by Yi Yin and Anneke Zuiderwijk (Delft University of Technology)
Collaboration among researchers from different disciplines is becoming an essential ingredient of scientific research. In order to solve increasingly complex scientific and social conundrums, research data needs to be shared among researchers from different disciplines. New technologies pave the way for unlimited potential for preserving, analysing and sharing research information. The methods used to leverage information technologies to deal with research data vary significantly among scientists, and likewise the requirements of individual scientists vary.
The need for data sharing
Science requires the collection and use of research data. The importance of data for science is equivalent to that of water for life. The proliferation of information communication technologies and other technological innovations has transformed how scientific research is conducted. The new trend in scientific research covers new research domains, funding sources and way of disseminating research results. More data is accumulated and scientists are more connected in the digital era, which is significantly changing all the phases of scientific research. Modern scientific research, which is increasingly data-intensive and complex, requires multidisciplinary collaboration as well as the support of large-scale research infrastructures and high-end experiment instruments, such as artificial intelligence, ecology science, health, biodiversity, culture and heritage research. In addition to obtaining funding from governments, research institutes or industries, crowdsourcing is also becoming a source of funding for scientific research: the widespread use of Kickstarter [L1] is a case in point. The way of communicating research results and output is also evolving. Open access and data sharing which allow everyone to access any research data, not limited to scientific publications and datasets, are increasingly favoured by society as a whole [1] [2]. Virtual research environments (VREs) can be used to support this more advanced type of research data sharing and to support collaboration among researchers.
The needs of researchers
In order to understand the fundamental needs of scientists to conduct various research activities in a VRE, ten interviews were conducted with scientists from different research domains. End-user requirements were collected for each of the activities in the lifecycle of scientific research. The requirements for developing a VRE fall into two categories: functional requirements, which describe what the system should do; and non-functional requirements, which include quality or performance attributes. Building on the outcomes of the ENVRIplus project [L2] the elicited functional requirements were grouped into seven categories:
- Data identification and citation, which covers the identification of various types of research data and associated metadata and provides clear references for specific datasets in terms of citation. The use of persistent and unique identifiers for both data and metadata we found to be crucial for data identification and citation.
- Data curation, which includes all processes and activities to manage acquired datasets, for instance, detailed data management planning and workflows for data management.
- Data Cataloguing, which refers to the collection and cataloguing of information for various categories associated with research activities, for instance experiment equipment, data processing software, data products, publications, research individuals and organisations, research events and research objectives.
- Data processing, which covers the functionalities related to searching, processing and analysing data.
- Data provenance, which covers the functionalities to track the changes of datasets.
- Collaboration, training and support, which covers the functionalities related to research collaboration and training, for instance user interface configuration, establishment of research groups and the supervision of research progress.
Besides the functional requirements, the non-functional requirements specify quality attributes related to functional requirements for a VRE. According to the interviewed researchers, a quickly-accessible, reliable, easy-to-use, low-cost VRE is needed. Besides the performance-related requirements, VREs also need to consider ethical, legal and privacy and security perspectives according to the guidelines and principles defined by the ‘European Charter for Access to Research Infrastructures’ [L3]. The non-functional requirements are categorised into:
- System performance related requirements defined by FURPS+ and ISO 25010:2011, for instance performance efficiency, usability, reliability, maintainability, compatibility and portability of the information system.
- Privacy, security, trust and legal requirements, which specify that the whole development of the VRE should comply with all legislations, especially how the use of the VRE should be robust against cyber-attacks in terms of protected information privacy and security regulated by the new General Data Protection Regulation [L4]. Trust requirements specify the acceptable behaviour of the stakeholders in the VRE system, such as users, system developers and service providers.
From the survey and from the existing VRE-related projects, we elicited various requirements regarding the whole lifecycle of scientific research. However, scientific research is evolving all the time, and it is unrealistic for the designed VRE system to cover all the evolving needs of scientists. The designed VRE system therefore needs to be adaptive and flexible to connect to other existing research infrastructures to collectively serve scientists’ needs.
Links:
[L1] https://www.kickstarter.com/
[L2] http://www.envriplus.eu/
[L3] http://ec.europa.eu/research/infrastructures/index_en.cfm?pg%20=access_ri
[L4] http://ec.europa.eu/justice/data-protection/reform/index_en.htm
References:
[1] Fecher, B., Friesike, S., & Hebing, M. (2015). What drives academic data sharing? Plos One, 10(2). doi:10.1371/journal.pone.0118053
[2] Zuiderwijk, A., & Janssen, M. (2014). Open data policies, their implementation and impact: A framework for comparison. Government Information Quarterly, 31(1), 17-29. doi:10.1016/j.giq.2013.04.003
Please contact:
Yi Yin, Anneke Zuiderwijk
Delft University of Technology, The Netherlands
