Conceptual Schema for Tribal Lands Collaboratory

The Esri-NEON Tribal Lands Collaboratory is a framework for facilitating natural resource-management based on a data-driven scientific understanding of tribal resources that are impacted by complex, large-scale environmental stressors that have differential effects at temporal and spatial scales

Schema demonstrating how phenology data can be used for cross-scalar research, education, and applications

<p>There are many value added services that need to be applied to data before it can become information that can be used for decision-support (upper half of diagram).  Phenology is a useful type of observation that is useful to demonstrate this value chain.  Its appeal lies with the ease by which multiple stakeholders with different levels of expertise and interests can be involved, and phenology fits very nicely into existing US national and international informatics initiatives (lower half of diagram).</p> <p></p

Data to Decisions for Climate Resilience: A Prototype Focused on Salmon in the Yakima River Basin

From 2015 - 2016, Northwest Indian College, the Tulalip Tribes, and the American Indian Higher Education Consortium collaborated in formulating the concepts for a prototype Tribal Lands Collaboratory (TLC). The TLC was used to explore how web-based collaboration technologies can be used to study the impacts of climate change on the tightly linked timings of salmonberry ripening, Swainson's thrush singing, and the return of salmon. In the fall of 2017, Neptune extended the TLC approach by initiating and leading a small experimental effort called "Data to Decisions for Climate Resilience" (D2D). D2D is co-sponsored by the US Global Change Research Program's National Climate Assessment network and the Federation of Earth Science Information Partners. The objectives of D2D included building a set of openly web-accessible concept maps to document climate adaptation planning methods, including a set of methodologies called "Structured Decision Making". To initiate D2D, we studied the challenges of the Yakama Nations' salmon harvests set against a complex landscape of socio-ecological actors in the Yakima River Basin. In this poster, we broadly describe the scope of D2D, which remains an experimental work-in-progress

Connecting LTAR-NEON Data to Science Policy

<p>Large-scale environmental changes pose challenges that straddle environmental, economic, and social boundaries. As we design and implement climate adaptation strategies at the Federal, state, local, and tribal levels, accessible and usable data are essential for implementing actions that are informed by the best available information. Data-intensive science has been heralded as an enabler for scientific breakthroughs powered by advanced computing capabilities and interoperable data systems. Those same capabilities can be applied to data and information systems that facilitate the transformation of data into highly processed products for use in research, policy, and decision-making.</p> <p>At the interface of scientifically informed public policy and data intensive science lies the potential for producers of credible, integrated, multi-scalar environmental data like the National Ecological Observatory Network (NEON) and its partners to capitalize on data and informatics interoperability initiatives that enable the integration of environmental data from across credible data sources. NEON is designed to provide high-quality, long-term environmental data for research. These data are also meant to be repurposed for operational needs, such as risk management, vulnerability assessments, resource management, and others. The recently established USDA Agriculture Research Service (ARS) Long Term Agro-ecosystem Research (LTAR) network is another example of such an environmental observatory that aims to produce credible data for environmental / agricultural forecasting and informing policy.</p> <p>To facilitate data fusion across observatories like NEON and LTAR, there is a growing call for observation systems to more closely coordinate and standardize how variables are measured. Together with observation standards, cyberinfrastructure standards enable the proliferation of an ecosystem of applications that utilize diverse, high-quality, credible data. Interoperability facilitates the integration of data from multiple credible sources of data, and enables the repurposing of data for use at different geographical scales. Metadata that captures the transformation of data into value-added products (“provenance”) lends reproducibility and transparency to the entire process. This way, the datasets and model code used to create any product can be examined by other parties.</p> <p>This poster outlines a pathway for transforming environmental data into value-added products by various stakeholders to better inform sustainable agriculture using data from environmental observatories including NEON and LTAR.</p

Framework for Data Informed Science Policy

<p>Large-scale environmental changes pose challenges that straddle environmental, economic, and social boundaries. As we design and implement climate adaptation strategies at the Federal, state, local, and tribal levels, accessible and usable data are essential for implementing actions that are informed by the best available information. Data-intensive science has been heralded as an enabler for scientific breakthroughs powered by advanced computing capabilities and interoperable data systems. Those same capabilities can be applied to data and information systems that facilitate the transformation of data into highly processed products.</p> <p>At the interface of scientifically informed public policy and data intensive science lies the potential for producers of credible, integrated, multi-scalar environmental data like the National Ecological Observatory Network (NEON) and its partners to capitalize on data and informatics interoperability initiatives that enable the integration of environmental data from across credible data sources. NSF’s large-scale environmental observatories such as NEON and the Ocean Observatories Initiative (OOI) are designed to provide high-quality, long-term environmental data for research. These data are also meant to be repurposed for operational needs that like risk management, vulnerability assessments, resource management, and others. The proposed USDA Agriculture Research Service (ARS) Long Term Agro-ecosystem Research (LTAR) network is another example of such an environmental observatory that will produce credible data for environmental / agricultural forecasting and informing policy.</p> <p>To facilitate data fusion across observatories, there is a growing call for observation systems to more closely coordinate and standardize how variables are measured. Together with observation standards, cyberinfrastructure standards enable the proliferation of an ecosystem of applications that utilize diverse, high-quality, credible data. Interoperability facilitates the integration of data from multiple credible sources of data, and enables the repurposing of data for use at different geographical scales. Metadata that captures the transformation of data into value-added products (“provenance”) lends reproducability and transparency to the entire process. This way, the datasets and model code used to create any product can be examined by other parties.</p

