## Links * **Course exercises and videos** * [Day One (7/28)](https://osf.io/r79wy/) * [Day Two (8/2)](https://osf.io/hcma2/) * [Day Three (8/4)](https://osf.io/9h5px/) * **Course website:** <https://ciakovx.github.io/> * **OSF home:** <https://osf.io/vpgbt/> * **FSCI schedule:** <https://fsci2021.sched.com/> * **Course Slack:** <https://fsci2021.slack.com/> * participants will already be added to the `w25_working_with_scholarly_literature_in_r` channel. * Click for the [direct link to the Slack channel](https://fsci2021.slack.com/archives/C0282717GPL). * **My email:** If you can't reach me on Slack, email me at clarke.iakovakis@okstate.edu * Use cases * Who relies on Crossref metadata? https://www.crossref.org/services/metadata-retrieval/user-stories/ * Introduction to roadoi (see “Use Case: Studying the compliance with open access policies”) https://cran.r-project.org/web/packages/roadoi/vignettes/intro.html * Using R packages to populate IR (Irine Tanudjaja) https://itanudjaja.github.io/project.html * Introduction to ORCID Researcher Identifiers in R with rorcid (Paul Oldham) https://www.pauloldham.net/introduction-to-orcid-with-rorcid/ * Creating a bibliography with rcrossref (Paul Oldham) https://www.pauloldham.net/creating-a-bibliography-with-rcrossref/ * Accessing the Scientific Literature with CrossRef (Paul Oldham) https://poldham.github.io/abs/crossref.html Updating your CV with packages (Erin Buchanan) https://www.aggieerin.com/post/updating-your-cv-with-packages/ **Launch the Binder for the entire repository:** [](https://mybinder.org/v2/gh/ciakovx/ciakovx.github.io/master) ## Description W25 - Working with Scholarly Literature in R: Pulling, Wrangling, Cleaning, and Analyzing Structured Bibliographic Metadata Clarke Iakovakis, Kay Bjornen, Megan Macken Abstract: "Developers have created a number of packages for accessing the scholarly literature in R over the last several years, among them rcrossref, rorcid, and roadoi. These packages make use of the APIs in their systems to allow users to execute specific queries and pull the structured data into R, where it can be reshaped, merged with other data, and analyzed. This session will be based on the workshop I provided at last year's FSCI. The course will assume no experience with R; however, a thorough explanation of the R programming language will not be provided. The course will a mixture of pre-recorded videos and synchronous meeting for discussion and Q&A sessions. Students will access IPNYB (Jupyter Notebooks) files containing the scripts for the workshop, created with Binder (https://mybinder.org/). The files will include executable code alongside descriptions of what the code is doing. Students can therefore run code that has already been written, but will also write and execute their own R scripts within the Jupyter Notebooks environment. Students will access these notebooks while watching the videos explaining the code. We will begin with a general orientation of the Jupyter Notebooks environment. We will then discuss R and provide a basic overview of how it works. This introduction will include reading data into R, installing packages, and some functions for cleaning and restructuring data. We will then discuss Crossref, ORCID, and Unpaywall, and the packages developed by the rOpenSci (https://ropensci.org/) organization to access the API services of these organizations, and walk through rcrossref, roadoi, and rorcid. rcrossref interfaces with the CrossRef API, allowing users to pull article metadata based on ISSN, filter queries by publication date and license information, running queries by title and author, getting funder data, getting citation counts, and exporting to BibTeX, RIS, and CSV. This can be immensely powerful for collecting citation data, conducting literature reviews, creating bibliographies, and more. roadoi interfaces with Unpaywall, allowing users to input a set of DOIs and return publication information along with potential locations of open access versions. rorcid interfaces with the ORCID API, allowing users to pull publication data based on a specific ORCID iD, or to input names and other identifying information to find a specific individual’s identifier. As we work through the tutorials, students will continue to learn R functions for working with data, including dplyr, purrr, and tidyr. By the conclusion of the session, students will be able to work with and analyze data in R. On a deeper level, they will have more powerful tools for gathering subsets of the scholarly literature in clean and structured formats based on specific parameters. Because they will be walking away with executable scripts, they will be able to modify those and collect data based on parameters they are interested in." Audience: Researchers, Librarians, Faculty/Scholars, Publishers, Administrators, Technical Support