Interdisciplinary research: the importance of learning other people’s language

Bowman, Adrian

doi:10.1007/s10182-007-0039-9

Cited by 9 publications

(3 citation statements)

References 6 publications

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“…The implementation of chromatography models that account for the biochemical and physicochemical properties of a system while following the rules and operations of algebra requires a bidirectional exchange of knowledge between experimenters and data scientists. This can be hampered by differences in problem solving approaches, limited knowledge about the possibilities and limitations of the complementary scientific domain, and the use of discipline-specific terminology and jargon ( Bracken and Oughton 2006 ; Bowman 2007 ; Monteiro and Keating 2009 ). For example, the term “transformation” has multiple distinct meanings in biology, chemistry, physics and mathematics, and it may not always be clear which sense is implied in a multidisciplinary context.…”

Section: Challenge I: Communication Barriers Can Slow Down Interdisci...mentioning

confidence: 99%

The use of predictive models to develop chromatography-based purification processes

Bernau

Knödler

Emonts

et al. 2022

Front. Bioeng. Biotechnol.

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Chromatography is the workhorse of biopharmaceutical downstream processing because it can selectively enrich a target product while removing impurities from complex feed streams. This is achieved by exploiting differences in molecular properties, such as size, charge and hydrophobicity (alone or in different combinations). Accordingly, many parameters must be tested during process development in order to maximize product purity and recovery, including resin and ligand types, conductivity, pH, gradient profiles, and the sequence of separation operations. The number of possible experimental conditions quickly becomes unmanageable. Although the range of suitable conditions can be narrowed based on experience, the time and cost of the work remain high even when using high-throughput laboratory automation. In contrast, chromatography modeling using inexpensive, parallelized computer hardware can provide expert knowledge, predicting conditions that achieve high purity and efficient recovery. The prediction of suitable conditions in silico reduces the number of empirical tests required and provides in-depth process understanding, which is recommended by regulatory authorities. In this article, we discuss the benefits and specific challenges of chromatography modeling. We describe the experimental characterization of chromatography devices and settings prior to modeling, such as the determination of column porosity. We also consider the challenges that must be overcome when models are set up and calibrated, including the cross-validation and verification of data-driven and hybrid (combined data-driven and mechanistic) models. This review will therefore support researchers intending to establish a chromatography modeling workflow in their laboratory.

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Section: Challenge I: Communication Barriers Can Slow Down Interdisci...mentioning

confidence: 99%

The use of predictive models to develop chromatography-based purification processes

Bernau

Knödler

Emonts

et al. 2022

Front. Bioeng. Biotechnol.

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“…Worse, they had not really come up for discussion at all. The research had not spoken to those at the workshop (it did not 'speak their language'; Bowman, 2007). As a consequence, the researcher voiced a keen sense of what we might call 'professional disappointment'.…”

Section: Statistical Publics and Theatres Of Proofmentioning

confidence: 99%

Statistical Practice: Putting Society on Display

Mair

Greiffenhagen

Sharrock

2015

Theory, Culture & Society

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As a contribution to current debates on the 'social life of methods', in this article we present an ethnomethodological study of the role of understanding within statistical practice. After reviewing the empirical turn in the methods literature and the challenges to the qualitative-quantitative divide it has given rise to, we argue such case studies are relevant because they enable us to see different ways in which 'methods', here quantitative methods, come to have a social life-by embodying and exhibiting understanding they 'make the social structures of everyday activities observable' (Garfinkel, 1967: 75), thereby putting society on display. Exhibited understandings rest on distinctive lines of practical social and cultural inquiry-ethnographic 'forays' into the worlds of the producers and users of statistics-which are central to good statistical work but are not themselves quantitative. In highlighting these non-statistical forms of social and cultural inquiry at work in statistical practice, our case study is an addition to understandings of statistics and usefully points to ways in which studies of the social life of methods might be further developed from here.

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“…[8] Other authors have pointed to broader factors, such as diversity in practices and perspectives offered by each discipline, and socio-cultural factors [9] that can negatively impact interdisciplinary collaboration. In particular, their insights point to communication as one of the most common barriers in interdisciplinary research, [10] , [11] and therefore they emphasize the need to learn the language of other specialists when working in interdisciplinary teams. Despite the interesting and potentially valuable concerns and suggestions expressed by these authors, their opinions are not based on evidence, which potentially precludes implementation of suggested procedures aimed at improving communication between these specialists.…”

Section: Introductionmentioning

confidence: 99%

Qualitative Analysis of the Interdisciplinary Interaction between Data Analysis Specialists and Novice Clinical Researchers

et al. 2010

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BackgroundThe inherent complexity of statistical methods and clinical phenomena compel researchers with diverse domains of expertise to work in interdisciplinary teams, where none of them have a complete knowledge in their counterpart's field. As a result, knowledge exchange may often be characterized by miscommunication leading to misinterpretation, ultimately resulting in errors in research and even clinical practice. Though communication has a central role in interdisciplinary collaboration and since miscommunication can have a negative impact on research processes, to the best of our knowledge, no study has yet explored how data analysis specialists and clinical researchers communicate over time.Methods/Principal FindingsWe conducted qualitative analysis of encounters between clinical researchers and data analysis specialists (epidemiologist, clinical epidemiologist, and data mining specialist). These encounters were recorded and systematically analyzed using a grounded theory methodology for extraction of emerging themes, followed by data triangulation and analysis of negative cases for validation. A policy analysis was then performed using a system dynamics methodology looking for potential interventions to improve this process. Four major emerging themes were found. Definitions using lay language were frequently employed as a way to bridge the language gap between the specialties. Thought experiments presented a series of “what if” situations that helped clarify how the method or information from the other field would behave, if exposed to alternative situations, ultimately aiding in explaining their main objective. Metaphors and analogies were used to translate concepts across fields, from the unfamiliar to the familiar. Prolepsis was used to anticipate study outcomes, thus helping specialists understand the current context based on an understanding of their final goal.Conclusion/SignificanceThe communication between clinical researchers and data analysis specialists presents multiple challenges that can lead to errors.

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Interdisciplinary research: the importance of learning other people’s language

Abstract: Communication, Consulting, Education, Interdisciplinary, Statistics,

Cited by 9 publications

References 6 publications

The use of predictive models to develop chromatography-based purification processes

The use of predictive models to develop chromatography-based purification processes

Statistical Practice: Putting Society on Display

Qualitative Analysis of the Interdisciplinary Interaction between Data Analysis Specialists and Novice Clinical Researchers

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