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First archived on: 2013-04-29

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Title: Digital Humanities Specialist | humanities software, visualization and analysis

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Original web address: http://dhs.stanford.edu/


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... a city composed not of the neighborhoods that it was actually made of but instead of neighborhoods most similar to each other based off the characteristics we measured You can see the interactive version here http bl ocks org emeeks 5218414 It s a great concept I think but it proved illegible to most users and as with many of these objects performed poorly when integrated into the larger site There s more on the floor For instance I ve pointed before to a topic model browser I made in Protoviz that allows the reader to compare writing about species in the IUCN Red List compared to the same entries for those species on Wikipedia to compare the differences in language of the two species databases In process is a new version of this using my much improved skills and D3 which will hopefully leverage the lessons folks have learned about representing the results of topic modeling It s my hope that at some point we ll have a better process for releasing and annotating these objects and I d hope there s a larger solution to better raise the visibility and understanding of this process for those currently on the outside Until then I ll try to keep posting the code on Github and giving some explanation of them here Posted in D3 Digital Scholarly Work Spatial Humanities Visualization Comments Off City Nature Posted on March 13 2013 by Elijah Meeks Today we re releasing City Nature the results of work exploring natural environments in urban areas using topic modeling GIS and data visualization The site has rich interactivity including an amazing parallel coordinates plot that allows you to explore the greenness and demography of 2600 US neighborhoods I created a few of the dataviz elements and helped with the research but the majority of the digital humanities development work was by Karl Grossner PIs on the project are Jon Christensen and Michael Kahan here at Stanford though Jon has since moved to UCLA and included a summer research project involving a dozen undergraduates here at Stanford who had the chance to work with remote sensing GIS topic modeling and information visualization Posted in Digital Humanities at Stanford Spatial Humanities Text Analysis Visualization Comments Off Toward a Connected Humanities Posted on March 4 2013 by Elijah Meeks Zephyr Frank and Erik Steiner were kind enough to give me a chance to discuss networks in humanities scholarship for the Visualizing Evidence course here at Stanford Here s the talk Posted in Algorithmic Literacy Graph Data Model Visualization Comments Off Color and Precision Posted on February 26 2013 by Elijah Meeks Color has been bothering me lately To get to color though we have to take a short digression into space You see a lesson you learn early on in spatial analysis is that just because your GIS package gives you 12 points of decimal precision when you add a point that doesn t mean you should use it False precision in the case of coordinates is well understood but I wonder why there is no analog in color As data visualization grows more prominent color theory becomes a practical consideration of modern scholarship just as geometry ontology formal logic and countless other seemingly unrelated fields have begun to intrude upon literature and history And while work has been done by folks like Cynthia Brewer and the team at Tableau to solve practical issues of palette and readability I m more interested in the issue of false precision in color representation and the use of functions to determine visual attributes rather than fixed values To better understand what random perturbation of color and visual elements would produce I wrote a little color perturbation toy in D3 that takes advantage of the range slider and color picker HTML elements these only work in Chrome and wrote a quick function to randomize the color displayed in 506 squares while displaying a single large square with the original color selected The original randomization function still in the code as slightyRandomColor just adjusted the individual Red Green and Blue RGB elements of the selected color in a completely random fashion function slightlyRandomColor r g b range r r Math floor Math random range Math floor range 2 g g Math floor Math random range Math floor range 2 b b Math floor Math random range Math floor range 2 return rgb r g b I updated this in the slightlyLessRandomColor function to take into account the distance from maximum value of that primary color Naturally there s some disagreement as to what a primary color is and a CMYK scale would treat this differently but this is just an initial foray In a finished version I d prefer this to be based on pure hues so that variation increases in the muddy regions The code is pretty simple function lessSlightlyRandomColor r g b range var scaleRamp d3 scale linear domain 256 0 range 5 2 clamp true var rRange range scaleRamp r var gRange range scaleRamp g var bRange range scaleRamp b r r Math floor Math random rRange Math floor rRange 2 g g Math floor Math random gRange Math floor gRange 2 b b Math floor Math random bRange Math floor bRange 2 return rgb r g b The effects are rather striking The idea is that rather than picking a single color out of the 256x256x256 or 16 78 million available colors you designate a small or large color region In a sense this is an imprecise color suitable for less precise data Now perhaps you re working with data where you can claim 1 in 16 78 million precision I don t typically have that at my disposal and that s one of the reasons I wanted to explore