Report of the project 'FAIR: geen woorden maar data' about the FAIRification of research data (in Dutch). It describes the proof of concept for implementation of the FAIR principles. The implementation is based on the resource description framework (RDF) and semantic knowledge representations using ontologies.
The ultimate goal of FAIR is to optimize the Reuse of data. To achieve this, metadata and data should be well-described and documented so that they can be replicated, understood and/or combined in different settings. Think of variable labels, codebooks, protocols and instruments used, attaching a license, etc. This checklist details what to include in a data package besides the data itself. The data package can be deposited in data repository such as UvA/HvA figshare.
This method paper presents a template solution for text mining of scientific literature using the R tm package. Literature to be analyzed can be collected manually or automatically using the code provided with this paper. Once the literature is collected, the three steps for conducting text mining can be performed as outlined below:• loading and cleaning of text from articles,• processing, statistical analysis, and clustering, and• presentation of results using generalized and tailor-made visualizations.The text mining steps can be applied to a single, multiple, or time series groups of documents.References are provided to three published peer reviewed articles that use the presented text mining methodology. The main advantages of our method are: (1) Its suitability for both research and educational purposes, (2) Compliance with the Findable Accessible Interoperable and Reproducible (FAIR) principles, and (3) code and example data are made available on GitHub under the open-source Apache V2 license.
Collaborative networks for sustainability are emerging rapidly to address urgent societal challenges. By bringing together organizations with different knowledge bases, resources and capabilities, collaborative networks enhance information exchange, knowledge sharing and learning opportunities to address these complex problems that cannot be solved by organizations individually. Nowhere is this more apparent than in the apparel sector, where examples of collaborative networks for sustainability are plenty, for example Sustainable Apparel Coalition, Zero Discharge Hazardous Chemicals, and the Fair Wear Foundation. Companies like C&A and H&M but also smaller players join these networks to take their social responsibility. Collaborative networks are unlike traditional forms of organizations; they are loosely structured collectives of different, often competing organizations, with dynamic membership and usually lack legal status. However, they do not emerge or organize on their own; they need network orchestrators who manage the network in terms of activities and participants. But network orchestrators face many challenges. They have to balance the interests of diverse companies and deal with tensions that often arise between them, like sharing their innovative knowledge. Orchestrators also have to “sell” the value of the network to potential new participants, who make decisions about which networks to join based on the benefits they expect to get from participating. Network orchestrators often do not know the best way to maintain engagement, commitment and enthusiasm or how to ensure knowledge and resource sharing, especially when competitors are involved. Furthermore, collaborative networks receive funding from grants or subsidies, creating financial uncertainty about its continuity. Raising financing from the private sector is difficult and network orchestrators compete more and more for resources. When networks dissolve or dysfunction (due to a lack of value creation and capture for participants, a lack of financing or a non-functioning business model), the collective value that has been created and accrued over time may be lost. This is problematic given that industrial transformations towards sustainability take many years and durable organizational forms are required to ensure ongoing support for this change. Network orchestration is a new profession. There are no guidelines, handbooks or good practices for how to perform this role, nor is there professional education or a professional association that represents network orchestrators. This is urgently needed as network orchestrators struggle with their role in governing networks so that they create and capture value for participants and ultimately ensure better network performance and survival. This project aims to foster the professionalization of the network orchestrator role by: (a) generating knowledge, developing and testing collaborative network governance models, facilitation tools and collaborative business modeling tools to enable network orchestrators to improve the performance of collaborative networks in terms of collective value creation (network level) and private value capture (network participant level) (b) organizing platform activities for network orchestrators to exchange ideas, best practices and learn from each other, thereby facilitating the formation of a professional identity, standards and community of network orchestrators.
Today, embedded devices such as banking/transportation cards, car keys, and mobile phones use cryptographic techniques to protect personal information and communication. Such devices are increasingly becoming the targets of attacks trying to capture the underlying secret information, e.g., cryptographic keys. Attacks not targeting the cryptographic algorithm but its implementation are especially devastating and the best-known examples are so-called side-channel and fault injection attacks. Such attacks, often jointly coined as physical (implementation) attacks, are difficult to preclude and if the key (or other data) is recovered the device is useless. To mitigate such attacks, security evaluators use the same techniques as attackers and look for possible weaknesses in order to “fix” them before deployment. Unfortunately, the attackers’ resourcefulness on the one hand and usually a short amount of time the security evaluators have (and human errors factor) on the other hand, makes this not a fair race. Consequently, researchers are looking into possible ways of making security evaluations more reliable and faster. To that end, machine learning techniques showed to be a viable candidate although the challenge is far from solved. Our project aims at the development of automatic frameworks able to assess various potential side-channel and fault injection threats coming from diverse sources. Such systems will enable security evaluators, and above all companies producing chips for security applications, an option to find the potential weaknesses early and to assess the trade-off between making the product more secure versus making the product more implementation-friendly. To this end, we plan to use machine learning techniques coupled with novel techniques not explored before for side-channel and fault analysis. In addition, we will design new techniques specially tailored to improve the performance of this evaluation process. Our research fills the gap between what is known in academia on physical attacks and what is needed in the industry to prevent such attacks. In the end, once our frameworks become operational, they could be also a useful tool for mitigating other types of threats like ransomware or rootkits.
Door COVID-19 crisis zijn er extra uitdagingen om de verdere doorontwikkeling van het praktijkgerichte onderzoek en de onderliggende infrastructuur en professionalisering kwalitatief en kwantitatief te realiseren. De Hogeschool van Arnhem en Nijmegen (HAN) zet de IMPULS 2020 middelen in om de rol van het praktijkgericht onderzoek hierin te bestendigen en versterken. Het betreft een academie overstijgende aanvraag. Het beschikbare budget vanuit de regeling bedraagt 550.000 euro en zal in 2021 via twee lijnen worden ingezet: 1. Netwerk- en visievorming Dit richt zich op de versterking van de strategische netwerkvorming en samenhang overstijgend aan de zwaartepunten als focus gebieden voor de samenwerking onderwijs, onderzoek en werkveld (deels is hier aandacht voor de ontwikkeling en samenwerking bij regelingen als SPRONG of MMIP). Dit moet leiden tot het ontwikkelen van een meerjarige roadmap SLIM, SCHOON & SOCIAAL (S3). De regie ligt bij dit deel bij het zwaartepunt management. (Sustainable Energy & Environment (SEE), Smart Region en Health). 2. Professionalisering onderzoeksondersteuning Dit gedeelte betreft het vervolg op het project professionalisering onderzoeksondersteuning en richt zich (in lijn met het nationale project DCC) op de doorontwikkeling van: datastewardship, FAIR data & open access, ICT kennisinfrastructuur en communicatie rondom onderzoek en ondersteuning, verdere ontwikkeling van een Open Science Platform en voorbereiding op een HAN Open Access Fonds. Dit deel zal vanuit Services Onderwijs, Onderzoek en Kwaliteitszorg gecoördineerd worden. Middels deze inzet geeft de HAN een extra stimulans aan de strategische samenwerking en de verdere ontwikkeling van een consistente en herkenbare onderzoeksprogrammering en -ondersteuning.
