Science, clients, and the state : a study of scientific knowledge production and use

This study addresses two main issues that refer to aspects of the relationship between producers and users of research. The first half of the study is focused on the stage of the research process where research problems are selected. The role that potential users of research play in the selection of research problems is investigated, and the extent to which potential applications of research results are defined as important in problem choice. Second, the study analyses the ways in which scientific information is used by government agencies and examines the factors that affect the use of such information in bureaucratic decision-making, and in other activities of government agencies. The two main issues are addressed with a core focus on the possible effects of the organisational context within which research processes and bureaucratic utilisation of research are conducted. The empirical setting of this study is the agricultural and fishery sectors in Norway. Norwegian fisheries and agriculture are sectors where nature, science and public management are intertwined. The two sectors have organisational arrangements that underline the strong ties between science and public administration

Putting Research into Use (RIU): Technology Development for the Poor Farmer in Low Income countries

The Research Into Use programme aims to help agricultural research projects put its existing stock of knowledge into practical use rather than investing in new scientific research. In doing this, it hopes to increase the reach of the scientific projects in low-income countries and set up productive, sustainable and high impact technologic development projects

The Role of Middle Range Publications in the Development of Engineering Knowledge

This paper explores the role of publications in the development of engineering knowledge. Previous studies of scientific and technical publications tend to assume that engineers are like scientists in their use of scientific journals as a means of communicating new technical knowledge. But science differs from technology and we should not expect scientists and engineers to use the same sources of knowledge. We contend that previous studies of publications have been flawed because they ignore other forms of publication more suited to the communication of technical and engineering knowledge. This paper argues that technologists use "middle range" publications to exchange knowledge and explore implications of their technological experiences. By providing more visual images, experience-based reports and background information on technologies and products, middle range publications better reflect the ways in which engineers think and work. They allow for visual conversations and support visual communities. The paper provides a detailed exploration of the role of middle range publications and suggests a framework for future research on patterns of publication by technologists and engineers.engineering knowledge, engineering and design organisations, construction, scientific publications, technical publications, innovation studies

Scientific Knowledge Object Patterns

Web technology is revolutionizing the way diverse scientific knowledge is produced and disseminated. In the past few years, a handful of discourse representation models have been proposed for the externalization of the rhetoric and argumentation captured within scientific publications. However, there hasn’t been a unified interoperable pattern that is commonly used in practice by publishers and individual users yet. In this paper, we introduce the Scientific Knowledge Object Patterns (SKO Patterns) towards a general scientific discourse representation model, especially for managing knowledge in emerging social web and semantic web. © ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is going to be published in "Proceedings of 15th European Conference on Pattern Languages of Programs", (2011) http://portal.acm.org/event.cfm?id=RE197&CFID=8795862&CFTOKEN=1476113

The use of paintings and sketches as scientific knowledge

This article is written in the field of the philosophy of science. The aim is to express how painting and drawing can be used as part of a phenomenological research method. The painter or drawer is a visual researcher in the process of capturing a holistic and truthful experience of a cultural phenomenon. We will highlight the visual researcher process and how the experience of truth is known throughout this process. The paining and sketches, which we present in this article, are part of a book, together with written narratives and pedagogical theory – on teaching as a phenomenon – called Lærerpraksis og Pedagogisk teori. The paintings and drawings present teaching in a way that complements and expands the written text. The sketches and painting of teaching attempt to establish the truth as unconcealment of a phenomenon. Our argumentation is based on the theories of Gadamer, Cassirer, Panofsky and Heidegger. Gadamer connects humanistic research with artistry and the experience of truth. Cassirer argues that the perception of a cultural phenomenon begins as a holistic understanding to bring forth the symbolic form or essence of the phenomenon. Panofsky transfers the theory of Cassirer into the field of painting. The concept of synthetic intuition is the intrinsic knowing of a painting, which corresponds to Cassirer’s concept of symbolic forms. Heidegger’s theory explores how art unfolds and preserves the truth. We will argue that the connection between art and truth could bring forth important perspectives on phenomenological science and turn the research activity closer to an artistic form.publishedVersio

Scientific knowledge and scientific uncertainty in bushfire and flood risk mitigation: literature review

