autoCV

A tool for automatic generation of a LaTeX-based curriculum vitae (CV)

Motivation

I recently wanted to update my CV to include all of my open science activities, such as links to open access papers, code/data, and include DOIs. Rather than doing this by hand for each publication, I decided to build an automated tool to generate a CV using PubMed and ORCID to download the publication information, and a set of text files containing other info. It's still a work in progress but it might be helpful for you; so far I have only tested it on my own CV, and it will almost certainly need work for others (especially if you have a common name that is not uniquely identified with a simple Pubmed query). It will be most useful for more advanced researchers whose CV may be many pages long.

The project takes advantage of the very nice LaTeX CV template from Dario Taraborelli.

Structure

The idea behind this package is to use PubMed and ORCID to obtain an up-to-date CV in a relatively automated way. Using it requires that the user first enter some relevant information into their ORCID account:

• Education
• Employment
• Invited Positions and Distinctions
• Membership and Service

In addition, it requires generating several CSV files containing other information that is not well organized or available within ORCID:

• conference.csv: Conference presentations
• talks.csv: Colloquium and other talks
• funding.csv: Grants and other funding
• editorial.csv: Editorial duties and reviewing
• additional_pubs.csv: Publications that are not found in PubMed/ORCID (including books, book chapters, and conference proceedings - note that ORCID allows addition of books but the metadata are a bit screwy, so I prefer entering them manually in this file)
• teaching.csv: Courses taught

It also allows addition of links to any reference using a csv file called links.csv.

Finally, you will need to generate a json file called params.json that contains some metadata about you - see example here.

You will need to take a look at the examples of these files in the repository to see their structure.

Running the code

The easiest way to run the code is using Docker, which removes the need to install a full LaTeX installation. After installing the Docker client, you can simply install the package:

pip install autocv

and then use this command from a directory containing the necessary files:

autoCV

This will download the data and generate the CV files, and then render them to PDF.

To test it out, you can run it from within the tests directory, which contains example files for my CV. You can see an example of the output here.