Socioenvironmental Team Science

Interlinking open science to team-based action research for socio-environmental cases

This paper discussed how open science, consisting of top-down open research data policies and bottom-up citizen science movements, can be interlinked with team-based action research for socio-environmental cases. Through the case studies of (1) waterweed recycling in the catchment of Lake Biwa, (2) participatory monitoring of alien species in Aso-Kuju, Oita, and (3) small-scale water supply management in Hokkaido, Japan, the authors found that team-based action research is often disrupted by socio-psychological boundaries, generated by asymmetric information, knowledge, value, socio-economic status, and power among actors, while such boundaries between actors can be spanned by sharing information, knowledge, and wisdom through appropriate visualization and dialogue. It also revealed the importance of inclusive and trans-sectoral knowledge-action networking in all three cases. Based on these notions, the authors are developing a methodology to interlink open science to team-based action research which consists of the following approaches, addressing socio-environmental issues. First, boundary spanning can be achieved by transcending, or discovering and sharing the goals that actors with different interests can tackle together, while carefully considering the ethical equity with special attention to empowering marginalized (or “small voice”) actors. Ethical equity is associated with fair data visualization, motivated by the FAIR (findable, accessible, interoperable, and reusable) data principles, and dialogue for solution. Civic Tech, or an open governance approach in which civic engineers develop a solution for local issues by using open government data and information and communication technologies, can be applied as a holistic approach.

To implement these concepts to technical operation of transdisciplinary research, open science is regarded as a movement of an open scientific knowledge production system rather than open scientific knowledge only. Open research data is implemented to the process as an input resource. As a method to engage societies to research experts, Civic Tech is applied to the team-based knowledge production, action, and networking to co-create a solution as outcome, which is then fed back to the resource to be used for next projects. This working hypothesis is being tested and further improved through the ongoing case studies. Through this process, open science and transdisciplinary theories will be integrated into a methodology of Open Team Science as a new research paradigm.

SciTS Presentation: Interlinking Open Science to Team-based Action Research for Socio-environmental cases

Adopting a Community Science Model of Team Science for Addressing Environmental Inequities

A team science model is generally used in academic, government, and industrial research settings to address large and complex problems.  While the transdisciplinary approach within team science assumes the creation of new methods to tackle difficult problems, the institutional foundation of team science limits the degree to which power can adequately be addressed.  We build on the team science model by reappropriating the term ‘community science’ in order to emphasize the significance of community members within the research team but also to diffusely connect the research team to the community in which it is engaging. Our project responds to silences created in the Flint water crisis narrative by using a community-driven research study aimed explicitly at elevating the frame of Flint residents in and around the Flint Water Crisis.  This paper describes the coming together of the research team and lessons learned over three interrelated research projects.  The three sub-projects include 1) a qualitative analysis of community sentiment provided during 17 recorded legislative, media, and community events, 2) an analysis of trust in the Flint community through nine focus groups across demographic groups (African American, Hispanic, seniors, and youth) of residents in Flint, and 3) an analysis of the role of the faith-based community in response to public health crises through two focus groups with faith based leaders from Flint involved with response efforts to the water crisis. 

The purpose of this paper is to describe a case analysis of the use of collaborative, community engaged, team science as a mechanism of community response to a crisis.  The key contributions of this paper include a community based participatory research (CBPR) approach to interpreting what a process of resiliency means for a community during and after a crisis. Through a CBPR approach to team science, we propose a new definition for team science that incorporates the transdisciplinarity of team science with the formal and informal educational experiences of community members in the bridged concept of “community science.” Throughout the process of research we have been deliberate in our transdisciplinary approach and have felt that the nature of the work in the Flint community in the context of the water crisis drives the development of new methods of engagement, collaboration, and research analysis.  We offer a model for community-based analysis of data for expressing community voice.  Finally, we suggest community science as a potential model for rebuilding trust in the scientific community in the context of violation by experts in a crisis situation. We define community science as this collaboration collectively operating through a team science model (while also extending beyond this model to emphasize a community orientation over an [academic] institutional frame) to address community member questions, analyze community-based study findings, and share an intentional community framed perspective on the interpretation of the research. We aim through this framework of community science to achieve true resident control in the conduct of research on communities.

SciTS Presentation: Adopting a Community Science Model of Team Science for Addressing Environmental Inequities

 Leading Large Interdisciplinary Research Teams: Lessons from LAGOS

Research to address large, complex, and interdisciplinary problems require scholars to work together as part of teams that include a variety of disciplines and perspectives, many of which are also distributed with a large number of people spread across institutions and nations. Although such large, interdisciplinary research teams have the capacity to accomplish a great deal, team leaders are also faced with challenges  both philosophical and logistical in nature. In this presentation, we describe our experiences co-leading two large interdisciplinary research teams associated with the LAGOS project (www.lagoslakes.org) that studies environmental research problems at the scale of the continental US. We focus on three areas that are especially important for science teams: (1) balancing individual and collaborative research modes; (2) identifying research questions that engage experts in multiple disciplines, thus advancing more than one discipline; and (3) keeping busy, productive, and creative scientists engaged when all are not at the same institution. We will present literature-based practices that we implemented to address these three important leadership challenges for our teams, share some recommended future research directions for science of team scientists, and share recommendations for other science team leaders.

