The objective of this Topic Area is to seek projects that develop innovative data integration, analysis, and visualization approaches that leverage historical and real-time datasets to enhance decision-making and predictive capabilities for natural resource managers. There is specific interest in:

• Developing innovative approaches for unified platforms and semantic interoperability to recover species or ecosystem monitoring data from past documents, such as current and historic INRMPs (Integrated Natural Resources Management Plans), Biological Assessments, Biological Opinions, Species Recovery Plans, State Wildlife Action Plans, U.S. Fish and Wildlife Service Species Recovery Plans.
• Developing automatic and efficient advanced data analytics and visualization approaches for analyzing recovered and real-time data through user-interfaces that make data accessible for applications at the installation, regional, or Service-wide scales.

Proposals should identify and apply emerging standards in ecological and environmental data management and metadata development, along with FAIR (Findable, Accessible, Interoperable, and Reusable) standards. Characterization and communication of uncertainty in recovered data and metadata products should be included in proposals.

Collaboration with natural resource managers at DoD installations is critical to identify and prioritize historical datasets. A DoD liaison will be assigned at the pre-proposal stage for those proposals selected to submit a full proposal.

DoD installations have made large investments in monitoring environmental and ecological variables to support installation planning, training and operations, and natural resource management, including compliance with legal mandates and regulations. Developing, refining, and applying tools to retrieve currently inaccessible historical data and make them more broadly available in appropriate formats would leverage these past investments, extend current monitoring baselines, sharpen ability to detect long-term trends, and enable better forecasting of environmental and ecological trajectories. While often gathered for specific purposes and under limited circumstances, these data can be repurposed for near-term comparisons to current conditions, as well as integrated and synthesized to better understand the contingent and complex responses of ecosystems to environmental drivers and therefore support more effective resource management on DoD installations.

Biodiversity management benefits from long-term environmental and ecological baselines, which provide information on system variability, allow identification of long-term trends, support hypothesis-testing concerning drivers and mechanisms of change, and feed into formal models to forecast future changes and anticipate responses to management activities. Large volumes of relevant data and reports from the past currently exist, but are often in paper form (written or printed) in file cabinets or in antiquated electronic media (e.g., tapes, floppy disks) and hence both currently unusable and liable to deterioration or destruction. Resource management reports may contain key insights into improving conservation efforts on military lands. Even when available on accessible media, many useful datasets were collected well before development of modern best practices for data management or suffer from lack of attention to these best practices due to pressure of time and lack of personnel trained in data management. Recovery of these archives and incorporation into state-of-the-art electronic media and databases offers many benefits for improving biodiversity management, but current approaches are labor-intensive and hence prohibitively expensive at scale.

Modern technologies (e.g., automated scanning, artificial intelligence, image enhancement) have the potential to accelerate the process of data rescue, ensure a high degree of accuracy, and characterize uncertainty in the resulting data products. These technologies can be applied to many of the steps involved in data rescue, including identifying and locating historic datasets, prioritizing datasets for rescue based on eventual utility for biodiversity management, compiling and transferring data to modern digital formats, cleaning and validating data, creating metadata, and archiving and sharing data. Errors can arise in all of these steps and it is therefore also critical to characterize the uncertainty in rescued datasets to ensure appropriate applications of data.