Open access

Overview

The ability to view, reproduce, and build upon other works is a core tenet of the scientific process that Redivis is built to uphold and facilitate. Redivis tools for data storage and analysis comply with federal mandates for open access and automatically generate artifacts documenting all steps in the data process, ensuring reproducibility.

Background

In August 2022, the United States Office of Science and Technology Policy (OSTP) released a memorandum on public access to federally funded research (known as “Nelson Memo”) which outlined new requirements affecting both faculty and students who conduct research using federal funding:

1. Make publications and their supporting data resulting from federally funded research publicly accessible without an embargo on their free and public release.

2. Enact transparent procedures that ensure scientific and research integrity is maintained in public access policies.

3. Ensure equitable delivery of federally funded research results and data.

1. Public accessibility

Redivis is built to make data sharing as easy and safe as possible, defaulting to fully accessible data, metadata, and documentation. However, some data must be restricted for privacy and to comply with data licensing agreements. In these cases Redivis follows an "as much as securely possible" sharing philosophy for sharing.

• Multiple levels of access allows data owners to grant access to documentation, metadata, samples, and data separately, so as much access as possible can be granted.

• When research is done using restricted data, analysis code is made available when possible.

• Viewing public data and analysis workflows on Redivis is completely public and does not require a Redivis account.

• If any piece of analysis or data is restricted, a user will need a Redivis account to apply for access. Redivis accounts are completely free with no restrictions.

2. Transparent procedures

Redivis systems are built to automatically capture and document any work done on datasets and analyses in standardized formats, while giving researchers and administrators the ability to supplement or override these when necessary.

• Datasets are automatically versioned. All changes to variables and rows are recorded and made available to administrators and data viewers.
• Redivis offers a no-code interface to build transform queries that compiles to SQL code. This code is available to the analyst and any viewers.
• Workflows are self-documenting. Every step taken in an analysis workflow is recorded sequentially in a visual format that is easy to follow.
• Workflows are version controlled. Every step is versioned and time-stamped, allowing users to revert to a previous iteration, or a viewer to understand how queries might have changed over time.
• Workflow can be forked, preserving relationships between workflows and datasets.

3. Equitable delivery

Redivis is free and accessible to any researcher, reviewer, or casual data browser. By taking multiple complex data storage and analysis systems and centralizing them with clear UI, it is clearer to understand full workflows without specific technical knowledge.

• Redivis uses transparent, open-source formats, allowing data, analyses, and workflows to be exported and used within other systems.

• APIs make data and workflows available to researchers and administrators using their own systems.
• No-code interfaces allow researchers at any skill level to build and understand research steps.

• Common, open-source coding languages (SQL, Python, R) are a foundation of tools and supported in analysis workflows.
• There is no cost to researchers to store personal data, or run data queries.