Tackling the challenge of interdisciplinary energy research: A research toolkit

Cohen, Jed; Azarova, Valeriya; Klöckner, Christian A.; Kollmann, Andrea; Löfström, Erica; Pellegrini‐Masini, Giuseppe; Polhill, Gary; Reichl, Johannes; Salt, Douglas

doi:10.1016/j.erss.2021.101966

Cited by 16 publications

(5 citation statements)

References 41 publications

(62 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A familiar analogy entity provides the audience with abundant information to relate immediately to their own experiences. While WordNet does not directly measure familiarity, many researchers use the frequency of each synset's occurrence in a semantic concordance as a familiarity score [14,30]. Therefore, we include the word frequency database as a component of our familiarity measurement.…”

Section: Familiaritymentioning

confidence: 99%

Beyond Numbers: Creating Analogies to Enhance Data Comprehension and Communication with Generative AI

Chen,

Shuai,

Zhang

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

Fig. 1. An example of the AnalogyMate system automatically suggests data analogy for the inputs. The system first suggests data analogies according to the input, then generates corresponding design solutions and visual representations for illustrations. Finally, we show a user-rendered version of one of the analogy designs. Unfamiliar measurements usually hinder readers from grasping the scale of the numerical data, understanding the content, and feeling engaged with the context. To enhance data comprehension and communication, we leverage analogies to bridge the gap between abstract data and familiar measurements. In this work, we first conduct semi-structured interviews with design experts to identify design problems and summarize design considerations. Then, we collect an analogy dataset of 138 cases from various online sources. Based on the collected dataset, we characterize a design space for creating data analogies. Next, we build a prototype system, AnalogyMate, that automatically suggests data analogies, their corresponding design solutions, and generated visual representations powered by generative AI. The study results show the usefulness of AnalogyMate in aiding the creation process of data analogies and the effectiveness of data analogy in enhancing data comprehension and communication.

show abstract

Section: Familiaritymentioning

confidence: 99%

Beyond Numbers: Creating Analogies to Enhance Data Comprehension and Communication with Generative AI

Chen,

Shuai,

Zhang

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

show abstract

“…In discussing the three key challenges we review in this paper, it is important to define the lexicon we will be using. MSD research is inherently interdisciplinary, and different communities of researchers focusing on different sectors often have different vocabularies, which is a fundamental challenge for interdisciplinary research teams (Bracken & Oughton, 2006; J. J. Cohen et al., 2021; Henson et al., 2020). MSD research, however, necessarily involves coupling and integration of simulation models and research outputs that may reflect differing disciplinary norms about the treatment of uncertainties.…”

Section: Types Of Uncertainty and Model Coupling Regimesmentioning

confidence: 99%

Uncertainty Analysis in Multi‐Sector Systems: Considerations for Risk Analysis, Projection, and Planning for Complex Systems

et al. 2022

View full text Add to dashboard Cite

Simulation models of multi‐sector systems are increasingly used to understand societal resilience to climate and economic shocks and change. However, multi‐sector systems are also subject to numerous uncertainties that prevent the direct application of simulation models for prediction and planning, particularly when extrapolating past behavior to a nonstationary future. Recent studies have developed a combination of methods to characterize, attribute, and quantify these uncertainties for both single‐ and multi‐sector systems. Here, we review challenges and complications to the idealized goal of fully quantifying all uncertainties in a multi‐sector model and their interactions with policy design as they emerge at different stages of analysis: (a) inference and model calibration; (b) projecting future outcomes; and (c) scenario discovery and identification of risk regimes. We also identify potential methods and research opportunities to help navigate the tradeoffs inherent in uncertainty analyses for complex systems. During this discussion, we provide a classification of uncertainty types and discuss model coupling frameworks to support interdisciplinary collaboration on multi‐sector dynamics (MSD) research. Finally, we conclude with recommendations for best practices to ensure that MSD research can be properly contextualized with respect to the underlying uncertainties.

show abstract

“…Indeed, over the last 20 years, there has been a plethora of studies that have focused explicitly on identifying the barriers to doing interdisciplinarity (e.g. Brewer 1999;Campbell 2005;Cohen et al 2021;Hein et al 2018;Kelly et al 2019;Lyall and Meagher 2012;Morse et al 2007;Wallace and Clark 2017). Such studies are often situated within a wider descriptive convention that lacks a conceptual bedrock to their discussion of interdisciplinarity.…”

Section: What Science and Technology Studies' Sociotechnical Underpinnings Have To Offermentioning

confidence: 99%

Introduction

Silvast

Foulds

2021

Sociology of Interdisciplinarity

View full text Add to dashboard Cite

This chapter provides background context on the calls for doing (more) interdisciplinarity and explains our own positioning as to what interdisciplinarity actually is, as well as what we believe this book contributes to the study of said interdisciplinarity. Specifically, we discuss mainstream arguments for why interdisciplinary research is deemed to be a worthwhile endeavour by many researchers, policymakers, funders, and so on. We build on this by arguing that there is a unique—and currently under-fulfilled—role to be played by Science and Technology Studies (STS) in exploring the sociological dimensions of how large-scale (energy) research projects are actually carried out. Alongside these wider landscape discussions, we explain what this book contributes to the study of interdisciplinarity and to energy research, through our empirics and STS-inspired ideas. We also make clear how we define interdisciplinarity and disciplines and explain how we focus on problem-focused research that may (or may not) involve external stakeholders.

show abstract

Tackling the challenge of interdisciplinary energy research: A research toolkit

Cited by 16 publications

References 41 publications

Beyond Numbers: Creating Analogies to Enhance Data Comprehension and Communication with Generative AI

Beyond Numbers: Creating Analogies to Enhance Data Comprehension and Communication with Generative AI

Uncertainty Analysis in Multi‐Sector Systems: Considerations for Risk Analysis, Projection, and Planning for Complex Systems

Introduction

Contact Info

Product

Resources

About