scholarly journals COEMS — open traces from the industry

10.29007/gp13 ◽  
2018 ◽  
Author(s):  
Svetlana Jakšić ◽  
Martin Leucker ◽  
Dan Li ◽  
Volker Stolz

The runtime verification community is still fragmented with non-interoperable specifications and tools. Within the EU H2020 project COEMS “Continuous Observation of Embedded Multicore Systems”, we are contributing to the European Open Research Data Pilot that makes scientific data available to other researchers. We describe our first contributions and experience with the required data management and discuss technical issues such as metadata management, format and storage on practical examples. Based on our experience, we make suggestions on tools and formats for future RV Competitions and for desired artefacts from the EU COST Action IC1402 "ARVI -- Runtime Verification Beyond Monitoring".

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Hendrikje Seifert ◽  
Marc Weber ◽  
Frank Oliver Glöckner ◽  
Ivaylo Kostadinov

Abstract The Nagoya Protocol on Access and Benefit Sharing is a transparent legal framework, which governs the access to genetic resources and the fair and equitable sharing of benefits arising from their utilization. Complying with the Nagoya regulations ensures legal use and re-use of data from genetic resources. Providing detailed provenance information and clear re-usage conditions plays a key role in ensuring the re-usability of research data according to the FAIR (findable, accessible, interoperable and re-usable) Guiding Principles for scientific data management and stewardship. Even with the framework provided by the ABS (access and benefit sharing) Clearing House and the support of the National Focal Points, establishing a direct link between the research data from genetic resources and the relevant Nagoya information remains a challenge. This is particularly true for re-using publicly available data. The Nagoya Lookup Service was developed for stakeholders in biological sciences with the aim at facilitating the legal and FAIR data management, specifically for data publication and re-use. The service provides up-to-date information on the Nagoya party status for a geolocation provided by GPS coordinates, directing the user to the relevant local authorities for further information. It integrates open data from the ABS Clearing House, Marine Regions, GeoNames and Wikidata. The service is accessible through a REST API and a user-friendly web form. Stakeholders include data librarians, data brokers, scientists and data archivists who may use this service before, during and after data acquisition or publication to check whether legal documents need to be prepared, considered or verified. The service allows researchers to estimate whether genetic data they plan to produce or re-use might fall under Nagoya regulations or not, within the limits of the technology and without constituting legal advice. It is implemented using portable Docker containers and can easily be deployed locally or on a cloud infrastructure. The source code for building the service is available under an open-source license on GitHub, with a functional image on Docker Hub and can be used by anyone free of charge.


2021 ◽  
Author(s):  
C. Soriano ◽  
R. Rossi ◽  
Q. Ayoul-Guilmard

The ExaQUte project participates in the Pilot on Open Research Data launched by the European Commission (EC) along with the H2020 program. This pilot is part of the Open Access to Scientific Publications and Research Data program in H2020. The goal of the program is to foster access to research data generated in H2020 projects. The use of a Data anagement Plan (DMP) is required for all projects participating in the Open Research Data Pilot, in which they will specify what data will be kept for the longer term. The underpinning idea is that Horizon 2020 beneficiaries have to make their research data findable, accessible, interoperable and re-usable (FAIR), to ensure it is soundly managed.


2021 ◽  
Vol 3 (1) ◽  
pp. 189-204
Author(s):  
Hua Nie ◽  
Pengcheng Luo ◽  
Ping Fu

Research Data Management (RDM) has become increasingly important for more and more academic institutions. Using the Peking University Open Research Data Repository (PKU-ORDR) project as an example, this paper will review a library-based university-wide open research data repository project and related RDM services implementation process including project kickoff, needs assessment, partnerships establishment, software investigation and selection, software customization, as well as data curation services and training. Through the review, some issues revealed during the stages of the implementation process are also discussed and addressed in the paper such as awareness of research data, demands from data providers and users, data policies and requirements from home institution, requirements from funding agencies and publishers, the collaboration between administrative units and libraries, and concerns from data providers and users. The significance of the study is that the paper shows an example of creating an Open Data repository and RDM services for other Chinese academic libraries planning to implement their RDM services for their home institutions. The authors of the paper have also observed since the PKU-ORDR and RDM services implemented in 2015, the Peking University Library (PKUL) has helped numerous researchers to support the entire research life cycle and enhanced Open Science (OS) practices on campus, as well as impacted the national OS movement in China through various national events and activities hosted by the PKUL.


