On January 6-8th, 2021, bioinformatics researchers from around the world came together virtually to collaboratively develop a framework for clinical reporting of RNA-Seq research. For the second year in a row, CMU Libraries was thrilled to provide support for a clinical genomics hackathon! This support included finding and organizing references, helping organize, review, and clean up the manuscript resulting from the hackathon, and giving feedback on READMEs and reproducibility. In the Components of this project, you can find direct links to the work of each of the four teams. These distinct teams worked collaboratively and iteratively with the same dataset to provide a framework for clinical reporting of RNA-Seq research.
Repository of videos from symposium sessions, slides from talks, and documentation of the program and speaker information.
Click on components for each session to view videos and slides from that speaker.
Event Website: https://events.library.cmu.edu/oss2020/
Event report: https://doi.org/10.1184/R1/12092193
Archive of videos from symposium talk sessions, slides from talks, the symposium program, speaker bios, and tweets from #CMUopenscience during the event.
Click on components for each session and talk to view videos and slides for that speaker.
Event website: https://events.library.cmu.edu/oss2019/
The inaugural Open Science Symposium (OSS) was held on October 18 and 19th, 2018 at the Mellon Institute of Carnegie Mellon University. You can find a repository of videos from symposium sessions, slides from talks, and documentation of the program and speaker information on Open Science Framework.
The OSS brought together students and researchers from a wide variety of departments at Carnegie Mellon University (CMU) and University of Pittsburgh to discuss open science. The first day, October 18th, was a series of panel discussions from researchers, publishers, and developers that are working on innovative open science practices and tools, both at CMU and other institutions.
A Scientific Speed Dating event encouraged researchers from different disciplines to informally discuss their work and look for collaboration opportunities.The second day, October 19th, was a series of hands-on workshops on popular and reliable tools and platforms for practicing open science.
The Open Science Symposium was a joint event by Mellon College of Science and University Libraries, supported by funding from the DSF Charitable Foundation. It was organized by Melanie Gainey, Ana Van Gulick, and Huajin Wang of University Libraries and Eric Yttri, assistant professor of Biological Sciences and Center for Neural Basis of Cognition (CNBC).
First hosted in 2019 as an NSF-funded conference, AIDR: Artificial Intelligence for Data Reuse aims 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.
The Data Collaborations Lab (dataCoLAB) at Carnegie Mellon University Libraries connects the research community across disciplinary borders and facilitates collaborations between data producers and data scientists. The program connects researchers who want more from their datasets with individuals who have data and computer science skills, creating opportunities for people with different technical and disciplinary backgrounds to work together. The ultimate goal of the dataCoLAB program is to help build a strong community and a healthy data ecosystem.
Anyone from the CMU or Pittsburgh community can participate. Perhaps you have an existing dataset and want help analyzing, organizing, or visualizing it. Or maybe you have data science skills and want to gain experience consulting on interesting real-world data problems. Learn more about whether dataCoLAB is for you!
Information professionals from the CMU University Libraries will help participants connect with collaborators and get started. Participants get support on research data management, project documentation, and other research methodologies. Your library support will help you find and use tools for collaborating and documenting your project, and provide guidance on best practices for making your project publically available and citable.
Planning Data Collaboration Workflow
A bioengineering researcher studying human motor control and motor learning sought dataCoLAB input on designing a collaborative data collection workflow. One goal was to enable multiple collaborators to independently contribute data points directly to a growing dataset. The conversation with dataCoLAB consultants focused on how to make the database accessible using tools like GitHub, Open Science Framework, and to visualize the data by building a Shiny app. The consultation also considered pros and cons of having participants entering data directly into a shared dataset, versus keeping separate datasets to be integrated later.
Visualization using Chord Diagrams
A researcher from the School of Nursing sought support in visualization of research data on technology use in promoting healthy behaviors among cancer survivors. When encountering problems in customizing graphs in R, the researcher suspected that the problem was in the code itself, but the dataCoLAB consultation revealed that file formatting issue were interfering with the machine readability of the data. The consultation provided guidance on how using a simple open data format from early on in the process could help avoid similar issues in future efforts.
Machine Learning to Predict Gait Intervention Outcomes
A researcher from University of Pittsburgh is examining gait intervention protocols for different demographics. In order to predict how people will respond to different types of intervention, the research relies on a database containing human demographics, training protocols, and movement outcomes. The researcher hopes to utilize machine learning to predict individual outcomes. Early consultation with dataCoLAB focused on critical aspects of data exploration to be completed prior to designing a machine learning or modeling approach. Subsequently the researcher was paired with a consultant from CMU, who is a Master's student in Data Analytics at Heinz College. The project is now in progress, and the participants will meet regularly and use Open Science Framework as a collaborative project platform.
View past online workshops
