Almost everybody in the research environment talks about FAIR data. The challenge is:
Researcher, make your research data public!
It seems easy, isn’t it? but truly, it’s not so easy. We must create infrastructures to do so, we must create interoperability protocols and so one, and essentially, we must persuade all the research community, especially researchers, how to make it, and about the benefits to do so, in short, thats worth it.
But why this data must be FAIR? because there are a large amount of data produced by the ICT in research that has immense value which should be unlocked. The exponential growth of data could drive and accelerate the societal challenges, scientific advances and productivity gains across the European economy. But now, some of this data is locked away in isolated and inaccessible silos. This isolation reduces the benefits that can be achieved if this data were open.
FAIR is the set of guiding principles to make data Findable, Accessible, Interoperable and re-usable. But, what does this words means in the OpenScience environment?
- (meta)data are assigned a globally unique persistent identifier
- data are described with rich metadata
- (meta)data are registered or indexed in a searchable resource
- metadata specify the data identifier
- (meta)data are retrievable by their identifier using a standardised communications protocol
- the protocol is open, free, and universally implementable
- the protocol allows for an authentication and authorisation procedure, where necessary
- metadata are accessible, even when the data are no longer available
- (meta)data use formal, accessible, shared and broadly applicable language for knowledge representation
- (meta)data use vocabularies that follow FAIR principles
- (meta)data include qualified references to other (meta)data
- (meta)data have a plurality of accurate and relevant attributes
- (meta)data are released with a clear and accessible data usage license
- (meta)data are associate with their provenance
- (meta)data meet domain-relevant community standards
One of the main actors in the OpenScience is the European Open Science Cloud (EOSC). EOSC is a programme identified by the European Comission (EC) as one of the most important projects for Research and Education across the community, in this way is the programme through which the EC proposes to achieve OpenScience. EOSC will enable the federation of existing and emerging data infrastructures, bridging the fragmentation and ad-hoc solutions which populate the e-infrastructures landscape today, so as to remove obstacles to wide access to publicly funded research publications and data. It will enable sharing and re-use of research data across disciplines and borders, taking into account relevant legal, security and privacy aspects. In this way, it’s one of the actors in the way to make data FAIR.
Sources of information:
- The magazine from the GÉANT community
- Research Data Alliance (RDA)
- European Open Science Cloud (EOSC )
Esta entrada fue publicada en Sin categoría y etiquetada como accessible, EOSC, FAIR, findable, GEANT, interoperable, metadata, OpenAire, OpenData, OpenScience, protocols, RDA, research, Researcher, reusable.
Can we see the bibliometric indicators as part of an ecosystem? I think so…
Let me explain. I’m involved in a project related to bibliometric analytics, seeing the first results, I realise, as I read many and many times, that the bibliometric indicators are a good quantitative indicators to measure the research results, but should not be the only or the main measures to evaluate a researcher.
The bibliometric indicators such as citations counts or downloads, have different biases depending on age, discipline and co-authorship. As it is good explained in this paper.
The number of citations increased with the age (number of years of active researcher) of the individual, so the square root of number of citations divided by the age is almost a constant.
Different disciplines have different citation cultures, so citation counts cannot be directly compared.
Must take the degree of co-authorship into account.
Finally we must take also into account that indicators such as provided by WOS, Scopus and Google Scholar (the most relevant), do not cover all areas of research, being Google Scholar the widest.
We can see the researcher environment as an ecosystem that depends on the researchers personal profile, their collaboration relationships, their research scope, their media activity (measured thru altmetrics), other indicators such eigenfactor among others. Only seeing the researchers as a whole, interacting in an environment, we can evaluate them, and not only thru two or three indicators.
In short, we can show the main bibliometric indicators, as we can see bellow, but it’s not a result or an evaluation as well, it’s only information that we need to analyse in order to evaluate the results.
see UVICs PPC