Since then, its website has remained the main resource for information about where to find anthropology archives, and how to encourage archiving practices among anthropologists. The Council on the Preservation of Anthropological Records (CoPAR) was an organization founded in the 1990s to promote archiving and discovery of archival materials for cultural materials around the world. Iris Yu, Anna Lavrentieva, Ethan Taggart, Christopher Way Keshav Gupta, Soham Pawaskar, Gabriel Duran Rhys Winter, Micah Tracy, Ryan Zhang, Kaosi Unini Matthew Purvis, Aidan Aguilar, Olivia Love, Danny Taylor Jimmy Nguyen, Anthony Capasso, John Dutan Xirui Han, Johannah Ryan, Tam Thu Doan, Kevin Weiner Jarrar Haider, Akhil Reddy, Eshan Agarwal, Chaitanya Pohnerkar By analyzing datasets of crash data, students can answer questions such as: what proportion of drivers are involved in crashes in communities where they live? Are there specific zip codes where higher-risk drivers tend to be more common than others?Ĭhallenge: Washington Fatal Crash Files – WTSC (Data Analysis, Level 4) However, we are curious to know if the people living in these communities are the same people involved in the crashes where occur there. The Washington Traffic Safety Commission (WTSC) manages traffic safety behavior programs and media outreach for various communities. Since the beginning of the COVID-19 pandemic, Washington State has experienced an unprecedented year-over-year increase in traffic fatalities. Malcolm Rivers, Ryan Grafman, Christopher HomĪngelina Cheng, Caroline Irwin, Harrison MorrowĪiden Patel, Chris Kim, Myles Evans, Ola Kwasniewski Thomas Cho, Jenna Han, Joshua Kang, Daniel Shin Gunakshi Sharma, Dhiraj Lahoti, Shweta Salelkar, Sakshi BR Patil Manas M Bhat, Shantanu Parab, Vineet Singh This analysis will be used by the Public Health Seattle-King County (PHSKC) to target specific subgroups for media outreach, materials, and infographics, which students can also contribute to in addition to their analysis.Īditya Kiran Aswin Kumar, Rajeevan Madabushi, Sravya Lenka, Srikanth Parvathala We are curious to know how public perceptions of this new act have changed over time, particularly among demographic subgroups. In 2018, Washington State enacted stronger distracted driving laws, and in response, a survey was developed to evaluate residents’ understanding of the new Driving Under the Influence of Electronics Act, which was conducted in one-month time periods over the past four years. Sehba Wani, Josue Tlapechco, Vienna Nguyen Ramith Wijesinghe, Matthew Chin, Kidus Solomon, Courtney Brandon Osinakachi Amaefule, Cecilia Chavez, Jaclyn Tran Harshitha Ramachandra, Shashank Ramprasad Students will use their creative insights on how variables such as gender, race, ethnicity, age, and residency from past student data can be used to compare diversity trends by semester, academic year, and student status.īrita Laveck, Ellen Kim, Tartela Tabassum After the COVID-19 pandemic, Global Classrooms courses and participants have increased significantly, and the Office of International Affairs seeks to understand student diversity trends for past semesters. The University of Maryland Global Classrooms program allows UMD students to gain international experience in a virtual learning environment. The dataset has a complex structure, numerous variables of interest, and spatial-temporal dimensions. The problem statement is open-ended and requires multitudes of analytical perspectives and visualizations. Level 4: Participants with advanced data analysis skills. Analyses from different angles by various techniques are encouraged. The dataset may contain many variables of interest. The problem statement is open-ended about what the final product may look like. Level 3: Participants with some data analysis background. Creative and interdisciplinary solutions are welcomed. The dataset has a standard structure suitable for beginners. The problem statement is open-ended yet straightforward. Level 2: Participants with basic data analysis knowledge. Start from the basics and create an interesting story. The dataset contains enough information to answer the questions in the problem statement. The problem statement is straightforward about what the final product may look like. Level 1: Participants with little to no knowledge in data science.
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