"Revolution Analytics will allow HRDAG to handle bigger data sets and leverage the power of R to accomplish this goal and uncover the truth." Director of Research Megan Price is quoted.
REVOLUTION ANALYTICS
Press release
February 4, 2014
Link to press release
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The online version of the 2019 Year-End Review will appear in January 2020.
Donating to HRDAG
Thank you for your interest in making a donation to the Human Rights Data Analysis Group to help us use science to support our partners in the human rights world.
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Las estimaciones se estratificaron por ubicación y perpetrador.
Propelled by the impact of data analysis in El Salvador, Patrick Ball applied his WDWTW model to human rights information in other countries. Throughout the 1990’s, Ball worked at the American Association for the Advancement of Science (AAAS) analyzing large-scale human rights violations in Ethiopia, South Africa, Haiti and Guatemala. Together with senior scientific colleagues, including statistician Dr. Herb Spirer, Ball developed new methods for analyzing state-sanctioned violence. This chapter documents how the research expanded when a group of nongovernmental organizations in Guatemala asked the scientific community to gather and analyze ...
We are pleased to announce that HRDAG will be supported by two additions to our Advisory Board, Julie Broome and Frank Schulenburg.
We’ve worked with Julie for many years, getting to know her when she was Director of Programmes at The Sigrid Rausing Trust. She is now the Director of London-based Ariadne, a network of European funders and philanthropists. She worked at the Trust for seven years, most notably Head of Human Rights, before becoming Director of Programmes in 2014. Before joining the Trust she was Programme Director at the CEELI Institute in Prague, where she was responsible for conducting rule of law-related trainings for judges and ...
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Some police officers avoid accountability by “wandering” to another agency. HRDAG and partners created a data tool that tracks officers’ employment history.
The HRDAG Tech Corner is where we collect the deeper and geekier content that we create for the website. You can browse by Category or scroll to view find all articles listed.
A new study has estimated that over 500 Tamils were forcibly disappeared in just three days, after surrendering to the Sri Lankan army in May 2009.
The study, carried out by the Human Rights Data Analysis Group and the International Truth and Justice Project, based on compiled lists which identify those who were known to have surrendered, estimated that 503 people had been forcibly disappeared between the 17th– 19th of May 2009.
IPFS is a peer-to-peer storage network that promotes the resiliency, immutability, and auditability of data. This README explains code written to shepherd the files from janky external USB drives to IPFS.
Kristian Lum and William Isaac (2016). To predict and serve? Significance. October 10, 2016. © 2016 The Royal Statistical Society.
Kristian Lum and William Isaac (2016). To predict and serve? Significance. October 10, 2016. © 2016 The Royal Statistical Society.
HRDAG analysis shows that the government figures are a gross underestimation of the drug-related killings in the Philippines.
This rigorous estimate shows that 1-2 percent of the country’s population was killed or disappeared during the civil war.
“The Rafto Prize 2021 is awarded to the Human Rights Data Analysis Group (HRDAG) for their wide-reaching documentation of grave human rights abuses. By using statistics and data science they uncover large-scale human rights violations that might otherwise go undetected. This novel approach has enabled courts to bring perpetrators to justice and given closure to affected victims and their families. HRDAG represents a new generation of human rights defenders that advances the enforcement of human rights globally.”
Megan Price (2022). Beautiful game, ugly truth? Significance, 19: 18-21. December 2022. © The Royal Statistical Society. https://doi.org/10.1111/1740-9713.01702
Megan Price (2022). Beautiful game, ugly truth? Significance, 19: 18-21. December 2022. © The Royal Statistical Society. https://doi.org/10.1111/1740-9713.01702
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Using machine learning to make sense
of massive caches of data
Som...