For many indigenous and other local communities in the rainforests of South America, survival in the rainforest has always depended on an intimate knowledge of their territory, passed down by their ancestors. Place-based stories help determine where food or resources are located, or where dangers lie hidden. Most importantly, the oral histories reinforce their historical and cultural connection to their homelands, which in turn informs their collective identity. However, in the contemporary context, oral history storytelling traditions are at risk of disappearing as younger community members leave their villages recurrently or permanently, in search of work. To prevent invaluable oral histories from disappearing into oblivion, we developed a methodology and built a FOSS application to help communities map their oral histories. Terrastories was born in 2018 after realizing the need for an offline-first geostorytelling tool that can work in very remote conditions such as the Amazon rainforest, and give communities the power to manage their own traditional knowledge and storytelling data. I will share how the project was born and why indigenous peoples needed this tool, how Terrastories works, and discuss some tough questions around data sovereignty, protection of sensitive data, and archiving traditional knowledge.
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\r\n\r\nSlides from presentations at csv,conf,v5 held online on May 13-14, 2020.
\r\n\r\ncsv,conf brings diverse groups together to discuss data topics, and features stories about data sharing and data analysis from science, journalism, government, and open source.
\r\n\r\ncsv,conf is a non-profit community conference run by folks who really love data and sharing knowledge. If you are as passionate about data and its application to society as we are, then this is the conference for you.
\r\n\r\ncsv,conf conferences aren't just about spreadsheets. We curate content on broader topics like advancing the art of data collaboration- from putting your data on public repos, to producing meaningful insight by running large scale distributed processing on a cluster.
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