---
title: Science and Open Source
date: 2024-06-11
description: Why academics need to think carefully about the software they use and teach.
lang: en
slug: science-and-open-source
translationSlug: wissenschaft-und-open-source
featuredImage: /images/neuigkeiten/science_and_open_source.avif
imageCaption: © Lukas / Pexels
tags: [Open Source, Science]
---

During my academic adventures, I was frequently confronted with the use of proprietary software. Indeed, different faculties at the University of Vienna are reliant on different software. Sociology Students are taught the use of IBM SPSS for statistical evaluation. For Political Science it's Stata. Geography Students must use ArcGIS for geospatial analysis.

Microsoft Office is, of course, free of charge and encouraged. Open Standards and Open Source Software (OSS) are only mentioned on the side. University staff often only shares material in proprietary, non-standard formats like .pptx and .docx. The effects and ethics of using proprietary software are never discussed. Courses based on OSS alternatives are only rarely offered.

As academics, it is imperative that we understand the consequences and implications of what software we use and teach.

## What is "proprietary" and what is "open source"

The key difference between the two terms is whether the software's code can be publicly reviewed. Proprietary software is usually controlled by a single company, like Microsoft Office. The exact workings of the software are obscured from public view. OSS like LibreOffice on the other hand is owned by communities or NGOs and makes all of its code publicly available. It can be scrutinized by anyone willing to take a look.

Another differentiator is licensing. OSS is usually available free of charge, and can be modified and redistributed by anyone. Proprietary software often requires frequent payments for the latest versions. Redistributing the software or modifying it is strictly forbidden.

Proprietary software seeks to control and lock-in its users. OSS fosters cooperation, transparency and accountability, things we consider to be good scientific practice.

## Ethics and good scientific practice

Young students are confronted with ethics and good scientific practice in academia early in their studies. Controlling and listing our sources as well as protecting the privacy of participants during research are a given. You will be heavily criticized if you do not abide by these rules. However we often exclusively apply them to our methodology, and not to the tools we use to process our data.

While the software we used in the past was of little consequence, nowadays it is important to carefully choose our tools of choice.

## Legal grey area

It is widely known that large companies collect all kinds of data about us in all kinds of places. That is nothing new. But only few realize that Microsoft's data collection practices go as far as to analyze every word and every number inputted into their Office Suite. While your research participants might have agreed to **your** privacy policy, have they also explicitly consented to Microsoft's?

The GDPR is fairly new regulation, and this question is hard to answer, since there is little to no precedence.

## Alternatives

You might be wondering, what software could be considered problematic based on the above, and what alternatives exist to carry on your research legally and with a good conscience. The following is a non-exhaustive list to get you started:

* ❌ Microsoft Office → ✅ LibreOffice *(Please keep in mind that the widely known "OpenOffice" has not received updates in many years, and is not actively developed anymore)*
* ❌ SPSS, Stata → ✅ R, Python *(highly recommended switch, as there is barely any real-world usage of SPSS and Stata)*
* ❌ ArcGIS → ✅ QGIS