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This essay discusses interdisciplinary research in the context of science policy and the practice of science. Comparisons between interdisciplinary research and other forms of cross-disciplinary research are made, and a brief discussion of the development of the concept of interdisciplinarity is provided. The overarching thesis of this essay is that interdisciplinary research is team research, that is, research conducted by a team. This notion is developed via recent policy discussions of team science and the need to understand interdisciplinary research in action. The author shows how it may be possible to consider the implementation of principles from teamwork and team training to improve interdisciplinary research and the practice of team science.

Cognitive processes in teams have been a valuable arena for team researchers to explore. Team cognition research advances and informs a variety of disciplines, including cognitive and social sciences, engineering, military science, organizational science, human factors, medicine, and communications. There has been a great deal of progress in the team cognition literature, yet the field is still in its early stages of maturity. There is much more to be gained from the field's insights and there is a need to unite the diverse array of scholarly ideas that permeate the field. This movement wil.

Knowledge integration in diverse teams depends on their integrative capacity—the social and cognitive processes, along with emergent states, that shape a team's ability to combine diverse knowledge. We argue that integrative capacity represents the potential that a team has to overcome various compositional, team, and contextual barriers to generating integrated and novel knowledge. This article focuses specifically on the unique challenges facing diverse science teams that have the goal of generating novel knowledge at the intersection of disciplinary, practice, and organizational boundaries. The integrative capacity of a science team is argued to help facilitate the social and cognitive integration processes necessary for effective team processes that enhance the likelihood of innovative team outcomes. Implications of our theoretical framework for practice and research on fostering innovation in diverse science teams are discussed.

Objective: A measure of play experience in video games was developed through literature review and two empirical validation studies. Background: Despite the considerable attention given to games in the behavioral sciences, play experience remains empirically underexamined. One reason for this gap is the absence of a scale that measures play experience. Method: In Study 1, the initial Play Experience Scale (PES) was tested through an online validation that featured three different games ( N = 203). In Study 2, a revised PES was assessed with a serious game in the laboratory ( N = 77). Results: Through principal component analysis of the Study 1 data, the initial 20-item PES was revised, resulting in the 16-item PES-16. Study 2 showed the PES-16 to be a robust instrument with the same patterns of correlations as in Study 1 via (a) internal consistency estimates, (b) correlations with established scales of motivation, (c) distributions of PES-16 scores in different game conditions, and (d) examination of the average variance extracted of the PES and the Intrinsic Motivation Scale. Conclusion: We suggest that the PES is appropriate for use in further validation studies. Additional examinations of the scale are required to determine its applicability to other contexts and its relationship with other constructs. Application: The PES is potentially relevant to human factors undertakings involving video games, including basic research into play, games, and learning; prototype testing; and exploratory learning studies.

Objective: This article presents a model for predicting complex collaborative processes as they arise in one-of-a-kind problem-solving situations to predict performance outcomes. The goal is to outline a set of key processes and their interrelationship and to describe how these can be used to predict collaboration processes embedded within problem-solving contexts. Background: Teams are increasingly called upon to address complex problem-solving tasks in novel situations. This represents a domain of performance that to date has been underrepresented in the research literature. Method: Multidisciplinary theoretical and empirical literature relating to knowledge work in teams is synthesized. Results: A set of propositions developed to guide research into how teams externalize cognition and build knowledge in service of problem solving is presented. First, a brief overview of macrocognition in teams is provided to distinguish the present work from other views of team cognition. Second, a description of the foundational theoretical concepts driving the theory of macrocognition in teams presented here is provided. Third, a set of propositions described within the context of a model of macrocognition in teams is forwarded. Conclusion: The theoretical framework described in this article provides a set of empirically testable propositions that can ultimately guide practitioners in efforts to support macrocognition in teams. Application: A theory of macrocognition in teams can provide guidance for the development of training interventions and the design of collaborative tools to facilitate knowledge-based performance in teams.

Swift starting action teams (STATs) are increasingly prevalent in organizations, and the development of trust is often a critical issue for their effectiveness. However, current theory and research do not provide a clear picture regarding how trust toward the team (i.e., the team as the target) is developed in these settings. The primary contribution of this article is to present a theoretical framework describing how individual-level trust toward one's team is developed in STAT contexts. This article integrates several existing trust theories into one comprehensive context-specific multilevel theory of how trust develops in STATs from cognitive, affective, behavioral, and contextual perspectives. This framework furthers our understanding of the unique antecedents of initial trust in STATs, how trust attitudes are adjusted over the short amount of time the team interacts, and how the team context influences this developmental process.

