The principal change this year: we used a large language model classifier to screen out false-positive matches and to categorize awards, steps performed by manual review in previous editions. The full documentation reports how each classifier performed against human-annotated data, along with its known limitations.

Briefly: 

1. Sources. We harvested data directly from funders’ websites when it was available; we also harvested from OpenAIRE, OpenAlex (new this year), 360giving, and usaspending.gov. For several private foundations, we reviewed grants reported on US IRS Form 990 filings. This dashboard covers awards with start dates from 2001-2025 inclusive; awards with no date are included in counts but not in amounts. The downloadable dataset also includes awards from before 2001. 
2. Selection. We searched award titles, descriptions, and recipients using a predefined list of search terms. A classifier then removed matches that mention an OI only in passing (e.g., as an illustrative example) or that coincide with an OI’s name; uncertain cases and awards with too little text were routed to human review. Based on our testing, roughly one in six false matches is retained.
3. Deduplication. We removed awards that appeared more than once, including the same award recorded by different sources under different identifiers. Deduplication at this scale is imperfect, and some duplicates remain.. 
4. Currency. We converted non-USD amounts to USD using European Central Bank historical rates at each award’s start date. Undated awards are counted but carry no USD amount. 
5. Categories. Direct awards are those where funds reach the OI or its host organization; this dataset and dashboard currently treats any award reaching the host as Direct, regardless of its stated purpose, a rule we are now reviewing [see errata]. Direct awards are further categorized as research and development, operations, and other. Adjacent awards do not flow to an OI but support an activity that uses or builds on one, for example depositing a research work to an open repository. Adoption awards support uptake of an OI by a user or community. Unknown awards are those whose relationship to the OI we could not determine. The classifier under-identifies Adoption and Unknown awards, so treat those counts as lower bounds.
6. Multi-recipient awards. We counted multi-recipient awards for each named OI, wherever total counts are reported. Because we rarely know how funds were divided among collaborators, their amounts are not attributed to individual OIs but are included in overall totals. 
7. Consortial awards. Where an OI is one participant in a large multi-partner consortium, the headline award total overstates its funding. For the largest awards (the top 20 by value, plus European Commission awards over $2M), we checked participant-level records: where an OI's share could be recovered, only that share is counted; where it could not, the award is counted without an amount. Smaller consortial awards were not reviewed and may still overstate funding for some OIs.

This work was generously supported by the sponsors and partners of the State of Open Infrastructure 2026 report: Special thanks go to Crossref, the report’s Supporting Sponsor, and to our Community Supporters: the Research Data Alliance (RDA), the Council on Library and Information Resources (CLIR), the American Geophysical Union (AGU), and Cassyni.

Some of the disruptions over the last two years have been very loud, with budget cuts and policy shifts across the globe. Herein, we try to spotlight some of the ripple effects that often are quieter and harder to see. The biggest take-away may well be that the fragility the field feels today is not new,: instead, it is a built-in characteristic of the structures and models used to fund open infrastructure for decades. 

Where the money goes.

We’ve analyzed more than US$663 million in open infrastructure grant funding across 1,065 award clusters, 47 funders, and 94 open infrastructures from 2021-2026. Here’s what that money actually paid for:

• $192 million (29%) funded open infrastructures directly
• $411 million funded research that depends on open infrastructures
• $23 million funded adoption of open infrastructures

We want this to be crystal clear to our readers: the vast majority of the money flow has gone to the work that infrastructure makes possible, not to the infrastructure itself. That isn’t a bad thing on the surface: we want infrastructure to be strong and to enable lots of exciting research discoveries, publications, and dissemination. But the gap here tells a more complicated tale: Creative Commons sees nearly $29 spent on research that uses its work for every $1 that reaches it directly. DOAJ sees nearly $24 spent for every dollar it receives, and RRID sees more than $22 per dollar.

Again, this could signify efficiency: a modest open infrastructure investment carrying an enormous research enterprise. It could also signal that there is a growing body of research resting on infrastructure that is not funded to grow with it. At IOI, we think both of these readings are true and that the second one should keep funders up at night.

We pay to build, not to run.

As this year’s report shows, only a thin slice of direct funding has supported open infrastructures. And an even thinner slice of that $192 million funded operations rather than innovations for these open infrastructures. 

So, rewinding, only 29% ($192 million) funded open infrastructures directly. Of that, $105 million went to research and development (R&D) while only $64 million went to operations, or the work of keeping systems available year after year.

This doesn’t stem from anyone’s bad judgment; it’s structural. Grant proposals are built around  projects, and we expect that projects will build things. This produces an ecosystem where launching new things is very lucrative, but keeping them alive thereafter is not. Infrastructure organizations end up running just to stay in place, raising money for new features that often complicate their codebases and services so that they can very quietly keep their lights on.

Global reliance, single-source support

Research that depends on open infrastructure is funded from all parts of the world. That is not the case for the infrastructure, though. Of the 34 infrastructures in this year’s analysis that had more than one direct grant, 22 drew their support from a single country, even though the research relying on them is funded from nearly 3 countries on average. Nine countries, for example, fund research that depends on arXiv; one country funds arXiv’s grants. For OSF, that ratio is seven to one.

Funders are, of course, similarly concentrated. The US National Science Foundation appears in the direct funding of 13 different infrastructures, Institute of Museum and Library Services in 10, and Mellon, Sloan, and the European Commission each appear in eight. When one of these funders shifts priorities, the risk isn’t isolated; it’s explosive. Right now, $42.1 million in active US federal grants to open infrastructure is exposed to immediate budget threats, indirect cost caps, and the near-elimation of some agency activity. Infrastructure that the world depends on is only as durable as the priorities of the one government that sustains it.

