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In this work we present the FAIRsFAIR service assessment framework, a framework for assessing how well research data infrastructure services support FAIR data. The framework focuses on providing guidelines on how services can be made to optimally improve the FAIRness of the data that they are used for. This work was inspired by a combination of literature describing the expectations users have from FAIR data services, and refined by the authors based on feedback from the community gained e.g. through workshops. This framework is the last deliverable of the T2.4 task group in the FAIRsFAIR project, and it will be presented to the European Open Science Cloud (EOSC) where we expect the most direct usage of the framework.

FAIRsFAIR invited providers of data services – and all other interested stakeholders - across the full range of scientific disciplines to participate in a workshop to validate and further develop the FAIR assessment framework for data services. The proposed framework is described in the recently published FAIRsFAIRReport on Basic Framework on FAIRness of Servicesand was introduced to the research community at adedicated webinar. This is the second workshop, after a first workshop in February. The purpose of the workshop, which will be hosted by the authors of the report, is to discuss the six key aspects of the framework with potential users to identify gaps and formulate improvements. Feedback received will be incorporated into a further iteration of the report to be published later in 2021. Why a FAIR Assessment Framework for Data Services? The FAIR assessment framework for data services aims to assist service providers to ensure their data services are ‘FAIR enabling’ and optimally support the FAIRification process. This is important because realizing the full potential of a FAIR ecosystem - in which research outputs can be easily shared and optimally reused - requires looking beyond the FAIRification of data and other digital objects to also consider the infrastructure and services that act on those objects. During the workshop the 3rd iteration of the Assessment Framework was reviewed, it is available here: https://doi.org/10.5281/zenodo.4771937. The recording of the workshop is available onYouTube.

This is an updated version of the initial basic framework on FAIRness of services as presented in detail in: Koers, Hylke, Herterich, Patricia, Hooft, Rob, Gruenpeter, Morane, & Aalto, Tero. (2020). M2.10 Report on basic framework on FAIRness of services (Version 1.0). Zenodo. https://doi.org/10.5281/zenodo.4292598 The initial milestone was based on literature review, interviews and feedback collected at an EOSC-hub week session. A second version (March 2021) incorporated feedback from the report launch webinar (Jan 2021) and workshop (Feb 2021) gathering more detailed feedback on the assessment framework. This current version (iteration 3, May 2021) introduces an update to the overall aspects as Trustworthiness has been split into two aspects for clarification: Transparency and Longevity. Additional changes cover rewording of recommendations as well as deletions of duplicate recommendations. We also clarified FAIR-enablement by highlighting that it covers both augmenting FAIRness of a digital object and facilitating a certain FAIR principle.

This document identifies and evaluates a set of business models for procuring services in EOSC and maps them to the EOSC ecosystem. It identifies open issues requiring more analysis, along with related opportunities and risks. A roadmap for activities beyond the life of the project is presented, including recommendations for key stakeholders.

This presentation is part of the 3rd edition of the Helis Academy FAIR data stewardship (for life sciences) course Day 2, March 18, 2021 https://www.aanmelder.nl/fair-data-stewardship-2021 https://helisacademy.com/en/data-analysis-stewardship Topic: infrastructure for storing and sharing data

A common pediatric disorder with posture and motor dysfunction in neurological diseases is known as cerebral palsy (CP). Recently, a series of effective techniques have been developed for treatment of CP. These promising methods need high-tech equipment for brain stimulation and mainly classified into invasive and no-invasive approaches. This study aimed to introduce these techniques for treatment of patients who suffer from CP. The potential and performance of currently available brain stimulation techniques have been mentioned in detail. Moreover, the clinical application, safety, efficacy and challenges of these methods have been discussed. Here we review the recent advances in the CP treatment with an emphasis on brain stimulation techniques.

Today, stem cell transplantation is a hot topic in scientific circles as a novel therapeutic approach to repair the structure and function of central nervous system. The safe and neuroprotective effects of cell therapy in models and traumatic brain injury patients were evaluated in many experimental and clinical studies in recent decade and somewhat promising results were provided to the scientific community. Nevertheless, there are still obstacles in translating experimental studies in the laboratory into clinical practice that should not be overlooked. In this review study, a brief explanation is provided about biological events and endogenic neurogenesis and angiogenesis after TBI the performance of transplanted cells in restoration of damaged neurons the role and potential use of mesenchymal stem cells as adult stem cells preferred in cell transplantation and clinical trials ever conducted in this area features of cell transplant candidates who will most benefit from transplantation, the type of and proper time for cell transplantation, optimal method for conducting transplant to deliver cells to the brain, and the best dose for effectiveness of transplantation. Finally, the various neuroimaging techniques are discussed, which are used to track and evaluate the survival and implantation of transplanted cells.

