Contents
Supporting Terms for Continuous Science
- Rapid
- Describes the ability to share, update, or respond to research outputs quickly and with minimal friction. In a Continuous Science context, rapid refers not just to speed or frequency of sharing, but to responsiveness — enabling timely feedback, early collaboration, and more agile scientific progress without sacrificing quality or rigor.
- Iterative
- A research and communication process characterized by small, incremental changes, frequent feedback, and continuous refinement over time. In an iterative model, scientific work is Shared early and often and then refined — enabling others to engage, reuse, or respond while the work is still evolving. This contrasts with traditional one-time publishing models, and supports faster learning, course correction, and collaboration.
- Complete
- A complete research object includes all the artifacts necessary to understand, verify, and build upon the work — such as the narrative, software, data, notebooks, protocols, and reviews. Completeness is foundational to Reproducible and Executable science, ensuring that others can validate and interact with the full research process, not just its summary. In Continuous Science, completeness also supports Composable reuse, where specific components of a project can be shared, granularly cited, and Integrated across different platforms and contexts.
- The complete research “package” is sometimes referred to as a research compendium or knowledge stack. For larger scale data or externally published artifacts, the contents should be Integrated not necessarily packaged as a single artifact.
- Integrated
- The user experience of navigating research should be integrated, navigating between various outputs should be seamless. Currently the experience is extremely disconnected: think of even the simple experience of navigating to a supplementary video or figure, it means downloading and scrolling to the page; this should be integrated into the experience of reading (hover over and see the supplementary figure or video) and extend to other artifacts and resources (software, interactive figures, embedded or linked datasets). An integrated experience between all of the aspects of what makes up the science (not just the narrative!) can enable reuse in new Networked ways.
- Networked
- Networked Science is a model of scientific practice where knowledge, data, software, and methods are openly shared and richly Integrated across platforms and formats, enabling discovery, reuse, and collaboration at scale. Networked science builds on the principles of FAIR data and reimagines science as a dynamic, distributed system of contributions rather than a sequence of static outputs.
- Reusable
- Reusable research artifacts are designed and shared in ways that allow others to confidently build upon, adapt, or incorporate them into new work. Reuse is the outcome of aligning with the FAIR principles — ensuring outputs are Interoperable, well-documented, and legally and technically accessible. In Continuous Science, reusability goes beyond full publications to include Granular and Composable components — like figures, methods, code blocks, datasets, paragraphs, or terms — that can be cited, executed, or extended in new contexts.
- FAIR
- Findable, Accessible, Interoperable, and Reusable are the FAIR Principles. “The principles emphasize machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data.”
- Automated
- The use of systems and workflows to reduce manual effort in scientific communication and research sharing. In Continuous Science, automation ensures that best practices — such as format conversion, metadata generation, reproducibility checks, and citation linking — happen by default or in an assisted way. This allows researchers to focus on their work while maintaining high standards for openness, integrity, and integration.
- Reproducible
- Reproducibility is the ability for others to independently validate or build upon a result using the same data, code, and methods. In Continuous Science, reproducibility depends on sharing Complete research artifacts — not just the final narrative, but also the data, software, protocols, and analysis environments. These components must be Integrated into the communication process, not isolated or relegated to supplemental materials. Reproducibility thrives in an Iterative workflow, where work is shared early and refined over time, making it easier to detect issues, receive feedback, and ensure that claims can be verified and reused.
- Collaborative
- Collaboration in Continuous Science refers to the ability of researchers to work together across teams, institutions, disciplines, and time — sharing not just ideas, but complete research artifacts such as data, software, and methods. Effective collaboration depends on integration of tools and outputs, enabling others to engage with and build on work more easily. When research is shared in an Iterative and Reusable way, collaboration can happen earlier, more frequently, and with greater depth — transforming isolated contributions into shared, networked progress.