The infrastructure of integrity

The report's second section asks how open infrastructure makes a published research output trustworthy, especially when generative AI is making research fabrication increasingly easier. The recently emerging threats include preprint flooding, AI-powered paper mills, ghost authorship, and hallucinated citations polluting the scholarly record.

The good news? We already have much of the infrastructure needed to validate and establish the provenance and trustedness of a research object. The bad news? Well, that’s consistent with the rest of this report. These are largely nonprofit, community-governed systems that are chronically underfunded relative to how much the ecosystem leans on them. Free to use does not mean free to run.

Signals we are watching.

The final report section tracks forces that are usually discussed separately but that we think are deeply intertwined: the AI investment boom, the severe pullback on public science budgets, and the rise of digital sovereignty. We sort seven signals into three groups: Risk, Watch, and Opportunity.

In “Risk,” we talk about the dangers posed by philanthropic funders leaving the ecosystem simultaneously with the federal open science pull backs. We also look at how an open tool, Tailwind, has been seeing its usage grow while its revenues are dropping alarmingly. In “Watch,” we share our perspectives on the EU’s new EDIC Digital Commons, COS’s decision to streamline OSF, and PLOS’s proposal to move beyond the article as the unit of value. And in “Opportunity,” we discuss the $500 million HumanityAI funder coalition and the Dutch government’s funding for infrastructure maintainers’ training in sustainability.

What could change the trajectory from fragility to resilience?

None of these patterns were designed on purpose, and no single organization created them. Responsibility has been passed around the same small table for years, and each player has a reasonable argument for why it belongs to someone else. This is the reason no single organization or stakeholder group can fix the problem alone.

Still, some of this is imminently fixable. The funder pool may need a new view of the ways it is funding research and infrastructure in order to strategically consider what reciprocal relationship those two things need to have. Keeping systems running may cost far less than rebuilding, and concretely, based on our findings in this report, we recommend:

• Fund infrastructure directly, not just the research it supports
• Treat maintenance as a budget line, not a one-time cost
• Build persistent identifiers into policy

Above all, this report demonstrates that all stakeholders today need to look hard at their dependencies and how and whether they are funding them.

Coordination begins when funders, institutions, and open infrastructure providers all can look at the same evidence. That’s why we produce this report.

• Read the 2026 State of Open Infrastructure Report
• Explore the Data Dashboards
• Download the Dataset (opens Zenodo in a new tab)

We're grateful to Crossref, our Supporting Sponsor, which was the first to step up and put its money where its values are. We also thank our Community Supporters, RDA, CLIR, AGU, and Cassyni, and our core funders, including the Andrew W. Mellon Foundation, Arcadia Fund, and the Wellcome Trust. That this report is funded by the communities it describes is perhaps a testament to the value of cross-sector coordination.

Get involved

We’d love to have your feedback on the report through our feedback form. Your feedback is very important and will help shape the topics for future editions of the report.

We hope this report sparks your curiosity about the tools and systems the research and scholarship community relies on and helps you to think about how your decisions line up with your vision for the future of research and scholarship. If you need additional information and support around these decisions, please reach out to us. We are experienced in providing tailored research and recommendations, including discovery, due diligence, and stakeholder feedback collection, to inform the decisions you are facing.

Funding insights at a level no single funder can see from inside its own portfolio.

Grant funding flows are one signal of how the research ecosystem values and takes responsibility for the infrastructure it shares.

The data and analyses we published in 2024 and 2025 primarily focused on better understanding the amount, impact, and limitations of grant funding to Open Infrastructures (OIs). In 2026, we are looking more closely at a related factor: how much funding is supporting infrastructure elements directly versus how much funding is supporting research that leans on and invokes infrastructure, but doesn’t contribute to it financially.

This section maps the funding dependencies for infrastructure and how narrowly the sustaining funding is sourced. We hope our analysis offers funders, institutions, and infrastructure providers a shared, independent picture of patterns that none of them created alone, and that none can address alone.

Our full data collection and preparation methods are described in detail in the documentation that accompanies the raw data — this is available for download.¹ We encourage readers to explore the full documentation for additional information on how we processed the data, as well as key assumptions and their likely trade offs. The funding described in this report is limited to grants we were able to identify through public sources and does not include other sources of revenue or funding for these infrastructures.

Figure 1

Ecosystem analysis overview

$663M

Total tracked funding

2001–2026

1,065

Awards analysed

965 dollar-bearing

47

Funders

canonical, ROR-resolved

94

Open infrastructures

Infra Finder catalogue

All financial data in the report is quoted in US dollars.

Figure 2

What the tracked funding pays for

$192M

$411M

$192M direct — funds an OI $411M adjacent — funds work that depends on an OI $23M adoption $37M unknown

Shares are of $663M across 965 dollar-bearing award clusters, out of 1,065 clusters in total. The remaining 100 carry no allocatable dollar figure and are counted by presence only.

Grant funding flows are one signal of how the research ecosystem values and takes responsibility for the infrastructure it shares.

Question 1: What does the money support?

Most grant funding supports research that depends on open infrastructure; far less sustains the infrastructure itself.

We classify grants as direct (funding an OI), adjacent (funding research that depends on an OI), or adoption (funding an entity to deploy an OI). The imbalance between direct and adjacent is the clearest signal in the data of how funders allocate funding: more heavily toward the research, and lightly toward the infrastructure beneath it. The ratio varies widely by infrastructure; for some, a few hundred thousand in direct support underwrites tens of millions in dependent research.

