Open Science

Badges distributed by Zephyr to recognize open and reproducible research will incentivize authors to archive and share data, code, and models associated with their research articles. This standard enables other authors to more easily build upon that research.

Governance

This project is overseen by a board-approved Project Management Group (PMG) as follows:

Andre Carrel Chair

The Ohio State University

Jason Hawkins

University of Calgary

Jawad Mahmud Hoque

WSP

Xuesong Zhou

Arizona State University

More to come! Please let Andre Carrel ([email protected]) know if you are interested in contributing.

Approach

Establishing a credible open science badge takes two parallel efforts: agreeing on what “open” and “reproducible” actually mean in practice, and building the buy-in needed for a badge to carry real weight in the research community. The workplan below breaks this into two steps:

Agree on a review and certification process

Before badges can be awarded, the PMG needs a clear, defensible process for reviewing submissions and certifying that they meet Zephyr’s standards. That means resolving a set of open questions about what counts as sufficient documentation, how rigorous the review should be, and how to handle the practical realities of proprietary and confidential data. The PMG is considering the following questions:

What are our standards?

Should model estimation files be archived when results are presented?

Should estimation scripts or code also be provided for the final models?

Should the data and geographic files underlying a paper's figures be archived?

Should the model runs themselves be archived when their results are presented?

Should metadata describing files and field names be required?

Should a knowledgeable reviewer be able to rerun the models/scripts/estimations to recreate the paper's results?

Beyond the standards themselves, a few open policy questions remain:

Badge Tiers

Is there a single badge, or is there value in considering different tiers (silver and gold)?

Proprietary Data

How do we deal with proprietary data?

Confidential Data

How do we deal with confidential data obtained from consumer surveys, etc.?

Get buy-in and implementation support from academics, journals, and Zephyr

A standard is only useful if people actually adopt it. Beyond defining the review process itself, the PMG is thinking through how to get authors, journals, and Zephyr on board:

What is the process for awarding badges? An additional stand-alone review, or one integrated with the normal peer review process?

What strategies can we employ to make a badge a positive incentive?

The Zephyr Foundation’s 5 Levels of Open Science Readiness (Z5OSR)

Proposed addendum to the Open Science guidelines — May 7, 2025

The Zephyr Foundation introduces a five-level Open Science Readiness (OSR) system tailored for transportation research. This system aligns with Zephyr’s mission to advance rigorous decision-making in transportation and land use for the public good. It underscores the importance of developing and implementing travel analysis methods that are valuable, credible, and transparent.

Manuscript & Report (MRL)

Planning

Identify platforms for open access publication.

Accessibility

Make the manuscript or pre-print version publicly accessible.

Open Specifications

Ensure the manuscript adheres to open specifications for methodology and reporting, facilitating broader understanding and application.

Validation

Manuscript findings are validated through peer review or community feedback.

Reuse

Demonstrate how the research can be applied or extended in other studies or real-world scenarios.

Code & Model (CML)

Planning

Outline the strategy for code sharing, considering various open-source options.

Development & Open Specifications

Develop code with logical abstraction, ensuring it meets open specifications for wider usability.

Sharing

Make the code available in a public repository, with comprehensive documentation.

Validation

Validate the code through real-world use cases or external verification.

Reuse

Code is reused in different contexts or projects, demonstrating its adaptability and robustness.

Data (DRL)

Planning

Develop a data sharing plan that respects privacy and proprietary concerns.

Documentation & Open Specifications

Ensure comprehensive documentation of data collection and processing, adhering to open data standards.

Sharing

Publicly share the dataset with a permanent link, making it accessible for replication and further research.

Validation

Validate the dataset through application in real-life use cases, confirming its reliability and relevance.

Reuse

The dataset is utilized in various contexts, proving its value beyond the original study.

This five-level structure emphasizes a progressive approach to achieving open science, starting with foundational planning and accessibility, incorporating standards and specifications for broader usability, and culminating in validation and reuse that demonstrate the practical impact and adaptability of research outputs.

Lead: Andre Carrel

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