YYYY-MM-DD

YYYY-MM-DD

Time/Data/Visualisation

Time/Data/Visualisation

MoMA on GitHub

The Museum of Modern Art has followed in the footsteps of Tate and Cooper Hewitt and published their collections data on GitHub.

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As I’m currently in the final phase of my PhD, I have to dedicate more time to writing and less to doing. Even so I can’t let MoMA’s datasets go by unnoticed.

The above screenshot is from a timeline tool I developed for visually analysing large cultural collections. I imported the MoMA dataset and visualised the object records along their production dates. We can see the timeframe the collection spans, with earliest pieces from the late 1700s and – obviously – a focus on twentieth century and contemporary items.

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The block shape around 1820 and the rectangular spike at 1900 represent large numbers of items that have the same, or very similar, production dates. Such anomalies can stand for series of items in the collection, they can be traces of curatorial decisions in cataloguing, they could be mistakes in dating, etc.

I inspected a few records in the 1900 spike and encountered a few photographs, which gave me the idea that the spike could represent a larger series of photographs – this would explain the high production output in a short timeframe. The tool allows me to colour records according to a field value, so I gave it a try and coloured all photographs in green:

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Tree of Time: ChronoZoom

I have sacrificed parts of my Christmas break to develop something for Visualizing.org’s Visualizing Time challenge: A Hierarchical Time Tree visualisation which offers new insights into the dataset that powers the original ChronoZoom interface.

Screen Shot 2014-01-02 at 15.51.59

In 2009, Walter Alvarez was looking for a way to communicate the enormous time frames that make up the history of the universe and conceived the idea of ChronoZoom, which was released as an early prototype in 2010. Since then, the visualisation evolved quite a bit and by now contains a rich collection of data. What I only found out recently, is that all of this data is also accessible separate from the visualisation through a dedicated API.

Recently, the makers of ChronoZoom launched a contest in collaboration with the platform Visualizing.org in order to, I’d guess, promote the use of this API and at the same time tackle some of the problems they encountered.
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Behind the Scenes: ChronoZoom

In this post I will try to reproduce the steps that lead to my visualisation of ChronoZoom timelines. I tried to save the important mile stones as individual files and you can find them at the beginning of this post. It is fairly technical and in a way written more as a record for myself than for a general audience. So bear with me, should you decide to read this and feel free to ask questions in the comment section.

evolution

Versions

v0.1, v0.2, v0.3, v0.4, v0.5, v0.6, v0.7, v0.8, v0.9, v0.10, v0.11, v0.12, v0.13, v0.14, v0.15, v0.16, v0.17, v0.18, v0.19, v0.20, v0.21, v0.22, v0.23, v0.24, v0.25, v0.26, v0.27, v0.28, v0.29, v0.30, v0.31, v0.32, v1.0b, Final

ChronoZoom

Visualizing.org has partnered up with the people behind ChronoZoom. ChronoZoom is both a dataset containing curated timelines of the history of the cosmos and the world, as well as visual interface for exploring those timelines. Much in the same tradition as some of the earliest timelines, which aimed to map all of time – from the Creation to the last Judgement – ChronoZoom contains events since the Big Bang up to our current times. If you somehow haven’t come across it yet, you should give it a try here.
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The Tate Collection on GitHub

OpenGLAM alerted me via Twitter that the entire digital collection of the Tate is available on GitHub. I haven’t heard of any other institution who makes their collection available through this platform. It does kind of give a lot away, but then again, that’s the whole point of open data.

Why not Linked Data, asks a fellow Tweeter and Tate’s web architect Rich Barrett-Small justifies their move to GitHub with it being the most time- and cost-effective solution to get the data out there – for now.

Yes, SPARQL endpoints are the weapon of choice these days, but what’s wrong with using GitHub? It’s an incredibly versatile platform by far not limited to programmers, but equally useful for thesis writing or democracy.

What’s great about using GitHub, as opposed to APIs, is that it doesn’t only give you access to the data, it gives you the data. Maybe I’m old school, but I do like having real files on my hard drive, as opposed to it all being locked off in a cloud. And it’s still possible to keep the data updated, by syncing it with the original repository.

But enough about pros and cons of technical details, let’s have a look at what Tate offers.

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Scrolling Through Dead Presidents

Ravi Parikh’s interactive timeline maps the lives of US presidents and reveals the overlaps in their lifespans.

I have just come across this visual timeline by Ravi Parikh. It maps the lifespans of every US president and totals the number of living future, current and past presidents per year in a plot. It is a continuation of a visualisation he did the day before, which only included current and past presidents in any one year.

From his description it seems that the first visualisation was drawn up to answer one specific question: what is the maximum amount of (current and past) presidents alive at any point in history. In case you’re curious, it’s 6 (1861, 1993 and 2001-2003).

The second visualisation was encouraged by commentators and, I’d guess, without a specific question in mind. On the one hand it offers a more complete picture, because it includes the entire lives of presidents and not just the periods between their election and death. On the other hand, the lack of a question makes room for discovery.

Ravi notes some observations, such as which presidents have been alive during which wars. It would be interesting to see those wars and also other major events somehow highlighted in the timeline. The timeline also reveals which presidents coexisted and therefore knew of each other and might even had conversed. For example, Theodore Roosevelt and Ronald Reagan were both alive at the same time. Although it is unlikely that 8 year old Ronald had a chance to chat with Roosevelt before he died in 1919.

I would also be interested in the reason for the peak in numbers of presidents alive during the 1840s. Is it because presidential periods were shorter? Or is it just due to the fact that we can not know how many potential presidents are alive in current and more recent times because simply we don’t know who will become president in future decades?

It would be useful if we could play with the lifespans of presidents more freely, for example to have the beginnings of all the presidential periods aligned or to keep some ‘landmark presidents’ always visible.

Currently, the passed away presidents scroll out of view when the cursor is moved in the overview plot. Incidentally it was this mode of navigation by linking a line plot to a timeline view that first attracted my attention. It’s not novel, but it works very well with both the visuals and the kind of data. I immediately started playing with it, scrolling back and forth and brushing through the data. It’s fun, as long as you’re not put off by the slightly morbid character of how the presidents all die the further you scroll.