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Academia and academic leaders have an incredible responsibility to lead the world in advancing research at the frontiers of our shared knowledge. Diversity is the most important tool for ensuring that every domain expert is advancing intellectual and broader impacts. Here are three similes to describe why diversity first is the most efficient strategy and critical requirement for effective research and education:

1) Reproduction of knowledge benefits from diversity like genetic diversity benefits from sexual reproduction. Diversity is key to the evolution of our species, and we need that evolution to include all intellectual contributions to continue adaptation.

2) Inspiration to build knowledge depends on diversity as much as music is defined by the synthesis of various parts. Without integrating a spectrum of tone, pitch, and rhythm, it’s not music at all – without diversity, our academic efforts are more likely to be just noise.

3) Innovation in research and education are generated by diversity like a healthy democracy benefits from a respectful dialogue presenting a range of perspectives and contrasting ideas. Innovation does not happen in a homogenous environment limited by the bounds of a common set of ideas.

Diversity yields reproduction of knowledge, inspiration, and innovation when the sum of the parts is crafted to coexist harmoniously. The sum of the parts is greater than the whole when the parts remain intact AND something new is created from the collaboration. It is a solemn task and hard work to respectfully synthesize the latest approaches to create learning environments where diverse contributors can thrive. In reality, most academics are products of a meritocracy with toxic hierarchical dependencies that require reframing, emotional intelligence, and a daily commitment to uncovering unconscious bias. Training and cultural awareness on how to integrate diversity and inclusion into our academic work is emerging as a new status quo. I expect the short and long-term outcomes will include both domain science merit and generation of a scientific workforce whom the public taxpayers trust and are committed to support. The chaos of life on our planet has synthesized a dynamic set of problems for scientists and engineers. These problems can be converted to opportunities when we put diversity to work in a scaffold of our collective knowledge.


Based on the distribution of the global population, our aim is to create hackweek cohorts and community that includes at least 50% of participants who identify as women, and up to 10% of participants who identify as non-binary or transgender. We also aim to create a cohort that consists of up to 50% people of color, 10% LGBTQ, and 10% of bi- or multi-lingual students. We focus on recruiting and promotions before the event, and inclusive approaches during the event to create an environment where every participant can benefit (including accommodating disabilities) from the community intersectionality of disciplinary and personal diversity.

We have three components to our workshop that are new to the hackweek model at UW eScience Institute:

1) A diverse advisory board is currently being recruited from outside the science domain leadership team. This group of advisors have been selected for both professional domain expertise, and also meet the diversity target.

2) Our participant selection process will require invited 50 of the anticipated 300 applicants. Our selection method will include using and advancing the Entrofy software[1].

3) We are working with an independent Education Evaluator to help us design, develop, and implement an evaluation that with determine the effectiveness of the training for individuals from currently underrepresented populations. Measuring this effectiveness requires thoughtful planning throughout the application design, event promotion, during the event activities, and follow-up to determine long-term community impacts.

4) Student leadership training as Certified Data Carpentry Instructors and cultural awareness to support expert knowledge at all career stages.

[1] See https://github.com/dhuppenkothen/entrofy

open source software on Github. Given a list of participants with various “attributes” (e.g., gender, career stage, subfield, geography, years since PhD), the code finds the distribution of values within each attribute (e.g, male, female) and generate a subset that approximates target value distributions (e.g, 50% male/female, 30% junior, 30% non-US). The attributes and values are user determined and based on the data present in uploaded CSV file.