Reproducible Research Reproducible Research


We try to make and promote reproducible research, with the objective that publications are available including software and data online. 

Other actions to promote it:

  • 14 September 2017. Data management and sharing in Large Research Infrastructures: how synchrotrons handle the big data challenge. Anne Bonnin, Paul Sherrer Insitute.
  • 16 March 2017. PhD seminars. Software development best practices for research reproduceability. Alastair Porter, MTG.

Abstract: In software development it is considered a best practice to test code, include documentation, use source code management tools, and make frequent backups. A lot of the time technical research tends to eschew these best practices, resulting in missing data, hard to reproduce results, and wasted time. For researchers who haven't worked in or studied software engineering roles, it can often be confusing to know where to start, or how these best practices improve code quality and save time. In this talk I will show some examples why software engineering best practices are a valuable part of technical research and how to start applying them if you do not know what tools and resources are available.


  • How many times have you got frustrated, because your code from last month refuses to run and your past self didn’t bother to leave you any guidelines?
  • Are you scared that one day a colleague will ask you the data/results of a paper, and you will have to excavate them out of a folder named ‘allDataResultsVersion8bFinalizedRecheckedPleaseEnd’?
  • Are you concerned that your contributions will not have much impact, because people may not be able to access them properly?

If yes, you are in the same shoes with me, when I started my PhD! In this talk, I will present how I embraced reproducibility to avoid some of these common mistakes. I will explain some best practices, simple tricks and habits I have learned throughout my doctoral research, which improved the quality of my research, made it reusable by others (and myself), and hence improved the accessibility and visibility of my work. I will give practical examples in where I failed (and then gradually improved) on organizing, developing, versioning, documenting, licensing and publishing the research material (data, code, experimental setups and results etc.). I will also introduce some of the available software tools and services, which would help you to achieve these goals. I hope that this talk will convince you to consider reproducibility as a major criteria in academic research, and give you a head start on achieving this objective.

Title: Imaging biomarkers: algorithms, open data and infrastructure for neurological disorders


Abstract: One of the major challenges in clinical neuroimaging is to detect quantitative signs of pathological evolution as early as possible in order to prevent disease progression, evaluate therapeutic protocols or even better understand and model the normal history of a given neurological pathology.

A particular challenge is to find correlations between brain structures at the morphometric, structural, metabolic or functional level through a large set of multimodal images. MRI is the premier means to study the human brain through various acquisition protocols. This presentation will illustrate this challenge through the use of novel cellular or structural MRI protocols able to provide relevant information at the cellular or micro-structural level.

Technological perspectives will be also provided about general issues of Medical Imaging as a Service in the context of the emerging open data services and digital infrastructures. After introducing the general context, some examples will be provided of how these new services can be implemented, and applied to neurological diseases, and especially Multiple Sclerosis.

Title: Scientific Dissemination, Online Repositories, and Author's Rights.

Title: Reproducibility in research.

Abstract: An objective of the María de Maeztu Strategic Research Program is “to increase the impact of our research by increasing the impact of the publications, datasets and software tools, and take advantage of this impact to establish and consolidate partnerships”. It includes actions to promote that the research results, datasets and tools are discoverable, interpretable and reusable, including the publication of the data and software together with the publications. During this session, we will discuss some of the topics linked to "reproducible research", including the increasing requirements in making datasets and computer code available by funding agencies, publishers and potential mechanisms to promote it in our organisation being elaborated in the context of the Maria de Maeztu program.

Ongoing draft document within MdM for good practices for discussion in this link

Video of the 2016 award ceremony

  • Data management: The UPF Library supports you in several aspects linked to Data Management, including the possibility to use the UPF repository to preserve your data. Check here for more details.