Abstract

Materials Cloud is a platform designed to enable open and seamless sharing of resources for computational science, driven by applications in materials modelling. It hosts 1) archival and dissemination services for raw and curated data, together with their provenance graph, 2) modelling services and virtual machines, 3) tools for data analytics, and pre-/post-processing, and 4) educational materials. Data is citable and archived persistently, providing a comprehensive embodiment of the FAIR principles that extends to computational workflows. Materials Cloud leverages the AiiDA framework to record the provenance of entire simulation pipelines (calculations performed, codes used, data generated) in the form of graphs that allow to retrace and reproduce any computed result. When an AiiDA database is shared on Materials Cloud, peers can browse the interconnected record of simulations, download individual files or the full database, and start their research from the results of the original authors. The infrastructure is agnostic to the specific simulation codes used and can support diverse applications in computational science that transcend its initial materials domain. Comment: 19 pages, 8 figure Document type: Article

Abstract

Initiatives in the smart mobility domain are increasingly characterized by a service-dominant mindset, focusing explicitly on the value that is created for the customer. As a result, the business models of these initiatives become more agile in nature, forcing them to be continuously and rapidly redesigned. However, given the wide landscape of potential stakeholders in the smart mobility domain, it may prove difficult to identify the stakeholders that should be considered for viable business model design. In response, this paper proposes a reference business model for the smart mobility domain which elaborates on a generic set of stakeholder categories and their characteristics to guide the design of these models. Following a design science research approach, we iteratively derived and evaluated the validity of the reference model using a set of business model design workshops. We illustrate the applicability of the reference model by means of a business model design case.

Abstract

BSTRACT"/jats:title""jats:p"The field of Pharmacogenomics presents great challenges for researchers that are willing to make their studies reproducible and shareable. This is attributed to the generation of large volumes of high-throughput multimodal data, and the lack of standardized workflows that are robust, scalable, and flexible to perform large-scale analyses. To address this issue, we developed pharmacogenomic workflows in the Common Workflow Language to process two breast cancer datasets in a reproducible and transparent manner. Our pipelines combine both pharmacological and molecular profiles into a portable data object that can be used for future analyses in cancer research. Our data objects and workflows are shared on Harvard Dataverse and Code Ocean where they have been assigned a unique Digital Object Identifier, providing a level of data provenance and a persistent location to access and share our data with the community. Document type: Preprint

Abstract

The simultaneous implementation of daily activity-travel schedules of individuals in a given spatial environment generally gives rise to time- and location-varying congestion levels, which affect the conditions for subsequent activity and travel choices. Although such dynamics are commonly recognized, current activity-based models typically ignore the adaptive behaviour of individuals. In this article, we propose an agent-based simulation system that allows one to simulate, in addition to activity-scheduling behaviour, also the execution of schedules in space and time. Congestion levels at specific times and places emerge in the system and may lead to discrepancies between scheduled and actual activity and travel times. Agents respond to such unforeseen events by reconsidering an existing schedule (within-day re-planning) and by adapting their expectations about traffic conditions for subsequent days (learning). The system is illustrated using the activity-travel diary data collected in the Eindhoven region, the Netherlands, to better understand the choice of urban parks in the study area. We discuss the merits of the system for transport and spatial planning and identify avenues for future research.

Abstract

We describe the Open Diffusion Data Derivatives (O3D) repository: an integrated collection of preserved brain data derivatives and processing pipelines, published together using a single digital-object-identifier. The data derivatives were generated using modern diffusion-weighted magnetic resonance imaging data (dMRI) with diverse properties of resolution and signal-to-noise ratio. In addition to the data, we publish all processing pipelines (also referred to as open cloud services). The pipelines utilize modern methods for neuroimaging data processing (diffusion-signal modelling, fiber tracking, tractography evaluation, white matter segmentation, and structural connectome construction). The O3D open services can allow cognitive and clinical neuroscientists to run the connectome mapping algorithms on new, user-uploaded, data. Open source code implementing all O3D services is also provided to allow computational and computer scientists to reuse and extend the processing methods. Publishing both data-derivatives and integrated processing pipeline promotes practices for scientific reproducibility and data upcycling by providing open access to the research assets for utilization by multiple scientific communities. Design Type(s)data integration objective • database creation objectiveMeasurement Type(s)brainTechnology Type(s)magnetic resonance imagingFactor Type(s)Voxel SizeSample Characteristic(s)Homo sapiens • brain Machine-accessible metadata file describing the reported data (ISA-Tab format) Document type: Preprint