Just how difficult can it be counting up R&amp;D funding for emerging technologies (and is tech mining with proxy measures going to be any better)?

Hopkins, Michael M.; Siepel, Josh

doi:10.1080/09537325.2013.801950

Cited by 24 publications

(13 citation statements)

References 62 publications

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“…The audiences for tech mining follow a structure similar to that of corporate R&D, but with a stronger focus on funding and strategy. Data sources are selected, and the inclusion criteria used reflect those audiences and purposes, with tech mining pulling data largely from the patent and engineering and technical databases, while systematic reviews extract data from a variety of health, life science, and social science databases, depending on the specific topic . Both of these bibliometric methods appropriately focus on data sources that match the domain of the question and target audience.…”

Section: Discussionmentioning

confidence: 99%

Systematic reviews and tech mining: A methodological comparison with case study

Anderson

Shannon

Bickett

et al. 2018

Research Synthesis Methods

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When the Medical Library Association identified questions critical for the future of the profession, it assigned groups to use systematic reviews to find the answers to these questions. Group 6, whose question was on emerging technologies, recognized early on that the systematic review process would not work well for this question, which looks forward to predict future trends, whereas the systematic review process looks back in time. We searched for new methodologies that were more appropriate to our question, developing a process that combined systematic review, text mining, and visualization techniques. We then discovered tech mining, which is very similar to the process we had created. In this paper, we describe our research design and compare tech mining and systematic review methodologies. There are similarities and differences in each process: Both use a defined research question, deliberate database selection, careful and iterative search strategy development, broad data collection, and thoughtful data analysis. However, the focus of the research differs significantly, with systematic reviews looking to the past and tech mining mainly to the future. Our comparison demonstrates that each process can be enhanced from a purposeful consideration of the procedures of the other. Tech mining would benefit from the inclusion of a librarian on their research team and a greater attention to standards and collaboration in the research project. Systematic reviews would gain from the use of tech mining tools to enrich their data analysis and corporate management communication techniques to promote the adoption of their findings.

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Section: Discussionmentioning

confidence: 99%

Systematic reviews and tech mining: A methodological comparison with case study

Anderson

Shannon

Bickett

et al. 2018

Research Synthesis Methods

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“…Nonetheless, the coverage of funding data remains limited (Hopkins and Siepel, 2013). A number of databases (e.g.…”

Section: Discussionmentioning

confidence: 99%

What is an Emerging Technology?

2015

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There is considerable and growing interest in the emergence of novel technologies, especially from the policy-making perspective. Yet as an area of study, emerging technologies lacks key foundational elements, namely a consensus on what classifies a technology as 'emergent' and strong research designs that operationalize central theoretical concepts. The present paper aims to fill this gap by developing a definition of 'emerging technologies' and linking this conceptual effort with the development of a framework for the operationalisation of technological emergence. The definition is developed by combining a basic understanding of the term and in particular the concept of 'emergence' with a review of key innovation studies dealing with definitional issues of technological emergence. The resulting definition identifies five attributes that feature in the emergence of novel technologies. These are: (i) radical novelty, (ii) relatively fast growth, (iii) coherence, (iv) prominent impact, and (v) uncertainty and ambiguity. The framework for operationalising emerging technologies is then elaborated on the basis of the proposed attributes. To do so, we identify and review major empirical approaches (mainly in, although not limited to, the scientometric domain) for the detection and study of emerging technologies (these include indicators and trend analysis, citation analysis, co-word analysis, overlay mapping, and combinations thereof) and elaborate on how these can be used to operationalise the different attributes of emergence.

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“…This section demonstrates how carefully gathered funding acknowledgements data, although labour intensive to prepare, can be used to provide research portfolio profiles for individual organisations as well as providing landscape overviews of wider funding environments. This is an important approach because alternatives based on self-reporting of funding inputs by research funders have a number of limitations including, inter alia, reliance on funders to provide data and different data collection and coding conventions amongst funders (Hopkins and Siepel, 2013). Although efforts are underway to bring together and classify funding inputs data (e.g.…”

Section: Exploring Uk Cancer Fundingmentioning

confidence: 99%

Funding Data from Publication Acknowledgements: Coverage, Uses and Limitations.

et al. 2016

Self Cite

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This article contributes to the development of methods for analysing research funding systems by exploring the robustness and comparability of emerging approaches to generate funding landscapes useful for policy making. We use a novel dataset of manually extracted and coded data on the funding acknowledgements of 7,510 publications representing UK cancer research in the year 2011 and compare these 'reference data' with funding data provided by Web of Science (WoS) and MEDLINE/PubMed. Findings show high recall (about 93%) of WoS funding data. By contrast, MEDLINE/PubMed data retrieved less than half of the UK cancer publications acknowledging at least one funder. Conversely, both databases have high precision (+90%): i.e. few cases of publications with no acknowledgement to funders are identified as having funding data. Nonetheless, funders acknowledged in UK cancer publications were not correctly listed by MEDLINE/PubMed and WoS in about 75% and 32% of the cases, respectively. 'Reference data' on the UK cancer research funding system are then used as a case-study to demonstrate the utility of funding data for strategic intelligence applications (e.g. mapping of funding landscape, comparison of funders' research portfolios).

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Just how difficult can it be counting up R&D funding for emerging technologies (and is tech mining with proxy measures going to be any better)?

Cited by 24 publications

References 62 publications

Systematic reviews and tech mining: A methodological comparison with case study

Systematic reviews and tech mining: A methodological comparison with case study

What is an Emerging Technology?

Funding Data from Publication Acknowledgements: Coverage, Uses and Limitations.

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