Integration and reuse of Library Carpentry content into curricula
By Jeff Oliver, Julie Goldman and Konrad Förstner
There is a growing need to teach students professional data handling skills. Luckily, nobody has to start from scratch for this. While the lessons of The Carpentries including the Library Carpentry lesson program are designed to be taught as defined combinations in two-day workshops, they can be reused in other contexts. In this blog post members of the Library Carpentry community describe how they include Library Carpentry lessons in academic curricula and use the training methods to teach computational skills to students.
The University of Arizona (Jeff Oliver)
New graduate students have considerable variation in their experience dealing with data. While some incoming students may have had prior research opportunities, many have not collected data, let alone developed experience in data collection pipelines and workflows. Many graduate programs have an implicit expectation of skills in the storage and manipulation of tabular data, but few programs explicitly assess or address the training necessary for such skills. To ensure these students are prepared for their graduate education, a pair of Library Carpentry lessons are ideal for integration into graduate student orientation curricula: Tidy Data and OpenRefine. We all know how rewarding it is when we teach students how to use library databases, and by offering these Library Carpentry lessons, libraries can further support graduate student skills development.
The Tidy Data lesson introduces software and best practices for working with tabular data. As many graduate students will be collecting data for their degrees, spreadsheet programs are going to be a major part of many students’ lives. Even when data are collected by hand and transcribed into a digital copy, knowing how the data will be stored digitally can significantly influence (in a good way!) how manually collected data are recorded. The Tidy Data lesson introduces common spreadsheet programs (e.g. Microsoft Excel, LibreOffice) and the concept of tidy data a la Hadley Wickham (one variable per column, one observation per row, one value per cell). The lesson also covers critical data formatting challenges and solutions, including how to deal with date data (aside: ISO format please, FTLOG!). Incorporating the Tidy Data lesson in orientation curricula can make for a much more pleasant, and ultimately more productive, graduate student experience.
However, many graduate students will be working with data collected by someone else. That is, incoming graduate students may inherit data from legacy research projects or they may end up working with data made available online. Who among us has not had the pleasure of opening a data set where the creators did not follow best practices for data collection (see previous aside regarding dates)? The Library Carpentry OpenRefine lesson provides solutions to many data interrogation, quality, and transformation challenges graduate students may face. The faceting and filtering functionality of OpenRefine provides an accessible means of initial investigations of data collected by someone else. Identifying and correcting mistakes in data (e.g. misspelling “Tucson” as “Tuscon”) is relatively painless with OpenRefine’s clustering tools. The lesson also briefly touches on the powerful and extensive data transformations available, including consistent case (lower, upper, title) and removal of extraneous whitespace. No longer does one need to manually search hundreds of cells to find the difference between “Arizona” and “Arizona “ that caused analyses to fail (don’t ask how long that took). OpenRefine makes such operations trivial. As available data grow at an increasing rate, graduate students will need such tools to apply best practices to data collected by others without re-entering entire datasets.
These two Library Carpentry lessons are ideal for incoming graduate students (and probably some who are years into their degrees, too). With the goal of providing baseline understanding of data best practices, incorporating these lessons into graduate student orientation will prepare graduate students on day one. Early adoption of such practices will make graduate students’ work more computable, shareable, and reproducible. Oh, and did I mention date formats?
Simmons University (Julie Goldman)
Building Data Skills for Librarians
Instruction and consultations are essential parts of the data librarian work. However, few librarians receive data literacy education or coursework around instruction. Providing professional development opportunities for current librarians is essential in order for them to build the data skills needed to be a part of and included in academic workflows. What are the skills data librarians need and where can they receive them? The field is constantly evaluating the skills needed by librarians to engage in data science (Burton et al. 2018 and Federer et al. 2020). But there is consensus that librarians should now have some basic data skills that involve best data practices and being able to organize, wrangle and visualize data. If librarians have these skills, they will be in a position to successfully provide data services, support and instruction sessions for researchers.
IPI Certificate Program
Simmons University and academic health sciences libraries across the USA are partnering to offer a post-master’s certificate program in the area of Inter-Professional Informationist (IPI), for the purpose of bridging the gap between traditional and emergent skills in health sciences librarianship and increasing the diversity in the workforce. A small cohort of librarians in the program will complete seven IPI courses, and partner institutions will connect them with researchers and clinical leaders who will mentor their capstone. This project was made possible in part by the Institute of Museum and Library Services with the Laura Bush 21st Century Librarian Program Grant [RE-17-19-0032-19]. Simmons University, School of Library and Information Science, College of Organizational, Computational and Information Science provides cost-share of the project.
