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Lessons In Development

See a list of lessons in development at our ph-submissions repository.

Active Lesson Requests

The Editorial Board of the English-language version of the Programming Historian would particularly welcome hearing from prospective authors for the following lesson topics. Anyone interested should consult the Author’s Guidelines for more information:

  1. How do you conduct spatial clustering of geographic data?

    We’ve got a great set of introductory mapping lessons, and while they are great for teaching how to make nice visualisations, we’ve not yet branched deeply enough into more advanced analysis skills. One of the most useful is the application of clustering algorithms, which identify logical groups of individual points in geographic space. Useful for forming conclusions on anything from trade to migration. But like with all analyses, it’s a space (no pun intended) fraught with pitfalls for the uninitiated. We’re looking for a great introduction that highlights both the strengths and the challenges of this form of analysis.

  2. When do you know your network analysis is meaningful?

    Again, one that we asked for previously, but haven’t yet seen. Ok, so we’ve built a great network diagram. How do we move to the next step and form meaningful conclusions? This is about starting with a graph and shifting into analysis mode. If you can help our readers take that step, we want to hear from you.

  3. How to Publish Digital Scholarly Editions using a Native XML Database

    Digital scholarly editions are often modelled as XML documents. Although there are some publication tools such available, the options can be bewildering and the solutions may not fit a user’s particular needs. Keeping with our open ethos, we’d like to see someone take readers through an open source solution that gives them a sustainable and flexible way to publish their digital edition.

  4. How to Analyse Audio Artefacts

    We have one lesson on how to use Audacity to edit audio files and another on how to transform you transform your data into audio to better understand it. But you can do much more! How are you using tools to get quantifiable data about your audio artefacts? Or, how can you use machine learning techniques to produce new understandings of an audio collection?

  5. How to Choose a Metadata Schema

    There are so many different metadata schema available. From Dublin Core to COins to MARC, METS, and MODS. If someone is embarking on a project that involves building a collection, how should they go about making a decision about which metadata schema to use? We believe this core lesson could really open up new areas for future lessons.

  6. How to Apply Multidimensional Scaling

    A means of identifying patterns in datasets, we’re looking for someone who can explain when this approach is useful, how to do it right, and how to interpret the results. This tutorial would be a great chance to show how you’ve used Multidimensional Scaling in your research to generate new historical knowledge.

  7. How to establish a podcast

    It may sound straightforward, but there’s more to it than just grabbing a microphone. We’re looking for someone that can outline the key questions one needs to consider, and sustainable solutions to help newbies make the plunge. Issues such as how to edit, host, distribute, and build an audience would be welcome.