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The Hitchhiker’s Guide to d3.js – Ian Johnson – Medium. Digital Humanities Pedagogy: Practices, Principles and Politics. TEI by Example. Overcoming the Challenges of Digital Research: A Tutorial – Portus Classroom. Timothy Walsh May 5, 2016 Professor David Thomas Introduction.

Overcoming the Challenges of Digital Research: A Tutorial – Portus Classroom

Quickstart Guide. Background A topic model is a simplified representation of a collection of documents.

Quickstart Guide

Topic modeling software identifies words with topic labels, such that words that often show up in the same document are more likely to receive the same label. It can identify common subjects in a collection of documents – clusters of words that have similar meanings and associations – and discourse trends over time and across geographical boundaries. This guide will help you use the tool and analyze the results it generates. If you’re familiar with the basic idea behind topic modeling, using the tool isn’t difficult. First Steps Install the tool Although the Topic Modeling Tool is built with Java, it is possible to run it as if it were a native application, without having to install Java at all.

For Macs Download TopicModelingTool.dmg. GitHub - scottythered/gratefuldata: Grateful Data isn't programming code, but an online tutorial about data acquisition, cleaning and enriching, using publicly accessible data on the band the Grateful Dead as examples. Read the Wiki to find out how to use. Detecting Locations with NER. By Aaron Braunstein, Clare Jensen, and Kaitlyn Sisk This page discusses the digital data collection tool Named Entity Recognition (NER) and its use in organizing the geographic information in runaway slave advertisements.

Detecting Locations with NER

Rationale In past analyses of runaway slave advertisements, the primary method utilized to collect data has been close reading, as illustrated in John Hope Franklin and Loren Schweninger’s book Runaway Slaves: Rebels on the Plantations. The authors chose fugitive slave advertisements as relatively credible sources since owners would have high incentives to provide accurate descriptions so their runaway(s) could easily be identified. They employed close reading to observe the nature of slavery on a personal level (Franklin and Scheninger, 295). Our goal was to understand differences in location references within runaway slave ads across the Texas, Arkansas, and Mississippi corpora.

Named Entity Recognition came to the rescue. Palaeography tutorial (how to read old handwriting) Palaeography is the study of old handwriting.

Palaeography tutorial (how to read old handwriting)

This web tutorial will help you learn to read the handwriting found in documents written in English between 1500 and 1800. At first glance, many documents written at this time look illegible to the modern reader. Text Analysis » Tooling Up for Digital Humanities. Topic Modeling for Humanists: A Guided Tour – the scottbot irregular. It’s that time again!

Topic Modeling for Humanists: A Guided Tour – the scottbot irregular

Somebody else posted a really clear and enlightening description of topic modeling on the internet. This time it was Allen Riddell, and it’s so good that it inspired me to write this post about topic modeling that includes no actual new information, but combines a lot of old information in a way that will hopefully be useful. If there’s anything I’ve missed, by all means let me know and I’ll update accordingly. Topic models represent a class of computer programs that automagically extracts topics from texts. What a topic actually is will be revealed shortly, but the crux of the matter is that if I feed the computer, say, the last few speeches of President Barack Obama, it’ll come back telling me that the president mainly talks about the economy, jobs, the Middle East, the upcoming election, and so forth.

Let’s Make a Map. Note: This article was written in 2012 and uses old versions of D3 and TopoJSON.

Let’s Make a Map

About these worksheets. These worksheets are intended to give you practice doing historical data analysis using the R programming language.

About these worksheets

You are probably encountering them as an assignment from one of my classes, such as “Data and Visualization in Digital History” and “Programming in History/New Media.” These materials are also a supplement to my book in progress, Digital History Methods in R. You can also get these worksheets from a GitHub repository. To clone them to your own computer, use this command: git clone Assumptions I’m going to assume that you have the most recent version of R installed on your computer.

Exploring Big Historical Data: The Historian's Macroscope. Welcome to the companion site for Exploring Big Historical Data: The Historian’s Macroscope, published by Imperial College Press.

Exploring Big Historical Data: The Historian's Macroscope

TxDHC Webinar Series. The TxDHC hosts a webinar 2-3 times per semester.

TxDHC Webinar Series

The webinars are open to all and are announced via the TxDHC website and listserv. We simply ask that you register beforehand. All webinars are recorded and made publicly available on the TxDHC YouTube channel. We try also to add transcriptions to the videos whenever possible. The webinars available so far include: Speaker Videos. The first video covers introductions from the workshop’s sponsors MITH and the NEH ODH, via Neil Fraistat, Jen Guiliano, and Jen Serventi; Matthew Jockers presenting on literary topic modeling with “Thematic Change and Authorial Innovation in the 19th Century Novel”; and Robert Nelson on historical topic modeling with”Analyzing Nationalism and Other Slippery ‘Isms’”.

Speaker Videos

Topic Modeling Workshop: Jockers and Nelson from MITH in MD on Vimeo. The second video covers Jo Guldi and Christopher Johnson-Roberson’s presentation “Paper Machines: A Tool for Analyzing Large-Scale Digital Corpora” Topic Modeling Workshop: Guldi and Johnson-Roberson from MITH in MD on Vimeo. The third video covers David Mimno’s presentation “The details: how we train big topic models on lots of text”. A Gentle Introduction to Correspondence Analysis. There are some digital humanists who are competent mathematicians, but most of us experience some anxiety about the more advanced mathematics involved in the text analysis methodologies that we use. Dammit Jim, I’m a humanist, not a mathematician! The problem of course is that there are clearly some statistical and graphical techniques that can be very powerful for humanities research (if you’re unconvinced by this claim, please read on anyway).

So one faces a choice: Learn R, Python & Data Science Online. Code School - Try R. Create with Fusion Tables - Fusion Tables Help. Fusion Tables is an experimental app. Create with Fusion Tables These tutorials step you through using Fusion Tables’ features to accomplish neat things with your data. See what others have done in the Example Gallery. Basic tutorials Get started using Fusion Tables: Create a map Turn a table of locations into a map. Extending your knowledge. Plain Text Note and Citation Management. Hacks Posted by W. Caleb McDaniel on May 5, 2014 When I started the research for my second book project, I decided to rethink my writing and note-taking process from the ground up. After switching to a plain-text writing workflow in the middle of writing my first academic book, I knew that I wanted to write and take notes exclusively in Markdown for this new project.

I knew that I wanted to experiment with open notebook research by hosting all of my notes in an online, version-controlled wiki. In this post, which serves as a sort of technical companion to my post on Open Notebook History, I will explain how I am managing my notes and citations using plain text files, Gitit, and Pandoc—John MacFarlane’s amazing open-source program for document conversion. In other words, my method for managing citations is similar to my plain-text GTD system. This method allows me to give every secondary source its own page in my Gitit wiki, so that I can link to specific sources within wiki pages.

Installing QGIS 2.0 and Adding Layers. Lesson Goals In this lesson you will install QGIS software, download geospatial files like shapefiles and GeoTIFFs, and create a map out of a number of vector and raster layers. Quantum or QGIS is an open source alternative to the industry leader, ArcGIS from ESRI. Machine Learning for Artists. Flexbox Game.