49 pags, 10 figs, 2 tabs. -- Supplementary data is available at the Publisher's web ; The goal of the Tropospheric Ozone Assessment Report (TOAR) is to provide the research community with an up-to-date scientific assessment of tropospheric ozone, from the surface to the tropopause. While a suite of observations provides significant information on the spatial and temporal distribution of tropospheric ozone, observational gaps make it necessary to use global atmospheric chemistry models to synthesize our understanding of the processes and variables that control tropospheric ozone abundance and its variability. Models facilitate the interpretation of the observations and allow us to make projections of future tropospheric ozone and trace gas distributions for different anthropogenic or natural perturbations. This paper assesses the skill of current-generation global atmospheric chemistry models in simulating the observed present-day tropospheric ozone distribution, variability, and trends. Drawing upon the results of recent international multi-model intercomparisons and using a range of model evaluation techniques, we demonstrate that global chemistry models are broadly skillful in capturing the spatio-temporal variations of tropospheric ozone over the seasonal cycle, for extreme pollution episodes, and changes over interannual to decadal periods. However, models are consistently biased high in the northern hemisphere and biased low in the southern hemisphere, throughout the depth of the troposphere, and are unable to replicate particular metrics that define the longer term trends in tropospheric ozone as derived from some background sites. When the models compare unfavorably against observations, we discuss the potential causes of model biases and propose directions for future developments, including improved evaluations that may be able to better diagnose the root cause of the model-observation disparity. Overall, model results should be approached critically, including determining whether the model performance is acceptable for the problem being addressed, whether biases can be tolerated or corrected, whether the model is appropriately constituted, and whether there is a way to satisfactorily quantify the uncertainty. ; A portion of the work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the NASA Aeronautics and Space Administration. A portion of the work was carried out the National Center for Atmospheric Research, which is operated by the University Corporation for Atmospheric Research under sponsorship of the National Science Foundation. PY acknowledges support from the Faculty of Science and Technology, Lancaster University. JB and UI acknowledge NordForsk under the Nordic Programme on Health and Welfare Project #75007: Understanding the link between air pollution and distribution of related health impacts and welfare in the Nordic countries (Nordic Welf Air); and the H2020-LCE project: Role of technologies in an energy efficient economy – model based analysis policy measures and transformation pathways to a sustainable energy system (REEEM), Grant agreement no.: 691739. GZ acknowledges the New Zealand Government's Strategic Science Investment Fund (SSIF) through the NIWA programme CACV. This work was supported by the Engineering and Physical Sciences Research Council [grant number EP/N027736/1] and the Natural Environment Research Council [grant number NE/N003411/1]. ; Peer reviewed

International audience ; Within ACCENT, a European Network of Excellence, eighteen atmospheric models from the U.S., Europe, and Japan calculated present (2000) and future (2030) concentrations of ozone at the Earth's surface with hourly temporal resolution. Comparison of model results with surface ozone measurements in 14 world regions indicates that levels and seasonality of surface ozone in North America and Europe are characterized well by global models, with annual average biases typically within 5–10 nmol/mol. However, comparison with rather sparse observations over some regions suggest that most models overestimate annual ozone by 15–20 nmol/mol in some locations. Two scenarios from the International Institute for Applied Systems Analysis (IIASA) and one from the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (IPCC SRES) have been implemented in the models. This study focuses on changes in near-surface ozone and their effects on human health and vegetation. Different indices and air quality standards are used to characterise air quality. We show that often the calculated changes in the different indices are closely inter-related. Indices using lower thresholds are more consistent between the models, and are recommended for global model analysis. Our analysis indicates that currently about two-thirds of the regions considered do not meet health air quality standards, whereas only 2–4 regions remain below the threshold. Calculated air quality exceedances show moderate deterioration by 2030 if current emissions legislation is followed and slight improvements if current emissions reduction technology is used optimally. For the "business as usual" scenario severe air quality problems are predicted. We show that model simulations of air quality indices are particularly sensitive to how well ozone is represented, and improved accuracy is needed for future projections. Additional measurements are needed to allow a more quantitative assessment of the risks to human health and vegetation from changing levels of surface ozone.

International audience ; Within ACCENT, a European Network of Excellence, eighteen atmospheric models from the U.S., Europe, and Japan calculated present (2000) and future (2030) concentrations of ozone at the Earth's surface with hourly temporal resolution. Comparison of model results with surface ozone measurements in 14 world regions indicates that levels and seasonality of surface ozone in North America and Europe are characterized well by global models, with annual average biases typically within 5–10 nmol/mol. However, comparison with rather sparse observations over some regions suggest that most models overestimate annual ozone by 15–20 nmol/mol in some locations. Two scenarios from the International Institute for Applied Systems Analysis (IIASA) and one from the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (IPCC SRES) have been implemented in the models. This study focuses on changes in near-surface ozone and their effects on human health and vegetation. Different indices and air quality standards are used to characterise air quality. We show that often the calculated changes in the different indices are closely inter-related. Indices using lower thresholds are more consistent between the models, and are recommended for global model analysis. Our analysis indicates that currently about two-thirds of the regions considered do not meet health air quality standards, whereas only 2–4 regions remain below the threshold. Calculated air quality exceedances show moderate deterioration by 2030 if current emissions legislation is followed and slight improvements if current emissions reduction technology is used optimally. For the "business as usual" scenario severe air quality problems are predicted. We show that model simulations of air quality indices are particularly sensitive to how well ozone is represented, and improved accuracy is needed for future projections. Additional measurements are needed to allow a more quantitative assessment of the risks to human health and ...

Within ACCENT, a European Network of Excellence, eighteen atmospheric models from the U.S., Europe, and Japan calculated present (2000) and future (2030) concentrations of ozone at the Earth's surface with hourly temporal resolution. Comparison of model results with surface ozone measurements in 14 world regions indicates that levels and seasonality of surface ozone in North America and Europe are characterized well by global models, with annual average biases typically within 5–10 nmol/mol. However, comparison with rather sparse observations over some regions suggest that most models overestimate annual ozone by 15–20 nmol/mol in some locations. Two scenarios from the International Institute for Applied Systems Analysis (IIASA) and one from the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (IPCC SRES) have been implemented in the models. This study focuses on changes in near-surface ozone and their effects on human health and vegetation. Different indices and air quality standards are used to characterise air quality. We show that often the calculated changes in the different indices are closely inter-related. Indices using lower thresholds are more consistent between the models, and are recommended for global model analysis. Our analysis indicates that currently about two-thirds of the regions considered do not meet health air quality standards, whereas only 2–4 regions remain below the threshold. Calculated air quality exceedances show moderate deterioration by 2030 if current emissions legislation is followed and slight improvements if current emissions reduction technology is used optimally. For the "business as usual" scenario severe air quality problems are predicted. We show that model simulations of air quality indices are particularly sensitive to how well ozone is represented, and improved accuracy is needed for future projections. Additional measurements are needed to allow a more quantitative assessment of the risks to human health and vegetation from changing levels of surface ozone.

International audience ; Within ACCENT, a European Network of Excellence, eighteen atmospheric models from the U.S., Europe, and Japan calculated present (2000) and future (2030) concentrations of ozone at the Earth's surface with hourly temporal resolution. Comparison of model results with surface ozone measurements in 14 world regions indicates that levels and seasonality of surface ozone in North America and Europe are characterized well by global models, with annual average biases typically within 5–10 nmol/mol. However, comparison with rather sparse observations over some regions suggest that most models overestimate annual ozone by 15–20 nmol/mol in some locations. Two scenarios from the International Institute for Applied Systems Analysis (IIASA) and one from the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (IPCC SRES) have been implemented in the models. This study focuses on changes in near-surface ozone and their effects on human health and vegetation. Different indices and air quality standards are used to characterise air quality. We show that often the calculated changes in the different indices are closely inter-related. Indices using lower thresholds are more consistent between the models, and are recommended for global model analysis. Our analysis indicates that currently about two-thirds of the regions considered do not meet health air quality standards, whereas only 2–4 regions remain below the threshold. Calculated air quality exceedances show moderate deterioration by 2030 if current emissions legislation is followed and slight improvements if current emissions reduction technology is used optimally. For the "business as usual" scenario severe air quality problems are predicted. We show that model simulations of air quality indices are particularly sensitive to how well ozone is represented, and improved accuracy is needed for future projections. Additional measurements are needed to allow a more quantitative assessment of the risks to human health and vegetation from changing levels of surface ozone.