Acronyms and Definitions

Acronyms and Abbreviations

  • ALMA: Assistance for Land-surface Modelling activities
  • APIs: Application Programming Interfaces
  • CI: Cyberinfrastructure
  • CSV: Comma-separated values
  • DMAC: Data Management and Cyberinfrastructure
  • EFI: Ecological Forecasting Initiative
  • HAB: Harmful Algal Bloom
  • IAAs: Interagency Agreements
  • IFCB: Imaging FlowCytobot
  • IMPACT: Inter-agency Implementation and Advanced Concepts Team
  • IOOS: Integrated Ocean Observing System
  • IoT: Internet of Things
  • LTER: Long Term Ecological Research
  • MIPs: Model Intercomparison Projects
  • NASA: National Aeronautics and Space Administration
  • NCCOS: National Centers for Coastal Ocean Science
  • NEON: National Ecological Observatory Network
  • NERACOOS: Northeastern Regional Association for Coastal Ocean Observing Systems
  • NetCDF: Network Common Data Form
  • NGOs: Non-Governmental Organizations
  • NHABON: National Harmful Algal Bloom Observing Network
  • NOAA: National Oceanic and Atmospheric Administration
  • OBIS: Ocean Biodiversity Information System
  • PEcAn: Predictive Ecosystem Analyzer
  • S3: Simple Storage Service
  • SCCOOS: Southern California Coastal Ocean Observing System
  • STAC: SpatioTemporal Asset Catalog
  • USGS: United States Geological Survey

Ecological Forecasting Cyberinfrastructure Terms

Design Justice Principles

(Retrieved from Design Justice Network)

  1. We use design to sustain, heal, and empower our communities as well as to seek liberation from exploitative and oppressive systems.
  2. We center the voices of those who are directly impacted by the outcomes of the design process.
  3. We prioritize design’s impact on the community over the intentions of the designer.
  4. We view change as emergent from an accountable, accessible, and collaborative process rather than as a point at the end of a process.
  5. We see the role of the designer as a facilitator rather than an expert.
  6. We believe that everyone is an expert based on their own lived experiences and that we all have unique and brilliant contributions to bring to a design process.
  7. We share design knowledge and tools with our communities.
  8. We work towards sustainable, community-led, and community-controlled outcomes.
  9. We work towards non-exploitative solutions that reconnect us to the earth and to each other.
  10. Before seeking new design solutions, we look for what is already working at the community level.

CARE Principles

From (Carroll et al. 2020)

  • Collective Benefit
    • C1: For inclusive development and innovation
    • C2: For improved governance and citizen engagement
    • C3: For equitable outcomes
  • Authority to Control
    • A1: Recognizing rights and interests
    • A2: Data for governance
    • A3: Governance of data
  • Responsibility
    • R1: For positive relationships
    • R2: For expanding capability and capacity
    • R3: For Indigenous languages and worldviews
  • Ethics
    • E1: For minimizing harm and maximizing benefit
    • E2: For justice
    • E3: For future use

FAIR Principles

From (Wilkinson et al. 2016)

  • To be Findable:
    • F1. (meta)data are assigned a globally unique and persistent identifier
    • F2. data are described with rich metadata (defined by R1 below)
    • F3. metadata clearly and explicitly include the identifier of the data it describes
    • F4. (meta)data are registered or indexed in a searchable resource
  • To be Accessible:
    • A1. (meta)data are retrievable by their identifier using a standardized communications protocol
      • A1.1 the protocol is open, free, and universally implementable
      • A1.2 the protocol allows for an authentication and authorization procedure where necessary
    • A2. metadata are accessible even when the data are no longer available
  • To be Interoperable:
    • I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation
    • I2. (meta)data use vocabularies that follow FAIR principles
    • I3. (meta)data include qualified references to other (meta)data
  • To be Reusable:
    • R1. meta(data) are richly described with a plurality of accurate and relevant attributes
      • R1.1. (meta)data are released with a clear and accessible data usage license
      • R1.2. (meta)data are associated with detailed provenance
      • R1.3. (meta)data meet domain-relevant community standards

Adapted Forecasting and Cyberinfrastructure Terms from (Fer et al. 2021)

