Bring Your Own Data (BYOD) Working Groups
Do you have research data waiting to be collected, cleaned, analyzed, or visualized? BYOD Working Groups provide structure, guidance, and accountability to help you make progress on data components of your research project. Groups are formed quarterly.
The program includes:
- Weekly virtual group check-ins
- Access and referrals to learning resources to support your work
- Additional meetings with RCDS consultants as needed
Groups of 4-7 researchers meet weekly with a RCDS staff member to discuss what they plan to accomplish on their research project, what progress they made in the last week, any issues that have come up or that they are looking for help with, and other challenges that arise in the process of doing research.
Program Benefits
Benefits include:
- Help maintaining productivity and motivation on your project
- Chance to de-stress, reprioritize, and refocus on the most relevant tasks each week
- Support from a community of researchers who will celebrate your progress and help you with your research challenges
- Get new perspectives on your analysis from other researchers outside your field
- Practice communicating about your research to others outside of your field
- Extended support from RCDS consultants beyond what’s available with an individual consultation
BYOD Working Groups are designed to help researchers focus on the next step of their project, get unstuck quickly when facing obstacles, and make forward progress on their work. Working Groups are deliberately designed to not create any extra work for researchers – you should show up for a weekly check-in even if you made little progress on your project over the previous week.
Who Should Apply
Researchers should apply who:
- Are Master’s Students, PhD Students, Professional Students, Postdocs, Faculty, and Staff. Advanced Undergraduate Students with clearly defined research projects may also apply.
- Are actively working on a research project with a well-defined goal in one or more of these areas:
- Data collection or experimental design
- Data cleaning or exploration
- Statistical analysis
- Bioinformatic or genomic analysis
- Coding of pipelines, algorithms, simulations, or research tools
- Data visualization, including static and interactive figures
- Modeling or running simulations
- Have working knowledge of the coding language or tool they are primarily using. BYOD cannot accommodate participants who are looking to learn a coding language, but please visit our Training page for workshops and learning resources.
- Are able to attend most weekly sessions for their chosen group
Email Ritika Giri with questions. Keep reading below for important dates and sample projects, or click the button to apply.
APPLY NOW FOR WINTER 2025 BYOD WORKING GROUPS
Winter 2025 Working Groups
| Group Focus | Meeting Time | Meeting Dates |
| Social Science | Tuesdays, 3 to 4 p.m. | January 21 - March 11 |
| Modeling and Simulations | Fridays, 12 to 1 p.m. | January 24 - March 14 |
| Bioinformatics and Genomics | Fridays, 1 to 2 p.m. | January 24 - March 14 |
Social Science Working Group: This group is open to those engaged in social science research projects using quantitative data, regardless of their experience level. It is led by a statistician and a data scientist, both of whom have social science backgrounds.
Bioinformatics and Genomics Working Group: This group is open to those working with genomics data or bioinformatics tools. It is led by a data scientist with expertise in biological data analysis and machine learning. Researchers with all levels of experience are encouraged to apply.
Modeling and Simulations: This group is open to everyone in the community regardless of modeling paradigm, as all types of modeling scenarios will be welcome (machine learning, game theory, epidemiology, networks, etc.), as well as simulation techniques that often come hand-in-hand with models (differential equations, agent-based models, etc.). It is led by a data scientist with expertise in this area.
Data Science and Visualization: This group is open to all researchers working on the analysis or visualization of their data. It is led by a data scientist with visualization expertise, and it will also have a statistician on call. We encourage researchers from any discipline covering any research topic who have projects involving data analysis and/or visualization to apply.
Sample BYOD Projects
To get the most out of BYOD, researchers should have a clearly defined project and adequate time to work on it during the quarter; this can range from 4 to 40 hours a week, depending on your role and whether or not this is your only active project.
Examples of previous BYOD participants’ projects:
- Building a pipeline to diagnose [a particular lung condition] in ICU patients. Characterizing the performance of the pipeline relative to other attempts. My tasks will be stitching together code written by many people into one cohesive workflow.
- How does affect and coping change over 18 months in individuals recently diagnosed with [a particular disease]? Does participation in a well-being intervention affect these relationships? I will run growth curve models to determine the 3-way interaction effect between coping, time, and intervention treatment condition on different aspects of emotional well-being.
- Studying intergenerational mobility in income in [country]. I will estimate a flexible child skill production function, which is a key mechanism for income persistence across generations. This quarter I will be finishing data cleaning and estimation using R and Python.
- What is the effect of race and place on political behavior? I will complete a statistical evaluation of survey data to understand the relationship between race and place. I am using R to create logit models to predict vote choice based on these factors.
- How can I show relevant information from EEG data at different time points surrounding a stimuli? Looking to explore possible data visualization choices and select at least one for inclusion in a manuscript. Would also love to streamline the processing pipeline and make it flexible for future studies.