2017 ◽  
Author(s):  
Kaisa Hartikainen ◽  
Tuula Rissanen

At the University of Eastern Finland (UEF) Library, the national Open Science and Research initiative (2014–2017, Ministry of Education and Culture of Finland) triggered the planning and construction of open science related research support services. Planning of support services with themes of open access scholarly publishing, open research data and open study material began at full throttle at the UEF Library in November 2015. Information specialists were grouped into teams, which orientated to separate aspects of open science and shared their knowledge by training the whole library staff. Teamwork continued actively over the year 2016. Open science continuously brings new tasks for the Library and has already notably changed the job profiles of the library specialists.Advancing open science has been considered highly important not only at the library but also at the university level. UEF has offered resources e.g. by recruiting new information specialists and a data protection officer and internal auditor. UEF Library has a vital role in conducting open science but it is practiced in close collaboration with University Services, especially that of Development Services, General Administration and Legal Services and IT Services. Open Science team has landed the departments to share information and to discuss about open science practices at UEF. Nowadays these roadshows concerning UEF publishing and data policy, open access (OA) publishing as well as research data management and sharing are our focal operation.Work continues but the results can already be seen: In the OA ranking of research organisations in Finland (Ministry of Education and Culture of Finland), UEF has achieved level four in the five-level maturity model. Also, UEF researchers can order tailored training sessions about open research and support services from the diverse training menu offered by the Library. Updated Open UEF web pages are available for everyone and multi-channeled informing directed to UEF staff and students continues online. One concrete output from conducted open science and active campaigning about self-archiving is UEF institutional publication repository, UEF//eRepository, which was launched in February 2017. At the moment about 31% of UEF scientific publications are open access, but substantial increase is expected. The next big challenge in open research is data management and opening. UEF Library is starting to build a metadata portal for research data in order to conceive the data produced by UEF researchers and to help finding data for potential reuse.Open science will also be included in information retrieval studies of master's degree and doctoral students. To encourage students and teachers in OA publishing, during the international open access week, one student publishing master's thesis openly will be rewarded with a stipend by the Library. Department having the highest rate of OA master's theses will also be adorned with flowers.Open science is a matter of teamwork, committing and keeping up to date.


2019 ◽  
Vol 15 (2) ◽  
Author(s):  
Patrícia Rocha Bello Bertin ◽  
Juliana Meireles Fortaleza ◽  
Adriana Cristina Da Silva ◽  
Massayuki Franco Okawachi ◽  
Márcia De Oliveira Cardoso

RESUMO O fenômeno Big Data e o quarto paradigma da ciência – a e-Science – demandam das instituições de ciência e tecnologia um apropriado gerenciamento e preservação dos dados de pesquisa, de modo a possibilitar o acesso, uso e compartilhamento dos dados originais e assim alcançar sustentabilidade e competitividade no sistema científico e tecnológico moderno. O presente trabalho comenta e analisa a Política de Governança de Dados, Informação e Conhecimento da Embrapa, com foco nas questões relacionadas à gestão de dados de pesquisa. Espera-se que essa Política possa ser instrumental para outras organizações do sistema de C&T nacional no desenvolvimento de seus próprios normativos.Palavras-chave: Dados Científicos; Ciência Intensiva em Dados; Acesso; Compartilhamento; Preservação; Gerenciamento.ABSTRACT The Big Data phenomenon and the fourth science paradigm - e-Science - demand from science and technology institutions proper management and preservation of research data, for access, use and sharing of original data and thus achieve sustainability. and competitiveness in the modern scientific and technological system. This paper comments and analyzes Embrapa’s Data Governance, Information and Knowledge Policy, focusing on issues related to scientific data management. It is hoped that this Policy can be instrumental to other organizations in the national S&T system in developing their own standards.Keywords: Scientific Data; Data Intensive Science; Access; Sharing; Preservation; Management.


