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[BOOK] Digital Research Methods with Mathematica, 2nd Ed (2019)

Posted 10 months ago
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This summer I completed the second edition of my open content, open access and open source textbook Digital Research Methods with Mathematica. It is freely available as a Mathematica notebook which can be accessed with either the Mathematica software or with Wolfram's free Wolfram Player. There is also a PDF version which is much less satisfactory, but which can give you an idea of the coverage if you are interested.

https://williamjturkel.net/digital-research-methods-with-mathematica

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The book focuses on learning to read code to the point where one can modify it to solve related research problems. Here are the topics that are covered.

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  1. Reading Code. Word frequency, word clouds and stopwords.
  2. Computable Knowledge. Entities, tables, timelines and maps.
  3. Text Content. Mathematica notebooks and expressions, strings and natural language processing.
  4. Data Structures. Lists, associations and datasets.
  5. Reusing Code. Defining and developing functions, keyword in context (KWIC).
  6. Networks. Metadata, matrices and social network analysis.
  7. Indexing and Searching. Pattern matching, topic classification and term distribution.
  8. Geospatial Analysis. Geographic information: raster, vector and attribute data.
  9. Images. Computer vision, face detection, feature extraction and image mining.
  10. Page Images. Optical character recognition (OCR), figure extraction and classification.
  11. Crawling. Browser automation, batch downloading, web archives and WARC files.
  12. Linked Open Data. Resource description framework (RDF), SPARQL queries and endpoints, JSON-LD.
  13. Markup Languages. Scraping and parsing, XML, really simple syndication (RSS) and text encoding initiative (TEI).
  14. Studying Societies. Computational social science, search data, social media and social networks.
  15. Extracting Keywords. Information retrieval, term frequency-inverse document frequency (TF-IDF) and rapid automatic keyword extraction (RAKE).
  16. Word and Document Vectors. Feature extraction, dimension reduction, word embeddings and global vectors.
  17. References, web services, bibliographic linked open data and citation networks.
  18. Natural Language. Multilingual analysis, computational linguistics and sentiment analysis.
  19. Web Services. Entity networks, publication search, dashboards, manipulating JSON.
  20. Databases. Parts, selections and transformations, computations and querying, relations.
  21. Measuring Images. Photogrammetry, georectification, handwriting and facial 3D reconstruction.
  22. Machine Learning. Unsupervised clustering, classify, predict and transfer learning.

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4 Replies

This is simply a fantastic resource – thank you!

Not even included in the table of contents above is an additional (nearly 50 page) section at the end of the textbook with ideas for further digital humanities projects and experiments often linking to existing data and sample code to get one started.

William, this is a wonderful work! Thank you for your inspiring effort and sharing the news here. I cannot wait to find sometime to start reading in detail :-) I hope educators will start adopting this as a textbook for their Computational-X courses.

Thanks for the kind words, Arno & Vitaliy!

I just signed into the Community for the first time and found this. What a treat!

Thank you so much for this wonderful resource.

W

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