Community concept mapping activity March 2020 through July 2020

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2020 ESIP Summer Meeting Activities

There are two ESIP summer meeting activities related to concept maps:

  1. Event #1, introductory tutorial to ontologies, 2020-07-13, Monday, 1200 hrs Eastern Daylight Time (GMT-4). The event is a free pre-conference learning opportunity co-organized by the ESIP Semantics Technology Committee and Agriculture and Climate Cluster. This introductory tutorial to ontologies provides an overview of what ontologies are, how ontologies foster collaboration, and how ontologies contribute to enhancing data and information interoperability. If you have heard about ontologies mentioned in the context of the FAIR Principles, or wondered how a machine is able to retrieve datasets described as "precipitation" when a user searches for "rain", or how concept maps and ontologies are related, you might be interested in attending this tutorial. Please register by 2020-07-09. Details can be found on this wiki page.
  2. Event #2, ESIP meeting session, 2020-07-14, Tuesday, 1400 - 1730 hrs Eastern Daylight Time (GMT-4). The ESIP summer session "Community concept mapping for data-to-decisions – Use cases in climate adaptation, disaster planning, and disaster response" provides an overview of the concept mapping activities described on this page, provides a forum for individuals to participate in new and ongoing work, and provides direction to future Agriculture and Climate Cluster activities.

Objective of this experimental concept map repository

Between March 2020 and July 2020, we aim to create a small collection of concept maps ("c-soup") that ESIP members are encouraged to contribute to. You could also use existing concepts in the c-soup to assemble a high-level concept-map that is really a narrative about how some technology you're working on (regardless of its technology readiness level) relates to an applied problem. That high-level concept-map may be thought of as a "data to decisions" pathway, or a traceability map of sorts.

This is an experiment in its infancy. Looking into the future, the full potential of such a tool include:

  1. Program managers can use these pathways as a narrative to rapidly assess the conceptual landscape that contextualizes your technology and its potential impact.
  2. Disaster mitigation teams or climate resilience planners can submit a query in natural language to a system that uses concept map data, fused with digital research object data, to retrieve candidate science-informed, data-driven solutions with information about the models and data used to inform decisions (see Figure 25 of this ESIP Lab funded deliverable that sketches out a potential approach using knowledge graphs and 'Resilience Genomes').

These ideas are summarized in the concept map below:

See the section below titled "Examples of data-to-decisions narratives" for example narratives on science-informed, data-driven planning activities that trace back to a NASA data product.

Ways you can use the concept maps

  1. Describe your own data-to-decisions narrative. You can pick and choose relevant concepts from the c-soup to communicate to other people (like your program manager!) about how data or technology you helped developed can ultimately be traced to science-informed, data-driven decisions. This you can do by assembling your own concept map embedded within a wiki, just like what we have done here on this wiki page. Or, use your concept map in any content management system that allows you to insert an HTML iframe into the body of your text. See the section below titled "Examples of data-to-decisions narratives" for examples of science-informed, data-driven risk mitigation activities.
  2. Use the c-soup as a just-in-time (i.e. as needed) educational resource. Human thinking and memory appears to be largely associative. People tend to assimilate new information by associating novel phenomena with the existing knowledge in one's head. Concept maps are useful for starting from some concept that you are already familiar with, and assimilating new knowledge through associations. That way, you're contextualizing your existing knowledge.
  3. 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.
  4. Experiment with ideas for 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. Could the concept maps 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, are great. Wouldn't it be even better if knowledge discovery algorithms retrieve digital research objects that somewhat reflect the complex web of concepts already in the human's head as expressed in a concept map?

Regarding context-aware knowledge discovery: a technical note to those who tend to lose sleep over knowledge representation with hopes of eventually watching a crossover movie titled Terminator vs. Westworld hosts vs. Blade Runner replicants vs. Wall-E trash robots. For the foreseeable future, the c-soup is almost certainly NOT machine-readable. It is true that the concept maps can be easily exported to an XML formatted file for parsing, but the nodes and relations (i.e. subject-predicate-object triples) contain text that are not necessarily well-formed expressions. Some nodes in the c-soup are semantically annotated on an experimental basis. Concepts may ultimately be represented as subject-predicate-object triples in a labeled property graph database (outside the scope of this experiment). Data-to-decisions traceability maps may be constructed by ordering the triples. These triples and ordered-triple-sequences may then further be exposed to algorithms for machine-assisted discovery.

Examples of concept maps that form the basis for narratives

These concept maps in the c-soup address topics that may be used to form human-readable narratives. Some of the concept maps below are "reachable" with one-click from the narratives in the next section. Other concept maps require following a number of links to related concepts starting from the narratives. The process of finding a "pathway" from one concept to another is known as "graph traversal" in computer science lingo.

