Accessible Content Optimization for Research Needs (ACORN) applies standardization, automation, linked data, and institutional knowledge to research activity data (RAD) to create actionable insights and ultimately enable new research. ACORN is a command line multitool that creates analysis-ready data from RAD. It can also run on remote continuous integration servers for shared RAD repositories. ACORN employs automated processes for informing and/or enforcing defined content schemas to create standardized, highly structured data. Because of its standardized data source, ACORN easily applies computer automation to generate communication assets such as PDFs, PPTs, and web pages. Built using memory-safe Rust, ACORN is portable and accessible for use on any Windows, Mac, or Linux machine. ACORN's standardized approach ingests and maintains data in a consistent format to enable immediate analysis and use, building progressively more powerful datasets.
# Statement of need
Communicating research can be challenging — from the high-level overview of a research institution, down to singular projects within that institution. Science data systems created to help communicate research are often isolated and/or specialized to individual suborganizations, teams, or domains. Research communication is further complicated by a lack of consistency and documentation in research data and metadata, preventing external audiences, such as jobseekers, policymakers, funders, and the general public, from finding the information they need, despite federal guidance for clear, consistent documentation.[@Lin,2020],[@OSTP:2022] True innovation requires reusable systems that can standardize data across domain boundaries and serve as a nexus for scientists, developers, and communicators.[@Sochat:2018],[@Puebla:2024]
Communicating research can be challenging — from the high-level overview of a research institution, down to singular projects within that institution. Science data systems created to help communicate research are often isolated and/or specialized to individual suborganizations, teams, or domains. Research communication is further complicated by a lack of consistency and documentation in research data and metadata, preventing external audiences, such as jobseekers, policymakers, funders, and the general public, from finding the information they need, despite federal guidance for clear, consistent documentation.[@Lin:2020],[@OSTP:2022] True innovation requires reusable systems that can standardize data across domain boundaries and serve as a nexus for scientists, developers, and communicators.[@Sochat:2018],[@Puebla:2024]
Traditional research practices are built on antiquated, habitual processes. These are particularly troublesome in an environment where publishing is critical to career survival.[@Grimes:2018] Researchers may be tempted to do the bare minimum, skip steps, and pursue sensational or novel paths in the name of journal acceptance and credibility.[@van_Dalen:2012] These practices have led to the twin reproducibility [@Baker:2016] and replicability [@Camerer:2018] crises.
