JP2 Conversions

Wanted to share a couple of hilarious and haunting images from creating and converting JP2s with the Kakadu JPEG2000 library. Full disclaimer: it is not the fault of Kakadu, it is most likely our free-wheeling, high-octane JP2 conversion approach we had in the pipeline for awhile.

Kakadu allows for tasks to get run over multiple processes, this is good! We also run these JP2 conversions with Celery, a background task infrastructure for Python. It is also not Celery’s fault. Finally, we’re queuing up multiple images for this pixel gauntlet. That, is most assuredly our fault.

The result, some pretty wild images. They are usually the combination of tiles and pixels from pictures nearby on the processing pipeline, at least that’s my working theory for now.


The Spreadsheet View

So I’ve been watching the EXCELLENT Collections as Data 2016 conference live stream all morning, and it’s really got the wheels going.

And the wheels were already going. A few weeks ago – when I finish corraling my thoughts, perhaps I can link to here – I attended a workshop in Maryland about Image Processing and Reunification.

These events, and the natural and mysterious evolution of ideas, have conspired to really hit home the idea of Collections as Data.

Doesn’t stop there. Thomas Padilla, a former nearby colleague of MSU and now in California I believe, also shared an IMLS grant just yesterday they had funded, “Collections as Data: Conditions of Possibility”.

I’m also serving on a committee about academic, R1 library collections.

And there’s no end in sight.

So, collections as data? What does that mean?

We do our best here in the Digital Publishing and with our Digital Collections to push the envelope of preservation and access, challenging ourselves to align digital objects in ways that will send them flying into the masses outstretch arms like mailbags on passing trains.


If I’m going to bury the lead, might as well throw one more blanket on the pile. I’ve also been working on a connector between the python ORM Peewee and DataTables for another project (which I hope to share at some point). As such, the scary efficient and well-understood mecahnics of a searchable, server-side processing spreadsheet has been on the brain.

So here’s the lead:

What about a spreadsheet-like view for digital collections?

You get it all. Thumbnails. Titles. Descriptions. Metadata. Filtering. Sorting. Speed. Search results already as structured data. Finesse. Fireworks.

Onwards and upwards! Putting the feelers out for a Solr-DataTables, python based connector, and we’re hoping to wire up just such an interface soon for our front-end.

Thermal Writing

Thermal Couplers of Writing

I’m penning this email with, as a foray into the worlds of an interface for a static, jekyll GitHub blog.

So far, so good.

  1. Authorized to interact with my GitHub.
  2. Found my blog repository, switched branches with ease.
  3. Clicked “new file” which put me in this editor I now find myself typing.
  4. I like this editor; so far I like it a lot.
  5. In fact, I’ve been able to preview my writing as I go.
  6. Jekyll keys off of filenaming convetions, and name of this file / post was conveniently started for me. I rue typing YYYY-DD-MM, my fingers simply can’t do it.
  7. And now, I’m hoping that clicking the adorable save icon in the corner will save this markdown file to my blog repository, and thus, publish this “post”.

If these writings to not ever make it to the Internet, may their voyage into /dev/null be quick(?) and painless.

If, however, they do find their way to a blog posting, then consider me tickled. I might end up using this for impromptu, tidy, enjoyable writing.

And here’s a picture for good measure: