Making Sure Open Science Stays Open
There was a time when everything on the internet seemed like it would be there forever, whether a Facebook status or a government website. But today the prospect of maintaining massive amounts of digital history is in doubt. This poses a particular issue for the open science movement, which has advocated for wide access to scientific data and papers over the last 25 years. The movement’s successes have led to the creation of digital research infrastructure such as data libraries and preservation systems that serve large and small research communities. Now, as the system is maturing, it’s becoming clear that open science requires investment and significant effort to stay alive. Without it, open research cannot be verified or advanced.
On this episode, host Megan Nicholson is joined by Jen Gibson and Kaitlin Thaney, authors of “Who Will Keep Research Data Infrastructure Open and Running?” in our Spring 2026 issue. Gibson is the executive director of Dryad, an open-access international research data repository and curation service. Thaney is the executive director of Invest in Open Infrastructure, a nonprofit that advances open-source solutions and systems for research communities.
Resources
- Read “Who Will Keep Research Data Infrastructure Open and Running?” for more on the importance of maintaining digital infrastructure.
- Visit Dryad and Invest in Open Infrastructure’s websites to learn more about open data, open infrastructure, and how you can support their work.
Transcript
Megan Nicholson: Welcome to The Ongoing Transformation, a podcast from Issues in Science and Technology. Issues is a quarterly journal published by the National Academy of Sciences and Arizona State University.
It feels like everything on the internet will be there forever, from Facebook statuses to news archives and government websites. But digital research infrastructure like data libraries and preservation systems requires investment and significant effort to stay alive. Without it, open research cannot be verified or advanced.
I’m Megan Nicholson, senior editor at Issues. I’m joined by Jen Gibson and Kaitlin Thaney, who wrote about this in our spring 2026 issue in a piece called “Who Will Keep Research Data Infrastructure Open and Running?” Jen is the executive director of Dryad, an open access international research data repository and curation service. And Kaitlin is the executive director of Invest in Open Infrastructure, a nonprofit that advances open-source solutions and systems for research communities.
Jen, Kaitlin, welcome. You have both spent years working in the open science movement, which aims to make science freely accessible to everyone. So I’m wondering, when did you start thinking about the infrastructure side of the movement and really how infrastructure enables open science?
Jennifer Gibson: I think that the open infrastructure side has been part and parcel of the story for a long time in my mind because so long as the systems limit access because they’re proprietary or because they have other legal and financial barriers built into them, the flow of knowledge is interfered with. Working with Dryad, where I joined because I wanted to work on open data, it became part of my job, my responsibility, my passion really to try and help it to thrive in the environment in which we operate. So it was kind of a conversation with infrastructures throughout my career and an opportunity to really take hold of it at Dryad about five years ago.
So long as the systems limit access because they’re proprietary or because they have other legal and financial barriers built into them, the flow of knowledge is interfered with.
Kaitlin Thaney: Yeah, and I would say on my end, the infrastructure… I’ve been in the access to knowledge and information space for a long time, but my entry into that was in the early 2000s at Creative Commons and was really rooted in some rare neglected disease research, very human experiences that were happening with close loved ones that were unable to get access to the information they needed to be able to get the right medical care. And so the work that we were originally doing to explore that kind of falling into a group at Creative Commons that started to explore what that application of not just copyright, which Creative Commons was very well known for, but the access to knowledge piece. There were other elements and infrastructure was referred to as very different things at different times as technology has also advanced. We take this view at IOI of infrastructure as the core elements that research relies on, the core systems, protocol, standards.
And so at that time they were the physical materials that needed to be the cell lines, the lab mice, the plasmids. It also was the cyber infrastructure, the systems that would allow for there to be initial compute. This was pre-GitHub. It was pre-sharing of code outside of some of the larger research instances. And so watching that evolve and then also thinking about the data components and how that then started to digitize. And in some disciplines, it still is very analog or it still may be reliant on one specific instrument. It was always a piece of the puzzle. It was a dimension of the work that we were doing, looking at the human elements and the sociotechnical side of things, but then also the actual brass tacks of what the research ecosystem needed to not only share knowledge but generate research, conduct research and focusing on that research workflow.
Nicholson: That’s fascinating. There’s this way of referring to open research infrastructure and its importance as a whole, as the system, which might give the impression that it’s treated like a utility or that it’s serviced by a few providers if you refer to it that way. But as your piece for Issues shows, and as you just spoke about a bit, operators and providers of open research infrastructure are actually really numerous and decentralized. And when you start to break down that ecosystem of providers, that picture becomes a lot more rich but also a lot more complicated. So Kaitlin, since your organization works across all of these infrastructures, can you talk about how you classify or categorize open infrastructures?