2014 ◽  
Vol 9 (1) ◽  
pp. 220-230 ◽  
Author(s):  
David Minor ◽  
Matt Critchlow ◽  
Arwen Hutt ◽  
Declan Fleming ◽  
Mary Linn Bergstrom ◽  
...  

In the spring of 2011, the UC San Diego Research Cyberinfrastructure (RCI) Implementation Team invited researchers and research teams to participate in a research curation and data management pilot program. This invitation took the form of a campus-wide solicitation. More than two dozen applications were received and, after due deliberation, the RCI Oversight Committee selected five curation-intensive projects. These projects were chosen based on a number of criteria, including how they represented campus research, varieties of topics, researcher engagement, and the various services required. The pilot process began in September 2011, and will be completed in early 2014. Extensive lessons learned from the pilots are being compiled and are being used in the on-going design and implementation of the permanent Research Data Curation Program in the UC San Diego Library. In this paper, we present specific implementation details of these various services, as well as lessons learned. The program focused on many aspects of contemporary scholarship, including data creation and storage, description and metadata creation, citation and publication, and long term preservation and access. Based on the lessons learned in our processes, the Research Data Curation Program will provide a suite of services from which campus users can pick and choose, as necessary. The program will provide support for the data management requirements from national funding agencies.


2021 ◽  
Author(s):  
J. García-Espinosa ◽  
C. Soriano

This document presents the deliverable D8.1 – the Data Management Plan (DMP) of work package 8 of the prodPhD project. It aims to present the plan for the management, generation, collection, security, preservation and sharing of data generated through the prodPhD project. The DMP is a key element for organizing the project’s data. It provides an analysis of the data, which will be collected, processed and published by the prodPhD consortium. The project embraces the initiatives of the European Commission to promote the open access to research data, aiming to improve and maximize access to and reuse of research data generated by Horizon 2020 projects. In this sense prodPhD will adhere to the Open Research Data Pilot (ORD Pilot) fostered by the European Commission, and this DMP will be developed following the standards of data storage, access and management. This plan will detail what data will be generated through the project, whether and how it will be made accessible for the verification and reuse and how it will be curated and preserved. In this context, the term data applies to the information generated during the different experimental campaigns carried out in the project, and specifically to the data, including associated metadata, to be used to validate the computational models and the technical solutions to be developed in the project. This document is the first version of the DMP and may be updated throughout the project, if significant changes (new data, changes in consortium policies, changes in consortium composition, etc.) arise.


2020 ◽  
Author(s):  
Mario Gollwitzer ◽  
Andrea Abele-Brehm ◽  
Christian Fiebach ◽  
Roland Ramthun ◽  
Anne M. Scheel ◽  
...  

Providing access to research data collected as part of scientific publications and publicly funded research projects is now regarded as a central aspect of an open and transparent scientific practice and is increasingly being called for by funding institutions and scientific journals. To this end, researchers should strive to comply with the so-called FAIR principles (of scientific data management), that is, research data should be findable, accessible, interoperable, and reusable. Systematic data management supports these goals and, at the same time, makes it possible to achieve them efficiently. With these revised recommendations on data management and data sharing, which also draw on feedback from a 2018 survey of its members, the German Psychological Society (Deutsche Gesellschaft für Psychologie; DGPs) specifies important basic principles of data management in psychology. Initially, based on discipline-specific definitions of raw data, primary data, secondary data, and metadata, we provide recommendations on the degree of data processing necessary when publishing data. We then discuss data protection as well as aspects of copyright and data usage before defining the qualitative requirements for trustworthy research data repositories. This is followed by a detailed discussion of pragmatic aspects of data sharing, such as the differences between Type 1 and Type 2 data publications, restrictions on use (embargo period), the definition of "scientific use" by secondary users of shared data, and recommendations on how to resolve potential disputes. Particularly noteworthy is the new recommendation of distinct "access categories" for data, each with different requirements in terms of data protection or research ethics. These range from completely open data without usage restrictions ("access category 0") to data shared under a set of standardized conditions (e.g., reuse restricted to scientific purposes; "access category 1"), individualized usage agreements ("access category 2"), and secure data access under strictly controlled conditions (e.g., in a research data center; “access category 3"). The practical implementation of this important innovation, however, will require data repositories to provide the necessary technical functionalities. In summary, the revised recommendations aim to present pragmatic guidelines for researchers to handle psychological research data in an open and transparent manner, while addressing structural challenges to data sharing solutions that are beneficial for all involved parties.


