As a member of euroCRIS organisation, I attended the Spring 2017 EuroCRIS Membership Meeting in Dublin taking place from the 29th and until de 31st of May. During the meeting I presented one of our leading projects in scientific management; the implementation of a SIGMA CRIS CERIF for Universitat Pompeu Fabra (UPF).
My presentation was the story of how a CRIS system implementation was able to boost scientific information both internally and externally for an institution leading in research. There, I presented through some real examples, how to use and reuse the scientific information with a goal:
“collect data once, reuse it many times”
I talked about the data quality, avoiding redundancy, the using of standards and the relationship with openAccess among others.
Later, I presented the 100% cloud SIGMA Research tools that gives support to the whole research lifecycle.
I have also the opportunity to attend all the presentations, were participants, showed various examples of CRISs in different European countries, focusing on the openScience (mainly openAccess, openData), and how to improve the use of the scientific information, highlighting the role of the libraries.
We also took the chance to establish the general tends of the state-of-the-art of the CRIS implementations as well as future needs. We are agree of the importance of CRIS for the future of research recognising there is still a lot to be done.
In terms of the future needs, we all emphasised the fact that researchers should improve their data management skills in order to make the information more useful and shareable. Another area to improve is the visibility of CRIS amongst institutions to prevent parallel and unilateral projects to resolve what CRIS already resolves. Finally, constant communication to guarantee the information is updated in the institutional repositories and accountability of the information were also highlighted as important milestones to move forward.
In short, very interesting presentations, in the beautiful city of Dublin!
AI: Artificial Intelligence, a concept created by Alan Turing. That is a concept that exists 66 years ago but, unlike science fiction films, has not had a realistic approach that has allowed its development and implementation in all these years. But no, I don’t want to talk about robots that act like people, witch develop feelings and others. No, I think that finally, the right approach to AI is coming, the approach that should have had from the beginning, but for some reasons it has not been possible to develop. I mean the use of the machines so that they can process the huge amount of data flowing through the network, and it has not been possible until the advent and popularisation of the Internet.
I remember a couple of years ago, I attended a conference in a Spanish public university where it was questioned if in the university were applicable concepts such as artificial intelligence (AI) and bigData, among others. Several of the speakers claimed that no, others with small mouth pointed that perhaps it was possible, but generally not seen how or where to apply.
Well, it’s only two years after and I think we can say that in the universities we can talk about bigData and many applications of AI. If we focus on one of the pillars of the university, the research area, with the development of models such as open access of publications and data as well as the internationalisation of research, where increasingly proliferate large research groups, international and multidisciplinary, no doubt, that there is an explosion of bigData in research data. And, in my view, the bigData, ie, generation and access many data in real time, leads to the need to apply artificial intelligence techniques for the analysis and processing of terabytes of data that proliferate in the network.
Thus, and related to my previous post, AI techniques are already required such as, machine learning, deep learning, computational linguistics, natural language processing and data mining, among others.
Our researchers do not have to, nor can, spend time searching and analysing raw data, ie process thousands of data to find what they seek. To do this, these algorithms and technologies need to facilitate access to information, which is not the same as access to data. So, in this way, we could say that the data is for machines, information for Researchers. I am currently working on these issues, to provide researchers with tools that can really help them in their work and enable them to move faster and to obtain the best results.
Enjoy your summer!