Figure 3

Adjacent funding per dollar of direct funding

Creative Commons

28.7×

$9.3M adjacent
$325K direct

DOAJ

23.9×

$934K adjacent
$39K direct

RRID

22.6×

$36.9M adjacent
$1.6M direct

DSpace

22.2×

$16.1M adjacent
$724K direct

Biodiversity Heritage Library

16.2×

$6.0M adjacent
$371K direct

OpenEdition

15.9×

$4.7M adjacent
$297K direct

Ratio shown only for OIs with both direct and adjacent funding. Ranking is restricted to OIs carrying at least $250K in adjacent funding and the top six are shown. Without that floor, a large ratio resting on a small adjacent total would top the chart. The adjacent figure depends on the classifier (F1 ≈ 0.93), so treat magnitudes as directional, not precise.

$1.65

Research funded

$1

Direct funding

Across the ecosystem, every $1 of direct funding that reaches an infrastructure sits beneath $1.65 of funded research that depends on that infrastructure. This could be interpreted in two ways:

Efficiency

Modest infrastructure investment carries a much larger research enterprise

Fragility

The wider the gap, the more output rests on support that hasn’t grown with it

Within that thin slice of direct funding, most pays to build, not to run.

Of the $192M reaching infrastructure directly, the majority funds new development rather than sustained operation. This is not a failure of any funder’s judgement — grant proposals are designed around projects, and projects tend to build things. But project-based funding leaves ongoing operations (the part that keeps infrastructure available year after year) structurally harder to fund.

$663M

Funding tracked across the ecosystem

2001–2026 · 1,065 award clusters

$192M

Reaches infrastructure directly (29%)

the rest funds work that depends on it, or its adoption

$64M

Of that pays to keep it running (10%)

paying for daily maintenance and costs

Figure 4

Distribution of direct funding to open infrastructures by category

Research & development$105Mnew capabilities

Operations$64Mkeeping it running

Other or not classified$23M

Question 2: How concentrated are the sources?

Research relies on open infrastructure globally. The funding that sustains infrastructure tends to come from single sources.

Direct funding is concentrated by geography: most infrastructures draw their sustaining grants from one country even when they support global research. Seen two ways — by geography, and by funder — the pattern is the same, and it is not one any single organisation created or can resolve alone.

Figure 5

Infrastructures supported by different funders

National Science Foundation (US)

13

Institute of Museum and Library Services (US)

10

Alfred P. Sloan Foundation

8

Andrew W. Mellon Foundation

8

European Commission

8

Each bar counts the infrastructures a funder has supported directly, of the 52 that received any direct funding.

22

Infrastructures funded directly from a single country

Of the 34 infrastructures with more than one direct grant, 22 receive that support from one country, while the research depending on them is funded from 2.6 countries on average. Single-source funding ties an infrastructure’s continuity to one country’s budget cycles and policy priorities, however global its users.

13

Distinct infrastructures the National Science Foundation (US) alone has funded directly

A small number of agencies and funders underwrite much of the ecosystem: NSF appears in the direct funding of 13 different infrastructures, IMLS 10, Mellon, Sloan and EC 8 each. When one funder’s priorities shift, the effects reach many infrastructures at once, and the risk is correlated.

The European Commission (EC) is treated as pan-European, not as Belgium (its registered country), so EC-funded OIs are not counted as single-country. Country diversity is measured by award presence (≥1 grant, any amount), which restores consortial grants that carry no allocatable dollar figure. This dataset undersamples non-US funders — so true single-country concentration is, if anything, lower than shown.

The asymmetry up close: Award counts by country.

Nine countries have funded research that depends on arXiv; one provided grant funds for arXiv itself. For Open Science Framework (OSF), this ratio is seven to one. The reliance is distributed across the research world; the funding that sustains the infrastructure is not.

Single-country concentration

Where that concentration meets current risk.

$42.1M

US Federal Funds for Open Infrastructure

Since January 2025, US federal research funding has faced sustained pressure: agency budget threats, indirect cost caps, and the near-elimination of IMLS. $42.1M in active federal grants run to open infrastructure through this period. The point is bigger than the US: infrastructure the world depends on, sustained mainly by one government, is only as durable as that government’s priorities.

Figure 8

Open infrastructure reliance on US federal funding

Infrastructure	Federal funder(s)	Active grant value
ICPSR	NSF · HHS	$18.1M
arXiv	NASA · NSF	$6.4M
Dataverse	NIH · HHS	$3.3M
Zenodo	HHS	$2.3M
Dryad	HHS	$2.2M
Galaxy	NIH · HHS	$1.9M
OSF	NSF	$1.5M
+ PUDL, DMPTool		$0.6M
Multi-recipient grants span several OIs		$5.8M
Total at risk		$42.1M

Cutoff held at Jan 2025 (last edition’s threshold): the harvest thins after 2024 (78 grants in 2024 → 41 in 2025 → 5 in 2026), so a later cutoff would undercount active grants not yet captured, not grants that have ended.

None of these patterns were intentionally designed, and none belongs to any single organisation to resolve. Our aim with this analysis is simpler: coordination starts when funders, institutions, and infrastructure providers are looking at the same evidence.

Data and software availability statement

The grant awards dataset used for the analysis presented here is available online at https://doi.org/10.5281/zenodo.21401121, together with full methods documentation and a data dictionary. The code used for processing and analysis are available online at https://github.com/investinopen/state_of_open_funding_data_processing/.

Interactive dashboards allowing direct exploration of the data are available at https://investinopen.org/grant-funding-data-dashboard/.

Questions or concerns? Please contact us at research@investinopen.org.

