||Lessons Learned: Creating Space for Inclusive Practices in Academia
||Antoinette Foster & Lucille Moore
||May 8 2019
||With the advent of big data, many people are beginning to explore fighting social inequity and structural systems of oppression with data in order to (1) define the problem and (2) affect changes in policy. We are learning that, for the most part, much of the data around these issues don’t exist, which largely reinforces systems of oppression.
At Oregon Health & Science University in Portland, OR, a group of people have come together to focus on the lack of representation of historically underrepresented minorities (URM) in science as well as the lack of inclusive culture within OHSU’s graduate programs. Our group is called the Alliance for Visible Diversity in Science (AVDS). We found that data on a variety of topics, e.g. statistics on the number of URM graduate students that are interviewed/accepted/decide to matriculate, and well-designed climate surveys to assess the culture of inclusivity are lacking. This leads to decision-making and policies based on incomplete data that disproportionately hurts already vulnerable populations. For example, many programs require that applicants report their score on a standardized test called the graduate record examination (GRE) despite the fact that research shows that GRE scores are more highly correlated with socioeconomic status than student success. We would like to share what we have learned through the process of forming AVDS: our successes, our challenges, and the imperative idea that we must in part approach social inequity issues with scientific and data-driven approaches.
||The n-of-many-ones: Fueling Community Science with Personal Data
||Bastian Greshake Tzovaras
||May 8 2019
||As we are becoming more and more digitized, we are creating and collecting more personal data than ever before, offering unprecedented chances for research. This potential wealth of data for research comes practical problems such as: How to merge data streams? And how can people responsibly share their personal information? In this talk we will explore how to enable responsible personal data sharing by giving individuals granular sharing options and how this can enable community science. Furthermore, we will also see how we can scale up personal data exploration from the n-of-one to an n-of-many-ones, using a JupyterHub setup built right into a community science platform.
||Measurement Lab - Open Data on Global Internet Health
||May 8 2019
||Daisy Bingham Room
||Measurement Lab (M-Lab) is the largest open internet measurement platform in the world, hosting internet-scale measurement experiments and releasing all data into the public domain (CC0). We are an open source project with contributors from civil society organizations, educational institutions, and private sector companies, and are a fiscally sponsored project of Code for Science & Society. Our mission is to Measure the Internet, save the data, and make it universally accessible and useful. M-Lab works to advance network research and empowers the public with useful information about broadband and mobile connections by maintaining a scalable, global platform for conducting internet measurements, and by supporting an ecosystem of external partners and users around the world interested in using the resulting open data. Our users are researchers, activists, analysts, journalists, experiment developers, hosting providers, regulators, municipalities, and every day consumers. M-Lab works to enhance internet transparency, and help to promote and sustain a healthy, innovative internet by supporting our users in their research and data analyses, developing and publicizing new use cases for our datasets, forming collaborative partnerships, and building open source measurement tools. In this talk we will introduce the M-Lab platform with the csvconf audience, share how our open data and open source tools are being used by communities around the world, and provide resources on how attendees might use them as well.