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Researchers are currently encouraged by their institutions and the funding agencies to deposit data resulting from projects. Activities related to research data management, namely organization, description, and deposit, are not obvious for researchers due to the lack of knowledge on metadata and the limited data publication experience. Institutions are looking for solutions to help researchers organize their data and make them ready for publication. We consider here the deposit process for a CKAN-powered data repository managed as part of the IT services of a large research institute. A simplified data deposit process is illustrated here by means of a set of examples where researchers describe their data and complete the publication in the repository. The process is organised around a Dublin Core-based dataset deposit form, filled by the researchers as preparation for data deposit. The contacts with researchers provided the opportunity to gather feedback about the Dublin Core metadata and the overall experience. Reflections on the ongoing process highlight a few difficulties in data description, but also show that researchers are motivated to get involved in data publication activities.

Abstract
Advances in genomics have driven to the recognition that tumours are populated by different minor subclones of malignant cells that control the way the tumour progresses. However, the spatial and temporal genomic heterogeneity of tumours has been a hurdle in clinical oncology. This is mainly because the standard methodology for genomic analysis is the biopsy, that besides being an invasive technique, it does not capture the entire tumour spatial state in a single exam. Radiographic medical imaging opens new opportunities for genomic analysis by providing full state visualisation of a tumour at a macroscopic level, in a non-invasive way. Having in mind that mutational testing of EGFR and KRAS is a routine in lung cancer treatment, it was studied whether clinical and imaging data are valuable for predicting EGFR and KRAS mutations in a cohort of NSCLC patients. A reliable predictive model was found for EGFR (AUC = 0.96) using both a Multi-layer Perceptron model and a Random Forest model but not for KRAS (AUC = 0.56). A feature importance analysis using Random Forest reported that the presence of emphysema and lung parenchymal features have the highest correlation with EGFR mutation status. This study opens new opportunities for radiogenomics on predicting molecular properties in a more readily available and non-invasive way. © 2019, Springer Nature Switzerland AG.

Abstract
Some might argue that ever so nimble and responsive innovation paradigms can rarely be managed scientifically. We propose a more inclusive perspective. Science of managing for innovation has certain characteristics which we identify through the acronym “ROTRUS”- Real-world, Observable, Testable, Replicable, Uncertain and Social. Real-world refers to the notion that innovation happens in practical settings, be bound by resources and capabilities. This real-world is the context in which the observable events occur. To progress the understanding of formative predictors and their impact on innovation, the innovation events need to be observable. This may be challenging if we are to believe that much of the innovation is driven by heuristics (see e.g. Lopez-Vega, Tell and Vanhaverbeke, 2016; Nisch and Veer, 2018). Observable evidence in our perspective does not mean it needs to be capable of being observed but includes events or phenomenon that were observed. In this sense, managerial heuristics once actioned become observed evidence, such that observable evidence is any evidence that can be or has been experienced by one or many, regardless of whether this can be observed by a third party. (...)

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