this The primary motivation is still aesthetic of course and I think that this minor perturbation will be more appealing and attractive to readers This can be taken beyond color elements and applied to line thickness as I ve done in the demo and curving on paths opacity et cetera These channels in the parlance of information visualization are all amenable to functional values that can be jostled based on known inaccuracies in individual data points or generally understood issues of uncertainty precision and accuracy of the project as a whole Again the curves end up looking like they ve been drawn though with only minor perturbation by someone with a steady hand and so the aesthetic motivation is there but the aesthetic enshrines a fact of data visualization used in representing the kinds of phenomena I m called on the represent The results give a different understanding of what it means to have the same level of variation when that variation is not just a simple value but a function based on the value of the color being affected Turning the variation up to a maximum of 50 which means 25 from the R G and B positions on the color already implies less variation for values at the top or bottom of the scale near 0 or 255 in other words and since the function further scales this variation so that it is more at 0 and less at 255 the result is that high variation has quite different visual results There are at least three issues at play here One is the capacity to optically distinguish between different parts of visual light spectrum which could itself be accounted for in the development of functional colors The second is the use of functions to perturb visual elements for aesthetic purposes as well as to address issues of visual representation of complexity and uncertainty The final is the idea of regions be they color regions or angle regions or line thickness regions to fight against false precision Naturally with high enough variation you can end up damaging the ability for a reader to distinguish between categories of elements as seen in the changing color ramp on the bottom of the demo The variability in the blue in this particular case and each time it will be different makes it impossible to distinguish between category 20 and category 18 and just as difficult to distinguish between category 17 and category 19 or if this is a continuous ramp to distinguish that entire region But it may be that this is imprecision is a more accurate representation of some very imprecise dataset You may therefore be able to use functional colors and imprecise colors to provide higher accuracy with lower precision Obviously this begs for a robust implementation which I hope to provide some time down the road The gist of the code can be found here Posted in Algorithmic Literacy D3 Spatial Humanities Visualization Comments Off The Digital Humanities as a Donkey Posted on February 19 2013 by Elijah Meeks Advice animals are a long established method of passing along knowledge and learning about subject matter especially academic But I have found no Digital Humanities advice animal and so I offer up the only slightly used ORBIS donkey I think we need a stalwart mosaic Digital Humanities Donkey to explain the subtle truths of this field to prospective undergraduates on Reddit Helpfully I ve also provided a superhip version Now maybe there s some kind of digital humanities emu or marmoset out there in which case I m not trying to muscle in on their territory But if not here s an example or three to get the ball rolling Posted in Natural Law The Digital Humanities as Comments Off Martin Evans Posted on February 16 2013 by Elijah Meeks When I first came to Stanford University and I was expected to do digital humanities without quite knowing what that meant I had the very good fortune to work with Martin Evans a professor in the English Department and a Miltonist While we never got around to representing the pan chronology of Paradise Lost we did manage to cobble together a small site in Flash remember this was 2010 before Flash was evil that presented people and places and texts linked together in a dynamic manner If you ve still got Flash installed you can see Authorial London here Martin Evans died on Monday February 11th 2013 In my time working with him and since he was always dynamic and incisive and ambitious When I first started doing digital humanities professionally I am a specialist after all I thought myself to be very much smarter than the folks who had neglected to learn how to code Dr Evans disabused me of this notion early on and not through any kind of browbeating but instead by simply demonstrating the kind of intellectual rigor and attention to detail it took to really understand the complexities of literature such as Lycidas In reading Martin s obituaries I ve found him quoted as a staunch defender of the humanities You would have to be as green as I was three years ago to think that such a defense would necessitate being an opponent of digital humanities Bitter constraint and sad occasion dear Compels me to disturb your season due Posted in Digital Humanities at Stanford Comments Off Digital Literacy and Digital Citizenship Posted on February 11 2013 by Elijah Meeks Visual notes of my talk about digital humanities in high school education On Friday I gave a talk for a Bay Area Teacher Development Collaborative workshop entitled Technology for Teaching and Learning What s Worthwhile What s the Next Chapter I was asked to speak broadly on the role of digital humanities in middle school and high school education and put together this slide deck which I thought I d explain a little more fully here Usually I don t explain the title page You already know the website address and likely my email address and probably my job title but there s a little piece of information that I ve felt the need to emphasize more and more as I go out into