The technology for digitizing analog videotape is continually evolving. Thanks to increases in data transfer-rates and hard drive write-speeds, as well as the availability of more powerful computer processors at cheaper price-points, the Digital Production Center recently decided to upgrade its video digitization system. Funding for the improved technology was procured by Winston Atkins, Duke Libraries Preservation Officer. Of all the materials we work with in the Digital Production Center, analog videotape has one of the shortest lifespans. Thus, it is high on the list of the Library’s priorities for long-term digital preservation. Thanks, Winston!
Thunderbolt is leaving USB in the dust.
Due to innovative design, ease of use, and dominance within the video and filmmaking communities, we decided to go with a combination of products designed by Apple Inc., and Blackmagic Design. A new computer hardware interface recently adopted by Apple and Blackmagic, called Thunderbolt, allows the the two companies’ products to work seamlessly together at an unprecedented data-transfer speed of 10 Gigabits per second, per channel. This is much faster than previously available interfaces such as Firewire and USB. Because video content incorporates an enormous amount of data, the improved data-transfer speed allows the computer to capture the video signal in real time, without interruption or dropped frames.
Blackmagic design converts the analog video signal to SDI (serial digital interface).
Our new data stream works as follows. Once a tape is playing on an analog videotape deck, the output signal travels through an Analog to SDI (serial digital interface) converter. This converts the content from analog to digital. Next, the digital signal travels via SDI cable through a Blackmagic SmartScope monitor, which allows for monitoring via waveform and vectorscope readouts. A veteran television engineer I know will talk to you for days regarding the physics of this, but, in layperson terms, these readouts let you verify the integrity of the color signal, and make sure your video levels are not too high (blown-out highlights) or too low (crushed shadows). If there is a problem, adjustments can be made via analog video signal processor or time-base corrector to bring the video signal within acceptable limits.
Blackmagic’s SmartScope allows for monitoring of the video’s waveform. The signal must stay between 0 and 700 (left side) or clipping will occur, which means you need to get that videotape to the emergency room, STAT!
Next, the video content travels via SDI cable to a Blackmagic Ultrastudio interface, which converts the signal from SDI to Thunderbolt, so it can now be recognized by a computer. The content then travels via Thunderbolt cable to a 27″ Apple iMac utilizing a 3.5 GHz Quad-core processor and NVIDIA GeForce graphics processor. Blackmagic’s Media Express software writes the data, via Thunderbolt cable, to a G-Drive Pro external storage system as a 10-bit, uncompressed preservation master file. After capture, editing can be done using Apple’s Final Cut Pro or QuickTime Pro. Compressed Mp4 access derivatives are then batch-processed using Apple’s Compressor software, or other utilities such as MPEG-Streamclip. Finally, the preservation master files are uploaded to Duke’s servers for long-term storage. Unless there are copyright restrictions, the access derivatives will be published online.
Video digitization happens in real time. A one-hour tape is digitized in, well, one hour, which is more than enough Bob Hope jokes for anyone.
Thanks for all you do throughout the year to make our lives better, brighter, and a bit more fun! From teaching us to fish to helping us move, fathers and father-figures have always been there to help children learn, grow and achieve. While parenting roles and identities continue to evolve, the love of family persists. So, this Father’s Day here is a Digital Collections salute to dads everywhere!
The pure joy of catching a fish under dad’s approving eye!
Thanks, Dad, for helping me move all my furniture (again)!
Who doesn’t love snacks in front of the TV?
As Raul Castro knows, work-life balance issues aren’t new for fathers.
And, finally, please keep in mind the family that shares a soap together, stays together.
This past week, we were excited to be able to publish a rare 1804 manuscript copy of the Haitian Declaration of Independence in our digital collections website. We used the project as a catalyst for improving our document-viewing user experience, since we knew our existing platforms just wouldn’t cut it for this particular treasure from the Rubenstein Library collection. In order to present the declaration online, we decided to implement the open-source Diva.js viewer. We’re happy with the results so far and look forward to making more strides in our ability to represent documents in our site as the year progresses.
Haitian Declaration of Independence as seen in Diva.js document viewer with full text transcription.