Leveraging Environmental Observation Infrastructure for the Benefit of Society

<p>Presented at an annual Congressional exhibition organized by the Coalition for National Science Funding (CNSF) in May 2013.</p

Proposed Requirements-driven User-scenario Development Protocol for the Belmont Forum E-Infrastructure and Data Management Cooperative Research Agreement

<p>The Belmont Forum E-Infrastructure and Data Management Cooperative Research Agreement (CRA) is designed to foster a global community to collaborate on e-infrastructure challenges. One of the deliverables is an implementation plan to address global data infrastructure interoperability challenges and align existing domestic and international capabilities. Work package three (WP3) of the CRA focuses on the harmonization of global data infrastructure for sharing environmental data. One of the subtasks under WP3 is the development of user scenarios that guide the development of applicable deliverables.</p> <p>This paper describes the proposed protocol for user scenario development. It enables the solicitation of user scenarios from a broad constituency, and exposes the mechanisms by which those solicitations are evaluated against requirements that map to the Belmont Challenge. The underlying principle of traceability forms the basis for a structured, requirements-driven approach resulting in work products amenable to trade-off analyses and objective prioritization.</p> <p>The protocol adopts the ISO Reference Model for Open Distributed Processing (RM-ODP) as a top level framework. User scenarios are developed within RM-ODP’s “Enterprise Viewpoint”. To harmonize with existing frameworks, the protocol utilizes the conceptual constructs of “scenarios”, “use cases”, “use case categories”, and use case templates as adopted by recent GEOSS Architecture Implementation Project (AIP) deliverables and CSIRO’s eReefs project. These constructs are encapsulated under the larger construct of “user scenarios”.</p> <p>Once user scenarios are ranked by goodness-of-fit to the Belmont Challenge, secondary scoring metrics may be generated, like goodness-of-fit to FutureEarth science themes. The protocol also facilitates an assessment of the ease of implementing given user scenario using existing GEOSS AIP deliverables.</p> <p>In summary, the protocol results in a traceability graph that can be extended to coordinate across research programmes. If implemented using appropriate technologies and harmonized with existing ontologies, this approach enables queries, sensitivity analyses, and visualization of complex relationships.</p

Esri-NEON Tribal Lands Collaboratory: An ODE to Phenology

Response of Tribal nations and Tribal communities to current and emerging climate change challenges requires active participation of stakeholders who have effective access to relevant data, information and analytical tools. The Tribal Lands Collaboratory (TLC), inspired by ESIP's Earth Science Collaboratory and currently under conceptual development, is a joint effort between the American Indian Higher Education Consortium (AIHEC), the Environmental Systems Research Institute (Esri), and the National Ecological Observatory Network (NEON). The vision of the TLC is to create an integrative platform that enables coordination between multiple stakeholders (e.g. Tribal resource managers, Tribal College faculty and students, farmers, ranchers, and other local community members) to collaborate on locally relevant climate change issues. The TLC is intended to facilitate the transformation of data into actionable information that can inform local climate response planning. The TLC will provide the technical mechanisms to access, collect and analyze data from both internal and external sources while also providing the social scaffolds to enable collaboration across Tribal communities and with members of the national climate change research community. The prototype project focuses on phenology, a branch of science focused on relationships between climate and the seasonal timing of biological phenomena. Monitoring changes in the timing and duration of phenological stages in plant and animal co­­­­mmunities on Tribal lands can provide insight to the direct impacts of climate change on culturally and economically significant Tribal resources. The project will leverage existing phenological observation protocols created by the USA-National Phenology Network and NEON to direct data collection efforts and will be tailored to the specific needs and concerns of the community. Phenology observations will be captured and managed within the Collaboratory environment where these data may then be correlated with regional climate data to investigate interactions between large-scale environmental changes and local impacts. Esri’s Story Maps is a candidate mechanism for sharing of those findings among Tribal stakeholders