Enabling Git based research data quality control for institutional repositories

Ayer V, Pietsch C, Vompras J, et al. Enabling Git based research data quality control for institutional repositories. Presented at the Research Data Alliance (RDA), Repository Platforms for Research Data IG, 9th Plenary meeting, Barcelona.Reproducibility of scientific research data has been error-prone for contextually sensitive studies like Psychology with pharmaceutical clinical trials having lower success rates. However, “analytical reproducibility” of a statistical analysis is mathematically achievable when the research data artifacts are public. The DFG-funded Conquaire project at Bielefeld University is developing a generic architecture based on GitLab to support computational reproducibility of research data. One aspect of the project is to examine and develop solutions to track released versions of research data and corresponding source code in institutional repositories for proper persistent identification, dissemination and visibility of computational research artefacts. In this respect, the presentation will focus on usage scenarios with Conquaire pilot partners which inform the functional requirements on deposition, ingestion and publication workflows of the institutional repository platform LibreCat

Expanding the Research Data Management Service Portfolio at Bielefeld University According to the Three-pillar Principle Towards Data FAIRness

Schirrwagen J, Cimiano P, Ayer V, et al. Expanding the Research Data Management Service Portfolio at Bielefeld University According to the Three-pillar Principle Towards Data FAIRness. Data Science Journal. 2019;18(1): 6.Research Data Management at Bielefeld University is considered as a cross-cutting task among central facilities and research groups at the faculties. While initially started as project “Bielefeld Data Informium” lasting over seven years (2010–2015), it is now being expanded by setting up a Competence Center for Research Data. The evolution of the institutional RDM is based on the three-pillar principle: 1. Policies, 2. Technical infrastructure and 3. Support structures. The problem of data quality and the issues with reproducibility of research data is addressed in the project Conquaire. It is creating an infrastructure for the processing and versioning of research data which will finally allow publishing of research data in the institutional repository. Conquaire extends the existing RDM infrastructure in three ways: with a Collaborative Platform, Data Quality Checking, and Reproducible Research

Expanding the Research Data Management Service Portfolio at Bielefeld University According to the Three-pillar Principle Towards Data FAIRness

Research Data Management at Bielefeld University is considered as a cross-cutting task among central facilities and research groups at the faculties. While initially started as project “Bielefeld Data Informium” lasting over seven years (2010–2015), it is now being expanded by setting up a Competence Center for Research Data. The evolution of the institutional RDM is based on the three-pillar principle: 1. Policies, 2. Technical infrastructure and 3. Support structures. The problem of data quality and the issues with reproducibility of research data is addressed in the project Conquaire. It is creating an infrastructure for the processing and versioning of research data which will finally allow publishing of research data in the institutional repository. Conquaire extends the existing RDM infrastructure in three ways: with a Collaborative Platform, Data Quality Checking, and Reproducible Research

Expanding the research data management service portfolio at Bielefeld University according to the three-pillar principle towards data FAIRness

Schirrwagen J, Cimiano P, Ayer V, et al. Expanding the research data management service portfolio at Bielefeld University according to the three-pillar principle towards data FAIRness. Presented at the Göttingen-CODATA RDM Symposium 2018, Göttingen

Continuous quality control for research data to ensure reproducibility

Ayer V, Pietsch C, Vompras J, et al. Conquaire: Towards an architecture supporting continuous quality control to ensure reproducibility of research. D-Lib Magazine. 2017;23(1/2).ABSTRACT Recently, analytical reproducibility in scientific research has been a keenly discussed topic within scientific research organizations and acknowledged as an important and fundamental goal to strive for. Scientific studies published recently have found that irreproducibility is widely prevalent within the research community even after releasing data openly. At Bielefeld University, nine research project groups from varied disciplines have embarked on a `reproducibility` journey by collaborating on the Conquaire project as case study partners. This paper introduces the Conquaire project. In particular, we describe the goals and objectives of the project as well as the underlying system architecture that relies on a DCVS system for storing data and on continuous integration principles to foster data quality. We describe a first prototype implementation of the system and discuss a running example to illustrate the functionality and behaviour of the system

Automatische Qualitätskontrolle von Forschungsdaten durch kontinuierliche Integration mit GitLab CI

Ayer V, Herrmann F, Peil V, et al. Automatische Qualitätskontrolle von Forschungsdaten durch kontinuierliche Integration mit GitLab CI. Presented at the Erste Konferenz für ForschungssoftwareentwicklerInnen in Deutschland (deRSE19), Potsdam.Im DFG-Projekt Conquaire erprobt die Universität Bielefeld, wie die analytische Reproduzierbarkeit von Forschungsergebnissen durch den Einsatz von Versionierung und automatischen Qualitätsrückmeldungen verbessert werden kann. Dafür betreiben wir eine GitLab-Instanz und bieten Forschenden an, ihre darin versionierten Daten durch automatische Tests in GitLab CI (Continuous Integration) bei jeder Änderung überprüfen zu lassen. Dabei wird auch – soweit automatisch möglich – auf die Einhaltung der FAIR-Prinzipien geachtet. Das Ergebnis sehen die teilnehmenden Forschenden nicht nur in GitLab, sondern können es auch im institutionellen Publikationsrepositorium PUB (eine Eigenentwicklung auf der Basis von LibreCat) neben einer DOI anzeigen lassen

Conquaire: Coupling a local GitLab instance with an institutional repository for instant research data publications

Pietsch C, Schirrwagen J, Peil V, et al. Conquaire: Coupling a local GitLab instance with an institutional repository for instant research data publications. Presented at the Open Science Conference, Berlin.Collecting and refining research data or writing software is a part of many researchers' daily routine. As a university library, we have been encouraging researchers to add their research output to our institutional repository called PUB which is based on the free and open-source LibreCat software. Within an international collaboration, we have been able to design and program this institutional repository according to our own specifications fairly flexibly. This allowed us to extend its scope to data publications recently. However, it remains a system outside the day-to-day work flow of researchers who, as we all know, dislike doing what appears to be administrative burdens. To remedy this situation, we set up a local GitLab instance and loosely coupled it with our institutional repository. We rolled out this instance (called GitLab.UB) university-wide and added it to the campus single sign-on system that is also used by our institutional repository. As GitLab is built around a versioning software, it becomes easy to mint a DOI that refers to a certain snapshot of a GitLab project (even retrospectively). This approach is similar to the service jointly offered by GitHub and Zenodo. In addition to an effortless way to publish snapshots of research data or software, integrating GitLab allows our institutional repository to display the results of automatic checks on the data supplied by researchers. Inspired by the badges that indicate a successful continuous integration run on sites like GitHub, we show badges that indicate good research data quality including wellformedness for certain data formats and adherence to FAIR data principles as far as these can be checked automatically. These checks are implemented within the GitLab CI framework that comes with GitLab. They will be made available for re-use free of charge at the end of this project. Although news of our local GitLab instance so far has spread largely by word of mouth, there has been a steady influx of users from various faculties and research institutes as well as their external partners. GitLab has also become an integral part of some undergraduate courses. GitLab's additional collaboration tools such as issue trackers and wikis have proven to facilitate joint work across groups and institutions, and have additionally increased the quality of results

Why scientists should learn to program in Python?

Ayer V, Miguez S, Toby B. Why scientists should learn to program in Python? Powder Diffraction Journal. 2014;29(S2):S48-S64