Challenges to Address
We have had two glaring limitations in providing access to digitized collections to date: 1) a less-than-stellar zoom & pan feature for images and 2) a suboptimal experience for navigating documents with multiple pages. For zooming and panning (see example), we use software called OpenLayers, which is primarily a mapping application. And for paginated items we’ve used two plugins designed to showcase image galleries, Galleria (example) and Colorbox (example). These tools are all pretty good at what they do, but we’ve been using them more as stopgap solutions for things they weren’t really created to do in the first place. As the old saying goes, when all you have is a hammer, everything looks like a nail.
Big (OR Zoom-Dependent) Things
A selection from our digitized Italian Cultural Posters. The “large” derivative is 11,000 x 8,000 pixels, a 28MB JPG.
Traditionally as we digitize images, whether freestanding or components of a multi-page object, at the end of the process we generate three JPG derivatives per page. We make a thumbnail (helpful in search results or other item sets), medium image (what you see on an item’s webpage), and large image (same dimensions as the preservation master, viewed via the ‘all sizes’ link). That’s a common approach, but there are several places where that doesn’t always work so well. Some things we’ve digitized are big, as in “shoot them in sections with a camera and stitch the images together” big. And we’ve got several more materials like this waiting in the wings to make available. A medium image doesn’t always do these things justice, but good luck downloading and navigating a giant 28MB JPG when all you want to do is zoom in a little bit.
Likewise, an object doesn’t have to be large to really need easy zooming to be part of the viewing experience. You might want to read the fine print on that newspaper ad, see the surgeon general’s warning on that billboard, or inspect the brushstrokes in that beautiful hand-painted glass lantern slide.
And finally, it’s not easy to anticipate the exact dimensions at which all our images will be useful to a person or program using them. Using our data to power an interactive display for a media wall? A mobile app? A slideshow on the web? You’ll probably want images that are different dimensions than what we’ve stored online. But to date, we haven’t been able to provide ways to specify different parameters (like height, width, and rotation angle) in the image URLs to help people use our images in environments beyond our website.
A page from Mary McCornack Thompson’s 1908 travel diary, limited by its presentation via an image gallery.
Paginated Things
We do love our documentary photography collections, but a lot of our digitized objects are represented by more than just a single image. Take an 11-page piece of sheet music or a 127-page diary, for example. Those aren’t just sequences or collections of images. Their paginated orientation is pretty essential to their representation online, but a lot of what characterizes those materials is unfortunately lost in translation when we use gallery tools to display them.
The Intersection of (Big OR Zoom-Dependent) AND Paginated
Here’s where things get interesting and quite a bit more complicated: when zooming, panning, page navigation, and system performance are all essential to interacting with a digital object. There are several tools out there that support these various aspects, but very few that do them all AND do them well. We knew we needed something that did.
Our Solution: Diva.js
We decided to use the open-source Diva.js (Document Image Viewer with AJAX). Developed at the Distributed Digital Music Archives and Libraries Lab (DDMAL) at McGill University, it’s “a Javascript frontend for viewing documents, designed to work with digital libraries to present multi-page documents as a single, continuous item” (see About page). We liked its combination of zooming, panning, and page navigation, as well as its extensibility. This Code4Lib article nicely summarizes how it works and why it was developed.
Setting up Diva.js required us to add a few new pieces to our infrastructure. The most significant was an image server (in our case, IIPImage) that could 1) deliver parts of a digital image upon request, and 2) deliver complete images at whatever size is requested via URL parameters.
Our Interface: How it Works
By default, we present a document in our usual item page template that provides branding, context, and metadata. You can scroll up and down to navigate pages, use Page Up or Page Down keys, or enter a page number to jump to a page directly. There’s a slider to zoom in or out, or alternatively you can double-click to zoom in / Ctrl-double-click to zoom out. You can toggle to a grid view of all pages and adjust how many pages to view at once in the grid. There’s a really handy full-screen option, too.
Fulltext transcription presented in fullscreen mode, thumbnail view.Page 4, zoom level 4, with link to download.