  1. Disaster Risk Management Concepts derived partially from UN related documents and how they relate to concepts of risk used in (1) the US Climate Resilience Toolkit (US CRT) and (2) a conceptual model used to inform the National Cohesive Wildland Fire Management Strategy
  2. Cheasapeake Bay Climate Resilience Planning using the US Climate Resilience Toolkit "steps to resilience" framework
  3. National Cohesive Wildland Fire Management Strategy
  4. Wildfire Conceptual Model Used to Inform the National Strategy for wildland fire management
  5. Colorado Wildfire Risk Assessment Portal (CO-WRAP)
  6. US policy instruments relevant to science, technology, and environment matters
  7. Driver-Pressure-State-Impact-Response DPSIR Framework

Examples of data-to-decisions narratives

Below are examples (i.e. use-cases) of how concepts in the c-soup can be sequenced into a narrative to demonstrate how data can be used to inform decisions. Narratives are human-readable concept maps that are not meant to be machine-readable. All three examples revolve around the concept of risk management that involve hazard, vulnerability, and exposure.

  1. Narrative 1: Mitigating the anticipated impacts of increased nutrient runoff from flashy precipitation from poultry agriculture in the Chesapeake watershed. This example narrative uses the US CRT as a planning framework to examine how climate change impacts to the Chesapeake watershed can be mitigated.
  2. Narrative 2: Tracing how Landsat data helps foster fire-adapted communities in Colorado through data-driven, science-informed planning. This example narrative follows how the 2012 fire season in Colorado led to the recommendation of a web-tool called CO-WRAP for drafting Community Wildfire Protection Plans to mitigate wildfire hazards in Wildland-Urban Interface areas.
  3. Narrative 3: Tracing how Landsat data is used to inform burnt acreage in an emergency stabilization Burned Area Emergency Response BAER for the 2012 High Park Fire in Colorado. This example narrative is based on the 2012 High Park Fire (west of Fort Collins, CO).

Organization of the concept map repository

For the time being, the concept maps are meant more as a communication and learning tool for humans rather than for machines to reason with. The c-soup is therefore meant to foment discussions about what it will take to: (1) use concept maps as stakeholder communication tools for applications like climate resilience planning, (2) re-use the concept maps for machine-assisted discovery of relevant digital research objects to aid in science-informed, data-driven decision making.

These ideas were first developed as part of an 2018 ESIP Lab Project and documented in one of the project deliverables (human-readable with lots of diagrams!).

The schema below represents one of many possible ways to conceptualize the c-soup. All the concept maps on this page are interactive and you can choose to drill down into connected concepts. This may get challenging for larger concept maps that are squeezed into fix-sized frames. Should you run into this issue with the interactive schema below, use this full-sized schema that will open in your browser window. Use your browser's back button to retrace your steps back "up" the concept maps.

What we need from you

We have sketched out a hypothetical "data to decisions" pathway below. Pathways that reflect real-world solutions are likely to involve the many disciplines that ESIP members hail from. Contribute your expertise by way of drafting concept maps along spots in the pathway that you feel need shoring up!

2020-03-20.Concept map icons for the data-to-decisions demo path wiki page v1.1.png
In the section below titled "Examples of data-to-decisions narratives", we have sketched out examples of data-to-decisions pathways.
Demo embedded Neo4j

To contribute and edit the c-soup

You can contribute to the c-soup by using either a desktop-based software called CmapTools, or the web-based version called CmapTools in the Cloud (henceforth referred to as "CmapToolsCloud". The desktop version, CmapTools, is recommended and available as freeware for the Windows, OSX, and Linux platforms.

To contribute and edit the c-soup, this is what you need to do at a conceptual level. You can find more detailed instructions right after the abstracted instructions:

  1. Create a free CmapToolsCloud account at
  2. Log into your CmapToolsCloud account, and add bwee --at-likethissign-@-- and/or william.l.teng --at-likethissign-@-- as a fellow Cmapper to your account.
  3. CmapToolsCloud will notify Brian Wee and/or Bill Teng to add you as a collaborator to the c-soup. Thereafter, you will be able to edit the c-soup.
  4. Download CmapTools from (optional, but recommended).
  5. Provide your CmapToolsCloud login account information to CmapTools so that you can edit the c-soup using CmapTools (optional, but recommended).

Here are the more detailed instructions:

  1. Create a free CmapToolsCloud account at
  2. Log into your CmapToolsCloud account.
  3. Find your Cmaps home page where you should see a tab labeled "Cmappers".
  4. Navigate to the page under the "Cmappers" tab, and click on "Find Cmappers".
  5. Look for either bwee --at-likethissign-@-- or william.l.teng --at-likethissign-@--
  6. Wait for a notification via email that you've been successfully added as a collaborator, which may take up to a day because it involves a manual action by the c-soup administrators. The email notification will be sent by to the email address you use for CmapToolsCloud.
  7. (Following steps are optional, but recommended) Download CmapTools from
  8. Fire up CmapTools on your desktop, navigate your way to the "Cloud Account" setup (under "Edit" - "Preferences" in the Windows version), and specify your CmapToolsCloud login information.
  9. Once the c-soup administrators grant you collaboration access, you should now be able to contribute to the knowledge repository named the "AgClimate Concept Map Schema" using CmapTools or CmapToolsCloud.