Interdisciplinary Data Curation for Socio-Environmental Research

1. Andrea Thormer, UI Urbana Champagne
Scientifically significant sites have unique data curation needs
    -resource managers and curators typically need information more typical of “metadata”
-researchers need datasets collected with certain data points in order to reuse data and assess fitness-for-use
-both need info about unique natural phenomena, info about methods and collecting events
Can be a long time lag between data collection and data publication
    -Apply standards at point of data collection?
        Is this feasible? A good idea?
        Phases of iterative collection of refinement that make observations into data
            Many decision points
Potential problem of using standards early on: risks removing the scientists from key decisions about study design
Challenge: balancing idiosyncrasy (researchers’ priorities, often) and normativity (curators’ priorities, often)
Case study: Geobiology at Yellowstone NP
-Challenges to geobiology data integration at YNP: interdisciplinarity, heterogeneity, sociotechnicality
Two proposed solutions:
Minimum information framework for geobiology research: set of recommended parameters for data collection that could inform future standards work
Research process modeling: document workflows to make data duration easier & documenting data provenance with PROV
Case study: Paleontology at Rancho La Brea
-Challenges: idiosyncrasy, standards creep (people who apply the standard do so differently, e.g.), sociotechnicality (lack of adequate computing infrastructure, hesitation to share data, etc.)
From site-based to socioenvironmental
    -similar challenges to data integration around sites
    -site-based perspective could inform integration and curation efforts

2. Jim Duncan, Vermont Monitoring Cooperative
Interdisciplinary data for environmental monitoring
Each agency has their own mandate in terms of archiving data → big advantage for the VMC
VMC houses data related to forest ecosystem condition
Lessons in working with interdisciplinary data
    -heterogeneity - the reason and the challenge
        -field data
        -real-time sensor data
        -remote sensing data
-less traditional forms of data (land ownership change data, etc.) -- how to represent these effectively
→ need flexibility in standards, and flexibility in user interface (in what is displayed and having ability to drill down into the metadata)
-importance of linking to other catalogs - creating a complementary space for scientists to connect
-drivers of access and control concerns
-beyond the usual suspects (control, quality, use, and pubs) there can be discipline-specific types of concerns
-political concerns, locations of threatened/endangered species, plot location privacy concerns (e.g. FIA plots)
-embargo periods, designating things as sensitive, etc.
    -improving communication between the data world and the science world
-how can we articulate value of semantic of metadata-driven data systems for improving science and credit in a way that is meaningful to the broader research and monitoring community?
-there are a lot of possible avenues for data science to pursue that could make data sharing “better” for scientific discovery and testing - how can we help cross-disciplinary repositories prioritize those?
    -increasing ecological and social perspectives in earth science data sharing
        → why might this be is this hard?

3. Lindsay “Bar” Barbieri, University of Vermont
Earth Science Data
Description of research project to look at treatments to ag fields → GHG emissions
Economics → biophysical data (ag management) → ecosystem services → scale up to adoption of ag practices? Livelihoods? Policy? Leverage points? Etc.
Lessons learned
-interdisciplinary work is important to address societal and env. challenges
-data management plan → researchers: “what does that mean?”
-synthesis? Lack of scaffolding to make this possible
-importance of scale: individual research project vs “big picture” synthesis

4. Kelly Hondula, SESYNC
Socio-environmental synthesis
Synthesize data, ideas, theories to understand coupled social and biophysical systems; feedbacks and nonlinear interactions common; emergent properties and surprises common
Description of SESYNC and what they do/how they work
Mismatch of data across regions, scales
Data and knowledge gaps: many related to data and data access
Data challenges:
    -knowing what data “is” and exist outside domain expertise
    -need to revise Q based on data
    -linking biophysical data to ecosystem services
    -differing scales and extent of data

The conversation continues! Contact Lindsay Barbieri at [email protected] if you want to participate!
    -distributed groups working in short bursts (this is often linked to funding)    -authorship norms across disciplines