Get fast answers to complex questions

Web-based content analysis for researchers

Quickly and accurately tag, classify and extract information from thousands of documents by tapping into the power of the crowd

 
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Features

 
 
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Complexity at scale

Tackle complex, large-scale projects with ease. Efficiently annotate, tag and classify thousands of documents to extract the information you need to answer your research questions. 

 
 
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The power of the crowd

Easily manage hundreds of crowd-workers to extract the information you need. Utilize human input at scale to identify subtle theoretical concepts that machines can't detect.

 
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Up to 10x faster

Complete what would normally be a decade-long project in around a year. TagWorks removes the need to train wave after wave of research assistants, saving you time and money.

 
 
 
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Validated results

See the full provenance of your data and share it with the research community. TagWorks is open and transparent, making it simple for others to review and validate your results.

 
 

How it works

To get started, all you need is a corpus or set of documents that you want to analyze and to know what information you're interested in extracting. 

  1. Upload your documents into the tool
  2. Tell us what you'd like to find out
  3. We'll ask the crowd detailed questions to extract the information you need
  4. Analyze your data
 
 
 

About

 
 
 
 Nick Adams, Ph.D. 
 

Nick Adams, Ph.D.

 

Nick Adams is TagWorks' inventor and co-creator. He came up with the initial design of the software when he faced the challenge of closely annotating nearly 10,000 news articles describing events of the Occupy movement.

Nick is a proponent of hybrid text analysis approaches, and he has taught, consulted, and mentored hundreds of researchers in a range of automated and human text analysis techniques. He founded the Computational Text Analysis Working Group at UC Berkeley's D-Lab, and the interdisciplinary Text Across Domains (Text XD) initiative at the Berkeley Institute for Data Science.

Nick earned his Ph.D. in sociology from Berkeley in 2015 and is now co-Founder of Thusly, Inc. and the Founder and Director of Goodly Labs, a CA non-profit dedicated to empowering the public with the data, tools, and insights of social science.

Nick is honored to sit on the SSRC's Digital Culture committee; to have helped form the Credibility Coalition; and to serve as a BITSS Catalyst (Berkeley Initiative for Transparency in the Social Sciences), promoting sustainable open science.

 
 
 
 Norman Gilmore
 

Norman Gilmore

 

Norman Gilmore is the Chief Technology Officer for Thusly, Inc. and co-creator of TagWorks. He has a long term interest in software that supports citizen science, visualization for exploratory data analysis, and complex problem solving, and is pleased that TagWorks brings all of those themes together. 

Norman likes to lead happy teams, and believes Scrum is a good way to do that. He uses Node and React to build advanced interfaces, Django for web application development, and deploys to AWS with docker containers.

Norman has worked at big companies like Disney Interactive and Boeing, and at small companies that you probably haven't heard of.

 
 

Register your interest

TagWorks is currently in Beta. If you're interested in using TagWorks for your research project, please fill in the form below and we'll be in touch.

 

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