Thaney: This is maybe my favorite question because we have infrastructure in the name. To us, we originally started with a view of what research infrastructure looked like, again, those systems, protocols, standards that research relies on. Now, when we’re talking about research, again, that can be very different if you’re looking at social science and humanities, if you’re looking at physical, natural sciences, et cetera. So over the course of the last six years of IOI, we started to realize that rather than having a definition and a classified box, that thinking of it more as the descriptive characteristics. And what I mean by that is for some disciplines, that might be a physical collection that that discipline relies on for their scholarship. For others, it might be a specific instrument or high-performance computing facilities and others, it may be the underlying systems. We do try to ensure that that less visible knowledge infrastructure, whether it’s disciplinary or more generalist, is at the core foundation layer of what we’re looking at.
There are areas where it becomes more specialized. I think to your point, Megan, of just the utility side of things where people assume that this is paid for in many ways and cared for by broader ecosystems, it is a hugely diverse space. In some cases, it may be a specialized repository for Indigenous scholars where scale is actually not the objective, it’s about depth. And so that may be something where you may have one or two specific funders for those infrastructures and it may be a very, very different scale than if you were looking at say a biobank or something along the scale of say Dryad or a larger publishing option that exists. And so we try to hold space for the fact that diversity is really important, but we do need to make some determinations on how we resource some of those pieces that are heavily relied on while also ensuring that there’s some opportunity there for those other more specialized services and tools are part of the picture too.
Nicholson: I want to come back to the funding piece in a bit, but first I want to let Jen talk a bit about the kind of infrastructure that Dryad provides. Can you give us a bit more information on that?
Gibson: Absolutely. Dryad is a class of infrastructure that ensures that the data underpinning research is available to evidence the research. So we’re called a data repository. Repositories fall into two camps broadly. There are specialist repositories like GenBank, PDB that contain specialist silos of data and then there are generalist repositories that catch everything else. Of course, any data that should belong in a specialist repository should go to that specialist repository.
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Generalist repositories catch the things that go in between but also provide support to fields that are new to data sharing. So there are over 3,000, I think, data repositories classified by re3data.org and we are home to, I think, billions of specific digital object identifiers. Dryad ourselves, we are hosted to 50 million files and 70,000 DOIs for specific data collections.
Nicholson: Wow. I think this is an important point to mention the kind of difference between a repository that everybody might recognize if you’re in the research community and then these other repositories that might not be as well known but still really integral to particular research communities. Can you talk a little bit more about the difference and maybe throw out some names?
Thaney: Oh, I mean there’s a whole host of specific groups. And I know that Jen, for Dryad and a number of others, the National Institutes of Health has been investing in collaboration across and coordination across a number of generalist repositories through the Generalist Repository Ecosystem Initiative known as GREI, which also brings a number of these groups together, including Dryad, Dataverse, Zenodo, which is housed at CERN. And again, this gets into what a specific discipline refers to as data, which might differ in terms of what that might look like. There’s a big focus right now on data repositories because of some of the changes that have happened in the United States and on the federal data side. And so there’s a number of other—whether it’s in climate, agriculture, weather, geospatial—a whole host of other groups that are now starting to look at coordination in a deeper way.
But I’ll just give a few other examples. So the Biodiversity Heritage Library, which recently spun out of the Smithsonian and is now housed at CLIR, which is a library organization. There’s also other groups such as the Global Biodiversity Information Facility, GBIF, which has been very widely known. As Jen knows, there’s a whole host of them in the biomedical space that we refer to as sort of data resources. Some of these also are very heavily used like UniProt, things that, again, bridge different types of data. Some of those might be physical materials or samples. Some of those might be more digital data and things there too.
And so there’s always a deep need to talk to those in the specific research discipline. It’s been fascinating to me to see some of the interplay between the generalist repositories and the specialist repositories have actually allowed for there to be much more use of that information and data and much more visibility and awareness, though it is not without its own challenges because again, duplication of data is something that we’re always trying to find ways to mitigate, but there’s a whole other host of infrastructures that have been working hard on that, which gets to some of that underlying plumbing side of the things that are not visible unless you are heads down in that technical solution and looking at providing service to the research community.
Gibson: Thanks, Kaitlin, for calling out GREI because GREI also encapsulates the challenges of scaling repositories and of helping many repositories to thrive. We’ve been tackling questions of standardization for metadata. Even those seven generalist repositories hadn’t agreed on what metadata schema to use. So long as everyone’s asking authors for something different, it creates friction. So through that collaboration, we’ve all adopted one standard metadata schema. And then within the metadata, of course, is, well, which ontology are you going to use for funding affiliation or university affiliation, et cetera.
So that has been a tremendous step forward. And the whole project is really a potential model for repository collaboration with the end that the data, the description of the data becomes more standardized and can therefore be made more discoverable across systems around the whole world. And as these opportunities are opened up, whilst also the financial pressures on repositories are increasing, I’m personally really excited and optimistic about the opportunities for strategic consolidation and that’s Kaitlin’s term, but how might repositories collaborate more effectively in order to leverage our respective strengths but consolidate on the functions that we have in common that don’t need to be held separately by each of us?
Nicholson: I think what I’m hearing, it’s so interesting to think of these infrastructures as being really like the vehicles for underpinning the discoverability and collaboration of open science. It’s really like the structures that are holding those ideals together. I wanted to come back to the funding piece. You both mentioned it. I think something really interesting about open infrastructure is that at first glance, it kind of looks like there’s a lot of funders of these services. You have federal agencies in the US like the National Science Foundation, National Institutes of Health, you have research agencies in other countries like French National Research Agency and UK Research Institute, and then you have philanthropic organizations like Simons Foundation, Arnold Ventures, Wellcome Trust. So what is going on with this funding ecosystem and is this a stable structure?
Thaney: So what happens when you get into some of the deeper dives for this, right? Invest in Open Infrastructure came about literally from a hallway conversation where a number of the individuals that have been pushing this Sisyphus and rock up the hill of building community-based, community-governed open infrastructure—those within institutions, those that had done it as separate tool provider, place, funders, users, open organizations, advocates, et cetera. Their frustration with the way in which this ecosystem was supported where it felt like there were a handful of funders that were supporting the majority of the open infrastructure for research or open science infrastructure ecosystem, but also knowing that the reliance on whether it was philanthropic or even some funding from government supports, like as you mentioned, from NIH or from National Science Foundation or from national funders outside of the US, that it usually was either smaller in size and not designed to be able to cover funding for the longer term or very time limited.
Many of these cycles are three years, if you’re lucky, five years at a max, and then there needs to be additional sort of innovation, diversification or moving off of that reliance on funding. Research is heavily supported and it has historically been heavily supported by government. We’re seeing some really significant shifts right now in that underlying assumption. And that has shaken down to institutions which have also supported this work, philanthropies that do need to innovate and there’s a whole sea of different leaderships coming in. And what is not necessarily noted and is harder to qualify from the analysis that we’ve done is a significant investment it takes to secure those grants, to do that business development. And the fact that the majority of these infrastructure organizations don’t have dedicated fundraising teams. We try to do as much as we can to open doors on our end and continually try to move that conversation forward to open the aperture of different funding sources.
If government also starts thinking like a venture capitalist and wanting to move into that broader innovation space, who helps keeps that plumbing supported?
But it makes it a really challenging space that unfortunately pits some of these infrastructures that want to be able to collaborate with one another. They don’t want to be, for the most part, stepping on each other’s toes. It’s not necessarily designed to be as competitive of a space, but the actual funding to be able to keep staff paid, keep the lights on, continue service to the research community unfortunately often pits them into that space. Jen and I have been hammering on for a longer time, but more specifically, I would say over the last six to 12 months is the fact that data is critical if you’re going to have all of these additional investments in innovation, in artificial intelligence, in accelerated discovery, and the shifts to look at, I’m going to say, the flashier side of the innovation stack.
And we’re seeing that not just happen by say postmodern philanthropy. We’re also seeing that from very traditional philanthropy funders that are very historically supported broader communities and organizations in our space that are moving into that, which I think is great, but also we need that balance. If that funding moves out, who replaces it? If government also starts thinking like a venture capitalist and wanting to move into that broader innovation space, who helps keeps that plumbing supported and who can help carry that? It’s one of the biggest challenges that we’ve seen particularly in the US but also in other global economies. The geopolitical shift where government has shifted away from that traditional support, the expectation that government still needs to support it because they are the only ones with deep enough pockets to support it, I think is false. I think we do need a radically different model. There’s a whole other set of conditions, different yardsticks, for lack of a better phrase, for how these infrastructures are evaluated, how their sustainability or runway is evaluated.
And I think it’s time that that needs to radically change. Jen and I have been trying to make that case to a number of different types of funders and those that could contribute to this space to hopefully be able to shift those tides.
Gibson: I would introduce Dryad as an example of all of that. Dryad is going to be 19 this year in 2026 and Dryad was established by researchers to take care of the data that they wanted to make sure was shared in association with their research articles with trusted home to 50 million files, 70,000 datasets, and 95% of those data sets are directly connected to a published research article. So if that data goes away, the evidence to prove that story goes away. And I think, Megan, you made the point earlier that we’re the kind of foundation of research today. So the funding absolutely needs to be transformed.
Looking at Dryad’s story for, let’s say, the first 12, 13 years of our existence, we went from NSF grant to NSF grant. So the NSF, the US National Science Foundation, has had a suite of important programs to support just these types of initiatives. So that was the first part of our journey and then in the second part of our journey, folks started to experiment with business models and different revenue streams. The US and global academic library community has been very, very engaged in the importance of open infrastructure and have been contributing through membership programs to different types of open infrastructures and that’s been very important and that has good potential.
In more recent years, we’ve been trying to take more of a business mentality. If open infrastructure is important to somebody, if there’s a user, is that user willing to support us financially? And if not, where else can we find support? Who might support that user? With Dryad, we deliver service that is so valuable that folks are willing to pay for it. So now our job is not just running the repository and curating the data and delivering on research integrity, but delivering a high-quality service to the individual researchers and the entities that partner with us in order to earn unrestricted revenue that will help us to break even and thrive over time and get away from that end-of-life scenario every three years, which is very, very stressful for any organization.
Nicholson: It sounds like, to some extent, at least, the flexibility that that model of being community-based, community-governed, community-oriented, it almost came at the sacrifice of being able to do any long-term planning. And I guess that brings the question up of, is financial instability the biggest threat to infrastructures or are there other kind of pressures on these service providers?
Gibson: Some people in this space hold that something must be nonprofit. It mustn’t pursue money. It mustn’t talk about sales, marketing, breakeven, to get too business-y. And that can be an impediment. It can be very difficult for any nonprofit and a data repository that’s been community-run for a long time to then introduce those conversations and make progress as a business. And again, I offer Dryad as an experimental organism in this case because as we rewrote our fees and decided that we are no longer a membership organization, it was very, very important that we articulate that we are a nonprofit organization operating a service in the community interest and our community via our board and others have directed that we must canonize the community interest in our operations to make sure that despite aspects of our business that will help us to thrive financially, we don’t lose the spirit of operating in the community interest because that’s as important.
Nicholson: What does it look like when one of these infrastructures goes down? What does it look like when a repository goes dark? Can you share an example of when that’s happened and how that’s affected research or a research community?
Thaney: We’re seeing this right now. We have separate work that I know Jen is also a part of where some of the cuts that have been made across the US, we’ve seen actual data get deleted that has been federally funded and mandated to be made publicly available. Conversations where and specific data sets, I believe we list a number of them in the Issues article. And I know there have been a couple of other pieces in the Spring issue that have also spoken about this for environmental reasons and atmospheric data sets.
I know in particular there was a data set that was heavily relied on by fishermen and fisheries that’s completely taken down and it was the fisheries lobby that had to make a lot of noise to get that put back online. We’re seeing other data sets that are getting restored because insurance companies are pointing out that it is heavily necessary. We’re seeing weather data that is being affected or things that measure water flow and the depth of water, which is not just necessary for looking at the polar ice caps, but it’s necessary for looking at natural disasters and disaster relief. That’s hampering our ability as society, not just in the research ecosystem, society to be able to do what is needed. And so many of the efforts around helping to safeguard some of this critical scientific research data is also looking at not just for the research ecosystem, the need for this information to be made available and made persistent and also for there to be backups for that, but also looking at when it comes to taking that away, who else publicly relies on that and where does that put us at more risk? There’s use cases for Ebola, there’s use cases in biomedical, and we’re seeing some of these things happen.
I know that on the scholarly communication infrastructure or research infrastructure side, there’ve also been a sea of other examples where various infrastructures have either been acquired and then dismantled. Or even on the for-profit side, bepress is a big example that was for-profit was bought by Elsevier. Many institutions couldn’t migrate easily off of bepress to other open solutions because it’s a significant expense and also a significant investment of effort to be able to do so. And so you can still find areas where people are and institutions are trying to find how to migrate off of bepress despite the fact that that acquisition was a number of years ago.
Without access to the data, progress slows because you don’t have the data to be able to build on to take the story forward.
Gibson: There are so many examples and it’s not just what’s happening recently, it’s at the end of every grant. At the beginning of a grant, you go off to do your research, you go off into the field, you collect all of your data, your grant ends, there’s no funding to keep the data available persistently. So it’s a real problem and I would try to drive this home with things that happen when a repository goes dark. The repository is the data to underpin how we know what we know. It’s the evidence. So in the first instance, we lose the ability to verify research. You click through and there’s nothing there. So how do you know that the findings are sound? How do you know that there wasn’t something missed or some rows replicated in the analysis? It’s impossible to verify without access to the data. Without access to the data, progress slows because you don’t have the data to be able to build on to take the story forward.
You have to invent it somehow. You have to recreate the work before you can actually build on it. So data is the foundation stone for progress. So everything just becomes more inefficient and more expensive. Experiments have to be run again needlessly, right? AI, as Kaitlin said at the top of the call, it has nothing to work with. We need well-organized open data to be available to AI so that AI can get us accurate answers quickly. And if the data isn’t available, AI is not able to give us a complete picture. And then finally is we don’t know what we won’t know because the data is absent. We don’t know that someone made an important discovery with tree frogs or lemurs, whatever the investigation may be, polar ice cap, if that data is gone, we don’t know what we won’t know and that’s really terrifying.
Nicholson: So when the funding ecosystem is global, when open infrastructures are global, what parts of this problem can be addressed by US policy by US institutions?
Gibson: I would suggest in the first instance that the US has the opportunity, potentially responsibility, to take a look at initiatives that are hosted within the US. So science is global, research is global. This is a strength, this is power about developing solutions for the globe more quickly is the international collaboration. However, every country is home to a number of important initiatives and there’s the opportunity for the US policymakers, funders to consider how can it help—CUAHSI in hydrology, the Environmental Data Initiative, Dryad to keep our lights on. These are US-hosted 501(c)(3)-registered entities. How can US policy ensure that we do our job in the US, that we do our job to help the US-hosted entities to keep the lights on and ensure the persistent availability of research data and then by in turn, how might other countries also contribute?
Thaney: I build on that in noting one of the things that has mobilized many around the data infrastructure space and around this data rescuing and the safeguarding is the fact that there was a belief that because government agencies are federally mandated to make this data publicly available and that assumption has been shaken up and there’s no backup solution. And so an investment in open infrastructure solutions is where we’re trying to mobilize and a number of others are working alongside, but that is a responsibility for not only philanthropies and other sorts of funding models of how they contribute to these open infrastructures is not at the baseline of procurement in the same sort of way that you would find for say compute infrastructure necessarily.
Jen and I have made the argument in front of a number of impassioned funders that were looking at what they could do. There was a whole study on the disruption to facilities and administration costs and what institutions could charge for government grants. Even just a 10% overhead, why is that not a 10% that’s going for all of these grants that are being made? Why is there not 10% that has to go to the broader data infrastructure? It is a rounding error, but a significant amount of investment that in a shared capacity could start going to these groups, groups that are integrating with these infrastructures, groups that are relying. And so again, I think yes on the policy side to start normalizing this cost, but also in terms of the shared responsibility that everyone should be inquiring about who is paying the bill and where they can make sure that that gets protected in their underlying budgets.
Nicholson: And beyond or maybe in addition to the financial structures, since there are so many stakeholders and actors as part of the ecosystem, how do you think infrastructure support can be better coordinated? It seems like that’s a really big issue.
Gibson: Can I make an overarching statement, a brief overarching statement? And Kaitlin can handle the details. The costs of making the research available, of sharing it and making it accessible forever should be sat with the costs of doing the research to begin with. It just has to be part of the same conversation.
Thaney: 100%. And in many cases, again, we’re looking at the operational expenses and then we’re also looking at the long term. If you’re looking at it, if we want to take a VC mentality, the majority of what they’re investing in is over a 10-year timeline expecting 80% to fail. We don’t look at that in the same sort of way for these infrastructures. We don’t look at the long-term costs versus the near-term operational expenses. And again, this is where some of the business acumen that Jen and others have brought into the space and that we’re trying to help support can help. And so, I would just say that there’s that component. And to your question, Megan, about where that coordination comes in, IOI exists to help not only provide broader guidance and targeted strategic support and helping to even pilot what can a future look like where we explore different models, doing some of that business development behind the scenes, a significant amount of it too, to try to see where we can unlock more resources.
