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There have been a few times recently where I’m showing someone how to do something in the lab and by the time we are finished, they have this look of unabashed glee on their face. It turns out this workflow or process was something they did in the past (and it took hours or days to complete) or that they feared would be extremely complex or time consuming. And instead, it was a few minutes and mostly learning the specifics around software settings or options for automating. These reactions make me think about the impact technology has had but more so, what kind of impact it can have going forward. It’s so easy to take for granted the years of research, tests, failures, and effort that has gone into each piece of…

This is a continuation of this post [link!] detailing our investigation of thresholding as a means to silhouette our images. In this post, we will see if using a contemporary machine learning model for image segmentation will yield better results. Introduction With the thresholding being prone to fairly disappointing results, a colleague recommended that we look into Meta/Facebook’s machine learning Segment Anything model, which is thankfully open source. We ideally did not want to rely on a program that could be taken down or otherwise become unusable. We want to be able to do this long-term, so this being open gave us more confidence in its sustainability. Setup The README for the repo is fairly detailed about getting dependencies and the actual model installed. The steps are fairly standard aside…

Introduction One of the questions we get periodically in the Digital Archaeology Lab is around photographing artifacts and silhouetting the objects (aka removing the background so that you have an image of just the object that can then be used in publications or presentations). In most cases, this process has been done manually using software like Adobe Photoshop or GIMP to digitally draw a mask over the object and then deleting the background pixels. However, this does not scale and there has always been this nagging thought of shouldn’t technology be able to do this for us by now? The Carpentries team published a workshop on image processing that reignited this thought and showed some possibilities for ecological data. Yet we know that the range of materials, composition, conditions, and…

This post was adapted from an article written by John Lynch, with contributions from Lisa Snyder, Annelie Rugg, Deidre Whitmore, Lucian Tucker, Todd Presner, Miriam Posner, and Patrik Svensson and originally posted on June 14, 2017. Every publication method, digital or analog, has a likely lifespan, that is, the length of time before it will stop being accessible unless it is significantly overhauled or replaced. The likely lifespan of well-cared-for paper books can be measured in decades, if not centuries; the likely lifespan of digital projects on the other hand is significantly dependent on the decisions made throughout the development process. This means that, without careful planning and management, digital scholarship is at high risk of being lost permanently. We’ve prepared this guide to help scholars understand the level of risk associated with…