326 results for search: 가평출장마사지☆［010-8226-1872） 매탄출장마사지✻청라출장마사지●수원출장마사지 일산출장마사지/feed/press-release-tchad-2010jan-fr
Lessons at HRDAG: Making More Syrian Records Usable
If we could glean key missing information from those fields, we would be able to use more records.
Analizando los patrones de violencia en Colombia con más de 100 bases de datos
El objetivo de esta institución temporal es conocer la verdad de lo ocurrido en el marco del conflicto armado.
Trove to IPFS
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.
How Machine Learning Protects Whistle-Blowers in Staten Island
People filed complaints against NYPD officers, and HRDAG went above and beyond to protect the privacy of the people who reported the offenses.
HRDAG Names New Board Member William Isaac
William Isaac joins HRDAG's Advisory Board, bringing expertise in fairness and artificial intelligence.
String matching for governorate information in unstructured text
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Donate with Cryptocurrency
Help HRDAG use data science to work for justice, accountability, and human rights. We are nonpartisan and nonprofit, but we are not neutral; we are always on the side of human rights.
Cryptocurrency donations to 501(c)3 charities receive the same tax treatment as stocks. Your donation is a non-taxable event, meaning you do not owe capital gains tax on the appreciated amount and can deduct it on your taxes. This makes Bitcoin and other cryptocurrency donations one of the most tax efficient ways to support us.
We are a team of experts in machine learning, applied and mathematical statistics, computer science, demography, and social science, and ...
How Data Analysis Reconsiders Deaths During Arrest
The US Bureau of Justice collects information on deaths during arrest, and HRDAG determined the efforts were incomplete. The conclusion: Approximately one third of all Americans killed by strangers are killed by police.
Mapping Mexico’s hidden graves
When Patrick Ball was introduced to Ibero’s database, the director of research at the Human Rights Data Analysis Group in San Francisco, California, saw an opportunity to turn the data into a predictive model. Ball, who has used similar models to document human rights violations from Syria to Guatemala, soon invited Data Cívica, a Mexico City–based nonprofit that creates tools for analyzing data, to join the project.
Controlled vocabulary
What is a controlled vocabulary?
A controlled vocabulary provides the ability to transform information that has been collected on violations, victims, and perpetrators into a countable set of data categories. It is important that this process be done without discarding relevant information and without misrepresenting the collected information.
Why is it necessary?
The data collected about human rights violations originates from a wide range of information sources – legal case files, newspaper articles, e-mails, faxes, letters, phone conversations, testimonies, interviews, radio and television programs, video clips, and photos. This wide range of ...
Using Math and Science to Count Killings in Syria
In this afternoon "Lightning Talk" at RightsCon 2014, Megan Price spoke about the importance of using models to adjust for variability when reporting human rights violations and mentioned innovative tools that can be used for tracking abuses.
RIGHTSCON
March 4, 2014
San Francisco, California
Link to RightsCon program
Back to Talks
HRDAG at Strata Conference 2014
Last Thursday, HRDAG co-founder and director of research Megan Price presented at Strata, the conference for data scientists and people who work with "big data." In her talk, she addressed the question of how we can know the actual number of conflict casualties in Syrian. Her short answer was, "We don't know." The longer answer was that we have a very good idea of how many conflict casualties have been reported, by several documentation groups, and that we're working on analyzing (more…)
Kosovo Data – Killings, Migrations and More
Much of the debate about the March–June 1999 war between NATO and Yugoslavia turned on how many people left their homes in particular places and at certain times. Solid information about the flow of refugees out of Kosovo has helped investigators to link patterns in the flow to patterns of NATO bombing, Yugoslav strategic plans for "cleansing" Kosovo, and Yugoslav and irregular troop deployments. At its heart, the debate was about whether refugees left their homes fleeing NATO attacks and fighting between the KLA and Yugoslav forces, or whether they left their homes after being threatened, assaulted, and robbed by Yugoslav police, army, and irregu...
Data on Kosovo Killings
The data on killings in Kosovo are in four files. All of the files are comma-delimited ASCII. The fields in each file are described below.
If you use these data on Kosovo killings, please cite them with the following citation, as well as this note:
“These are convenience sample data, and as such they are not a statistically representative sample of events in this conflict. These data do not support conclusions about patterns, trends, or other substantive comparisons (such as over time, space, ethnicity, age, etc.).”
Patrick Ball, Wendy Betts, Fritz Scheuren, Jana Dudukovich, and Jana Asher. (2002). AAAS/ABA-CEELI/Human Rights Data ...
Data on Kosovo Migration
[popup citation="For migrations: Ball, Patrick. (2000). AAAS/ Human Rights Data Analysis Group database of migrations in Albania and Kosovo. For killings: Patrick Ball, Wendy Betts, Fritz Scheuren, Jana Dudukovich, and Jana Asher. (2002). AAAS/ABA-CEELI/Human Rights Data Analysis Group database of killings in Kosovo. For other data: Human Rights Data Analysis Group. (2002). Database of NATO airstrikes, geographic coding, and KLA activity in Kosovo."]
The data on migration from Kosovo are in seven files. All of the files are comma-delimited ASCII. The fields in each file are described below. For more information, see Policy or Panic, section A1, pp. ...
Welcoming our new Technical Lead
After almost two months of searching for the perfect fit, we’re very pleased to announce that Josh Shadlen has joined HRDAG as our new technical lead. Finding Josh was no easy feat. We were looking for what many people would call a “data scientist,” that is, someone with expertise in both computer science and statistics. These days, “data science” is one of the hottest fields out there.
Bringing the perfect mix of academic depth and thoughtful reflection, Josh stood out for us. With prior jobs including gigs at Silicon Valley startups and Twitter, he’s got high-level (more…)
Historic verdict in Guatemala—Gen.Efraín Ríos Montt found guilty
I've been working with various projects in Guatemala to document mass violence since 1993, so in 2011, when Claudia Paz y Paz asked me to revisit the analysis I did for the Commission for Historical Clarification examining the differential mortality rates due to homicide for indigenous and non-indigenous people in the Ixil region, I was delighted. We have far better data processing and statistical methods than we had in 1998, plus much more data. I think the resulting analysis is a conservative lower bound on total homicides of indigenous people. (more…)
Data Mining for Good: CJA Drink + Think
At the Center for Justice and Accountability's happy hour, "Drink and Think," Patrick Ball spoke about "Data Mining for Good." The talk included a discussion of how HRDAG brings human rights abusers to justice through data analysis, and HRDAG's work conducting quantitative analysis for truth commissions, NGOs, the UN and other partners. The event was held at Eventbrite. More photos are below.
The Center for Justice and Accountability
Young Professionals' Committee for Human Rights
September 16, 2014
San Francisco, California
Link to CJA event page
Back to Talks
All photos © 2014 Carter Brooks.
Multiple Systems Estimation: The Matching Process
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Q8. What do you mean by "overlap," and why are overlaps important?
Q9. [In depth] Why is automated matching so important, and what process do you use to match records?
Q8. What do you mean by "overlap," and why are overlaps important?
MSE estimates the total number of violations by comparing the size of the overlap(s) between lists of human rights violations to the sizes of the lists themselves. By "overlap," we mean the set of incidents, such as deaths, that appear on more than one list of human rights violations. Accurately and efficiently identifying overlaps between ...