EXECUTIVE SUMMARY The Scientific Diversity, Scientific Uncertainty and Risk Mitigation Policy and Planning (RMPP) project aims to investigate the diversity and uncertainty of bushfire and flood science, and its contribution to risk mitigation policy and planning. The project investigates how policy makers, practitioners, courts, inquiries and the community differentiate, understand and use scientific knowledge in relation to bushfire and flood risk. It uses qualitative social science methods and case studies to analyse how diverse types of knowledge are ordered and judged as salient, credible and authoritative, and the pragmatic meaning this holds for emergency management across the PPRR spectrum. This research report is the second literature review of the RMPP project and was written before any of the case studies had been completed. It synthesises approximately 250 academic sources on bushfire and flood risk science, including research on hazard modelling, prescribed burning, hydrological engineering, development planning, meteorology, climatology and evacuation planning. The report also incorporates theoretical insights from the fields of risk studies and science and technology studies (STS), as well as indicative research regarding the public understandings of science, risk communication and deliberative planning. This report outlines the key scientific practices (methods and knowledge) and scientific uncertainties in bushfire and flood risk mitigation in Australia. Scientific uncertainties are those ‘known unknowns’ and ‘unknown unknowns’ that emerge from the development and utilisation of scientific knowledge. Risk mitigation involves those processes through which agencies attempt to limit the vulnerability of assets and values to a given hazard. The focus of this report is the uncertainties encountered and managed by risk mitigation professionals in regards to these two hazards, though literature regarding natural sciences and the scientific method more generally are also included where appropriate. It is important to note that while this report excludes professional experience and local knowledge from its consideration of uncertainties and knowledge, these are also very important aspects of risk mitigation which will be addressed in the RMPP project’s case studies. Key findings of this report include: Risk and scientific knowledge are both constructed categories, indicating that attempts to understand any individual instance of risk or scientific knowledge should be understood in light of the social, political, economic, and ecological context in which they emerge. Uncertainty is a necessary element of scientific methods, and as such risk mitigation practitioners and researchers alike should seek to ‘embrace uncertainty’ (Moore et al., 2005) as part of navigating bushfire and flood risk mitigation

Unsupervised word embeddings capture latent knowledge from materials science literature.

The overwhelming majority of scientific knowledge is published as text, which is difficult to analyse by either traditional statistical analysis or modern machine learning methods. By contrast, the main source of machine-interpretable data for the materials research community has come from structured property databases1,2, which encompass only a small fraction of the knowledge present in the research literature. Beyond property values, publications contain valuable knowledge regarding the connections and relationships between data items as interpreted by the authors. To improve the identification and use of this knowledge, several studies have focused on the retrieval of information from scientific literature using supervised natural language processing3-10, which requires large hand-labelled datasets for training. Here we show that materials science knowledge present in the published literature can be efficiently encoded as information-dense word embeddings11-13 (vector representations of words) without human labelling or supervision. Without any explicit insertion of chemical knowledge, these embeddings capture complex materials science concepts such as the underlying structure of the periodic table and structure-property relationships in materials. Furthermore, we demonstrate that an unsupervised method can recommend materials for functional applications several years before their discovery. This suggests that latent knowledge regarding future discoveries is to a large extent embedded in past publications. Our findings highlight the possibility of extracting knowledge and relationships from the massive body of scientific literature in a collective manner, and point towards a generalized approach to the mining of scientific literature

Simplifying the Development, Use and Sustainability of HPC Software

Developing software to undertake complex, compute-intensive scientific processes requires a challenging combination of both specialist domain knowledge and software development skills to convert this knowledge into efficient code. As computational platforms become increasingly heterogeneous and newer types of platform such as Infrastructure-as-a-Service (IaaS) cloud computing become more widely accepted for HPC computations, scientists require more support from computer scientists and resource providers to develop efficient code and make optimal use of the resources available to them. As part of the libhpc stage 1 and 2 projects we are developing a framework to provide a richer means of job specification and efficient execution of complex scientific software on heterogeneous infrastructure. The use of such frameworks has implications for the sustainability of scientific software. In this paper we set out our developing understanding of these challenges based on work carried out in the libhpc project.Comment: 4 page position paper, submission to WSSSPE13 worksho

Capturing Scientific Knowledge on Medical Risk Factors

In this paper, we describe a model for representing scientific knowledge of risk factors in medicine in an explicit format which enables its use for automated reasoning. The resulting model supports linking the conclusions of up-to-date clinical research with data relating to individual patients. This model, which we have implemented as an ontology-based system using Linked Data, enables the capture of risk factor knowledge and serves as a translational research tool to apply that knowledge to assist with patient treatment, lifestyle, and education. Knowledge captured using this model can be disseminated for other intelligent systems to use for a variety of purposes, for example, to explore the state of the available medical knowledge