SciTS Presentation: Co-leading large interdisciplinary research teams: Lessons from LAGOS

Deep Knowledge Integration Across Disciplines: The EMBeRS Method

Dr. Deana Pennington, University of Texas at El Paso; Dr. Kate Thompson, Griffith University; Dr. Shirley Vincent, Vincent Evaluation Consulting, LLC; Dr. David Gosselin, University of Nebraska at Lincoln

Deep knowledge integration across disciplines has been identified as one of seven key challenges confronting interdisciplinary team science. Deep knowledge integration depends on identifying and linking relevant conceptual frameworks in multiple disciplines that could contribute synergistically towards research on a shared problem; yet which frameworks are relevant depends on the problem that is going to be addressed, which can itself be framed in a multitude of ways. Such “ill-defined” problems are known to be inherently complex, requiring different skills than needed for addressing well defined problems. Common approaches for tacking ill-defined problems depend on effective team processes that facilitate participation and knowledge sharing.  Yet in interdisciplinary teams, such processes are inadequate because of extreme differences in vocabulary and epistemology. Facilitating team interactions that enhance participation does not address the issue of team members not understanding the deep knowledge that is being shared by their colleagues from other disciplines.

The EMBeRS project (Employing Model-Based Reasoning in Socio-Environmental Synthesis) is investigating the challenge of deep knowledge integration across disciplines as a learning problem. Team members must learn enough about each other’s research and the basic concepts that are fundamental to understanding it to be able to generate an internal mental model of each colleague’s research and connect those with their mental model of their own research. This must happen on-the-fly during teamwork and is an example of experiential learning. Once team members have learned enough to generate such connections across mental models, they have the capacity to generate a synergistic shared mental model of the problem. These connections and a shared mental model of the problem contribute towards development of a distributed cognitive system, from which a shared vision of research emerge. Shared vision is known to be a key factor determining the outcomes from team work.

This presentation will summarize the EMBeRS method for learning across disciplines to purposefully identify potential conceptual linkages between team members and converge through time on a shared mental model of the problem. The presentation will briefly summarize learning theories on which the method is based; provide a heuristic model for practices that facilitate learning in science teams derived from synthesizing those theories; describe the PhD workshops that are being used to test the EMBeRS method; and present results from qualitative and quantitative analysis of data collected during the workshops. Results show promise for development of a set of generic activities that can be used in interdisciplinary science teams to intentionally and purposefully integrate deep knowledge across disciplines, leading to development of a shared mental model of the problem and a path towards identifying a shared vision of synergistic research.

SciTS Presentation: Deep Knowledge Integration Across Disciplines: The EMBeRS Method

Managing Interdisciplinary Teams: Lessons Learned from Coupled Natural and Human Systems Modeling in Lake Catchments

Interdisciplinary team science is increasingly common in fields such as ecology, where it is used to investigate the multifaceted and reciprocal interactions between humans and their environment. Bringing together multiple disciplines allows researchers to examine ecological systems in a more holistic way, which is necessary to understand system behavior and forecast environmental outcomes. However, the increased effort and flexibility required to conduct interdisciplinary team science can pose significant roadblocks for many projects.

Here we present lessons learned from a coupled natural and human systems (CNHS) modeling project focused on freshwater lake catchments. Our project brings together more than 20 researchers from disciplines such as economics, hydrology, agronomy, and social psychology. These team members span more than six institutions that are distributed geographically across the eastern and Midwestern United States. Furthermore, the team includes scientists from a diversity of career stages, including undergraduate students all the way to professors emeritus.

Two separate threads of the scientific literature examine the science of team science and address the factors that support project management. However, little exists at the intersection of these two lines of inquiry, which defines the practical, day-to-day aspects of conducting interdisciplinary research. Given that the study of CNHS in particular involves a suite of activities and characteristics that can compound team science related challenges, operational guidelines for approaching these projects are likely to be beneficial for similar teams. Our objective is to use our case study to develop best practices for CNHS research including a blueprint for potential risks and challenges as well as a set of strategies for dealing with them.

Over the past three years, our team has grappled with large team size, geographically distributed researchers, complex interdisciplinary integration, and more. Drawing on established frameworks from the science of team science and project management literatures, we delve into the lessons we have learned to support CNHS research in general. Examples of questions we address include: How do we build QA/QC into the process of moving datasets between sub-teams by establishing metadata standards, platforms for dataset sharing, and versioning control? How do we formalize our expectations for collaborative manuscript writing, while allowing for the flexibility to incorporate new types of research products and disciplinary publication standards? Which management techniques and meeting strategies have been most successful in driving forward coupled modeling efforts?

Our answers to these questions contribute to the growing body of science of team science knowledge. They highlight the types of challenges that arise in interdisciplinary team science in the field of ecology, and identify potential strategies teams may use to address them. Insights from our experience provide a starting point that may be adapted to support projects that share some similarities to ours, whether in content or structure. Ultimately, we seek to support effective and efficient collaborative team science in order to advance the state of CNHS modeling. Such advancement will help the scientific community move toward developing a holistic understanding of complex freshwater systems and support policy development and management of these scarce resources.

SciTS Presentation: Managing interdisciplinary teams: Lessons learned from coupled natural human systems modeling in lake catchments