Footnotes

1. 1 https://doi.org/10.5281/zenodo.21401121

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Generative AI is prompting a research integrity crisis

Open Infrastructure interventions bolster trustworthy knowledge

What needs to happen

Works Cited

We wanted to move from strategic planning and naming of technical considerations to testing approaches in a tangible way – and we’re excited to share more about what we’ve been working on as a first attempt at answering some of the questions we believe the data rescue movement hasn’t fully tackled yet, including what happens once the data is rescued. This work sits alongside countless efforts and existing open data infrastructure solutions working to mirror, host, improve discoverability, and map out intersection points, and is designed to test a few assumptions before committing to a full build and catalyze further action. 

We started with a few baseline questions such as once critical scientific data is saved from disappearing, how do you actually make it usable again, by the people who need it most, across the many disciplines a real emergency touches? And what does it look like to build with and across existing open data infrastructures and repositories in a networked way, without duplication and without them colliding with each other? The in-between reality is even messier to follow. Data rarely goes dark all at once. Some federal sources stay up while others disappear, some come back, some are replaced quietly with something incomplete. What does a workable interim solution look like when the picture is that uneven, rather than a clean before and after?

Back in June, we wrote about what it actually looks like when the dependencies underneath research break down. That piece used the example of wildfire smoke maps in Ruidoso, New Mexico, tools that help people figure out where smoke is drifting and which immunocompromised neighbors might be at risk, built with Forest Service research funding that was, at the time, disappearing in real time. It ended with a promise – that we were starting to build what we refer to as an “existence proof”, a working demonstration of what a genuinely open, federated infrastructure solution that draws on existing investments in open data could look like for this class of problem. 

Today we want to share more about that proof, in its first form – and why we’re taking the approach we are (and what we’re learning).

A focus on wildfire (as a start) 

We picked wildfire as our test case for a simple reason. It's emblematic of a rare data problem that everyone already understands is urgent – and that by its very nature involves users and data sources beyond traditional academic research walls. The story underneath is bigger than fire. It's about whether we can build a system that pulls from many different data sources, disciplines, and infrastructures at once and keeps answering questions even when one of those sources goes dark, whether that's a NOAA smoke feed in New Mexico, public health data or a Forest Service research dataset losing its funding mid stream, or something in between where one agency's data is fine and its neighbor's isn't. 

There’s some interesting aspects in querying data across a variety of sources, mirroring of data if a source goes dark, and also perhaps a bigger question (as we at IOI look to drive more attention and investment to data infrastructures and the organisations behind them) – is the data being rescued usable and getting into the hands of those who need it most? 

As we built the proof-of-concept, we spoke with volunteer firefighters and emergency managers about what they actually rely on today. One collaborator, a volunteer firefighter and emergency manager in Colorado, put it plainly.

"There is very little data analysis in emergency management and the fire service, and relatively little technical expertise. Nobody writes code. In essence the role of Emergency Management during an emergency is to create and support a human decision making and information dissemination machine that is not reliant on digital technologies."

He sent us a photo of his department's map binder. It was dated 2011.

Between that binder and the Ruidoso smoke maps sit a whole range of reasons to rethink data resilience, from someone deciding whether it's safe to send their kid outside, to a department trying to get better information than a fifteen year old paper map during an active incident. A wildfire scenario forces you to reckon with weather, terrain, vegetation, public health, energy infrastructure, and socioeconomic data all at once. That cross domain assembly problem is exactly what a resilient data system needs to be able to handle, and it's much easier to make it real through a scenario like this than to explain in the abstract.

The gap we're responding to

Over the past year and a half, IOI has been a part of a series of coalitions that have mobilized to rescue federal data at risk from funding cuts, policy changes, and takedowns and design new approaches and business models to build back better. This has included collaborations with groups convened by the Center for Open Science, the American Geophysical Union, the Internet Archive, Data Rescue Project, Public Environmental Data Partners, Source.coop, Dryad, ICPSR, our own mappings of over 87 initiatives and collaboration efforts, and that’s just scratching the surface. 

That work has mattered, and it continues to matter. Efforts are shifting from saving bytes, archiving copies, and moving data somewhere safer to starting to explore what’s next. At IOI, we’re working to do our part to push on the infrastructure elements we know best . We’re building on and with existing investments in open data and repository infrastructure, looking at where we can (and need to) build better given geopolitical and funding volatility, and making the case for increased resourcing for these critical systems at a time we need to increase speed, not lose momentum. 

Some of what's been rescued so far is now sitting in archives without stable identifiers, consistent metadata, or any clear sense of who maintains access to it over time. That's not a criticism of the people doing that work, most of it has been done under real duress, on volunteer time. It's a reflection of where the investment has and hasn't gone, and where we think it needs to go next.

We watched something similar happen after COVID. A wave of energy moved toward open science, shared data, and collective infrastructure. Much of that energy receded once the acute phase passed, and a lot of research and discovery drifted back toward business as usual. We can't afford for critical scientific and disaster response data to make the same round trip, especially with funding and political conditions as volatile as they are right now. The data crisis facing federally funded research isn't a moment that passes and then gets fixed. It's a structural condition, and it needs a response built to outlast any single news cycle.

We've watched ambitious plans to build a full alternative to federal data infrastructure struggle to get off the ground, not for lack of good ideas, but because replicating something at the scale of a national government system is a genuinely hard problem to solve all at once – and funders are reticent to take on that capital cost alone. So instead of trying to build the whole thing, we scoped something much smaller and testable, a working proof of concept aimed at a specific set of real questions for a timely issue we’re seeing quickly affect not just fire prone areas in North America, but across Europe and beyond.