It’s optimized for performance via AJAX-driven “lazy loading”: only the page of the document that you’re currently viewing has to load in your browser, and likewise only the visible part of that page image in the viewer must load (via square tiles). You can also download a complete JPG for a page at the current resolution by clicking the grey arrow.
We extended Diva.js by building a synchronized fulltext pane that displays the transcript of the current page alongside the image (and beneath it in full-screen view). That doesn’t come out-of-the-box, but Diva.js provides some useful hooks into its various functions to enable developing this sort of thing. We also slightly modified the styles.
A tile delivered by IIPImage server
Behind the scenes, we have pyramid TIFF images (one for each page), served up as JPGs by IIPImage server. These files comprise arrays of 256×256 JPG tiles for each available zoom level for the image. Let’s take page 1 of the declaration for example. At zoom level 0 (all the way zoomed out), there’s only one image tile: it’s under 256×256 pixels; level 1 is 4 tiles, level 2 is 12, level 3 is 48, level 4 is 176. The page image at level 5 (all the way zoomed in) includes 682 tiles (example of one), which sounds like a lot, but then again the server only has to deliver the parts that you’re currently viewing.
Every item using Diva.js also needs to load a JSON stream including the dimensions for each page within the document, so we had to generate that data. If there’s a transcript present, we store it as a single HTML file, then use AJAX to dynamically pull in the part of that file that corresponds to the currently-viewed page in the document.
Diva.js & IIPImage Limitations
It’s a good interface, and is the best document representation we’ve been able to provide to date. Yet it’s far from perfect. There are several areas that are limiting or that we want to explore more as we look to make more documents available in the future.
Out of the box, Diva.js doesn’t support page metadata, transcriptions, or search & retrieval within a document. We do display a synchronized transcript, but there’s currently no mapping between the text and the location within each page where each word appears, nor can you perform a search and discover which pages contain a given keyword. Other folks using Diva.js are working on robust applications that handle these kinds of interactions, but the degree to which they must customize the application is high. See for example, the Salzinnes Antiphonal: a 485-page liturgical manuscript w/text and music or a prototype for the Liber Usualis: a 2,000+ page manuscript using optical music recognition to encode melodic fragments.
Diva.js also has discrete zooming, which can feel a little jarring when you jump between zoom levels. It’s not the smooth, continuous zoom experience that is becoming more commonplace in other viewers.
With the IIPImage server, we’ll likely re-evaluate using Pyramid TIFFs vs. JPEG2000s to see which file format works best for our digitization and publication workflow. In either case, there are several compression and caching variables to tinker with to find an ideal balance between image quality, storage space required, and system performance. We also discovered that the IIP server unfortunately strips out the images’ ICC color profiles when it delivers JPGs, so users may not be getting a true-to-form representation of the image colors we captured during digitization.
Next Steps
Launching our first project using Diva.js gives us a solid jumping-off point for expanding our ability to provide useful, compelling representations of our digitized documents online. We’ll assess how well this same approach would scale to other potential projects and in the meantime keep an eye on the landscape to see how things evolve. We’re better equipped now than ever to investigate alternative approaches and complementary tools for doing this work.
We’ll also engage more closely with our esteemed colleagues in the Duke Collaboratory for Classics Computing (DC3), who are at the forefront of building tools and services in support of digital scholarship. Well beyond supporting discovery and access to documents, their work enables a community of scholars to collaboratively transcribe and annotate items (an incredible–and incredibly useful–feat!). There’s a lot we’re eager to learn as we look ahead.
The Digital Production Center at the Perkins Library has a clearly stated mission to “create digital captures of unique, valuable, or compelling primary resources for the purpose of preservation, access, and publication.” Our mission statement goes on to say, “Our operating principle is to achieve consistent results of a measurable quality. We plan and perform our work in a structured and scalable way, so that our results are predictable and repeatable, and our digital collections are uniform.”
That’s a mouthful!
What it means is the images have to be consistent not only from image to image within a collection but also from collection to collection over time. And if that isn’t complex enough this has to be done using many different capture devices. Each capture device has its own characteristics, which record and reproduce color in different ways.
How do we produce consistent images?
