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09 02 2018
Karin van Es: Data-driven approaches are often particularly good at raising new questions, which may need to be answered with different methods
On data-driven research and visualization with Karin van Es from Utrecht Data School, who gave a lecture at Data (for) culture conference in Katowice
Łukasz Mirocha, Karin van Es
20 08 2017
Researching Facebook and researching with Facebook. The story behind the app to verify the declared participation in cultural events
We present a step-by-step account of the design and implementation of a Facebook application that will allow us to study the participants of cultural events promoted as part of Poland’s most popular social networking site.
Hanna Kostrzewska
11 08 2017
Data » information » knowledge: Medialab designers discuss the role of visualisation in the research process
In addition to data acquisition and analysis, the subsequent step of data visualisation is a central task that the Medialab team faces as part of the study of the culture cycles in Katowice under the Shared Cities project. As one of our previous postings has already covered the challenges facing our coders, this time we talk to Medialab designers. Paulina Urbańska and Waldek Węgrzyn work on the visual side of the project on a daily basis, while constantly improving our research methodologies and internal team communication.
Łukasz Mirocha, Paulina Urbańska, Waldemar Węgrzyn
08 05 2017
Medialab coders talk about the challenges of data acquisition and analysis
Successful data analysis in cultural projects would not be possible without the work of a team of coders who work hand in hand with designers and researchers to create the best tools and methods of working with data. We asked our experts – Dawid Górny and Marcin Chojnacki – to talk about their daily work and the challenges they face during each stage of the project.
Marcin Chojnacki, Dawid Górny, Łukasz Mirocha
25 04 2017
Visualisation of Facebook data. What you will not learn about your fan page from admin stats. Part 2.
We typically expect the analyses of large data sets to provide us with simple clues that will increase the number and engagement of visitors to our page. Detailed research on interactions with Facebook posts shows that trends tend to vary from month to month, making it difficult to find universal rules governing social media. Our visualisations, therefore, do not provide ready-made solutions, but rather help users embark on data exploration for a broader look at fan page statistics.
Karol Piekarski, Waldemar Węgrzyn
14 04 2017
How to transform data sets into tools for exploring cultural phenomena?
Explorative data analysis is at the heart of any data-driven research process. Having spent several months collecting data from various sources: social media, web sites, and our own surveys, we were able to perform prototype analyses and visualisations in order to test the quality of the collected data and its usefulness at a later stage of work. Being half-way into our study, we are anxious to see how the work done so far will help us explore the issues we are interested in.
Karol Piekarski, Waldemar Węgrzyn
21 03 2017
Where’s the Culture? A Cultural Event Geography by Facebook
Is it possible to map cultural activity across the city based on social data? Well, we gave it a go! Using the information available on Facebook, we set out to trace the dynamics of the local cultural life to find out whether the city’s geographical centre is also where its cultural heart beats.
Hanna Kostrzewska, Paweł Jaworski
09 03 2017
Visualisation of Facebook data. What you will not learn about your fan page from admin insights
Administrators of Facebook pages have at their disposal a set of advanced statistical tools to track and study the engagement of its users. However, if they want to compare their own performance in relation to similar institutions or competitors, they are forced to seek the paid services of analyst firms. Alternatively, they may refer to the data from another source, i.e. Facebook’s API. This is exactly the way we decided to check whether researchers or cultural event organisers can gain a basic understanding of any fan page, while performing their own studies and visualisations.
Karol Piekarski, Waldemar Węgrzyn
20 02 2017
Studio NAND: There is nothing worse than a visualisation that tries to convey too much information at once
On January 9–10, Medialab once again had the pleasure of hosting Steffen Fiedler and Stephan Thiel of Berlin’s design studio NAND, who gave an open lecture, and conducted a two-day workshop for representatives of institutions working jointly on the Shared Cities project. In an interview given to Medialab, the data visualisation experts talked about their work, best visualisation practices and the future of the industry.
Łukasz Mirocha
30 01 2017
Cultural data analysis is consistent with the positive smart city vision
Working under guidance from data science expert Piotr Migdał as part of our Shared Cities project, the workgroups are developing a range of tools for the analysis and visualisation of social media data. Network analysis conducted with these tools allows us to study the declared participation in Katowice’s cultural activities and present it in different forms.
Łukasz Mirocha
12 01 2017
From response to visualisation: how to streamline survey data processing
Before a response given in a survey makes it into a report or spectacular visualisation, it must go through a multistage data handling process. We provide a step-by-step guide to improving data acquisition and cleaning in order to accelerate the presentation of research outcomes and produce better quality responses.
Karol Piekarski
10 01 2017
What does data tell us about culture? Data-driven research
Thanks to data-driven research we are able to explore the relationship between knowledge about the behaviours of social media users with the research on consumers of culture conducted by traditional methods. This way, we combine the accuracy of surveys and their capacity for revealing the motivations of respondents with the advantages of automated analysis of large data sets, which makes it easy to discover the so-far unnoticed trends and relationships.
Karol Piekarski