One of the courses included in the IPI program is “Scientific Research Data Management” was taught Fall 2020 by Elaine Martin and Julie Goldman. This course had been an elective in the Simmons School of Library and Information Science curriculum for many years, but underwent a redesign to include and address many of the newer emerging areas related to data services in libraries. For example, the course included “Special Topics” that included Data Curation, Data Skills, Reproducibility, and Informationists. While basic understanding of data management is critical for librarians to work with researchers, there are these emerging areas where librarians can provide even more specialized help to their communities. It is one of the IPI’s project’s goals to bridge the gap between traditional and emergent skills in health sciences librarianship.
Carpentry Lessons for Online LIS Curriculum
Library Carpentry already successfully provides full lesson plans and materials that address these data and software needs. So as an instructor, there is no reason to reinvent the wheel, but to incorporate the already established materials and pedagogical concepts into a course. Also, the response from the Carpentries community regarding moving workshops and coding instruction online has been fabulous. These recent blog posts from Darya Vanichkina are extremely useful for teaching Library Carpentry lessons online.
Therefore, we used some of the core Library Carpentry lessons for the IPI program students to become familiar with data skills and also build confidence in using these skills. Students were provided a lecture video on “Data Skills'' which incorporated an overview of the Carpentries, and overview of topics related to the Library Carpentry curriculum lessons: two core (UNIX Shell and OpenRefine) and two extended (Tidydata and Intro to R). For an assignment, students had the opportunity to work through one of the presented lessons and then answer questions based on their experience and provide feedback. Here is some of the student feedback:
- “I have definitely bookmarked [Tidydata] for further use.”
- ”[The Tidydata lesson is] pretty heavy in specifics and details, but there’s a lot of great best practices and tips & tricks within the content”
- “I really liked the key points at the bottom, especially on the pages with lots of commands because it was easy to forget what I did/read at the top of the page. I also really appreciated that there were references for each lesson so if I wanted to dive deeper I could.”
- "I think it’s great! Anytime the library can find something on campus where there is a gap in knowledge and can bring those skills to the researchers/students/etc, the library is proving how valuable a resource it is.”
- “At [my POW], I know we have thought these, but my only concern would be that another unit at [my POW] is teaching similar classes. [Some] charge for their services, so they might not be happy if the library moves into the space with free resources."
It is wonderful to hear LIS students feel these skills are important for librarians to foster and also teach to their research communities. Future iterations of this lesson plan could incorporate some of the Carpentry Instructor Training approaches. For instance, having students record a short example of teaching a lesson, then everyone provides constructive feedback on each of the videos. You can see the entire RDM Course Syllabus on OSF (https://osf.io/yzwpk). Stay tuned for our next exploration of incorporating Carpentries training into LIS curriculum!
TH Köln – University of Applied Sciences (Konrad Förstner)
At the Institute for Information Sciences of the TH Köln – University of Applied Sciences (Cologne, Germany) several courses have integrated Library Carpentry lessons. The MALIS (Master in Library and Information Science) study course offers the elective course "Data Science / Practical IT") which builds upon three Library Carpentry lessons namely the Unix Shell, Introduction to Git and Introduction to Python. The lessons are taught spread over several weeks. Equipped with these skills the students start to work on projects in which data must be cleaned, processed and analyzed using the Unix Shell as well as Python and results submitted as git commits. A similar setup is applied in an IT class as part of the bachelor program “Library and digital communication” in which Library Carpentry lessons lay the foundation for project work. In another class of that bachelor program Python is shortly and intensively taught based on the Library Carpentry lesson. After that an introduction to Wikidata based on the early-phase Library Carpentry lesson is given.
Besides being included in these classical academic curricula the Shell, Python and Git Library Carpentry lessons represent the main content in the first module of the certificate course "Data Librarian" run at the ZBWI (Center for Library and Information Science Education) of the TH Köln. The skills acquired during this module are then later further extended by further modules that include the application of statistical methods and basic machine learning with Python.
Furthermore, it is planned to adapt the Library Carpentry and further Carpentries lessons as part of the DaLI (Data Literacy) program of the TH Köln. The aim of this project is to provide an interdisciplinary model and curriculum to teach data literacy across faculty borders.
I personally am extremely happy to be able to build upon the efforts of the Library Carpentry community. Keep in mind that not only the actual content but more importantly the teaching methodology (yes, also sticky notes) is used. This included frequent collection of feedback which is overall very positive although the topics are challenging. Due to these wide applications, it is motivating to improve and extend the material and by that reach a global audience. In future, we would like to include students further into the improvement of the content. That could be smaller contributions like translation for Glosario but also the inclusion into the development of new lessons. The best example is the above mentioned Wikidata lesson which was improved by Rabea Müller as part of her Bachelor thesis.
What is your Library Carpentry lesson re-cycle story?
Jeff OliverJulie GoldmanKonrad Förstner
Library Carpentry The Carpentries The University of Arizona Simmons University TH Köln Curricula Data Science Data Skills Workshops Lessons
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