  • Abstraction: The process of focusing on the design of the IT system architecture and how components relate to one another.
  • Accessibility: Availability of software and data in a public location with complete metadata and the protocols to use, install, implement, and employ them shared.
  • Algorithms: A set of rules designed to solve a computational problem.
  • Application Programming Interfaces (APIs): Tools for programmatically interacting with data online.
  • Assistance for Land-surface Modeling activities (ALMA): A data convention that ensures consistency in naming, unit, and sign standards for variable exchange in model-data activities.
  • Benchmarking: Assessing a model’s skill based on a priori performance expectations.
  • Comma-separated values (CSV): Text files using commas to separate values.
  • Cyberinfrastructure: IT systems with components for data storage and advanced informatics for simulating natural phenomena and scientific interpretation.
  • Data assimilation: Combining model predictions with observations to update system understanding.
  • Database: Electronic systems that store and organize data and metadata.
  • Internet of Things (IoT): Network of interconnected sensors and smart devices for automated environmental monitoring.
  • Interoperability: The ability of software to work with other products.
  • Metadata: Descriptive data providing documentation about other data or software.
  • Model Intercomparison Projects (MIPs): Multi-institutional efforts for model evaluation and benchmarking.
  • Network Common Data Form (NetCDF): A file format for storing array-oriented scientific data and metadata.
  • Ontologies: Standardized collections of terms.
  • Open file format: A file format without restrictions on use by any software.
  • Provenance: Historical information of all analysis steps and components.
  • Repository: Online storage for hosting software and code with related documentation.
  • Reusability: The versatility of data and software in different settings.
  • Scheduler: Software for managing scientific workflows periodically.
  • Software container: An application package with dependencies for code execution.
  • Workflow: Computational tasks executed in sequence with data flow.
  • Virtual machines: Software providing basic OS functionality in a contained environment.

Adapted Forecasting and Cyberinfrastructure Terms from (Hendry 2023)

  • Hypothesis: An expectation on system workings for testable predictions.
  • Prediction: Formal assertion about a state or outcome before it is known.
  • Expectation: A weaker form of prediction with higher uncertainty.
  • Question: Inquiry about system information for generating predictions.
  • Predictable: A precise and accurate state or outcome given adequate system knowledge.
  • Prophecy (prediction as …): A formal prediction about the future state of a system.
  • Diagnosis (prediction as …): A formal prediction about the current state of a system.
  • History (prediction as …): A formal prediction about the past state of a system.
  • Repeatability (prediction as …): Phenomena that are repeatable could also be predictable with sufficient knowledge.
  • Fate (prediction as …): When evidence suggests an outcome is inevitable.
  • Parallel or convergent (in the sense of evolution): Cases where evolution generates similar outcomes under similar conditions.
  • Forecasting: Using past or present data to predict the future.
  • Hindcasting: Using current or past data to predict another past state.
  • Uncertainty: The level of confidence in a prediction or outcome test.

Other Technical Terminology

  • Cloud-native architecture: A way of designing applications specifically to run on cloud platforms, making them easy to scale, flexible, and built from smaller, interchangeable parts.
  • Containerization: A method of packaging software so it can run consistently across different computing environments.
  • Docker: A popular platform for containerization that simplifies creating, deploying, and running applications by using containers.
  • Event-driven workflow: A system where actions are triggered by specific events like a sensor sending data, often used in cloud computing to automate processes.
  • Fairweather Report: A 2003 report by the National Academies Press on improving the provision of civilian weather and climate services.
  • I/O (Input/Output): Communication between a computer system and the outside world, involving receiving (input) and sending (output) data.
  • Kerchunk: A software tool that helps organize and store data efficiently in the cloud.
  • Loosely-coupled interfaces: Connections between software systems that are designed to interact with each other with minimal dependency, making it easier to update or change parts without affecting the whole system.
  • Modular architecture: A design approach that builds a system from separate, interchangeable parts, making it easier to update and maintain.
  • Object storage: A method of storing data in the cloud where each piece of data is stored as a distinct unit or “object” along with its associated metadata.
  • OpenShift: A commercial cloud-based platform by Red Hat that helps developers build and manage containerized applications.
  • Provenance tracking: Keeping detailed records of where data comes from and all the steps it goes through, ensuring data integrity and traceability.
  • Reproducible compute environments: Setups for running software that can be exactly replicated, ensuring that the same code always produces the same results, often using container technologies like Docker.
  • Serverless computing: A cloud service model where the cloud provider manages the servers, allowing developers to run code without worrying about server maintenance.
  • STAC (SpatioTemporal Asset Catalog): A standard that helps organize and share spatiotemporal data.
  • Stack Catalog: A specification for organizing and searching metadata about spatiotemporal data, making it easier to find and share such data.
  • Thredds: A server that makes it easy to access and share scientific data using common standards.
  • Zarr: A format for efficiently storing large multi-dimensional arrays of data, often used in scientific research.

References

Carroll, Stephanie, Ibrahim Garba, Oscar Figueroa-Rodrı́guez, Jarita Holbrook, Raymond Lovett, Simeon Materechera, Mark Parsons, et al. 2020. “The CARE Principles for Indigenous Data Governance.” Data Science Journal 19.
Fer, Istem, Anthony K Gardella, Alexey N Shiklomanov, Eleanor E Campbell, Elizabeth M Cowdery, Martin G De Kauwe, Ankur Desai, et al. 2021. “Beyond Ecosystem Modeling: A Roadmap to Community Cyberinfrastructure for Ecological Data-Model Integration.” Global Change Biology 27 (1): 13–26.
Hendry, Andrew P. 2023. “Prediction in Ecology and Evolution.” BioScience 73 (11): 785–99.
Wilkinson, Mark D, Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, et al. 2016. “The FAIR Guiding Principles for Scientific Data Management and Stewardship.” Scientific Data 3 (1): 1–9.