There are many variables to consider when solving the puzzle of “consistent results of a measurable quality.” First, we start with the viewing environment, then move to monitor calibration and profiling, and end with capture device profiling. All of these variables play a part in producing consistent results.
Full spectrum lighting is used in the Digital Production Center to create a neutral environment for viewing the original material. Lighting that is not full spectrum often has a blue, magenta, green or yellow color shift, which we often don’t notice because our eyes are able to adjust effortlessly. In the image below you can see the difference between tungsten lighting and neutral lighting.
Tungsten light (left) Neutral light (right)
Our walls are also painted 18 percent gray, which is neutral, so that no color is reflected from the walls onto the image while comparing it to the digital image.
Now that we have a neutral viewing environment, the next variable to consider is the computer monitors used to view our digitized images. We use a spectrophotometer (straight out of the Jetsons, right?) made by xrite to measure the color accuracy, luminance and contrast of the monitor. This hardware/software combination uses the spectrophotometer as it’s attached to the computer screen to read the brightness (luminance), contrast, white point and gamma of your monitor and makes adjustments for optimal viewing. This is called monitor calibration. The software then displays a series of color patches with known RGB values which the spectrophotometer measures and records the difference. The result is an icc display profile. This profile is saved to your operating system and is used to translate colors from what your monitor natively produces to a more accurate color representation.
Now our environment is neutral and our monitor is calibrated and profiled. The next step in the process is to profile your capture device, whether it is a high-end digital scan back like the Phase One or BetterLight or an overhead scanner like a Zeutschel. From Epson flatbed scanners to Nikon slide scanners, all of these devices can be calibrated in the same way. With all of the auto settings on your scanner turned off, a color target is digitized on the device you wish to calibrate. The swatches on the color target are known values similar to the series of color patches used for profiling the monitor. The digitized target is fed to the profiling software. Each patch is measured and compared against its known value. The differences are recorded and the result is an icc device profile.
Now that we have a neutral viewing environment for viewing the original material, our eyes don’t need to compensate for any color shift from the overhead lights or reflection from the walls. Our monitors are calibrated/profiled so that the digitized images display correctly and our devices are profiled so they are able to produce consistent images regardless of what brand or type of capture device we use.
Gretag Macbeth color checker
During our daily workflow we a Gretag Macbeth color checker to measure the output of the capture devices every day before we begin digitizing material to verify that the device is still working properly.
All of this work is done before we push the “scan” button to ensure that our results are predictable and repeatable, measurable and scalable. Amen.
I started working on the metadata of Sidney D. Gamble photographs in January 2008 on a spreadsheet with no matching images. The nitrate negatives from the collection had just been digitized and resided in a different location. I was, however still amazed by the richness of the content as I tried very hard to figure out the locations of each picture, half of them were so challenging that I must have guessed wrong for most of them in my struggle to meet the project deadline. It was after the digital collection was published that I started to study more thoroughly these images of Chinese life more than 100 years ago. And they have since then continued to amaze me as I understand more of their content and context with the various projects I’ve done; and to puzzle me as I dig deeper into their historical backgrounds. I’ve imagined China in those times in readings, enhanced by films early and recent, yet Gamble’s photographs help me to get closer to what life really looked like and how similar or different things appeared. Recently the hand-colored lantern slides in the collection have made me feel even more so.
Zagunao, Sichuan
Lantern slides are often hand-colored glass slides, commonly used in the first half of the twentieth century to project photographs or illustrations onto walls for better visualization. We are yet to find out whether Gamble colored these slides himself or instructed the work by giving details of the description of the objects. I find the colors in these images strikingly true, suggesting that they were done by someone familiar with the scene or the culture. Whether it is a remote hillside village in a minority region in Sichuan as shown above or the famous Temple of Heaven in Beijing below, the color versions are vivid and lively as if they were taken by a recent visitor.
Temple of Heaven, Beijing
Gamble used these color slides in his talks introducing China to his countrymen. He included both images of Chinese scenery and those of Chinese people and their lives. The large amount of images of Chinese life in the collection is a record of his social survey work in China, the earliest of its kind ever done in China; as well as a reflection of his curiosity and sympathy in Chinese people and their culture. Funeral is one of Gamble’s favorite subjects, and I have no clue whether green was the color for people’s clothes working at funerals as I see several images with men dressed in green doing all sort of jobs, such as this man carrying the umbrella, the color is not offensive but needs to be studied.
