McGill Library recently completed a project to add the full-text and metadata of over 40,000 theses from 1881-present to McGill’s institutional repository, eScholarship. This corpus of McGill student scholarship from over a century is a unique dataset with strong potential to be text mined and visualized by researchers. As such, McGill Library is happy to announce a Computational Research Fellowship, open to any student or researcher who is interested in exploring research questions related to this dataset. Research could include, but is not limited to, using the data to explore historical, social, political, or cultural research questions. Please see a small sample of the thesis data files, including metadata, here.
This Fellowship is open to all disciplines and applications would be evaluated on the project's originality, the clarity of the research proposal, and the appropriateness of the research design. The successful applicant will be given access to a dataset of the full text and metadata for the complete electronic theses collection and an honorarium of $3500 in support of the work.
Applicants must submit an cover letter outlining their proposed research project (500-800 words) as well as provide their CV to di.library [at] mcgill.ca (subject: Computational%20Research%20Fellowship%20application)
Please make sure to include the following in your CV and cover letter:
- Educational history: include any programs in which you are currently enrolled. Include the institutions, the fields of study, degree receive (if any), and the dates of enrolment.
- Research and professional experience: include all work experience that you consider relevant to this Fellowship.
- Professional and academic honours:
- Publications and Research interests: include any publications (e.g.journal articles, book chapters, books or other publications, theses and dissertations etc.). Include also any relevant coursework such as courses taken, papers written for assignments etc.
- Technical background/skills relevant to the proposed project (e.g. familiarity with data visualization tools such as Voyant, qualitative data analysis tools, such as NVivo, basic scripting skills in Python, Perl)
The selected Fellow must give a presentation at the end of their project to members of the Library as well as make a copy of their code and any visualizations available openly on a public repository like GitHub. A clean version of the dataset will be made available to future researchers and the fellowship work will be promoted via the library communication channels.
Fellowship will commence Summer 2017 with work to be completed by December 31, 2017 at the latest.
Open to all researchers and students including those external to McGill.
Please see a small sample of the thesis data files, including metadata, here.
2017: Rajat Bhateja