But we also sit outside of institutions, we sit outside of funders, we sit outside infrastructures to be able to provide insight across the broader ecosystem. So we have a tool called Infra Finder. It has information, I think, on 143 and growing infrastructures that you can go through to help decision makers. But part of what we’re also in conversation with infrastructures behind the scenes about, and I know Jen is as well, is where can we work smarter, not harder? To that earlier point of the broader insight into specific community needs and the depth of and the ways in which these infrastructures service specific communities to me is at the core of the special sauce of what these infrastructures provide. Now, trying to have larger businesses sustained at a scale for many of these groups without having some supports or shared infrastructure or release of different levels of capital make that incredibly hard to be able to sustain.
And that’s where we see some of these groups really start to struggle with having the sustained runway or the unrestricted reserve so that they can provide a little bit more breathing room to their staff. And so I think there’s a lot of space there to think of some different models. I’d love if there is a funder that wants to help get that off the ground to be able to see where we can prove what is possible for that, for there to be resourcing so we can explore it. And it’s something I know Jen and I have been talking about for a number of years along with a number of others.
Gibson: I must also point to GREI. The National Institutes of Health pioneering effort at the Office of Data Science Strategy has been to bring together commercial and nonprofit entities to build and perpetuate open standards for data. And the NIH doesn’t just sponsor research that ends up in generalist repositories. They also directly fund 150 or 200 separate knowledge bases and they’re asking themselves, what is the long-term financial plan to support all of these resources? We’ve spoken already around the standardization of various things amongst these commercial and nonprofit entities. There’s a lot of potential in this model. And from a government point of view, if each of the big US agencies took a look at this initiative, that could be significant progress.
Nicholson: I want to close with a kind of bigger question. It feels like open science has sort of served as a guiding light for the research community over the last several years and it is a fairly idealistic cause. So I’m wondering if you can both weigh in on how open research infrastructure has helped to uphold the ideals of this movement.
Gibson: I’d say it’s more than an idealism. There are people who are very passionate, but it is a practical solution. If the outcomes of research are made openly available on the internet, then research and progress can happen more effectively and more efficiently. The internet’s been around for what? 35 years now and it’s a bit embarrassing to compare progress for shopping and finding a yellow pair of shoes compared to trying to make progress in different types of cancer. It’s really a bit shocking. So I thank the folks who were idealistic about it and got us on this path, but really the implications are practical and very important for the whole world.
Thaney: I’ll underscore that because if you look at the creation of the web that came out of CERN, that came out of our research. I mean, it’s maybe the ultimate research infrastructure, though I know that there was a lot of work that predated that. And the fundamental assumption there was that access to knowledge was a fundamental human right and that being able to connect and have that be available as a global way to empower communities and innovation and business and economic development. I mean, it was not just for the research ecosystem. I will say, again, having had 20-plus years of my career in this broader ecosystem, what I find empowering about the open infrastructure space and I’d also encourage those that are creating closed infrastructure to think about some of these broader areas of where you can make sure that you are servicing community and not focusing on profit over service to the broader good.
I came into this space because a friend of mine could not get access to the doctors that he needed to keep himself in an ability to walk. I don’t want everybody to have to have that use case to be mobilized to be able to do this sort of work. We shouldn’t have to have that case making still be at the forefront of everything, but it is a reality for many of these broader spaces and having those that are pushing on solutions that are continually keeping in mind that you can build successful businesses, but also be really clear about who you are serving and why for the broader good to me is just a core essence of humanity and broader kind of public access. So it seems to me less of an ideal. There’s a lot of pragmatism, as Jen has mentioned, but it’s a necessity. And I think that without that, we’d be in a much darker place.
Nicholson: It’s been so interesting to talk to you both about this and I really appreciate your time and your insights and your work. Thank you again for this.
Thaney: Thank you, Megan.
Gibson: Thank you.
Nicholson: Learn more about the importance of digital infrastructure by reading Jen Gibson and Kaitlin Thaney’s piece called “Who Will Keep Research Data Infrastructure Open and Running?”
Find links to this and more in our show notes. Thanks to our podcast producer, Kimberly Quach, and our podcast editor, Shannon Lynch. I’m Megan Nicholson, senior editor at Issues. Thanks for listening.