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Concepts

ACORN is built around a few core concepts that help you understand how to use it effectively. This section will introduce you to these concepts and explain their significance in the context of ACORN and science.

Motivation

For the last few decades, scientific progress has been driven by publishing papers in, ideally peer reviewed, scientific journals. Publishing affords researchers with recognition, career advancement, and funding opportunities. This model has worked well for a long time, but it has some serious limitations that ACORN aims to address.

  1. Publish or perish1: Scientists and researchers often care more about being published than about sharing scientific knowledge. This is not without reason, as publications are a key metric for career advancement and funding. However, this can lead to a focus on quantity over quality, and a reluctance to share negative results or data that does not support a hypothesis.
  2. Reproducibility crisis2: Many scientific results are difficult or impossible to reproduce, leading to questions about their validity. This is compounded by the fact that many publications do not provide access to the underlying data or code used in the research.
  3. No clear way to demonstrate science: Publications, people, and budgets do not tell the whole story. Science needs a cross-domain standard to collect and communicate the full context of scientific endeavors, including data, code, methods, and results.

ACORN addresses these issues by reducing the administrative burden of codifying the details of a research project and providing an automated framework for the sharing and analysis of scientific knowledge. Predicated on the idea of applying “science all the way down”, ACORN applies rigorous scientific principles to the management and dissemination of scientific knowledge itself.

Research Enablement

The research enablement initiative at Oak Ridge National Laboratory (ORNL) aims to improve the way scientific research is conducted, shared, and evaluated. It is a team of developers, communication experts, and information scientists passionate about open science, transparency, and improving the researcher experience.

REI works toward its goals by providing:

  • A cross-domain model of research activity data (RAD)
    • ACORN - Schema, controlled vocabularies, and ontologies for describing research activities
    • ASPECT - Attribute of ACORN that describes technology associated with a given research activity
  • A command-line application written in Rust - acorn-cli
  • A Rust crate for working with RAD - acorn-lib
  • A Python package for working with RAD - acorn-py
  • A catalog of research activity data at ORNL - research.ornl.gov

🪣 Buckets

Research activity data is persisted in versioned folder collections called “buckets”. Each bucket contains a set of files and media assets that describe research activities. Buckets can be stored locally or in a remote repository, such as a GitLab or GitHub repository. For multiple examples, see ORNL’s buckets.

Buckets can be combined via a flat-file3 configuration file using the acorn CLI tool. This allows users to aggregate data from multiple sources and generate reports or other outputs based on the combined data.

Buckets are designed to be flexible and extensible - buckets do not require a cloud provider or expensive infrastructure to use. They can be stored in any version-controlled repository, such as GitLab, GitHub, or even a local file system.

Buckets are designed to enable federation and scaling while maintaining low level control over permissions and access. This allows organizations to share data across teams and departments while maintaining control over who can access and modify the data.

Tip

See the research enablement wiki for more information on buckets.

  1. D. R. Grimes, C. T. Bauch, and J. P. A. Ioannidis, “Modelling science trustworthiness under publish or perish pressure,”“ Royal Society Open Science, vol. 5, no. 1, p. 171511, Jan. 2018, doi: 10.1098/rsos.171511. ↩

  2. M. Baker, “1,500 scientists lift the lid on reproducibility,”“ Nature, vol. 533, no. 7604, Art. no. 7604, May 2016, doi: 10.1038/533452a. ↩

  3. A flat-file is a simple text file that contains data in a structured format, such as JSON or YAML. Flat-files are easy to read and write, and can be used to store configuration data for applications. ↩