Funeral Men Umbrella Carrier Blowing Horn
The Lama Temple, or Yonghegong, is an imperial Tibetan Buddhist Temple. Every year in early March, masked lamas performed their annual “devil dance”, a ritual to ward off bad spirits and disasters on a Monday. I learned about this performance through Gamble’s photographs and the color images have simply added more life. A search online for images taken today brought back photos that look just similar.
Devil dance at Lama Temple, Beijing
There are nearly 600 colored slides in the collection, one can imagine the reaction of the audience when Gamble projected them on the wall in his talk about the mysterious China in the Far East. With the help of a capable intern, I was able to create an inventory last fall, matching most of them with existing black and white one in the collection. A project was proposed and approved quickly to digitize these lantern slides. The project was done quickly and a blog post by one of our digitization experts provided some interesting details. In June this year, selected color images will appear in the travelling exhibit that professor Guo-Juin Hong and I curated and started in Beijing last summer when it opens at Shanghai Archives’ museum on bund. I believe they will fascinate the Chinese audience today as much as they had when Gamble showed them to the American audience.
Post Contributed by Luo Zhou, Chinese Studies Librarian, Duke University Libraries
Part of my job is to track our Duke Digital Collections google analytics data. As a part of this work, I like to keep tabs on the most popular digital collections items each month. There is generally some variation among the most popular items from month to month. For example in May, a post on the New Yorker blog pointed to some motherhood oriented ads and our traffic to these items spiked as a result.
Be-Ro Home Recipes, our most popular item.
However there is one item that persists as one of our most popular items: the Be-Ro Home Recipes: Scones, Cakes, Pastry, Puddings.Looking back at analytics since 2010 this is the most popular item by about 2000 hits (the book has seen 18,447 pageviews since Jan 1 2010). In the six months that I’ve been studying our digital collections analytics I consistently wonder, why this item? no really, why? Sure all the recipes call for lard, but that cannot be the only reason.
“Researching” the cookbook (conducting a few google searches) shows that the Be-Ro company was established in 1875 by the creator of the worlds first self rising flour. Home Recipes was originally published as a pamphlet to promote use of the flour as early as the 1880s. Our version includes over 50 recipes, was published in the 1920s, and is the 13th edition of the cookbook.
Duke’s Home Recipes claims that baking at home with Be-Ro is more economical and inspires the a better home, thanks to the woman of the house’s baking: “In ninety-nine cases out of a hundred she has a happy home, because good cooking means good food and good food means good health” (from page 2). This cookbook has a storied history to be sure, but that still doesn’t explain why our version is so popular.
I kept searching, and found that there is a fervent and passionate following for the Be-Ro Cookbook. Several UK cooking blog posts swoon over the book, saying they grew up with the recipes and first learned to bake from it. The community aspect of the cookbook jives with our traffic as most of the users of the item on our website come from the UK. Another factor driving traffic to our site is that Duke Digital Collections’ version of the cookbook tends to be the 4th hit on Google, when you search for “Be-Ro Cookbook”.
This investigation left me with a better understanding of why this cookbook is so popular, but I’m still surprised and amused that among all the significant holdings we have digitized and available online, this cookbook is consistently the most visited. Are there conclusions we can take away from this? We are not going to start only digitizing cookbooks as a result of this knowledge, I can promise you that. However analytics shows us that in addition to the more traditionally significant items online, items like this cookbook can tap into and find a strong and consistent audience. And that is data we can use to build better and more resonant digital collections.
Ever find yourself with a pile of data that you want to plot on a map? You’ve got names of places and lots of other data associated with those places, maybe even images? Well, this happened to me recently. Let me explain.
A few years ago we published the Broadsides and Ephemera digital collection, which consists of over 4,100 items representing almost every U.S. state. When we cataloged the items in the collection, we made sure to identify, if possible, the state, county, and city of each broadside. We put quite a bit of effort into this part of the metadata work, but recently I got to thinking…what do we have to show for all of that work? Sure, we have a browseable list of place terms and someone can easily search for something like “Ninety-Six, South Carolina.” But, wouldn’t it be more interesting (and useful) if we could see all of the places represented in the Broadsides collection on one interactive map? Of course it would.
