Qualitative data sharing and re-use for socio-environmental systems research: A synthesis of opportunities, challenges, resources and approaches (original) (raw)

Qualitative data sharing and synthesis for sustainability science

Nature Sustainability, 2019

Socio-environmental synthesis as a research approach contributes to broader sustainability policy and practice by reusing data from disparate disciplines in innovative ways. Synthesizing diverse data sources and types of evidence can help to better conceptualize, investigate and address increasingly complex socio-environmental problems. However, sharing qualitative data for re-use remains uncommon when compared to sharing quantitative data. We argue that qualitative data present untapped opportunities for sustainability science, and discuss practical pathways to facilitate and realize the benefits from sharing and reusing qualitative data. However, these opportunities and benefits are also hindered by practical, ethical and epistemological challenges. To address these challenges and accelerate qualitative data sharing, we outline enabling conditions and suggest actions for researchers, institutions, funders, data repository managers and publishers.

Setting the stage for a Shared Environmental Information System

Environmental Science & Policy, 2019

There has been a significant increase in efforts to improve environmental data sharing practices in the past decade. One such initiative is the Shared Environmental Information System (SEIS), initiated by the European Commission in 2008, as part of a process to facilitate regular environmental assessments and State-of-the-Environment Reporting (SOER). Using SEIS as a case study example, this paper takes its departure from the 8th Environment for Europe (EFE) Ministerial conference to identify ongoing processes and challenges surrounding environmental data and information sharing. The paper relies on data obtained for the 2016 report on progress in establishing SEIS in support of regular reporting in the pan-European region. The article demonstrates a number of gaps with regards to the availability and accessibility of certain environmental datasets and indicators and highlights the suboptimal use of information, where comprehensive data flows and high-quality information is not being used adequately in support of policymaking or where there is selective use of environmental indicators. Against this background, questions arise as to whether applied models for data sharing can be implemented with equal success across different regions and countries that are characterized by heterogeneous and complex data practices and data flows. Most importantly, results from the SEIS progress report demonstrate the pressing need for a better understanding of environmental data types, data packaging and data flows across multiples contexts, epistemic cultures and policy making.

Revisiting Qualitative Data Reuse

SAGE Open, 2017

Secondary analysis of qualitative data entails reusing data created from previous research projects for new purposes. Reuse provides an opportunity to study the raw materials of past research projects to gain methodological and substantive insights. In the past decade, use of the approach has grown rapidly in the United Kingdom to become sufficiently accepted that it must now be regarded as mainstream. Several factors explain this growth: the open data movement, research funders’ and publishers’ policies supporting data sharing, and researchers seeing benefits from sharing resources, including data. Another factor enabling qualitative data reuse has been improved services and infrastructure that facilitate access to thousands of data collections. The UK Data Service is an example of a well-established facility; more recent has been the proliferation of repositories being established within universities. This article will provide evidence of the growth of data reuse in the United Kin...

Frontiers in socio-environmental research: components, connections, scale, and context

Ecology & Society, 2018

The complex and interdisciplinary nature of socio-environmental (SE) problems has led to numerous efforts to develop organizing frameworks to capture the structural and functional elements of SE systems. We evaluate six leading SE frameworks, i.e., human ecosystem framework, resilience, integrated assessment of ecosystem services, vulnerability framework, coupled human-natural systems, and social-ecological systems framework, with the dual goals of (1) investigating the theoretical core of SE systems research emerging across diverse frameworks and (2) highlighting the gaps and research frontiers brought to the fore by a comparative evaluation. The discussion of the emergent theoretical core is centered on four shared structuring elements of SE systems: components, connections, scale, and context. Cross-cutting research frontiers include: moving beyond singular case studies and small-n studies to meta-analytic comparative work on outcomes in related SE systems; combining descriptive and data-driven modeling approaches to SE systems analysis; and promoting the evolution and refinement of frameworks through empirical application and testing, and interframework learning.

