Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2022-11-29 Cooperative journals: 《数据智能（英文）》
Abstract： It is easy to argue that open data is critical to enabling faster and more effective research discovery. In this article, we describe the approach we have taken at Wiley to support open data and to start enabling more data to be FAIR data (Findable, Accessible, Interoperable and Reusable) with the implementation of four data policies: “Encourages”, “Expects”, “Mandates” and “Mandates and Peer Reviews Data”. We describe the rationale for these policies and levels of adoption so far. In the coming months we plan to measure and monitor the implementation of these policies via the publication of data availability statements and data citations. With this information, we’ll be able to celebrate adoption of data-sharing practices by the research communities we work with and serve, and we hope to showcase researchers from those communities leading in open research.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2022-11-29 Cooperative journals: 《数据智能（英文）》
Abstract： Over the past five years, Elsevier has focused on implementing FAIR and best practices in data management, from data preservation through reuse. In this paper we describe a series of efforts undertaken in this time to support proper data management practices. In particular, we discuss our journal data policies and their implementation, the current status and future goals for the research data management platform Mendeley Data, and clear and persistent linkages to individual data sets stored on external data repositories from corresponding published papers through partnership with Scholix. Early analysis of our data policies implementation confirms significant disparities at the subject level regarding data sharing practices, with most uptake within disciplines of Physical Sciences. Future directions at Elsevier include implementing better discoverability of linked data within an article and incorporating research data usage metrics.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2022-11-25 Cooperative journals: 《数据智能（英文）》
Abstract： As the world population continues to increase, world food production is not keeping up. This means that to continue to feed the world, we will need to optimize the production and utilization of food around the globe. Optimization of a process on a global scale requires massive data. Agriculture is no exception, but also brings its own unique issues, based on how wide spread agricultural data are, and the wide variety of data that is relevant to optimization of food production and supply. This suggests that we need a global data ecosystem for agriculture and nutrition. Such an ecosystem already exists to some extent, made up of data sets, metadata sets and even search engines that help to locate and utilize data sets. A key concept behind this is sustainability—how do we sustain our data sets, so that we can sustain our production and distribution of food? In order to make this vision a reality, we need to navigate the challenges for sustainable data management on a global scale. Starting from the current state of practice, how do we move forward to a practice in which we make use of global data to have an impact on world hunger? In particular, how do we find, collect and manage the data? How can this be effectively deployed to improve practice in the field? And how can we make sure that these practices are leading to the global goals of improving production, distribution and sustainability of the global food supply? These questions cannot be answered yet, but they are the focus of ongoing and future research to be published in this journal and elsewhere.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2022-11-16 Cooperative journals: 《数据智能（英文）》
Landi, Annalisa
Thompson, Mark
Giannuzzi, Viviana
Bonifazi, Fedele
Labastida, Ignasi
Santos, Luiz Olavo Bonino da Silva
Roos, Marco
Abstract：
In order to provide responsible access to health data by reconciling benefits of data sharing with privacy rights and ethical and regulatory requirements, Findable, Accessible, Interoperable and Reusable (FAIR) metadata should be developed. According to the H2020 Program Guidelines on FAIR Data, data should be "as open as possible and as closed as necessary", "open" in order to foster the reusability and to accelerate research, but at the same time they should be "closed" to safeguard the privacy of the subjects. Additional provisions on the protection of natural persons with regard to the processing of personal data have been endorsed by the European General Data Protection Regulation (GDPR), Reg (EU) 2016/679, that came into force in May 2018. This work aims to solve accessibility problems related to the protection of personal data in the digital era and to achieve a responsible access to and responsible use of health data. We strongly suggest associating each data set with FAIR metadata describing both the type of data collected and the accessibility conditions by considering data protection obligations and ethical and regulatory requirements. Finally, an existing FAIR infrastructure component has been used as an example to explain how FAIR metadata could facilitate data sharing while ensuring protection of individuals.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2022-11-27 Cooperative journals: 《数据智能（英文）》
Abstract： 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.
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