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OS Tools: Finding papers & data

VO Sharing is daring: Open Science approaches to Digital Humanities

Please read the lesson script below and complete the tasks.

Questions, remarks, issues? Participate in the Zoom meeting on Mon, 18.05.2020, 5 p.m. - 6 p.m.!
This week's topic of discussion:
What's the difference between having to find Open Access papers (and other reading resources) and having to find open data sets for research purposes? Do we need different resources and strategies to solve these two tasks or are they essentially the same?

Mon, 18.05., 16:45 - 18:15: OS Tools: Finding papers & data

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Here are a few remarks before we get into the topic of this week's session:

  1. Please be reminded that we will have a guest lecture on 15 June 2020: Bernhard Schubert of the University of Vienna Open Access Office will give us insights into the institutional dimensions of Open Access. I hope that many of you will join this session, which will also be recorded and made available online. Be aware that the content of this guest lecture will be covered on the exam.
  2. The Association of European Research Libraries LIBER is a big advocate for Openness, particularly Open Access. LIBER is offering a webinar on "Open Science Skilling & Training Programmes Across Europe", which will give participants an insight into possibilities for learning more about Opn Science. The webinar is open to all and free of charge, it will take place on Tuesday, 19 May 2020, at 2 p.m. You are strongly encouraged to participate! You can register here.

Having made these remarks, we can begin to work on our topic of the week, which is "Finding papers and data". Naturally, when we speak about finding resources in this course, we always mean OPEN - and after what we discussed last week, ideally also FAIR - resources, so the more precise title for this session would be "Finding, accessing, and reusing open papers and interoperable data". In our opening remarks, we already encountered Association of European Research Libraries LIBER and learned that part of LIBER's work is to offer education opportunities for students and researchers on Open topics that are relevant to the research community. Among other things, they offer these training opportunities in the form of webinars (not only since the current crisis, but for a longer time, as LIBER is aiming to support the entire European community). These webinars are not only available as live teachings you can participate in, but they are also recorded and made available as video courses.

Task 1

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Visit the LIBER website and check out the long, long list of webinars that is offered there. Can you spot webinars on topics we have already covered in this course?

Task 2

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While on this page, you surely noticed that LIBER has also already covered the topic we are discussing this week: "Finding and Reusing Research Data" was a webinar presented by Kathleen Gregory. Please watch the entire video (on the LIBER site or on YouTube) - the accompanying slides are also available to you (you guessed it: on Zenodo).

As you can see, Zenodo is once again our repository of reference for finding a resource, in this case the slides for the webinar. Zenodo is our most prominent repository that we encounter most often, but also all other repositories that we have come across in this course are great resources for finding research data (and papers, slides, materials,...). Even ORCiD could be a good resource for finding papers and data, for instance if you came across a certain researcher's work and want to see what other things they might have published (remember that we looked at Jon Tennant's work on his ORCiD profile to do exactly that).

In the webinar we watched, Kathleen Gregory references the paper "Eleven quick tips for finding research data". These tips are very useful and will help you streamline your research process, so let's look at these tips in more detail:

  1. Think about the data you need and why you need them.
  2. Select the most appropriate resource.
  3. Construct your query strategically.
  4. Make the repository work for you.
  5. Refine your search.
  6. Assess data relevance and fitness-for-use.
  7. Save your search and data-source details.
  8. Look for data services, not just data.
  9. Monitor the latest data.
  10. Treat sensitive data responsibly.
  11. Give back (cite and share data).

This is basically a list of all steps that we have to do anyway if we are trying to strategically answer our research questions - which once again proves that "Open Science is just science done right" (Jon Tennant). Still, we sometimes wish we had some support or tools that would make the process less tedious, especially for the first steps. There is a tool that can support us greatly in this area: Open Knowledge Maps is a visual interface to the world's scientific knowledge. Let's take a more detailed look.

Task 3

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The aim of Open Knowledge Maps (OKM) is to provide easy, immediate, intuitive access to scientific knowledge for anyone. Therefore, the tool should work for you without explanation. So - let's try! Think of a research topic you are interested in (this can be very general "Open Science", or very specific, "Handke, Serbia"), go to the OKM website and see what you can find.

If you find this tool useful, you should stay tuned and regularly check out the updates, as it is constantly being improved and developed further. If you know how to (read) code, you can look at how the tool is made on GitHub (of course it is entirely Open Source). In the future, OKM will even allow you to create and edit your knowledge maps, therefore building the bibliography for anything you write within the tool (watch this short video to understand how that is supposed to work).

Task 4

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Jump back to the list and look at all the eleven tips above. Do you understand each of the tips, is it clear to you what they mean im practice? Identify the tips that you need more information on and read the corresponding chapters in the paper "Eleven quick tips for finding research data" by Kathleen Gregory and colleagues.

Task 5

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Up to here, we spoke exclusively about "research data", where to find them, and how to access and use them, not papers. Now think about how the strategies we learned are equally useful for finding papers. Do you feel that you are sufficiently equipped to find openly available papers with the methods and resources we learned about today? If not, what is missing, what do you need? This will serve as input for our discussion this week.

We have now covered the more established aspects of Open Science - Open Access and Open Data - in detail. In next week's session, we will move on to talk about the "border areas" or "orchid (not ORCiD!) topics" of the Open Science world, i.e. the less established approaches and areas such as Open Methodology, Open Evaluation, Open Educational Resources (one of which you are looking at right now), and so on. We will also briefly touch upon Open Source and Open Code, as this is of special interest to the Digital Humanities. But before that:

Do you have any yuestions, remarks, issues? Do you want to share your thoughts on task 5? Participate in the Zoom meeting on Mon, 18.05.2020, 5 p.m. - 6 p.m.!
This week's topic of discussion:
What's the difference between having to find Open Access papers (and other reading resources) and having to find open data sets for research purposes? Do we need different resources and strategies to solve these two tasks or are they essentially the same?

Reading & Resources

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