the world for conferences and talks I work for the library I never imagined how much of a difference this would make in different venues It s an alt ac position and so you receive the usual discourtesies if someone thinks that you re pretending to be Stanford faculty I ve found it best to clearly mark and emphasize that This wasn t the case for the BATDC conference and maybe that s why I m comfortable making this digression The abstract network in the background is TVTropes of course In any talk about digital humanities even those held at conferences that have digital and or humanities in the name it helps to suggest another definition of digital humanities Here s a rather practical one It s the application and integration of buzzwords and acronyms into humanistic inquiry I ve thought of GIS NLP SNA DataViz as the 3 1 pillars of digital humanities for a while but I think more than those particular methods digital humanities is the demystification of computational methods and their application in new and untraditional ways So it helps to mock them It may be that a better definition of digital humanities Careful application of computational methods to humanistic inquiry paired with careful application of skepticism toward computational methods for humanistic inquiry The examples for each are an isophoretric map of the Roman World topic clouds a network visualization of several generations of Darwins and a parallel coordinates visualization of neighborhoods These latter two examples are from projects which we hope to release in the coming weeks GIS There are so many good examples of GIS used for digital humanities research This might be bias on my part since spatial analysis was my first experience with this kind of work and so I m more aware of projects like ORBIS Vision of Britain and Civil War Washington all of which provide ready made resources for middle school and high school teachers that want to bring geospatial information visualization into their classes Because we have such a long and rich history of representing abstract concepts and data on maps it makes a good gateway into more exotic data analysis and visualization methods So while these sites teach us about space and history they also provide object examples of data visualization that because of our familiarity and literacy with maps doesn t seem so arcane as network and text and other information visualization Each of the slides for GIS NLP SNA and DataViz is meant to not only give resources for course material but also provide avenues for teachers that want to get started with creating and developing more material using these methods To that end Neatline Google Fusion Tables and Quantum GIS are all freely available and provide the capacity for teachers to build their own dynamic and interactive geospatial projects NLP Natural Language Processing is a harder thing to demystify than GIS Maps and directions are a large and highly visible part of our life but text analysis tends to be hidden away But tools like Voyant provide teachers with the capacity to apply a wide variety of NLP processes to whatever text they would like to examine whether it s assigned reading or student essays Importantly the accessibility and user friendliness of Voyant means that teachers and students can playfully engage with NLP and learn the methods through using the tools Somehow I forgot to mention Wordle during the talk but one of the teachers pointed it out during Q A SNA While network analysis is not only social network analysis it s what people know and so part of explaining networks is starting with social networks and then introducing transportation networks genealogies administrative networks and so on Like NLP it is difficult to point to straightforward examples that are easy to integrate into courses but for those that want to get started I pointed them to my Interactive Introduction to Network Analysis as well as my Network Analysis toolkit of choice Gephi Data Information Visualization Data visualization as a fourth category is interesting from an ontological perspective since it overlaps and conflicts in scope with the previous three But I ve noticed that it occupies a distinct space in the mental map of the digital humanities among practitioners and provides a very accessible entry into the various more intimidating methods above My only link is to D3 js because it provides not only a great library with which to build data viz but an excellent gallery of examples of data visualization There s so much dataviz on the Internet that providing examples seems pointless So that s what and how but why The remaining slides in the deck deal not with what digital humanities is are was nor how to do it or find it but why it s important The broad definition of digital humanities makes it more difficult to make this case and so that s why I settled on the constrained practical definition I started with Practically speaking integration of these methods and techniques makes sense for the following reasons Reason 1 Digital humanities is fun I touched on this a while back in regard to the popular appeal of ORBIS By bringing innovative interactive and highly visual methods into the exploration of humanities subjects you engage students in a way that just text does not This is the basic principle behind gamification except that the digital humanities isn t trying to dress up an experience with the appearance of interactivity but rather is predicated by it Using Voyant is fun You play with Gephi People have tweeted that ORBIS is awesome Reason 2 Digital humanities is inherently collaborative I won t repost the slide for this one which is simply three lists of contributors to various DH projects Collaboration is important from both a professional perspective and a social perspective The world that your students are going to go out into...

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