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)
- We use design to sustain, heal, and empower our communities as well as to seek liberation from exploitative and oppressive systems.
- We center the voices of those who are directly impacted by the outcomes of the design process.
- We prioritize design’s impact on the community over the intentions of the designer.
- We view change as emergent from an accountable, accessible, and collaborative process rather than as a point at the end of a process.
- We see the role of the designer as a facilitator rather than an expert.
- 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.
- We share design knowledge and tools with our communities.
- We work towards sustainable, community-led, and community-controlled outcomes.
- We work towards non-exploitative solutions that reconnect us to the earth and to each other.
- 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
- A1. (meta)data are retrievable by their identifier using a standardized communications protocol
- 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
- R1. meta(data) are richly described with a plurality of accurate and relevant attributes
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.