- Executable
- An executable research artifact can be run or interacted with directly, allowing others to validate, reproduce, or extend the results. This typically includes code and data bundled in notebooks, scripts, or environments that support re-running analyses or simulations. Executable content is essential for Reproducible research and is often Integrated into Composable and Interoperable workflows that support dynamic exploration and reuse.
- Granular
- Granular refers to the fine resolution at which research contributions can be identified, attributed, and reused — from individual figures and equations to paragraphs, methods, or code blocks. Granular sharing enables more precise collaboration and reuse, allowing others to cite or incorporate specific parts of a work. In Continuous Science, this level of detail supports truly Composable and Versioned research objects.
- Composable
- A research artifact is composable when its parts — such as figures, equations, protocols, analyses, paragraphs, terms, or datasets — can be reused or recombined independently across contexts. Composability supports Granular credit and enables researchers to build directly on each other’s work. In Continuous Science, composability is a key feature of Reusable and Interoperable workflows that promote collaboration and faster progress.
- Versioned
- Versioned research artifacts are tracked across their development, with clear histories of change and the ability to reference or cite specific iterations. Versioning supports Iterative workflows, enabling feedback, correction, and refinement without loss of provenance. In Continuous Science, versioned outputs help maintain trust, transparency, and accountability — especially for Executable or Composable work.
- Interoperable
- Interoperable artifacts can be used across different platforms, tools, and workflows without custom translation. Interoperability enables seamless exchange and combination of complete research outputs — such as data, software, and metadata — in ways that preserve their meaning and utility. It is a foundational aspect of FAIR data and underpins Reusable, Executable, and Composable science.
- Control
- Control refers to the researcher’s ability to independently decide how, when, and in what form their work is Shared. In Continuous Science, control means that researchers are no longer limited by journal timelines, centralized infrastructure, or formatting restrictions. With modern tools and workflows, researchers can iterate and produce Complete, Structured artifacts in an Automated and professional way — without needing to wait for final publication or external validation.
- Shared refers to the intentional act of making research accessible to others — whether to a collaborator, a community, or the public. In Continuous Science, sharing is not a one-time act but an ongoing part of the research process. Sharing Complete, Structured, and versioned artifacts allows for earlier feedback, more frequent collaboration, and Iterative refinement. While sharing is often aligned with open science values, it does not need to be public from the start — private, group, or staged sharing can all support continuous practices.
- Structured
- Structured research outputs are organized in a way that is understandable by both humans and machines — using consistent formats, FAIR metadata, and identifiers. Structure supports Automated workflows like conversion, validation, and linking, and ensures that outputs are Interoperable, Reusable and Networked. In Continuous Science, structure is essential to enabling artifacts to be Shared, versioned, and recomposed across tools and contexts.
- Open
- Open Science is a broad movement that advocates for making scientific outputs — including publications, data, methods, and software — freely accessible and legally reusable by anyone. Openness is foundational to transparency, equity, and reproducibility, and underpins the FAIR principles.
- Assisted
- Assisted refers to the experience of receiving real-time guidance and feedback while authoring, integrating, or preparing research artifacts. In Continuous Science, assistance is provided by tools and systems that help researchers follow best practices — offering suggestions, checks, or enhancements for things like metadata completeness, citation formatting, interoperability and structure, and integration of data or software. These Automated helpers act like “linting for research”, enabling researchers to improve quality, transparency, and interoperability as they work — without waiting until the end of the process.
- Interactive
- Interactive research artifacts allow readers, reviewers, or collaborators to actively engage with the content — whether by exploring data, running code, adjusting parameters, or viewing dynamic visualizations. In Continuous Science, interactivity transforms static outputs into living components of the research process, supporting deeper understanding, validation, and reuse. Interactivity often demonstrates reproducibility and completeness and depends on Executable and Integrated formats that allow content to be explored, manipulated, or rerun — making scientific work more transparent, engaging, and useful across a variety of contexts.
