Open Science & Data Collaborations banner

A University Libraries program supporting collaborative, transparent, openly accessible, and reproducible research across all disciplines at Carnegie Mellon University. We recognize that having well-documented and automated research workflows, code, and datasets is essential to making research more interdisciplinary, efficient, and reusable as well as allowing researchers to leverage data science techniques. This program provides services and infrastructure for open research at CMU through digital tools, training opportunities for research tools and practices, collaboration opportunities on data science projects, special events and advocacy, and a team of experts available as research consultants and collaborators.

Icons for Tools, Trainings, Events, Collaboration, Assessment


• Open Science Framework  – manage research data & projects, share work with collaborators, & register with this cloud-based platform

•  – document & share step-by-step methods & protocols

• LabArchives  – Electronic Research Notebook to securely record & share research notes & data

• KiltHub Repository  – make all of the products of your research openly available, citable, & reusable with CMU's institutional repository


Carpentries Workshops – 2-day introductions to coding with R & Python

• Libraries Workshops – short workshops on getting started with research tools from Zotero to Jupyter Notebooks & research practices from literature searching to data management

OSDC MiniSeries: Reproducible Research – 2 to 4-hour workshops that focus on basic concepts, skills, & tools for conducting research in a reproducible manner

• Citizen Science – support & training for citizen science projects


• Open Science Symposium: An annual symposium that brings together researchers, funders, publishers, & tool developers to discuss the challenges & opportunities of open research. Program & video of talks are available online for OSS2018 (video), OSS 2019 (video), and OSS 2020 (video).

• AIDR: Artificial Intelligence for Data Discovery & Reuse: First hosted in 2019 as an NSF-funded conference aiming to find innovative solutions to accelerate the dissemination & reuse of scientific data in the data science revolution. In 2020, a 1-day symposium version was held as a joint event with the Open Science Symposium.

Love Data Week – an international celebration of data each February.


• Data Collaborations Lab (dataCoLAB) — We match up researchers who want help with their datasets with consultants who have data and computer science skills, and create opportunities for people with different technical and disciplinary backgrounds to work together, following best practices that enhance reproducibility.

• Collaboration Bioinformatics Hackathon — Academic and industry researchers from around the world come together to collaboratively work on crucial problems and opportunities in clinical bioinformatics.


• Consultations – Get in touch for help with any stage of the research process from grant writing and data management plans, to reproducible analysis workflows, to making any or all products of your research open.

• Collaborations – We’re researchers too and would be glad to be partners on your research projects to contribute to data management or open data components of the work.

• Outreach – We’ll come to you! Get in touch if you’d like us to pay a visit to your lab, program, department, or other CMU community group.

The Open Science Team

Katie Behrman, KiltHub Repository Coordinator

Neelam Bharti, Chemistry, Chemical Engineering, Materials Science, Open Access

Patrick Campbell, Project Coordinator, Citizen Science

Julie Chen, Civil & Environmental Engineering, Mechanical Engineering, Engineering & Public Policy

Melanie Gainey, Biological Sciences, Biomedical Engineering, Neuroscience

Hannah Gunderman, Research Data Management Consultant

Emma Slayton, Data Curation, Visualization, & GIS Consultant

Huajin Wang, Biological Sciences, Computer Science, Data Collaborations & Reproducibility

Sarah Young, Social Sciences, Public Policy & Information Systems; Evidence Synthesis

Advisory Board

John Chin, Research Coordinator, Institute for Politics & Strategy, CMU

Jeffrey Flagg, Lab Manager, Dynamic Decision Making Laboratory, CMU

Brian Isett, Postdoctoral Fellow, Gittis Lab, CMU

Nynke Niezink, Assistant Professor, Department of Statistics and Data Science, CMU

Albert Presto, Research Professor, Mechanical Engineering, CMU

Thiago Rodrigues, Doctoral Student, Civil & Environmental Engineering, CMU

Cathy Su, Doctoral Student, Computational Biology, CMU

Sarah Werner, Doctoral Student, Biological Sciences, CMU