Finding Common Ground in Managing Data Used for Regional Environmental Assessments

Environmental Monitoring and Assessment

Evaluating the overall environmental health of a region invariably involves using databases from multiple organizations. Several approaches to deal with the related technological and sociological issues have been used by various programs. Flexible data systems are required to deal with rapid changes in technology, the social and political climate for sharing and integrating data, and expectations of diverse users. Here we describe how the Environmental Monitoring and Assessment Program and the Chesapeake Bay Program manage their data for regional studies. These programs, which encompass areas of different geographic scales but face similar issues, have adopted some solutions in common, but also have tried some unique solutions suited to their needs. Understanding the tribulations and successes of these programs may help others attempting similar assessments. Both these programs have embraced distributed data systems that are managed by the organizations owning them. Both use common ...

Promoting synergy in the innovative use of environmental data—Workshop summary

Open-File Report

From December 2 to 4, 2015, NatureServe and the U.S. Geological Survey organized and hosted a biodiversity and ecological informatics workshop at the U.S. Department of the Interior in Washington, D.C. The workshop objective • Management and delivery of the necessary data, tools, and analyses to sustain our Nation's environmental capital must be a collaborative effort between Federal, State, and local governments, academia, nonprofits, and the commercial sector, even though the responsibilities of each sector are different.

Qualitative data preservation and sharing in the social sciences: On whose philosophical terms?

Australian Journal of Social Issues, …, 2009

Over the past decade, an academic debate has developed surrounding qualitative data preservation and sharing in the social sciences, and has been characterised as one between supporters and opponents of this movement. We reframe the debate by suggesting that so-called 'opponents' are not resistant to the principle of data preservation and sharing, but ambivalent about how this principle is being put into practice. Specifically, qualitative researchers are uneasy about the foundational assumptions underpinning current data preservation and sharing policies and practices. Efforts to address these concerns argue that the inclusion of the 'contexts' of data generation, preservation and reuse will adequately resolve the epistemological concerns held by the qualitative research community. However, these 'solutions' reproduce foundational assumptions by treating 'context' as ontologically separate from, rather than constitutive of, data. The future of qualitative data preservation and sharing in the social sciences is dependent on shedding its implicit unitary foundational model of qualitative research, and embracing 'epistemic pluralism' and the diversity of philosophical perspectives representing the qualitative researcher community.

A review of the social-ecological systems framework: applications, methods, modifications, and challenges

Ecology and Society, 2018

The social-ecological systems framework (SESF) is arguably the most comprehensive conceptual framework for diagnosing interactions and outcomes in social-ecological systems (SES). This article systematically reviews the literature applying and developing the SESF and discusses methodological challenges for its continued use and development. Six types of research approaches using the SESF are identified, as well as the context of application, types of data used, and commonly associated concepts. The frequency of how each second-tier variable is used across articles is analyzed. A summary list of indicators used to measure each second-tier variable is provided. Articles suggesting modifications to the framework are summarized and linked to the specific variables. The discussion reflects on the results and focuses on methodological challenges for applying the framework. First, how the SESF is historically related to commons and collective action research. This affects its continued development in relation to inclusion criteria for variable modification and discourse in the literature. The framework may evolve into separate modified versions for specific resource use sectors (e.g., forestry, fisheries, food production, etc.), and a general framework would aggregate the generalizable commonalities between them. Methodological challenges for applying the SESF are discussed related to research design, transparency, and cross-case comparison. These are referred to as "methodological gaps" that allow the framework to be malleable to context but create transparency, comparability, and data abstraction issues. These include the variable-definition gap, variable-indicator gap, the indicator-measurement gap, and the data transformation gap. A benefit of the framework has been its ability to be malleable and multipurpose, bringing a welcomed pluralism of methods, data, and associated concepts. However, pluralism creates challenges for synthesis, data comparison, and mutually agreed-upon methods for modifications. Databases are a promising direction forward to help solve this problem. In conclusion, future research is discussed by reflecting on the different ways the SESF may continue to be a useful tool through (1) being a general but adaptable framework, (2) enabling comparison, and (3) as a diagnostic tool for theory building.