Stumbld on an interesting access challenge for our Digital Collections today.
When searching for the keyword,
library, all records in the Digital Collections are returned because
library exists for each record somewhere in the metadata (probably embedded somewhere like
Wayne State University Library).
Though Solr’s stellar ranking boosts relevant records to the top of the results – items that have library in the title, or prominently in the description – it’s still a little unnerving that it returns so many positive results.
An option would be to limit particular Solr fields from being indexed. BUT, should we start accepting records from other institutions, with varying metadata, that might become an important field to search and facet on. I’m sure there are workarounds for this scenario, but interesting all the same, and indicative of the iterative nature of tuning search and discovery systems.
I am following a thread on the IIIF Google group forum, ruminating on how IIIF and the Image API might support more advanced image processing. I am probably mis-characterizing, or reading too much into the conversation a bit, but something interesting to me emerged from some of the early comments.
There was the acknowledgement that as stewards of digital images, looking to the future, it’s likely that we will start undertaking image processing - OCR, classification, etc. – on the images we have at our disposal. Perhaps for metadata enrichment, digital humanities work, the possibilities are extensive. IIIF, and the Image API, provide an excellent and standardized way to access images. Image processing is helped by preparing images in particular ways, such as converting to grayscale to help detect nodes and edges, that IIIF might be able to help with. What if the API, in addition to rotating, scaling, selecting, and some limited color options, could help facilitate image processing of our visual resources?
This conversation has been fascinating on many levels.
Robert Casties responded to the thread, pointing out that the project digilib has some methods and functionality that would do just such things. I wasn’t familiar with digilib, but what a neat project! Appears to be out of Germany, dating back to the early to mid 2000’s. In many ways, it mirrors the IIIF ecosystem of image servers, and standardized APIs for requesting these images. Details drift and overlap here and there, but it’s devilishly similar to image servers such as Loris (which we use here at Wayne) or Canteloupe.
IIIF has what it calls the “Image API”, the particular
GET parameters used to request images. Digilib appears to have something called the “Scaler API” that does the same. Digilib appears to also support IIIF, perhaps an update to a project that seems to pre-date the IIIF movement, that acknowledges the increasing prevlance of IIIF in the digital repository spheres.
Though I’ve yet to install or interact with digilib, something deep in the fingers and toes tells me I like it. It has a page called, “Ancient History”, in German, which makes sense given where digilib was engendered. In principle and architecture, it very much mirrors what I have found so appealing about IIIF when I first stumbled on it in 2011 or 2012. This “Ancient History” page dates this project back the late 1990’s, where this kind of thinking for serving digital images online was pretty revolutionary.
I’ve strayed a bit from the original impetus for penning this post, that being ruminating on how standards like IIIF can support downstream image processing, but as I like to say, that’s okay! It’s been a fascinating thread to follow, and I’m hoping more will weigh in on how they envision emerging image delivery standards can help get these images into machine learning environements.
At least 10-12 inches of snow are falling outside the window, and I spent the evening moving between various states of slipping in snow, to sliding in cars, to overheating indoors. And throughout these transitions of state, the term “rewiring” is flickering through my thoughts.
Old English wir “metal drawn out into a fine thread,” from Proto-Germanic *wira- (source also of Old Norse viravirka “filigree work,” Swedish vira “to twist,” Old High German wiara “fine gold work”), from PIE *wei- (1) “to turn, twist, plait” (source also of Old Irish fiar, Welsh gwyr “bent, crooked;” Latin viere “to bend, twist,” viriæ “bracelets,” of Celtic origin).
Like working glass from a rod to an ornate and beautiful capture of heat and time, these definitions suggest that modest wire is a tangible expression of effort, or, a platted braid of circumstance (think of hanging cords, twisted around from radiating vents or curious cats, turned into something resembling the industrious form of wire).
Wire is dangerous. Wire is necessary. And wire is precise. This is no hip-shooting, boat wrangling rope rodeo. No, wire is the conduit of the 21st century’s most precision modes of communication, that by which the finest of movements are illicited from airplane rudders, and much more. Wire is a forgotten pillar of our modern infrastructure, with roots back to aesthetically pleasing arrangements of precious metals.
Rewiring, then, must be a reversal of some precise or circumstantial expression. At least temporarily to make way for a new pattern. Rewiring is slow. Rewiring is understanding potentially complex networks. And rewiring is permanent. It’s rare that we rewire a house, pull out the old, install the new, and ever rerewire the house with the old. Sure, importantly, that network of old wire can be reused, repurposed, or revered, but it’s unlikely it will ever power that same rotary dialer again.
And so, “rewiring” represents to me evolution. Irreversible change. Which isn’t always a bad thing.