So, I decided to make a map. It was about 4:30pm on a Friday and I don’t work past 5, especially on a Friday. Here’s what I came up with in 30 minutes, a Map of Broadside Places. Below, I’ll explain how I used some free and easy-to-use tools like Excel, Open Refine, and Google Fusion Tables to put this together before quittin’ time.
Step 1: Get some structured data with geographic information
Mapping only works if your data contain some geographic information. You don’t necessarily need coordinates, just a list of place names, addresses, zip codes, etc. It helps if the geographic information is separated from any other data in your source, like in a separate spreadsheet column or database field. The more precise, structured, and consistent your geographic data, the easier it will be to map accurately. To produce the Broadsides Map, I simply exported all of the metadata records from our metadata management system (CONTENTdm) as a tab delimited text file, opened it in Excel, and removed some of the columns that I didn’t want to display on the map.
Step 2: Clean up any messy data..
For the best results, you’ll want to clean your data. After opening my tabbed file in Excel, I noticed that the place name column contained values for country, state, county, and city all strung together in the same cell but separated with semicolons (e.g. United States; North Carolina; Durham County (N.C.); Durham (N.C.)). Because I was only really interested in plotting the cities on the map, I decided to split the place name column into several columns in order to isolate the city values.
To do this, you have a couple of options. You can use Excel’s “text to columns” feature, instructing it to split the column into new columns based on the semicolon delimiter or you can load your tabbed file into Open Refine and use its “split columns into several columns” feature. Both tools work well for this task, but I prefer OpenRefine because it includes several more advanced data cleaning features. If you’ve never used OpenRefine before, I highly recommend it. It’s “cluster and edit” feature will blow your mind (if you’re a metadata librarian).
Step 3: Load the cleaned data into Google Fusion Tables Google Fusion Tables is a great tool for merging two or more data sets and for mapping geographic data. You can access Fusion Tables from your Google Drive (formerly Google Docs) account. Just upload your spreadsheet to Fusion Tables and typically the application will automatically detect if one of your columns contains geographic or location data. If so, it will create a map view in a separate tab, and then begin geocoding the location data.
If Fusion Tables doesn’t automatically detect the geographic data in your source file, you can explicitly change a column’s data type in Fusion Tables to “Location” to trigger the geocoding process. Once the geocoding process begins, Fusion Tables will process every place name in your spreadsheet through the Google Maps API and attempt to plot that place on the map. In essence, it’s as if you were searching for each one of those terms in Google Maps and putting the results of all of those searches on the same map.
Once the geocoding process is complete, you’re left with a map that features a placemark for every place term the service was able to geocode. If you click on any of the placemarks, you’ll see a pop-up information window that, by default, lists all of the other metadata elements and values associated with that record. You’ll notice that the field labels in the info window match the column headers in your spreadsheet. You’ll probably want to tweak some settings to make this info window a little more user-friendly.
Step 4: Make some simple tweaks to add images and clickable links to your map
To change the appearance of the information window, select the “change” option under the map tab then choose “change info window.” From here, you can add or remove fields from the info window display, change the data labels, or add some custom HTML code to turn the titles into clickable links or add thumbnail images. If your spreadsheet contains any sort of URL, or identifier that you can use to reliably construct a URL, adding these links and images is quite simple. You can call any value in your spreadsheet by referencing the column name in braces (e.g. {Identifier-DukeID}). Below is the custom HTML code I used to style the info window for my Broadsides map. Notice how the data in the {Identifier-DukeID} column is used to construct the links for the titles and image thumbnails in the info window.
Step 5: Publish your map
Once you’re satisfied with you map, you can share a link to it or embed the map in your own web page or blog…like this one. Just choose tools->publish to grab the link or copy and paste the HTML code into your web page or blog.
To learn more about creating maps in Google Fusion Tables, see this Tutorial or contact the Duke Library’s Data and GIS Services.