What we are building

Under the hood, this is a demonstration of what it looks like to query across existing open infrastructures as if they were one system, rather than a pile of disconnected repositories, and to do it without asking any single one of them to become the new central point of failure.

The proof-of-concept pulls from a mix of data sources chosen specifically because none of them alone can answer the questions an emergency manager actually needs answered. We’ve included data from Source Cooperative such as cloud native spatial risk data, fire perimeters, burn probability, and wetlands data, all queryable directly over the open web. Data from Dryad provides research grade, DOI backed datasets like a quality controlled multi decade wildfire database and modeled smoke exposure data, so a dataset keeps a stable, citable identity even if the agency that originally published it goes dark. Live federal feeds from NOAA and NASA FIRMS supply near real time fire detections, Red Flag Warnings, and smoke plumes, the ground truth layer that shows what's happening right now, the same category of data behind the Ruidoso smoke maps. OpenStreetMap fills in the human and built environment layer, roads, power lines, and hydrants, the pieces that matter most once you're thinking about response capacity rather than just risk. (And that’s just a sampling.)

We query across all of that using DuckDB, which can reach directly into remote data without requiring us to first copy and store it ourselves. That matters for two reasons. It keeps compute on the researcher's own machine rather than centralizing it somewhere new, and it means the project stays useful even if we, IOI, weren't around to keep something running.

Then there's the fallback layer, which is really the heart of the demo, and where the uneven, partial outage problem actually gets handled. If a source dataset becomes unavailable, whether because an agency takes it down or a server quietly disappears, the system checks whether it's already been rescued elsewhere, using the Data Rescue Project's growing catalog of known mirrors, and routes the request there instead, while everything still reachable keeps flowing through normally. If a resource isn't already archived anywhere, the same tooling passively triggers an Internet Archive capture the moment someone tries to use it, so future users aren't left stranded the way today's users so often are. This is a proxy and a small set of tools, not a new central repository. We're intentionally not trying to become the permanent home for this data. We're trying to show what the connective tissue between existing homes could look like, including in the messy middle state where some sources are healthy and others aren't.

What this is, and what it isn't

This is a proof, not a product. We built it at a concentrated, testable scale specifically so we could learn quickly rather than spend years building toward something that might not hold up in practice. It's meant to sit alongside, and make more visible, the tremendous work already being done by the repositories, archives, and rescue efforts we're drawing from, not to replace or compete with any of it. And it names its gaps and tradeoffs openly rather than papering over them, because the honest accounting of what a durable version of this would actually cost is as much a part of the point as the demo itself.

What we're testing is a hypothesis. That rescued and existing open data, spread across research repositories, federal systems, and citizen science platforms, can function as one queryable, resilient system, that it can do so without duplicating effort or stepping on the infrastructure already doing this work well, and that the people who most need that data, not just researchers, but the operational people making decisions in the field, can actually get something usable out of it, even when the underlying sources are only partly working.

We think the answer so far is promising enough to be worth a much deeper investment in the infrastructure layer that data resilience work has largely skipped, the coordination layer that sits on top of and between repositories rather than inside any one of them. That layer doesn't currently exist as a shared resource, and it's still unclear who is building it. We'll be sharing more from the proof-of-concept, and what we learned building it, in the weeks ahead.

Want to learn more about getting involved or supporting our work? Contact contribute@investinopen.org – we’d love to talk. To take a peek under the hood, you can also check out our work in progress here.

A finales de 2025, Invest in Open Infrastructure lanzó el Fondo de IOI para la Adopción en Redes, un nuevo tipo de inversión diseñado para acelerar la adopción global de infraestructura abierta para la investigación y el intercambio de datos. En lugar de financiar herramientas individuales o instituciones aisladas, el Fondo invierte en redes: consorcios de instituciones e investigadores con relaciones ya establecidas, una gobernanza activa de sus miembros y un compromiso demostrado con la ciencia abierta. La lógica es que fortalecer la infraestructura compartida que una red ya opera puede multiplicar el impacto en todas sus instituciones miembro al mismo tiempo.

El Fondo surgió del diálogo directo con las comunidades de investigación para identificar los proyectos de infraestructura abierta que más necesitan apoyo sostenido, y reúne contribuciones de un grupo de financiadores interesados en fortalecer el ecosistema global de investigación abierta. Cada red seleccionada recibe hasta 1,5 millones de dólares durante un período de dos a tres años, junto con apoyo continuo del equipo de IOI para la implementación en ámbitos como gobernanza, participación comunitaria y modelos de negocio. El objetivo de IOI con el Fondo no es solamente permitir que las organizaciones beneficiarias desarrollen proyectos capaces de generar un gran impacto en sus redes, sino también contribuir a la resiliencia de esas redes mucho después de finalizada la subvención.

LA Referencia fue seleccionada como una de las dos primeras beneficiarias del Fondo, entre más de 100 postulaciones provenientes de 22 países. En conjunto, las dos primeras organizaciones beneficiarias del Fondo están en condiciones de beneficiar a más de 1.300 instituciones de investigación en 36 países de América Latina y África. Este artículo ofrece una visión general de lo que LA Referencia ha realizado durante los primeros seis meses de esta inversión. En los próximos meses publicaremos también una actualización sobre la otra organización beneficiaria inaugural, UbuntuNet Alliance.

