Open Data and Open Science: Opportunities for Strategic Alignment
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Transcript: English(auto-generated)
00:00
Thank you for having me. It's a pleasure to be here, and it's also really great to really hear about the work from USGS and also from you, Johan. Open access is also another issue that's close to my heart. So today, I'd like to discuss a bit about some
00:22
of the differences that we see from this. This is me coming from the outside in. Some of the differences we see between the open data movements and the open science movements. And in recognizing some of those differences, what kind of opportunities can we
00:43
uncover for alignment, for collaboration? And I think we all agree that there is a real need for more of that. So if you don't get anything out of this talk, please do try to take home the few following messages.
01:04
First, I just want to just kind of state upfront, from our perspective, open data isn't the same thing as open science. That's just something to kind of recognize. And some of that really comes down to some arguments I'll have around agendas and purpose.
01:23
And really, when we talk about transparency, transparency can have different goals around transparency. You can have different objectives. And so what you use transparency for can mean something for different communities.
01:41
And what we really might want to do if we want to collaborate better is really unpack those differences and find commonalities and really look for alignment around strategy and also common problem areas.
02:00
So the Open Data Charter, as you've just heard, we are a network of governments and civil society organizations. And we really coalesce around a set of principles that was set out originally from a G8 meeting on open data.
02:22
And this was really taken on by now the Open Data Charter, the organization. And we promote and advocate for open data policy at the national level and the subnational level. So I'm assuming everybody's very familiar
02:43
with FAIR principles. And you'll see some of the core components of FAIR principles also reflected here in the Open Data Charter principles. Some of the principles here correspond to what we want to see out of data,
03:01
like what are the characteristics of data that we want that we think of as good data. So we want data to be interoperable. So we think about standards. But some of the principles here are also reflective of a desire for change. What kind of change do we want to occur from opening up data?
03:24
Some of this could be process change, which is reflected in open by default. And some of this is in the impacts or externalities of opening data when we talk about governance and engagement.
03:41
An open data ecosystem, this is a very simplified model I've grabbed from a well-known paper. But what we really have here are primarily government organizations, use data, tools, platforms, standards
04:02
to provide data to users, of course. And if we think about the principles that you saw in the previous slide, some of those principles, some of them are really about shaping that relationship between the user and the data supplier or the government.
04:21
So when we think about open by default, it's really about creating processes in which users can really see into the workings of government. So I hope here I'm starting to outline some nuances around how transparency is seen in the open data movement
04:42
and how that can be compared to open science. So with open science, of course, and this is from a reporter from the government of Canada, open science can be much less jurisdictionally bound.
05:05
Of course, when we deal with research data networks, research data management networks, we're also talking about research collaborations and these can be transnational. Data providers and data suppliers can really exist under a broad range of institutions
05:25
frameworks. What I have here is a very, very simplified view of who's involved, who are those actors involved, and what kinds of connections we're dealing with. And under what kind of agendas are they operating
05:41
between open science and open data. There is, of course, a little bleeding between the two, of course. And I do want to acknowledge that there are also really different schools of thought when it comes to open science. And depending on your leading, you may be more focused on the open access side of publication
06:02
or maybe you're more focused on providing primary data, for example. So I also want to acknowledge that some of the arguments for open science have very strong similarities to open data, particularly around the arguments
06:21
that publicly funded research should also make researchers themselves more accountable to the public because that's where they get the money from. That being said, I think that some of that argumentation or the use of that common argumentation
06:41
does have some limitations. What we're dealing with with open data and open government discourses is really the existing relationship between citizens and the state. And we're talking about democratic frameworks here. And this means transparency in the open data community
07:01
is really rooted in the democratic rights. Whereas in, and this is from my view in Canada, a lot of the open sciences course is a little more angled towards innovation agendas, for example.