In computer-aided drug design, software tools are used to narrow down possible drug candidates, thereby reducing the amount of expensive in vitro research, by a process called virtual screening. This process includes large computations that require advanced computing infrastructure; however, using rapidly evolving high-performance computing platforms can be difficult for biochemists. In this paper, we present a science gateway for virtual screening that has been tailored to the specific needs of our local users. The gateway provides user-friendly access to distributed computing infrastructures for high-throughput experiments with a few clicks. Its design is based on the generic layer developed for another gateway for neuroimaging data analysis, including data and computation management, as well as support for its operation. To facilitate scalability, the system architecture allows for adding new computing platforms to the back-end without affecting the front-end, from which the user can dynamically choose the preferred infrastructure. This paper describes the user-centered design process, the system architecture and a performance assessment using gLite grid, Hadoop, and a local cluster. The empirical results show considerable speed-ups and ease of use, as well as user satisfaction. Copyright (c) 2015John Wiley & Sons, Ltd

Scientific workflow management systems provide a useful layer for defining and executing applications supported by science gateways. In various optimization or simulation applications that need to run for a long time, the users may be satisfied with an incomplete execution. The system should, therefore, allow users to evaluate partial results of the workflow execution. This entails performing a consolidation step, that would normally run only at the end of the workflow. In this paper, we present two new workflow patterns that formally define how the consolidation step should be executed (on partial inputs) whenever the user pro-actively requests evaluation of the partial results. This changes the traditional workflow behavior, in which every step runs once, when all its data dependencies are satisfied. We evaluate implementing these patterns in various workflow management systems and finally present a DIRAC-based implementation of this feature for the use case of a molecular docking gateway.

Low-voltage radial electricity cables will have more and more difficulties to carry the increasing load of novel consumption devices (e.g. electric vehicles) and the expected generated input of decentrally-generated power (e.g. from photovoltaic cells). One solution to avoid replacement is to install a battery at the end of a cable which is expected to be overloaded frequently. The intelligent operation of this battery needs to combine the protection of the cable with optimizing its revenue, in order to be economically viable. This paper formulates the offline optimization problem and proposes two robust heuristic online strategies. We show in computer simulations that these heuristics, which make fast just-in-time responses, reliably deliver good results. Our second heuristic, H2, reaches up to 83% of the approximated theoretical optimum.

Dynamic Pricing (DyP) is a form of Revenue Management in which the price of a (usually) perishable good is changed over time to increase revenue. It is an effective method that has become even more relevant and useful with the emergence of Internet firms and the possibility of readily and frequently updating prices. In this paper a new approach to DyP is presented. We design adaptive dynamic pricing strategies and optimize their parameters with an Evolutionary Algorithm (EA) offline while the strategies can deal with stochastic market dynamics quickly online. We design two adaptive heuristic dynamic pricing strategies in a duopoly where each firm has a finite inventory of a single type of good. We consider two cases, one in which the average of a customer population’s stochastic valuation for each of the goods is constant throughout the selling horizon and one in which the average customer valuation for each good is changed according to a random Brownian motion. We also design an agent-based software framework for simulating various dynamic pricing strategies in agent-based marketplaces with multiple firms in a bounded time horizon. We use an EA to optimize the parameters for each of the pricing strategies in each of the settings and compare the strategies with other strategies from the literature. We also perform sensitivity a analysis and show that the optimized strategies work well even when used in settings with varied demand functions.

We study different aspects of the multiagent resource allocation problem when the objective is to find an allocation that maximizes Nash social welfare, the product of the utilities of the individual agents. The Nash solution is an important welfare criterion that combines efficiency and fairness considerations. We show that the problem of finding an optimal outcome is NP-hard for a number of different languages for representing agent preferences; we establish new results regarding convergence to Nash-optimal outcomes in a distributed negotiation framework; and we design and test algorithms similar to those applied in combinatorial auctions for computing such an outcome directly.