This module is focused on developing a basic understanding of data and how it permeates different aspects of society. This involves knowledge of how it is structured, the politics behind this and implications regarding transparency. We understand data as a set of values of qualitative or quantitative variables about one or more persons or objects. It is transformed into information when data is created, extracted, elaborated and used with pre-established objectives. The information system, made up of data of the same or different form (the data set is defined as a “dataset”), is transformed into knowledge when it is interpreted through tools, applications, methods, indicators, etc.
The first lesson presents an overview of data and introduces the concepts of open data, showcasing different techno-political elements of its structure including principles and standards for publication. The second lesson presents an introduction to open science and open science data, and furthermore, principles for scientific data management. Finally, the third lesson showcases the importance of fostering innovation with open data to promote the creation of sustainable business models for responsible innovation.
We recommend that before you start this module you read this article where Mor Rubinstein asks the question if we are losing the battle for data literacy. Mor urges us all to pay attention to this critical skill as it will support us to navigate a world that is increasingly driven by (not neutral) data and riddled with misinformation. There is no time to waste, Mor sustains, in sharing these skills far and wide.
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- Understanding the basics of data
- Understanding the key principles of data
- Understanding the key concepts of data management
- Acquiring skills to publish data in open formats
- Developing ideas to innovate using open data
Nigel Shadbolt “The Value of Openness – The Open Data Institute and Publically Funded Open Data”
- Data is a symbolic representation that describes facts, conditions, values or situations
- Information is an organised set of processed and related data in a way that allows us to communicate or acquire knowledge
- Open data are data that can be freely used, reused and redistributed by anyone, subject only, at most, to attribution and distribution requirements with the same licence
- Open data use licence is the authorisation to use the data issued by the source that owns the copyright of the data
- Dataset is a collection of organised data records where each element has the same structure, ordered for processing by a computer
- Open data catalogue is an information management system that aims to be a single point of reference for those who want to search for and access data. It is made up of a management system for datasets and their metadata, which provides users with tools to speed up the publication, access, search and navigation of data.
- Co-creation those processes or activities where at least two actors (for example, public, private, governmental or civic) collaborate in the realisation of a project to achieve a certain result
- Hackathon a marathon of ideas, design and software prototyping, which brings together specialists, technical programmers, designers and entrepreneurs to work collaboratively. The days tend to last between 24 and 48 hours and usually have specific challenges to guide the event
- Digital innovation platform offers functionalities on which independent companies, developers or innovators can build complementary products, services or technologies
- Johnson, J. A. (2014) ‘From open data to information justice’, Ethics and Information Technology, vol. 16, no. 4, pp. 263–274. doi: 10.1007/s10676-014-9351-8 https://dx.doi.org/10.1007/s10676-014-9351-8
- Baack, S. (2015) ‘Datafication and empowerment: how the open data movement re-articulates notions of democracy, participation, and journalism’, Big Data and Society, vol. 2, no. 2, pp. 1–11. doi: 10.1177/2053951715594634 https://dx.doi.org/10.1177/2053951715594634
- Arzberger, P., et al., (2004) ‘Promoting access to public research data for scientific, economic, and social development’, Data Science Journal, vol. 3 (November), pp. 135–152. doi: 10.2481/dsj.3.135 https://dx.doi.org/10.2481/dsj.3.135
- Bezjak, S., Clyburne-Sherin, A., Conzett, P., Fernandes, P. L., Görögh, E., Helbig, K., ... & Ross-Hellauer, T. (2018). The open science training handbook https://www.fosteropenscience.eu/content/open-science-training-handbook
- Davies, T., Walker, S. B., Rubinstein, M., & Perini, F. (2019). The state of open data: Histories and horizons (p. 592). African Minds. https://stateofopendata.od4d.net/
- Gurin, J., Bonina, C., & Verhulst, S. (2019) Open Data Stakeholders - Private Sector. In T. Davies, S. Walker, M. Rubinstein, & F. Perini (Eds.), The State of Open Data: Histories and Horizons. Cape Town and Ottawa: African Minds and International Development Research Centre. Print version DOI: 10.5281/zenodo.2677777 https://doi.org/10.5281/zenodo.2677777
Key Complementary Resources
- Open Standards for Data https://standards.theodi.org/#:~:text=Open%20standards%20for%20data%20are,adopt%20open%20standards%20for%20data
- Tennant, J. (2020). A value proposition for Open Science. https://osf.io/preprints/socarxiv/k9qhv/
- Train-the-trainer card game for Open Science training | FOSTER https://www.fosteropenscience.eu/content/train-trainer-card-game-open-science-training
- Open Data Innovation Week tools https://labs.webfoundation.org/projects-2/open-data-innovation-week-2/