Improving reproducibility and reuse of modelling results in the life sciences

Research results are complex and include a variety of heterogeneous data. This entails major computational challenges to (i) to manage simulation studies, (ii) to ensure model exchangeability, stability and validity, and (iii) to foster communication between partners. I describe techniques to improve the reproducibility and reuse of modelling results. First, I introduce a method to characterise differences in computational models. Second, I present approaches to obtain shareable and reproducible research results. Altogether, my methods and tools foster exchange and reuse of modelling results.Die verteilte Entwicklung von komplexen Simulationsstudien birgt eine große Zahl an informationstechnischen Herausforderungen: (i) Modelle müssen verwaltet werden; (ii) Reproduzierbarkeit, Stabilität und Gültigkeit von Ergebnissen muss sichergestellt werden; und (iii) die Kommunikation zwischen Partnern muss verbessert werden. Ich stelle Techniken vor, um die Reproduzierbarkeit und Wiederverwendbarkeit von Modellierungsergebnissen zu verbessern. Meine Implementierungen wurden erfolgreich in internationalen Anwendungen integriert und fördern das Teilen von wissenschaftlichen Ergebnissen

Linked Data for the Natural Sciences. Two Use Cases in Chemistry and Biology

Wiljes C, Cimiano P. Linked Data for the Natural Sciences. Two Use Cases in Chemistry and Biology. In: Proceedings of the Workshop on the Semantic Publishing (SePublica 2012). 2012: 48-59.The Web was designed to improve the way people work together. The Semantic Web extends the Web with a layer of Linked Data that offers new paths for scientific publishing and co-operation. Experimental raw data, released as Linked Data, could be discovered automatically, fostering its reuse and validation by scientists in different contexts and across the boundaries of disciplines. However, the technological barrier for scientists who want to publish and share their research data as Linked Data remains rather high. We present two real-life use cases in the fields of chemistry and biology and outline a general methodology for transforming research data into Linked Data. A key element of our methodology is the role of a scientific data curator, who is proficient in Linked Data technologies and works in close co-operation with the scientist

The need for standardisation in life science research - an approach to excellence and trust

Today, academic researchers benefit from the changes driven by digital technologies and the enormous growth of knowledge and data, on globalisation, enlargement of the scientific community, and the linkage between different scientific communities and the society. To fully benefit from this development, however, information needs to be shared openly and transparently. Digitalisation plays a major role here because it permeates all areas of business, science and society and is one of the key drivers for innovation and international cooperation. To address the resulting opportunities, the EU promotes the development and use of collaborative ways to produce and share knowledge and data as early as possible in the research process, but also to appropriately secure results with the European strategy for Open Science (OS). It is now widely recognised that making research results more accessible to all societal actors contributes to more effective and efficient science; it also serves as a boost for innovation in the public and private sectors. However for research data to be findable, accessible, interoperable and reusable the use of standards is essential. At the metadata level, considerable efforts in standardisation have already been made (e.g. Data Management Plan and FAIR Principle etc.), whereas in context with the raw data these fundamental efforts are still fragmented and in some cases completely missing. The CHARME consortium, funded by the European Cooperation in Science and Technology (COST) Agency, has identified needs and gaps in the field of standardisation in the life sciences and also discussed potential hurdles for implementation of standards in current practice. Here, the authors suggest four measures in response to current challenges to ensure a high quality of life science research data and their re-usability for research and innovation

Skills and Knowledge for Data-Intensive Environmental Research.

The scale and magnitude of complex and pressing environmental issues lend urgency to the need for integrative and reproducible analysis and synthesis, facilitated by data-intensive research approaches. However, the recent pace of technological change has been such that appropriate skills to accomplish data-intensive research are lacking among environmental scientists, who more than ever need greater access to training and mentorship in computational skills. Here, we provide a roadmap for raising data competencies of current and next-generation environmental researchers by describing the concepts and skills needed for effectively engaging with the heterogeneous, distributed, and rapidly growing volumes of available data. We articulate five key skills: (1) data management and processing, (2) analysis, (3) software skills for science, (4) visualization, and (5) communication methods for collaboration and dissemination. We provide an overview of the current suite of training initiatives available to environmental scientists and models for closing the skill-transfer gap

Improving energy research practices: guidance for transparency, reproducibility and quality

Energy use is of crucial importance for the global challenge of climate change, and also is an essential part of daily life. Hence, research on energy needs to be robust and valid. Other scientific disciplines have experienced a reproducibility crisis, i.e. existing findings could not be reproduced in new studies. The ‘TReQ’ approach is recommended to improve research practices in the energy field and arrive at greater transparency, reproducibility and quality. A highly adaptable suite of tools is presented that can be applied to energy research approaches across this multidisciplinary and fast-changing field. In particular, the following tools are introduced – preregistration of studies, making data and code publicly available, using preprints, and employing reporting guidelines – to heighten the standard of research practices within the energy field. The wider adoption of these tools can facilitate greater trust in the findings of research used to inform evidence-based policy and practice in the energy field