The Research Data and Digital Skills Summer School will run the whole week from 30th May to 3rd June in the Glucksman Library building and will provide hands-on instructions on a range of digital tools to improve digital skills and data literacy of UL PhD students and researchers. The sessions aim to open new perspectives on potential data-oriented research approaches and to facilitate exchange between the participants.

The summer school does not assume previous knowledge of the tools covered, but participants with experience in some of the topics are most welcome to join and help their peers master new skills during the week. The tools covered will be beneficial to researchers from all disciplines. Participants are expected to attend all sessions.

 

Schedule

Monday

10am-1pm:    Research Data Management (training room GL0-068)

2pm-3pm:      The computational approach to research data (The Edge GL1-025)

3pm-5pm:      Regular expressions (The Edge GL1-025)

Tuesday

10am-1pm:    Tidy Data in Spreadsheets (The Edge GL1-025)

2pm-5pm:      OpenRefine (The Edge GL1-025)

Wednesday

10am-1pm:    Python I: Introduction and concepts (The Edge GL1-025)

2pm-4pm:      Presenting and exhibiting your data (The Edge GL1-025)

Thursday

10am-1pm:    Python II: Writing scripts (The Edge GL1-025)

2pm-5pm:     Open access publishing and measuring research impact (training room GL0-068)

Friday             

10am-1pm:    Beginner’s guide to 3D design and printing using Tinkercad (The Edge GL1-025 and Makerspace GL1-035)

2pm-5pm:      Python III: Data analysis and visualization (The Edge GL1-025)

 

Session: Research Data Management

Instructor: Armin Straube

Description:

The availability of data that is findable, accessible, interoperable, and reusable (FAIR) is improving research in all disciplines but requires data management throughout the research process. This session will enable the participants to plan their own data management according to FAIR data principles from the start of their research.  The hands-on planning exercise will be based on the research projects of the participants.

 

Session: The computational approach to research data

Instructor: Armin Straube

Description:
In this short session, we will reflect on how computers and data science approaches reshape research in all disciplines and how the acquisition of computational skills can benefit the research projects of participants.
 

Session: Regular expressions

Instructor: Armin Straube

Description:

Regular expressions are a powerful way of finding, extracting, structuring, and cleaning data originating from textual resources and are supported by many software packages and programming languages. Besides the direct benefits of knowing some regular expressions, it is a good starting point to understanding how computational approaches work.   

 

Session: Tidy Data in Spreadsheets

Instructors: Armin Straube

Description:

The session will teach “tidy” data practices in spreadsheets, structuring data in a machine-readable way to maximize the utility of the data (so that it can be used with other software). The training covers good data entry and formatting practices, basic quality control, and data manipulation in spreadsheets and approaches to avoid problems with dates as data. Demonstrations and explanations will be alternating with small hands-on exercises done by the participants with an example data set.

 

Session: OpenRefine

Instructor: Sinéad Keogh & Armin Straube

Description: OpenRefine is a tool to effectively clean, structure, enrich and format (tabular) data. Practical exercises will highlight the potential to save endless hours of manual data correction and to effectively use large datasets from various sources.

 

Session: Python I-III

Instructor: Armin Straube

Description:

Python is a general-purpose programming language widely used in data analysis and visualisation. These three sessions are aimed at researchers with no previous programming experience and will make participants familiar with the underlying concepts and principles, enable them to write basic scripts themselves and understand and re-use scripts written by others, and make a start in using packages for data analysis and visualisation. Finally, it will lay the foundations for further self-guided engagement with programming.   

 

Session: Presenting and Exhibiting Your Data

Instructor: Caleb Derven

Description:

This session will provide brief overviews of a number of freely available, high-level tools to display and exhibit data, including tools for topic modelling, spatial visualisation and textual analysis. The session will also introduce Omeka, a platform to exhibit your research and data and IIIF, a tool used by the library to share cultural heritage data.

 

Session:  Open access publishing and measuring research impact

Instructor: Ashling Hayes

Description:

Publishing your research outputs is a key component of academia. In this session we will give an overview of publishing Open Access and look at ways to measure the impact of those publications. It is extremely difficult to “measure” the impact of an individual researcher or a research group on the discipline or society. Bibliometrics can help to broadly inform evaluation alongside qualitative, expert assessment and review. However, it is important that research metrics are used responsibly, in a fair, transparent and robust way.

Session: Beginner’s guide to 3D design and printing using Tinkercad

Instructors: Ryan O'Keeffe & Louise O’Shea

Description:

The Glucksman Library Makerspace will offer a beginner’s guide to creating and printing a 3D model using the free online software, Tinkercad.  The library can supply laptops for use, or you can bring your own. If possible, please sign up to a Tinkercad account before the workshop. Basic computer skills are desirable.