Introducción al proyecto de LA Referencia

LA Referencia es una red regional de cooperación que agrega y proporciona infraestructura de  acceso abierto a la producción académica y científica de repositorios nacionales de América Latina y España, basada en un modelo de gobernanza federada y en una infraestructura de propiedad pública y no comercial. Con el apoyo del Fondo de IOI, LA Referencia está desarrollando un proyecto para el período 2026–2028 destinado a ampliar el alcance regional y la profundidad técnica de esta infraestructura mediante cuatro grandes objetivos: incorporar más países y repositorios a la red, modernizar la forma en que los investigadores descubren contenidos en múltiples idiomas, construir un sistema soberano y verificable de identificadores persistentes y poner en marcha un repositorio regional compartido junto con un programa de capacitación para datos de investigación. El avance completo del proyecto está documentado en detalle en el sitio de informes del proyecto de LA Referencia, y el relato que sigue se basa directamente en el informe ejecutivo del equipo correspondiente a enero–julio de 2026.

El proyecto está organizado en tres componentes complementarios que conectan contenidos, tecnologías de descubrimiento, infraestructura de identificadores y capacidades humanas:

• Componente A — Expansión de la infraestructura de agregación de LA Referencia: ampliar la cobertura regional, mejorar y normalizar los metadatos e introducir búsqueda semántica multilingüe en la capa de descubrimiento de LA Referencia.
• Componente B — Identificadores ARK descentralizados y preservación de metadatos (dARK): desarrollar dARK 2.0, una infraestructura modular basada en blockchain e ipfs para asignar, publicar y resolver identificadores persistentes.
• Componente C — Repositorio regional de datos  datos para instituiones sin infraestructura local y soporte para Dataverse: poner en marcha un repositorio regional para datos de investigación basado en Dataverse, acompañado de un programa federado de capacitación y curaduría.

En el resto de este artículo profundizamos en los avances realizados en cada uno de estos componentes y compartimos productos y resultados del trabajo de LA Referencia que ya pueden ser explorados.

Componente A: Expansión de la infraestructura de agregación de LA Referencia

El descubrimiento depende de contar con buenos metadatos para las colecciones, por lo que el Componente A combina la ampliación de la cobertura regional de LA Referencia con el trabajo sobre calidad de metadatos y una nueva capacidad de búsqueda multilingüe que permite descubrir contenidos independientemente del idioma utilizado en la búsqueda o del idioma del contenido.

Avances hasta la fecha

Durante el período del informe, LA Referencia implementó una metodología regional para identificar instituciones y repositorios candidatos a ser cosechados con el objetivo de ampliar su colección. Este trabajo incluyó la verificación de servicios OAI-PMH, la revisión de metadatos, la actualización de la Interoperability and Registry Database (IRD) de COAR y la verificación cruzada de información institucional con ROR. El trabajo de curaduría se completó para los países miembros Ecuador, Uruguay y Costa Rica, y para los países no miembros Colombia, Paraguay, Venezuela, Bolivia y Cuba; los registros de otros países se encuentran en proceso.

En total, el equipo actualizó los registros de 362 repositorios: 187 fueron editados, 77 incorporados como nuevos y 98 archivados. Venezuela fue el país con mayores avances, con dos repositorios cosechados exitosamente como experiencia piloto para la incorporación de contenidos provenientes de países que todavía no operan como nodos completos de LA Referencia.

En relación con esta expansión geográfica, LA Referencia está desarrollando una solución para un problema común identificado en su comunidad: muchos investigadores realizan búsquedas en un idioma mientras que el trabajo que necesitan está descrito en otro. Un registro de un repositorio en español podría no aparecer nunca para una persona que realiza una búsqueda en inglés, o viceversa, porque la búsqueda tradicional por palabras clave solo puede hacer coincidir las palabras exactas presentes en la página. El Componente A busca cerrar esa brecha mediante búsqueda semántica multilingüe, que encuentra resultados haciendo coincidir significados en lugar de palabras exactas. Para determinar la mejor manera de hacerlo, el equipo realizó una evaluación rigurosa y reproducible, comparando aproximadamente treinta modelos diferentes de IA en dos colecciones de prueba de entre 30.000 y 50.000 documentos, con miles de búsquedas de prueba en hasta 10 idiomas. Este trabajo produjo resultados prometedores: en el piloto, la búsqueda semántica y la búsqueda combinada recuperaron aproximadamente el doble de resultados relevantes entre los primeros 10 resultados —pasando de un promedio de 4,5 a 9,1— en comparación con la búsqueda únicamente por palabras clave. Las mayores mejoras se observaron en idiomas que suelen presentar mayores dificultades para la búsqueda por palabras clave, como chino, japonés y árabe. El equipo evaluó distintos modelos y seleccionó uno que ofrecía un equilibrio entre resultados de alta calidad, eficiencia y velocidad para operar sobre millones de registros.

Esta evaluación ya fue incorporada a un sistema beta funcional dentro de la plataforma de búsqueda de LA Referencia. Cuando una persona realiza una búsqueda, puede elegir entre la coincidencia tradicional por palabras clave, una coincidencia “basada en significado” que funciona entre distintos idiomas o un modo combinado que integra ambas aproximaciones. La reindexación se vuelve considerablemente más rápida cuando se reutilizan embeddings generados previamente. Reconstruir el índice completo para un conjunto de prueba de aproximadamente 28.000 registros requirió alrededor de 5 horas la primera vez, pero solamente unos 10 minutos cuando los datos basados en significado ya estaban almacenados, lo que hace posible realizar actualizaciones regulares a medida que la colección continúa creciendo. Paralelamente, el equipo incorporó un WAF abierto que filtra bots automatizados y scrapers, junto con una forma de aplicar esa misma protección simultáneamente en múltiples sitios de LA Referencia.

También es posible profundizar en los detalles técnicos consultando el informe completo del Componente A de LA Referencia.

