What are Data Jams?

The Data Jam Initiative is a flexible curriculum designed to create spaces for colleagues to work together on real data analysis projects. In all iterations, colleagues make real research or evaluation products, for example in the form of write-ups and visualizations; in all data jams, you collaboratively work on data that are professionally relevant for your colleagues or co-researchers.

Data Jams are inspired by the concept of Game Jams. In Game Jams, game developers meet for a short amount of time in order to produce quick prototypes of games. In Data Jams, the goal is to collaboratively produce concrete write-ups, models, initial theories and visualizations; these products are then shared with colleagues, partners and relevant stakeholders.

Click on the player on the left to listen to an interview on Data Jams on Bob Bertsch’s podcast “Working Differently in Extension” podcast. Learn more about Data Jams as organizational/institutional responses to the Data Imperative on the eXtension Foundation’s blog site: A Response to a Data Challenge Software as a Teaching Tool for Data Analysis;  Institutional Outcomes of Data Jams


Monthly One-Day Data Jams

djred1For a full day, colleagues work in small groups to analyze data. Their task is to produce and share a write-up of their initial findings. After the Data Jam, the write-ups are shared with colleagues across the organization in our Data Jam blog.

A colleague or a team of colleagues bring their data to the Data Jam, and they provide the group with a broad research question. The data set could be a program evaluation, a research project conducted with or for a community partner, a scholarly research project, or research done for our own program development.

One-day Data Jams across the institution form the backbone of the initiative. They lay the groundwork for the dissemination of analytic and technical skills, and they serve as a setting in which colleagues learn how to utilize the data jam format.


Multi-Day Data Jams - Retreat Format


In this format,  teams from across the institution meet for three to four days in order to jumpstart their research projects. This format can also be used to produce evaluation reports from start to finish.

Teams effectively integrate qualitative research work flows and qualitative data analysis software. Working with a dedicated facilitator, each team works on their existing research or evaluation project.

In this format, the task is to make products (e.g. models, coding systems, initial theories) that kick start the research project.  In addition, teams create project road maps and teamwork plans for their continued work.  If this format is chosen for a complete evaluation, the finished (pre-layout) report is the product. It is created through several rapid prototyping and analysis sprints, and undergoes several rapid feedback cycles.

Multi-Day Data Jams - In-Service Format


In series of half-day and full-day sessions, teams analyze their data, working towards project findings, publications, and further studies. The Data Jam Curriculum creates a flexible frame for both research teams and evaluation specialists in consulting roles to both guide analysts through their research and to build capacity in qualitative analysis and software use. The in-service format can be used for a small series of half-day analysis sessions and can be expanded to provide a framework for ongoing research projects involving multiple, and fluctuating members.  


Data Jams as Methods & Service Learning Classes

 djpurple1Its focus on creating a qualitative research maker-space makes the Data Jam Curriculum  a powerful asset and addition to traditional qualitative methods education. This curriculum can be used in academic departments and programs, summer schools, and as a capacity building framework in service learning programs. Students are engaged in a semester-long research project using a common data set, and they produce final papers in the form of research  reports. This format allows research teams and institutions to distribute the analytic work on large qualitative data sets in a structured, and engaging way.  


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