Data ◽  
2021 ◽  
Vol 6 (9) ◽  
pp. 96
Author(s):  
Timothy Austin ◽  
Kyriaki Bei ◽  
Theodoros Efthymiadis ◽  
Elias P. Koumoulos

Trends in the sciences are indicative of data management becoming established as a feature of the mainstream research process. In this context, the European Commission introduced an Open Research Data pilot at the start of the Horizon 2020 research programme. This initiative followed the success of the Open Access pilot implemented in the prior (FP7) research programme, which thereafter became an integral component of Horizon 2020. While the Open Access phenomenon can reasonably be argued to be one of many instances of web technologies disrupting established business models (namely publication practices and workflows established over several centuries in the case of Open Access), initiatives designed to promote research data management have no established foundation on which to build. For Open Data to become a reality and, more importantly, to contribute to the scientific process, data management best practices and workflows are required. Furthermore, with the scientific community having operated to good effect in the absence of data management, there is a need to demonstrate the merits of data management. This circumstance is complicated by the lack of the necessary ICT infrastructures, especially interoperability standards, required to facilitate the seamless transfer, aggregation and analysis of research data. Any activity aiming to promote Open Data thus needs to overcome a number of cultural and technological challenges. It is in this context that this paper examines the data management activities and outcomes of a number of projects participating in the Horizon 2020 Open Research Data pilot. The result has been to identify a number of commonly encountered benefits and issues; to assess the utilisation of data management plans; and through the close examination of specific cases, to gain insights into obstacles to data management and potential solutions. Although primarily anecdotal and difficult to quantify, the experiences reported in this paper tend to favour developing data management best practices rather than doggedly pursue the Open Data mantra. While Open Data may prove valuable in certain circumstances, there is good reason to claim that managed access to scientific data of high inherent intellectual and financial value will prove more effective in driving knowledge discovery and innovation.


2015 ◽  
Vol 33 (3) ◽  
pp. 417-449 ◽  
Author(s):  
Li Si ◽  
Wenming Xing ◽  
Xiaozhe Zhuang ◽  
Xiaoqin Hua ◽  
Limei Zhou

Purpose – This paper aims to find the current situation of research data services by academic libraries and summarize some strategies for university libraries to reference. Recent years have seen an increasing number of university libraries extended their traditional roles and provided research data services. Design/methodology/approach – This paper selected 87 libraries of the top 100 universities listed in the World’s Best Universities released by the USA News in October 2012 as samples and conducted a Web site investigation to check if there were any research data services provided. In addition, it made an interview with the Wuhan University Library’s Research Data Service Workgroup to understand the procedure, difficulties and experiences of their research data service. Based on the survey and interview, it analyzed the current status and difficulties of research data services in university libraries and proposed some strategies for others to reference. Findings – Of the 87 university libraries investigated, 50 libraries have offered research data services. Most of the services can be divided into six aspects: research data introduction, data management guideline, data curation and storage service, data management training, data management reference and resource recommendation. Among these services, research data introduction is the most frequently provided (47.13 per cent), followed by data curation and storage services (43.68 per cent), data management guideline (42.53 per cent), data management reference (41.38 per cent), resource recommendation (41.38 per cent) and data management training (24.14 per cent). The difficulties met by research data service of Chinese academic libraries are also concluded. Originality/value – Through Web site investigation and interview with the Wuhan University Library’s Research Data Service, this paper presented an overall picture of research data services in university libraries and identified the difficulties and experiences of research data services of the Wuhan University Library. Based on some successful examples, it put forward some strategies for university libraries to reference. This study is very useful for academic libraries to promote their research data services.


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