Zanele Kukuma

Zanele Kukuma is a Computer Science graduate, currently working as a software developer at SARAO

Accepted Talks:

Unlocking Efficiency: Python-Powered DOI Creation Automation

In the current time of digital information, the need for an efficient and accurate way of tracking and monitoring scholarly articles and datasets is essential. The Digital Object Identifier (DOI) system, vital to this process, ensures unique, persistent identifiers for digital objects. Previously, we used a manual process of creating DOIs which was daunting and error-prone, especially for scholarly articles with multiple authors. This talk illustrates a Python solution to mitigate these challenges by automating the DOI creation.

Python is the ideal tool for this endeavour because of its simplicity and adaptability. We will discuss the nature of the DOI system, how to cite data using it, and the challenges associated with manually producing DOIs. Furthermore, the presentation will take attendees through a hands-on example of a Python script constructed to speed up a DOI creation.

The powerful libraries that Python provides, grant us access to make DOI creation as smooth as possible. Thus contributing to the research workflow and enabling the creation to be less error-prone. Creating DOIs helps to cite papers better, making research more transparent and available for reuse. The intended impact of the attendees is to understand how Python can play a critical role to automate DOI creation, furthermore gaining the necessary skills to leverage Python libraries to automate such solutions in their own research. Regardless of whether you are a Software engineer, a librarian, a researcher or a data manager, this session will give information on how one can use Python to improve data citation practices and add to the greater quality of the digital scholarly infrastructure.

Bonus, Now that attendees are familiar with how we have used Python to solve a tedious manual process. We are also cognisant that our Python code is of great quality. We measure that code is of great quality by how high or low the value of the piece of code is. Generally, code that is easy to interpret and well-documented is considered to be of high quality. Here are some of the common quantifiers of code that are of high quality. Code that is functional, consistent, easy to understand, meets the client’s needs, is testable, reusable, free of bugs and well documented. These quantifiers are used to check if the automation of DOI creation code is of high or low quality.

Python Software Foundation
Thinkst Canary Afrolabs