GBS analysis presents a range of computational challenges. Among them are technical challenges (e.g. wrangling large NGS data files and acquiring and administering the computational hardware necessary or accessing the campus shared resources), software engineering issues (e.g. deploying and maintaining pipelines) and research questions. Also, we continually need to train scientists to use these analysis pipelines.
Typically, research groups on campus deal with these challenges in isolation. The focus of this session will be on the question: What can we do better by working together? The desired outcome is to identify a specific project (or projects) that will be mutually beneficial and to set a project plan in motion.
The City of Madison passed an Open Data Ordinance in 2012 (https://www.cityofmadison.com/news/new-city-of-madison-open-data-ordinance). Since then, the city has published a wealth of data to their open portal. Laura Larsen (LLarsen@cityofmadison.com) and others at the City of Madison have collaborated with me to get students involved in projects based on this data (https://wisc-ds-projects.github.io/f19/index.html). We are proposing a join session with the following content: (1) a tour of the data portal by a presenter from the City of Madison, (2) a couple short talks by students about their projects based on the data, and (3) a tutorial session by myself on how to use the data to create maps and animations.
Participants—please bring a laptop with internet access to this session, along with any software (R, Python, SAS, Excel, etc) that you usually use to explore a dataset.
This concurrent session will focus on how data and algorithms can undermine equity and perpetuate bias in the fields of public health and criminal justice. Topics could include bias in algorithms, such as the COMPAS Recidivism Algorithm, as well as the dissemination of health data in ways that confer blame on individuals. The audience is people who work with, write about, or use health and/or criminal justice data, and the goal would be for everyone to leave with an understanding of how to combat bias in the use and dissemination of health data, a vital skill in the data science community.
Games have many elements that promote active learning and engage learners more deeply in the topic. These games will provide an opportunity for players to explore and implement what they have learned in a fun, risk-free environment and engage users in new ways.To incorporate this trend into Research Data Management instruction, we are designing an escape room game that will motivate researchers to acquire, advocate for, and apply research data management best practices. In this presentation, we will explain our process and lead a play-testing activity for puzzles from the escape room game.
Openness and sharing of information are fundamental to the progress of science and to the effective functioning of the research enterprise. The free flow of information enables the research community to scrutinize scientific claims and to gain confidence over time in results and inferences that have stood up to repeated testing. Central to these topics are good data management practices that elevate data to first class research products that are citable and are attributed to creators. This workshop focuses on data management best practices occurring throughout the research life cycle and will focus on:
- Data management planning - Collecting data - Storing and sharing data during the research project - Data quality management - Metadata - Data curation and publishing
Bring your questions so we can address all your data management challenges and use cases, and consider bringing a dataset. We find learning within the context of your own data is helpful.
Geospatial analysis is often underutilized in the social sciences and in higher education administration. In this session, learn how analysts at any skill level can use the free software QGIS to take advantage of the latent geographic information that goes unused in so many datasets. Learn how QGIS can be used for feature engineering, classification, and smarter aggregation. Then, in a real-world example, see how OSFA uses it to identify target populations among enrolled students and understand their distribution across campus.
