Community concept mapping activity March 2020 through July 2020

From Earth Science Information Partners (ESIP)

Objective

Between March 2020 and July 2020, we aim to create a small collection of concept maps ("soup") that ESIP members are encouraged to contribute to. Concept maps in the soup may be viewed from the perspective of the schema below. The ultimate goal is to avail to the community a collection of concepts that can be used to assemble a "data to decisions" pathway, where individual concepts are represented as subject-predicate-object triples. Such pathways may be constructed from an ordered sequence of concepts (i.e. ordered triples) that traces how data is transformed into information and knowledge for decisions. These triples and ordered-triple-sequences may then further be exposed to algorithms for machine-assisted discovery.

These ideas were first developed as part of an 2018 ESIP Lab Project and documented in one of the project deliverables. Click here to render the full-scale schema in your browser window.


What we need from you

There is likely no one individual who has the span of knowledge to credibly reproduce a comprehensive data-to-decisions pathway. We have sketched out a notional pathway, and hope that you can contribute filling in that pathway by contributing your expertise at various spots along the pathway. The figure below sketches out a hypothetical pathway to gives you a rough idea of what we mean, and shows you how you can plug-in your own concept map:

2020-03-20.Concept map icons for the data-to-decisions demo path wiki page v1.1.png

Why we're doing this

  • Context awareness and knowing where you fit in the big picture. So that professionals like yourself are able to see how technology you develop may be traced to a real-world applied problem, or vice versa.
  • Context-aware knowledge discovery. So that the community can start experimenting with using natural language processing methods to parse human-readable concept maps. Such maps are not as machine-readable as, say, an ontology crafted in OWL. However, such maps are an effective way to communicate with stakeholders, and can also be repurposed to inform machine-assisted discovery of data and information relevant to a real-world challenge. Queries with boolean operators, coupled with semantic technology, is great, but wouldn't it be even better if knowledge discovery algorithms reflect the inference and association patterns exhibited by human thinking that allows us to reason in the abstract?


Real-world agriculture resilience scenario used to drive the concept map development

To anchor the "decision" end of the "data to decisions" pipeline, we shall focus on a use-case centered on climate adaptation challenges in the Chesapeake watershed. In particular, we focus on poultry agriculture in the Delmarva Peninsula (derived from the names of the States of Delaware, Maryland, and Virginia). The figure below shows USDA agriculture census data representing the value of poultry and eggs sold as a percentage of the total market value of agricultural products in 2017.

The hypothetical use-case involves an inter-county task force ("task force") charged with drafting a regional resilience plan to ameliorate the impacts of climate change on the economy and biodiversity of the Chesapeake watershed. First, the task force convened a multitude of stakeholders to derive a common understanding of the risks and vulnerabilities. Stakeholders included business owners, farmers, critical infrastructure operators, economic planners, scientists, environmental engineers, and government representatives. A shared understanding of the challenges facing the region was captured in a number of concept maps. These concept maps proved to be effective as visual communication tools to share the stakeholders' collective perception of the challenges.

Subsequently, the task force charged a team of transdisciplinary ESIP folks to develop a technology plan to support decision-making that was science-informed, data-driven, and technically defensible. The ESIP folks were asked to first focus on meeting the needs of the poultry agriculture industry. However, the technology plan should also be scalable to meet the needs of other areas of concern like enhancing coastal flood abatement civil infrastructure, enhancing the long-term sustainability of Chesapeake oyster farms, etc.

Value of Poultry and Eggs Sold as Percent of Total Market Value of Agricultural Products Sold in 2017.png
(Image source: https://www.nass.usda.gov/Publications/AgCensus/2017/Online_Resources/Ag_Census_Web_Maps/, retrieved 2020-03-28 1615 hrs GMT.)

Starting from decisions and tracing back to data and technology

Click only on the orange-colored nodes as you navigate the concept map soup. These orange colored nodes contain a pathway that includes technology components that have been partially filled in. We are seeking concept map contributions from you for concepts along this partially complete pathway.

If you see a node that ends with the expression ~sem.ann, that node contains a semantic annotation. Mouse-over the node to see the annotation. Annotations are based on OBO Foundry ontologies like ENVO (Environment Ontology), AGRO (Agronomy Ontology), and SDGIO (Sustainable Development Goals Interface Ontology). Please excuse the inconsistency in node annotation, because this is a phase of active experimentation. The experiment may be described as: "will semantic annotations of human-readable concepts (and perhaps predicates that connect concepts) enable these concept maps to be used for machine-assisted discovery of pertinent digital research objects?"



To contribute and edit the map

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