The European Open Science Cloud
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Transcript: English(auto-generated)
00:05
Klaus Dochtermann, it's a great pleasure to have you here and to talk with you about Open Science. And so you had a very interesting talk about the European Open Science Cloud. Is it really a cloud we are used to know from other repositories or is there a special
00:23
thing behind it? We use the term cloud just as a metaphor. We had not in mind to establish a cloud solution for European data management. European cloud solution, like in the original sense, would mean to establish a central repository
00:41
for research data management. And this is definitely not what we have in mind. What we want to do, and this is what the cloud metaphor serves for, is connect existing and future research data centers with one another. And this entire ecosystem of infrastructures, this is what we refer to as cloud.
01:00
So it's a decentralized thing, European-wide. And the basic idea is to interconnect a number of research institutions. What research institutions are we talking about? Anything or other special disciplines? Well, we have well-established research data centers in the different disciplines, like
01:21
Pangaea, for example, in environmental sciences. And like the research data centers, which should become part of the European Open Science Cloud, must meet some standards. For example, they should offer like an open interface so that we can access the metadata
01:41
describing the research data in their centers. They should also provide other technologies which are based on certain standards because standards are the prerequisite for a successful interconnection of these data repositories. What kind of standards are we talking about? Reporting standards or these are not really standards as, let's say, established by the
02:04
German Institute for Standards, the Dean. It's a special case of standards. What kind of standards are we talking about? One standard, for example, is the use of metadata standards. Standards describing the research data.
02:22
Another standard is there are standards for the APIs, the interfaces through which we can access the content of a repository. Or a third example are authentication standards. So because some repositories require their users to authentify and for that we have
02:42
European-wide standards. I see. So not only technical standards but also standards for best practices standards. When we talk about open science and open access, we also have to look at FAIR.
03:02
Could you say something about FAIR? FAIR data, it means that we want all researchers to make FAIR use of their data. And FAIR means the data should be findable, accessible, interoperable and reusable. So the idea is that the researchers and the research in the future does not protect
03:24
and shield the data but make it findable, accessible, interoperable to support cross-disciplinary research and of course reusable. How far is European Open Science cloud from FAIR data? Our aim is that in the European Open Science cloud, all data should be FAIR.
03:48
So this is the idea. So the two paradigms are closely connected to one another. European Open Science cloud is the infrastructure and the data used in that infrastructure should be FAIR.
04:00
I see. I mean, we are talking on the one hand about the infrastructure, but we also need to keep the scientists in our minds. We have to convince scientists to contribute to the Open Science idea. What kind of awards do you have in mind to persuade and convince scientists
04:24
to contribute to these structures? First, I completely agree in the high level expert group on the European Open Science cloud, we argued that 80 percent about the change is cultural change and only 20 percent is technological change, infrastructure change.
04:44
What we need are incentives, incentives for the researchers to share and make available their FAIR data. These days, we don't have these incentives. We have many incentives for publications, like, for example, Impact Factor or the
05:01
8 index, like number of citations of a publication. We don't have similar measures and metrics for research data. And this is why the European Commission also established an expert group on alternative metrics. And this group should work on new evaluation forms for an open science world.
05:23
Can you already say anything about these alternative metrics? Is there anything still alive or away alive? So far, the expert group just coined the frame conditions under which the new metrics should be used. For example, we only want to count numbers which measures, matters, excuse me,
05:45
which matters, so only measure what matters is the idea. For example, the number of tweets concerning a certain publication does not really matter. In our own research, we identify that the number of citations do not correlate with
06:00
the number of tweets of that publication and vice versa. If there is a higher mentioning of a publication on Twitter, it doesn't necessarily mean that it has higher citations. So this is one example showing that it doesn't work and it's much easier to find these examples which are not suitable and it's quite challenging.
06:20
And the group isn't that far. The group has just defined like the frame conditions under which we should develop these metrics.