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The most typical data types are outlined in the image below. But in the large scope the kinds of data being collected will be the same. but they are nonetheless real consequences.Įach project will have a variety of data it is interested in using or developing or manipulating.

These digital consequences are not in the mind of every consumer.

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This is not too unlike users asking which is better Android or iPhone, and then deciding what works not just with a given device but where they will buy their music, their digital books, and how they will get those digital assets to a new device, when the phone they are about to buy no-longer serves them. That is, they want to evaluate how software will work in a given scenario ( problem space) and to understand and make informed decisions based on the eco-system that the software will lead them into. New users to software want to know top level organizational assumptions made by software developers. Now, it is true that every language documentation program is different and will have different goals and outputs, but many of these goals are the same across projects. Many of these software development teams do not take the approach that potential software users coming to their website want to be oriented to how these software solutions work together to solve specific problems in the language documentation problem space.

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One of my ongoing concerns of linguistic software development teams (like SIL International's Palaso or LSDev, or MPI's archive software group, or a host of other niche software products adapted from main stream open-source projects) is the approach they take in communicating how to use the various elements of their software together to create useful workflows for linguists participating in field research on minority languages. In this post I take a look at some of the software needs of a language documentation team.