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Open access

Yi Shen

Abstract

Currently, we are witnessing the emergence and abundance of many different data repositories and archival systems for scientific data discovery, use, and analysis. With the burgeoning of available data-sharing platforms, this study addresses how scientists working in the fields of natural resources and environmental sciences navigate these diverse data sources, what their concerns and value propositions are toward multiple data discovery channels, and most importantly, how they perceive the characteristics and compare the functionalities of different types of data repository systems. Through a user community research of domain scientists on their data use dynamics and insights, this research provides strategies and discusses ideas on how to leverage these different platforms. Furthermore, it proposes a top–down, novel approach to the processes of searching, browsing, and visualizing for the dynamic exploration of environmental data.

Open access

David Froelicher, Patricia Egger, João Sá Sousa, Jean Louis Raisaro, Zhicong Huang, Christian Mouchet, Bryan Ford and Jean-Pierre Hubaux

Abstract

Current solutions for privacy-preserving data sharing among multiple parties either depend on a centralized authority that must be trusted and provides only weakest-link security (e.g., the entity that manages private/secret cryptographic keys), or leverage on decentralized but impractical approaches (e.g., secure multi-party computation). When the data to be shared are of a sensitive nature and the number of data providers is high, these solutions are not appropriate. Therefore, we present UnLynx, a new decentralized system for efficient privacy-preserving data sharing. We consider m servers that constitute a collective authority whose goal is to verifiably compute on data sent from n data providers. UnLynx guarantees the confidentiality, unlinkability between data providers and their data, privacy of the end result and the correctness of computations by the servers. Furthermore, to support differentially private queries, UnLynx can collectively add noise under encryption. All of this is achieved through a combination of a set of new distributed and secure protocols that are based on homomorphic cryptography, verifiable shuffling and zero-knowledge proofs. UnLynx is highly parallelizable and modular by design as it enables multiple security/privacy vs. runtime tradeoffs. Our evaluation shows that UnLynx can execute a secure survey on 400,000 personal data records containing 5 encrypted attributes, distributed over 20 independent databases, for a total of 2,000,000 ciphertexts, in 24 minutes.

Open access

Václav Šimandl and Jakub Novotný

Abstract

The article looks at the ways lower secondary and primary school pupils and teachers make use of ICT services (particularly user accounts and network drives). The description of implemented approaches is complemented by a discussion of the factors that influence schools in their choice of a particular solution. Attention has been devoted to both the various benefits that a chosen solution has brought to teaching and the complications that have been encountered during lessons. Our research has covered a wide range of schools that use services provided by servers to a varying extent as well as schools that are not in possession of servers. In-depth, semi-structured interviews have been carried out with school network managers. Our investigation has been supported by triangulation, consisting of interviews with teachers selected from the given schools. Data gained from the interviews has been processed using open coding. The results show that despite user accounts being found to be beneficial to teaching and lesson management, not all schools have access to such a solution. As well as being able to use personal and shared network drives, this solution can make it easier for schools to monitor their pupils′ Internet activity. Schools have their own specific procedures to deal with pupils that forget their login details, which could lead to lessons being disrupted. Schools that do not make use of user accounts have developed methods to overcome such a problem. It does not seem to be a lack of suitable solutions that prevents the more effective use of ICT in teaching. The problem is more likely to lie in the fact that many teachers lack knowledge of the various possibilities offered by available solutions and are often unwilling to make use of such solutions.

Open access

Lasse Metso and Mirka Kans

Abstract

Big Data and Internet of Things will increase the amount of data on asset management exceedingly. Data sharing with an increased number of partners in the area of asset management is important when developing business opportunities and new ecosystems. An asset management ecosystem is a complex set of relationships between parties taking part in asset management actions. In this paper, the current barriers and benefits of data sharing are identified based on the results of an interview study. The main benefits are transparency, access to data and reuse of data. New services can be created by taking advantage of data sharing. The main barriers to sharing data are an unclear view of the data sharing process and difficulties to recognize the benefits of data sharing. For overcoming the barriers in data sharing, this paper applies the ecosystem perspective on asset management information. The approach is explained by using the Swedish railway industry as an example.

Open access

Fernando Alfonso, Karlen Adamyan, Jean-Yves Artigou, Michael Aschermann, Michael Boehm, Alfonso Buendia, Pao-Hsien Chu, Ariel Cohen, Livio Dei Cas, Mirza Dilic, Anton Doubell, Dario Echeverri, Nuray Enç, Ignacio Ferreira-González, Krzysztof J. Filipiak, Andreas Flammer, Eckart Fleck, Plamen Gatzov, Carmen Ginghina, Lino Goncalves, Habib Haouala, Mahmoud Hassanein, Gerd Heusch, Kurt Huber, Ivan Hulín, Mario Ivanusa, Rungroj Krittayaphong, Chu-Pak Lau, Germanas Marinskis, François Mach, Luiz Felipe Moreira, Tuomo Nieminen, Latifa Oukerraj, Stefan Perings, Luc Pierard, Tatjana Potpara, Walter Reyes-Caorsi, Se-Joong Rim, Olaf Rødevand, Georges Saade, Mikael Sander, Evgeny Shlyakhto, Bilgin Timuralp, Dimitris Tousoulis, Dilek Ural, J.J. Piek, Albert Varga and Thomas F. Lüscher

Summary

The International Committee of Medical Journal Editors (ICMJE) provides recommendations to improve the editorial standards and scientific quality of biomedical journals. These recommendations range from uniform technical requirements to more complex and elusive editorial issues including ethical aspects of the scientific process. Recently, registration of clinical trials, conflicts of interest disclosure, and new criteria for authorship, emphasizing the importance of responsibility and accountability, have been proposed. Last year, a new editorial initiative to foster sharing of clinical trial data was launched. This review discusses this novel initiative with the aim of increasing awareness among readers, investigators, authors and editors belonging to the Editors’ Network of the European Society of Cardiology.

Open access

Zhigang Ma and Wenyi Liu

Abstract

Several methods, such as polling, multithread, timing, and so on, can be used in data receiving course. Low-efficiency and high level of system resource consumption may bring about data loss in polling and multithread methods when the data transmission rate is very high. Software timing methods are discussed and analyzed in Visual C++. Timing method would improve the system resource availability and decrease the risk of data loss. According to the demand ofatesting system,areal-time monitoring system based on memory sharing and multimedia timer is presented. After testing, the average timing error of the multimedia timer in the given instance is not bigger than 0.05%, so the continuity and integrity of data receiving can be assured under the conditions of high-speed data transmission.

Open access

Neil R. Smalheiser and Aaron M. Cohen

Abstract

Many investigators have carried out text mining of the biomedical literature for a variety of purposes, ranging from the assignment of indexing terms to the disambiguation of author names. A common approach is to define positive and negative training examples, extract features from article metadata, and use machine learning algorithms. At present, each research group tackles each problem from scratch, in isolation of other projects, which causes redundancy and a great waste of effort. Here, we propose and describe the design of a generic platform for biomedical text mining, which can serve as a shared resource for machine learning projects and as a public repository for their outputs. We initially focus on a specific goal, namely, classifying articles according to publication type and emphasize how feature sets can be made more powerful and robust through the use of multiple, heterogeneous similarity measures as input to machine learning models. We then discuss how the generic platform can be extended to include a wide variety of other machine learning-based goals and projects and can be used as a public platform for disseminating the results of natural language processing (NLP) tools to end-users as well.