and assessment.
Education for eScience Professionals: Integrating Data Curation and Cyberinfrastructure
International Journal of Digital Curation, 2011
Large, collaboratively managed datasets have become essential to many scientific and engineering endeavors, and their management has increased the need for "eScience professionals" who solve large scale information management problems for researchers and engineers. This paper considers the dimensions of work, worker, and workplace, including the knowledge, skills, and abilities needed for eScience professionals. We used focus groups and interviews to explore the needs of scientific researchers and how these needs may translate into curricular and program development choices. A cohort of five masters students also worked in targeted internship settings and completed internship logs. We organized this evidence into a job analysis that can be used for curriculum and program development at schools of library and information science. 1