Próximos pasos

LA Referencia continúa revisando e incorporando repositorios de otros países miembros y de nuevos países fuera de su red. El equipo planea ampliar la evaluación de la búsqueda semántica multilingüe a toda la colección regional y consolidar la versión beta de búsqueda semántica antes de avanzar hacia un despliegue más amplio.

Componente B: Identificadores ARK descentralizados (dARK) y preservación de metadatos

El Componente B comprende el desarrollo de dARK 2.0, la próxima generación de la infraestructura de LA Referencia para asignar, publicar y resolver identificadores persistentes ARK. Este componente contempla una infraestructura regional, soberana y sostenible, construida específicamente para la región a la que sirve LA Referencia.

Avances hasta la fecha

Los identificadores persistentes son los que permiten que una producción de investigación continúe siendo localizable y citable a largo plazo mediante un enlace permanente que sigue conduciendo al artículo, conjunto de datos o informe correcto años después, incluso si el archivo cambia de ubicación o si cambia el sistema que lo aloja. Actualmente, gran parte de esta infraestructura es gestionada por un pequeño grupo de proveedores externos. dARK 2.0 representa la visión de LA Referencia de una infraestructura que la propia región pueda operar, gobernar y considerar confiable, sin depender de una única organización como centro de control.

Para lograrlo, el equipo dividió dARK 2.0 en componentes separados que pueden ser verificados, actualizados o escalados de manera independiente. Un registro compartido y resistente a manipulaciones —un tipo de blockchain— mantiene el registro autoritativo de qué identificadores existen y hacia dónde apuntan, mientras que los metadatos descriptivos de cada registro se almacenan por separado, permitiendo que cualquiera pueda verificar que no han sido alterados. Esta separación mantiene la capa de “prueba” simple y confiable, mientras que los datos descriptivos pueden continuar creciendo sin necesidad de modificar esa capa de “prueba”. El equipo también construyó todo el flujo de procesamiento que requiere el sistema: reservar nuevos identificadores, preparar y almacenar sus metadatos, publicar la referencia en el registro y permitir que cualquier persona consulte un identificador y lo resuelva hacia el registro correcto.

El equipo sometió este flujo de trabajo a pruebas de estrés con millones de registros y ejecutó cientos de verificaciones automatizadas para confirmar su robustez, además de construir herramientas administrativas para que el personal pueda monitorear identificadores, actividad y consultas. El flujo de dARK ya está listo para ser desplegado y conectado con el sistema regional de cosecha de LA Referencia, por lo que el proceso de cosecha se está integrando con el flujo de dARK para permitir la asignación de identificadores a medida que los registros son procesados, incluso en colecciones grandes y en constante cambio.

Profundizar en los detalles en el informe completo del Componente B.

Próximos pasos

El siguiente paso consiste en distribuir la infraestructura dARK entre distintas instituciones en lugar de ejecutarla desde un único lugar. Se está preparando una instalación equivalente en RNP, en Brasil, y la institución socia IBICT está preparando la migración de sus identificadores desde la versión original 1.0 de dARK. Con múltiples instituciones utilizando el sistema, LA Referencia planea validar su funcionamiento entre distintos sitios y formalizar el modelo de seguridad y gobernanza de esta infraestructura regional federada.

Componente C: Repositorio regional para datos sin infraestructura institucional y soporte para Dataverse

El Componente C combina un nuevo repositorio regional basado en Dataverse con un programa federado de capacitación y curaduría, cuyo objetivo es ofrecer un espacio para aquellos datos de investigación que carecen de infraestructura institucional, al mismo tiempo que se difunden las capacidades necesarias para operar servicios de datos de manera sostenible en toda la región.

Avances hasta la fecha

El equipo comenzó con un diagnóstico regional que identificó aproximadamente 96 repositorios de datos registrados y alrededor de 30 instalaciones de Dataverse en América Latina, concentradas principalmente en Brasil, Argentina, México y Colombia. El diagnóstico se basó en cuestionarios y entrevistas que abordaron gobernanza, infraestructura, curaduría, preservación, soporte e interoperabilidad. Este análisis confirmó que la región ya cuenta con una experiencia y conocimientos significativos, lo que dio forma a una estrategia de colaboración entre pares que concibe al repositorio como un servicio sociotécnico: software e infraestructura combinados con políticas, equipos multidisciplinarios, apoyo a investigadores y capacitación continua.

El principal resultado técnico es una versión de prueba funcional y en línea del repositorio —un “Dataverse Alpha”— que funciona sobre la propia infraestructura de LA Referencia. El repositorio está disponible en inglés, español y portugués, incluye campos para indicar el país de origen de un conjunto de datos y con cuáles de los 17 Objetivos de Desarrollo Sostenible de las Naciones Unidas se relaciona, y utiliza licencias abiertas (CC0 y CC BY) para que los datos puedan ser reutilizados libremente. Ya se han cargado conjuntos de datos de ejemplo para que el equipo pueda probar la carga, descripción, búsqueda y acceso a archivos antes de que comiencen a recibirse depósitos institucionales reales.

Junto con el desarrollo técnico, el equipo está elaborando un MOOC asincrónico de 16 horas, Introducción a la Gestión y Curaduría de Datos en Dataverse, que abarca fundamentos y publicación, metadatos y documentación, verificación técnica y formatos, y ética, licenciamiento y aprobación. Este es uno de los tres productos de capacitación planificados; los otros incluyen capacitación práctica en curaduría y apoyo directo a las instituciones piloto. El curso será construido e instalado en un entorno Moodle de producción, con materiales en español y portugués.

Invitamos a los lectores a explorar todos los detalles técnicos en el informe completo del Componente C.

