| Reference : Visualizing Visions: Explorative Methods and Discovering the Rijksmuseum Digital Coll... |
| Scientific congresses, symposiums and conference proceedings : Unpublished conference | |||
| Arts & humanities : History | |||
| http://hdl.handle.net/10993/40998 | |||
| Visualizing Visions: Explorative Methods and Discovering the Rijksmuseum Digital Collection | |
| English | |
Koeleman, Floor  [University of Luxembourg > Faculty of Language and Literature, Humanities, Arts and Education (FLSHASE) > Identités, Politiques, Sociétés, Espaces (IPSE) >] | |
| 28-Nov-2017 | |
| No | |
| International | |
| dhnord2017: (De)constructing Digital History | |
| from 27-11-2017 to 29-11-2017 | |
| Maison Européenne des Sciences de l'Homme et de la Société | |
| Lille | |
| France | |
| [en] There is an ongoing trend to digitize museum collections. This ambitious task requires large amounts of time, money and other resources. Nevertheless, these institutions think it is a worthy investment. But why? The consensus is that it will revolutionize art historical research. We look for opportunities using different tools and datasets and how useful they actually are for researchers.
The dataset is usually a big hurdle when we want to get started with ‘digital art history’. This term refers to the transformative effect digital methods may have on the discipline of art history. We can easily overcome this hurdle by making use of already existing datasets. Especially since collecting data in itself is not a scholarly activity. For instance, the collection of the Rijksmuseum Amsterdam consists of about a million items, of which many are annotated and made available online. For this presentation we used the Rijksmuseum API and developed our own tools to search the digital paintings collection. A first experimental interface is shown in the screenshot above, allowing a simple comparison of artists and certain predefined themes. Using a number of basic elements we can quickly get an idea of the composition of the dataset. We can use a scatter plot to visualize dimensions and bring them into proportion to the largest artwork, The Night Watch. Or use a timeline that reveals at a glance that the vast majority of the paintings collection dates back to the Dutch Golden Age. We are now taking this first experiment to the next level. And over the course of this project we do not only want to study datasets of visual culture. We also want to (learn how to) show our findings visually. To this end we follow the ‘Seven Stages of Visualizing Data’ by Ben Fry. Of great importance to us is step 7, interact, to achieve interactive information visualizations. As a result we are delighted to present to you our story of the Rijksmuseum digital collection. We have chosen storytelling to communicate our data insights. But how do such methods contribute to the history of art? And how useful are existing datasets for answering academic research questions? Lastly we will reflect on our experiences with the Rijksmuseum dataset. | |
| Luxembourg Centre for Contemporary and Digital History (C2DH) > Doctoral Training Unit (DTU) | |
| http://hdl.handle.net/10993/40998 | |
| FnR ; FNR10929115 > Andreas Fickers > DHH > Digital History and Hermeneutics > 01/03/2017 > 31/08/2023 > 2016 |
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