The third Open Knowledge Network event in early July 2017, focused on discussing topics put forward by participants and exploring ways forward for the Network. The following topics were discussed – open data sets, data literacy, building a network of open practitioners.
Open Data Sets
This discussion focused on raising the profile of open data, identifying university data sets that could potentially be opened, encouraging data owners to share their data, collecting usecases around how open data sets might be used in order to demonstrate the value of open data to the wider community.
- We need to raise the profile of open data sets by making them discoverable in accessible repositories, and encouraging others to do the same.
- Encourage people to share their data – openness is a cost-saving measure in the long term.
- Open doesn’t necessarily mean usable. We need to provide information and tools so users can work with data.
- Show potential data creators what analytic tools are available and how users can manipulate their data.
- Demonstrate how data can be used outside its original remit.
- Although we can’t predict how open data will be used, it can be useful to have a particular set of users in mind, with the flexibility to reach wider audiences.
- We need to think about the resource implications around checking and anonymising data.
Examples of data sets that could be opened
- Energy per building, waste and recycling, procurement, investments, travel, survey data, learning analytics, research outputs – project database, catering sales.
- Technical equipment – useful to have an open list of technical equipment and the people who are able to use it.
- Admissions data – data that is currently open relates to number of applications and admissions. Demographic data is currently withheld. More explicit data would potentially be more useful to students deciding which programmes to apply to as they are only allowed a limited number of university applications. (How much of this data is made available through the KIS?) New General Data Protection Regulation (GDPR) guidelines may have an impact here.
Actions and recommendations
- Collect usecases demonstrating demand for open data and how it might be used.
- Collate case studies to demonstrate how data sets have already been used.
- Identify what data students want.
- Make list of Data Stewards accessible and open.
This discussion focused on exploring what constitutes data and information literacy, how our understanding of data literacy is changing and how we can support staff and students to become more data literate.
- Data literacy is not about being an expert on a particular data set, it’s about understanding what data means and how one might access it.
- Information literacy is changing from retaining information, to knowing where to find information.
- Interpretations of data can be different and subjective.
- It’s important to be able to determine the reliability of data sets.
- Need to be aware of inconsistencies in data and unconscious bias in algorithms, and how they encode the bias of their creators.
- Need reassurance that the data creator has a degree of understanding of how the data works.
- Should we be able to query and manipulate data in order to be data literate? Do we need programming knowledge, or simply better tools?
- Data literacy should incorporate thinking about what happens to personal data. Who gathers data about you? Who retains and has access to this data? What do they do with it?
Actions and recommendations
- Create an Information Literacy Hub providing access to resources and support on digital literacy, data literacy, information literacy, and copyright.
- Wikipedia and Wiki Data are great resources for teaching people how data is created. You can see how it has been pieced together by different authors, which in turn encourages students to question and interrogate data.
Building a Network of Open Practitioners
This discussion explored how we can build a network of open practitioners and help to enable colleagues to find other open practitioners working in the university and further afield.
- How can we encourage more networking within the university around Open Knowledge? Social media works more widely with open educators all over the world. But what about internally, can we replicate this?
- Use existing networks rather than creating new ones.
- Face to face meetings are useful. OKN is an existing network, continuing these meetings on a regular basis would support the development of a network.
- Events are good for networking, but how do we get researchers involved? Data literacy training events, and thematic events might be useful, to draw together people with similar interests.
- Promote open practice in our everyday work and focus on being open advocates.
- Promote and encourage the use of existing hashtags and social media. Hashtags may not reach people who are not currently engaged with social media but are useful for connecting people who are already engaged.
Actions and recommendations
- Use the IS Innovation Fund. Allocating funding to projects using open resources would encourage colleagues to engage with open practice and open data.
- Incorporating hashtags (e.g. #oer, #opendata, #openscot, #opendataEDB, #ukoer) in staff profiles would help to broaden existing networks within the university. This would improve discoverability as the University website has good search functionality. This would raise the visibility of open practitioners within the institution – linking, locating and advocating. Consider include an option for hashtags in the Edweb staff profiles template?
UoE OKN Sustainability
How can we build a sustainable Open Knowledge Network. Has the network been useful and beneficial? Should it continue?
- While there is value in sharing experiences of open practice within the univeristy, we could further this by linking with Open Knowledge Scotland.
- In order the get the best outcome, it may be helpful to split internal university goals and wider external goals and consider them separately. Researchers/teachers/administrators may have different priorities. It’s useful to bring communities together but maybe we should also think about differences?
- Think about running practical workshops rather than information events. E.g. How to use data visualisation tools. Embedding data literacy in courses. IPR and open licensing. Identify areas to focus on and then run practical sessions.
- Further embedding open practice into policy might be a useful objective for the future.
- Consider passing the network onto another community within the University? E.g. those involved in open research or open data?
- Alternatively consider a model similar to the elearning@ed forum, where the committee and aims change annually.
- Demonstrate the purpose and value of the network. Report back to the IS innovation fund with achievements so far, a list of further goals and apply for continued funding.