4

These blog posts by master Windows programmer Charles Petzold contain a few tables by country, and list a few other sources, notably the FBI. The analyses are brilliant - they look effortless and to-the-point. http://charlespetzold.com/blog/2015/07/De-Obfuscating-the-Statistics-of-Mass-Shootings.html http://charlespetzold.com/blog/2015/10/More-Bogus-Gun-...


3

Assumption: you are interested in United States data. This is all I really know about, sorry. Unfortunately, I don't think you are going to find raw/disaggregated data at a national level. You probably already know about the aggregated FBI UCR datasets. I think what you are looking for is what the public safety ecosystem calls "Incident" from a system ...


3

Take a look at UTAH: https://github.com/sonalake/utah-parser It's a good tool for handling files like this.


3

pyparsing is really a powerful tool. As the name implies, it's a module for Python. It's like zooming out one step from regexp. There are many examples at http://pyparsing.wikispaces.com/Examples Here's another example which also makes use of regexp, in case you also want to use that for more exact pattern matching.


2

This link is the Bureau of Justice Statistics start page on fire-arm related crimes: http://www.bjs.gov/content/guns.cfm


2

The Los Angeles Times did some very interesting work using natural language processing (NLP) to analyze their archival recipes and convert them into structured data. An interview with the team ran on OpenNews Source, and Anthony Pesce wrote more (including some code samples) on the LAT Data Desk blog.


1

I think you are asking about what to do with headers in tabular data, if so, here are my thoughts: Headers should be one column/one row and only one column/one row. If you have to label across rows, simply add the name of the label to the rows you want to apply it to. This does lead to extremely long headers, so also another thing to apply here is some ...


1

I have personally found that using Perl and Spreadsheet::ParseExcel was relatively simple and useful for extracting data from Excel sheets. Another approach that may work is to upload into Google and use the Google APIs to extract the data.


1

4.5gb twitter data. 41.7 million user profiles, 1.47 billion social relations, 4,262 trending topics, and 106 million tweets. http://an.kaist.ac.kr/traces/WWW2010.html download link bellow: http://an.kaist.ac.kr/~haewoon/release/twitter_social_graph/twitter_rv.zip also you can check site SNAP from Stanford University https://snap.stanford.edu/data/#web or ...


1

Amazon Mechanical Turk is pretty well setup for these kinds of tasks. Of course, you have to pay workers to complete the tasks, but there are plenty of open-source clients to access the service. You might especially want to look at boto for Python and MTurkR for R (note: I am the developer of this package).


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