07:20
And when we talk about an innovation agenda, transparency in that context, it really has a purpose. We want transparency. We want openness to lead to some new kinds of outputs. But transparency in an open data context,
07:42
sometimes transparency is just the end goal itself or one of the end goals. And because of that, for example, you can have data that you put out where it doesn't necessarily matter if it is not accessed and it's really the fact that the data is being put out
08:01
that matters. So here's another diagram. This is from a paper that outlines a sort of the policy implementation
08:21
landscape in Canada. And as you can see, it's quite complex. We will zoom in in the next slide. But what I really want to note here is that open science as part of government policy, it operates within a network of other policies, right?
08:45
And there's a jurisdictional component to this. So if we look at a little more closely in Canada, the Open Science Roadmap published in 2020
09:00
really falls under the Office of the Chief Science Advisor of Canada and also under the auspices of ISED, or Innovation Science and Economic Development Canada. And so this places open science a little closer to innovation agendas, whereas open data, which
09:24
falls under the open government portfolio, that really resides under the Treasury Board of Canada Secretariat, a completely different ministry. So of course, the government of Canada has already made those connections and already advocates for cross-pollination
09:45
and collaboration at the individual level between your CIOs, your chief scientific officers, and whatnot. And that's definitely a great thing, but I just want to highlight that there can still
10:01
be a challenge of that separation at the policy level of that separation between who has jurisdiction over implementation. Open science and open government in Canada has resulted in different ministries producing their own separate open science action plans or open government
10:21
action plans, for example. And so that creates more needs for governments to make those connections. There can also be a problem of alignment in these cases,
10:41
so alignment in terms of who are you talking to, which communities are you engaging, for example. With open government, I am pretty confident it's not going to overlap much with the open science community.
11:00
With open government in Canada, we have the multi-stakeholder forum on open government, which is mandated as part of Canada's commitment to the open government partnership. So that is really an engagement between government and civil society actors, advocates, nonprofits,
11:22
and a few academics. But these circles may not necessarily be speaking the same language or surfacing the same ideas as in other circles that discuss open science. So really, there are probably more connections
11:40
that could be made between the two communities. And this is not necessarily a bad problem either. I just want to note that. The fact that both are on the policy map and hopefully are not going away anytime soon, I think that's already a very good thing.
12:04
So what can we think about when it comes to alignment? Common challenges are definitely something that we could be working more on. So when it comes to data infrastructure, some of what we do in the open data communities
12:24
is we also use data management life cycles, like you from USGS, to help ourselves advocate to government and structure our conversations on what we want to see from open data from government.
12:40
And there are also common data needs when it comes to scientific or advocacy communities, common data needs like road networks, your data infrastructure type data sets. Multi-stakeholder problems as well
13:00
are also another potential area for convergence. So think about any kind of area where there's a convergence between an environmental and a social issue. Watershed management is one classic example where you have input required from policymakers,
13:21
from the public, from advocacy organizations, and also from scientists themselves. And when you have that coming together of this variety of stakeholders, there's probably opportunities there to make more connections between open science discourses
13:41
and agendas and open data agendas. And finally, wicked policy problems, the kinds that don't have optimal solutions. Transparency and citizen involvement is also increasingly being seen as just increasingly important as part
14:03
of that kind of approach. And finally, cross-community learning. So a lot of what we do is we also help governments create the policies that they want to put in place around, for example, anti-corruption.
14:21
So we help civil servants embed notions of or principles of open data and transparency into other policy areas to help make those connections between different government units,
14:40
but also their implementation. On the engagement side, there's been a lot of work done on what are the best practices for community engagement. So open data and open government communities have been doing this for quite a while, as has open science.
15:02
But the open data community can be very broad and brings together, brings in a host of people with different domains or different issues that they need resolved. And that forces us to really try to come together and really coalesce
15:22
around problem framing approaches to structuring discussions on what data is needed. So those are just a few takes. I'd be really open to having further discussion about what are the opportunities you see, how much you even
15:42
do engage with the open data community in your specific area. And we would be very open to discussing, of course. I have a few. These can be shared afterwards, but I have a few readings as well, suggested readings on open science and open data.
16:02
Happy to share that in the chat. Thank you very much. Please visit us and stay connected.