A group from Duke Libraries recently visited Dr. Maurizio Forte’s Digital Archaeology Initiative (a.k.a. “Dig@Lab”) to learn more about digital imaging of three-dimensional objects and to explore opportunities for collaboration between the lab and the library.
These glasses and stylus allow you to disassemble the layers of a virtual site and rearrange and resize each part.
Dr. Forte (a Professor of Classical Studies, Art, and Visual Studies) and his colleagues were kind enough to demonstrate how they are using 3D imaging technology to “dig for information” in simulated archaeological sites and objects. Their lab is a fascinating blend of cutting-edge software and display interfaces, such as the Unity 3D software being used in the photo above, and consumer video gaming equipment (recognize that joystick?).
Zeke tries not to laugh as Noah dons the virtual reality goggles.
Using the goggles and joystick above, we took turns exploring the streets and buildings of the ancient city of Regium Lepedi in Northern Italy. The experience was extremely immersive and somewhat disorienting, from getting lost in narrow alleys to climbing winding staircases for an overhead view of the entire landscape. The feeling of vertigo from the roof was visceral. None of us took the challenge to jump off of the roof, which apparently you can do (and which is also very scary according to the lab researchers). After taking the goggles off, I felt a heaviness and solidity return to my body as I readjusted to the “real world” around me, similar to the sensation of gravity after stepping off a trampoline.
Alex–can you hear me?
The Libraries and Digital Projects team look forward to working more with Dr. Forte and bringing 3D imaging into our digital collections.
More information about the lab’s work can be found at:
In the 1960s, an unstoppable group of student activists partnered with black southerners to mount an all-out attack on Jim Crow. One person, one vote – that was the idea that drove them when they woke up each morning. In some of the most remote and forgotten areas of the deep South, the Student Nonviolent Coordinating Coordinating Committee (SNCC) and local people risked their lives to secure the right to vote for all Americans. Fifty years later, that struggle is as central as ever.
Voting rights and SNCC activist Fannie Lou Hamer in Hattiesburg, Mississippi in 1964. Image courtesy of the Civil Rights Movement Veterans site.
The SNCC Legacy Project and Duke University are teaming up to chronicle SNCC’s historic campaign for voting rights. The pilot phase of that partnership, a project titled “One Person, One Vote: The Legacy of SNCC and the fight for voting rights,” will reexamine SNCC’s activism in light of current struggles for an inclusive democracy.
The OPOV pilot site will feature documents, photos, and audiovisual materials born out of SNCC’s fight for voting rights. From this material, SNCC veterans will use oral histories, critical curations, and featured exhibits to rethink the impact of their activism.
We’re looking for a talented, Triangle-based design team to help us connect the past to the present. Designers will create a WordPress theme that brings clarity and flow to the overlapping narratives of voting rights activism. See the prospectus for candidate contractors linked below. We’d like to make contact with you now, with a more extensive Call for Proposals to follow in May.
The inevitable result, when an IT manager suffering from Acute Administrivia-Induced Ennui gets ahold of dia and starts browsing The Noun Project.
We have all these plans and do all this work with the digital collections and the projects and what have you. Plan-plan-plan, work-work-work, and plan and work some more. Some things get done, others don’t, as we journey for that distant horizon, just on the other side of which lies, “Hooray, we finally got it!”
I started to draw a map for the next phases of that journey a few days ago, and it was going to be really serious. All these plans – repository migration, exhibit platform, workflow management, ArchivesSpace – would be represented in this exacting diagram of our content types and platforms and their interrelations. It might even have multiple sheets, which would layer atop one another like transparencies, to show the evolution of our stuff over time. UML books more than ten years old would be dusted off in the production of this diagram.
Then my brain started to hurt, and I found myself doodling in response. I started having fun with it. You might even say I completely dorked out on it. Thus you have the “Sketch for an almanac of digital projects at Duke University Libraries” above.
Placing whimsical sea monster icons on a field of faux design elements took a lot of my time this week, so I’m afraid I’m not able to write any more about the diagram right now. However, provided it doesn’t prove a source of embarrassment and regret, I might revisit it in the near future.
Notes from the Duke University Libraries Digital Projects Team
