emilyrenderingdata-blog

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Week 6 : Materialising data

In week 6 the topic was Materialising data and what it actually means to materialise data. We started off by having a look at the artist Natalie Miebach who creates sculptures based off statistical information and displays them in a very colourful and quirky way. Although this way of display the data is very accurate and visually impacting, I don’t particularly like it as i find it very confronting and i couldn’t actually gather information from it easily. If i didn’t know anything about the concept behind her work, from a first glimpse i wouldn’t understand what is going on in her work. After reading Miebach’s artist statement as part of the weekly reading, this gave me a greater understanding of her thought and process behind her practice, its greatly influenced by the mixing of art and science. Another example we looked at was Mitchell Whitelaws ‘Weather Bracelet’. This bracelet is created through collecting data sets of the maximum and minimum temperatures and displaying the data on the bracelet. Although this bracelet is very appealing, i don’t think its very practical. This week we got out groups and this is when we first started talking about the data set we were going to chose. We decided to work with the Ozone layer set as we thought it was very interesting and liked how neat and easy the data was to read.

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Week 5 Data sonification: art + science, sound vs. image

This week we focused on sound and how it can be used through data in a scientific or artistic way. In the weekly reading ‘Soundscape, sanitation and soon activism’ A Polli talks about human interaction through the act of listening builds social and environmental awareness. The term ’soundscape’ is described as a collection of sounds in an environment and can be listened to as music. The act of data sonification allows data sets to be used as non-speech sound. It is basically similar to visualization, but where visualizations use elements such as lines, shapes, and colours, sonification relies on sound properties such as volume, pitch, and rhythm. I personally find data sonifications confusing, but very interesting. It would be seen confusing at times as its purely of sound and visualisation. Due to the development of data sonifcation through science and sound is now has allowed real time monitoring athletes by connecting sensors to their body and collecting information on their force, posture and motions. It allows new sorts of knowledge to be discovered which can engage with a wide range of new audiences.  

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Week 4 : Data Visualisation, Art vs information

In week 4 we focused on data visualisation, infographic visual language and how data can be portrayed through both an informative and artistic way. 

One of the first things we established in a class discussion is that data visualisations aim is to give an objective outcome. When we get a visual representation of something it becomes more interactive within the brain and we process looking at a image very differently compared to a paragraph of text very differently. By using visual methods it allows the audience to determine their own outcomes and feelings towards piece of work. In the weekly reading, Artistic Data Visualization: Beyond Visual Analytics' something that i found really interesting was how “the artworks must be based on actual data, rather than the metaphors or surface appearance of visualisation”. I thought this was a interesting statement because artists tend to take more subjective approaches to their works but in this case they are working off actual data and they have to find ways to represent this data accurately. 

We did some discussion questions in small groups, where we had to compare two different websites and evaluate and compare them. The two my group looked at were Disappearing Planet and Ocean Studies. What we discovered was that Ocean Studies was more of a direct visualisation about global warming giving the viewer an immersive experience. The black and white video installation gave a good explanation of what is happening although no visual information was given. Compared to Disappearing Planet which was very informative and interactive. The very detailed website gave hundreds of statistics and numbers on a timeline letting you have a great understanding of the extinction rates for different species of animals. 

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Week 3 culture, database as form, systems and networks.

This week was about data culture, the form, systems and networks used in order to control and maintain massive amounts of data and information. We talked how how we view and use the systems in place and also about places are structured to organise the data, for example a library who conform to a number coding system which makes books easily accessible in different categories and can be found very quick. From the lecture Christine Paul says that the database system is a place where it stores, filters and retrieves data and i find this very interesting because the more i thought about it, the more i realised how much goes on in these databases and how the computer has to construct and organise the information so efficiently. One of the tasks we had to do today was to go an hour without our electronic devices, although this was very easy to do it was interesting with the emotions i was feeling when they got taken away from me. It was very intriguing to see people actually engaging in conversations with one another and not having their heads dug into their phones the hole time. It really shows how technology has taken over out lives and how data can be seen in most aspects of out everyday tasks. 

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Week 2 : Raw data, pure data, structured, formatted and classified data

This week involved thinking about ways to construct Raw data. One of the first things we had a discussion about was how raw data is always in some way prepared even though it is classified as ‘raw’. This brings up the question of what is classified as being raw data. In the lecture raw data is described as data collected from or by a source. This can also be linked with what Geoffrey Bowker says in the lecture powerpoint about how Raw data and ‘cooked’ data can not be separated because data always comes from a source and that source is always situated. I thought this was a really interesting statement because it takes awhile for me to process this and fully understand what he's saying. One of the exercises we did involved looking at Musical instrument classification and how the systems were different and some limitations that could be raised. The two areas we looked at were Chinese and Western Classification. The Chinese was grouped based off materials and Western was divided into groups like string and wind instruments etc. What we came to a conclusion about these classifications is that neither of them can fully reveal a systematic way to divide and classify instruments into groups as the Chinese and Western classifications are very specific. This links to the weekly topic through the construction of the data and how people make the decisions to group specific things. 

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Week 1: Introduction: the datafication of everything

The first week of this course was very interesting and the main topic we focused on was what actually is data, where is it used and where it goes.

I was extremely intrigued by the class discussion we had about this topic. Some of the main points that came up that stood out to me was that data is part of your everyday life, every hour, every minute, even when we don’t think it is it. Data is evolving rapidly and this highly influences the way we carry out specific tasks every day and influence the way we interact with one another. Another strong part of this topic we discussed was how data can be very relational. This can be seen through for example, instagram where there is a page that is called ‘things you may like’ which is calculated from all the other type of posts you look at and the type of accounts you follow. To me this brings up the topic of privacy, as their is a sort of data sequence being produced from your profile. Privacy can also been seen in many different other aspects of data and where your private information is stored. It also brings up the question of how secure private information is and who has access to this information.  What i still don’t quite understand is how data is processed on such a large scale and what happens with the data when it goes into these big neural networks and computer automated algorithms. This weeks class was a very intriguing and engaging lesson and i am every excited for the weeks to come.