Próximos pasos

El equipo finalizará los contenidos del curso, validará de extremo a extremo los flujos de trabajo del repositorio, seleccionará las primeras instituciones piloto y transformará las lecciones aprendidas durante la fase Alpha en un modelo estable para el servicio regional.

El proyecto de LA Referencia: apoyando los objetivos del Fondo de IOI para la Adopción en Redes

A seis meses de iniciado el proyecto, LA Referencia ha pasado del diseño y el trabajo de diagnóstico a sistemas funcionales: una experiencia de descubrimiento multilingüe con mejoras medibles, una arquitectura funcional para identificadores persistentes soberanos y verificables, y un repositorio regional de datos en funcionamiento acompañado de un proceso de capacitación diseñado para perdurar más allá de una única subvención. Nada de esto fue construido para una sola institución; cada mejora contribuye a una infraestructura compartida y federada de la que ya dependen los repositorios miembros de LA Referencia en toda América Latina.

Esta es la premisa del Fondo de IOI para la Adopción en Redes: que la forma más rápida y duradera de hacer crecer la adopción de infraestructura abierta es invertir en redes que ya cuentan con la gobernanza, la confianza y el alcance necesarios para poner nuevas capacidades al servicio de muchas instituciones simultáneamente. Los primeros seis meses de LA Referencia muestran esta tesis en acción. La base técnica que están construyendo permitirá avanzar hacia la próxima fase del proyecto: expandirse hacia nuevos países e idiomas, validar dARK entre distintas instituciones e incorporar las primeras instituciones piloto al Dataverse regional.

Leé más sobre el proyecto en el sitio de informes del proyecto de LA Referencia, incluido el informe ejecutivo completoen el que se basa este artículo, o conocé más sobre el trabajo de IOI con LA Referencia y el Fondo de IOI para la Adopción en Redes.

In late 2025, Invest in Open Infrastructure launched the IOI Fund for Network Adoption, a new kind of investment designed to accelerate the global adoption of open infrastructure for research and data sharing. Rather than funding individual tools or single institutions, the Fund invests in networks: consortia of institutions and researchers with established relationships, active member governance, and a demonstrated commitment to open science. The logic is that strengthening the shared infrastructure that a network already runs can multiply impact across every member institution at once.

The Fund grew out of direct engagement with research communities to identify the open infrastructure projects most in need of sustained support, and it pools contributions from a group of funders all interested in strengthening the global open research ecosystem. Each selected network receives up to $1.5 million over two to three years, paired with ongoing implementation support from IOI staff on governance, community engagement, and business modeling. IOI’s goal with the Fund is not only to enable grantees to do projects that can have a big impact on their networks, but also to support the resilience of these networks long after the grant ends.

LA Referencia was named one of the Fund's two inaugural grantees, selected from more than 100 applications spanning 22 countries. Together, the Fund's first two grantees are positioned to benefit more than 1,300 research institutions across 36 countries in Latin America and Africa. This post provides an overview of what LA Referencia has done with the first six months of that investment. Look for an update post about our other inaugural grantee, UbuntuNet Alliance, in the coming months. 

An introduction to LA Referencia’s project

LA Referencia is a regional cooperation network that aggregates and provides open access to the scholarly and scientific output of national repositories across Latin America and Spain, built on a model of federated governance and publicly owned, non-commercial infrastructure. With IOI Fund support, LA Referencia is running a 2026–2028 project to expand that infrastructure's regional reach and technical depth with four major goals: bringing more countries and repositories into the network, modernizing how researchers discover content in multiple languages, building a sovereign and verifiable system of persistent identifiers, and standing up a shared regional repository and training program for research data. The project's full progress is documented in detail on LA Referencia's project reporting site, and the account below draws directly on the team's own executive report for January–July 2026. 

The project is organized into three complementary components that connect content, discovery technology, identifier infrastructure, and human capacity:

• Component A — Expanding LA Referencia’s Aggregation Infrastructure: growing regional coverage, improving and normalizing metadata, and introducing multilingual semantic search to LA Referencia's discovery layer.
• Component B — Decentralized ARK Identifiers and Metadata Preservation (dARK): building dARK 2.0, a modular, blockchain-backed infrastructure for assigning, publishing, and resolving persistent identifiers.
• Component C — Regional Orphan Data Repository & Dataverse Support: standing up a Dataverse-based regional repository for research data alongside a federated training and curation program.

In the rest of this post, we dive deeper into the progress made on each of these components and share explorable outputs of LA Referencia’s work to date. 

Component A: Expanding LA Referencia’s Aggregation Infrastructure

Discovery depends on good metadata for the collections, so Component A pairs the expansion of LA Referencia's regional coverage with metadata quality work and a new multilingual search capability that enables discovery regardless of the language of the search or the content.

Progress to date

Over the reporting period, LA Referencia enacted a regional methodology for identifying candidate institutions and repositories for harvesting to bolster their collection. This work involved verifying OAI-PMH services, reviewing metadata, updating COAR's Interoperability and Registry Database (IRD), and cross-checking institutional information against ROR. Curation work was completed for member countries Ecuador, Uruguay, and Costa Rica, and non-member countries Colombia, Paraguay, Venezuela, Bolivia, and Cuba; records for another countries are in progress. 

In total, the team updated records for 362 repositories: 187 edited, 77 newly added, and 98 archived. Venezuela was the country with the greatest progress, with two repositories successfully harvested as a pilot for incorporating content from countries that do not yet operate as full nodes in LA Referencia. 

Related to this geographic expansion, LA Referencia is building a solution for a common problem they encountered in their community: many researchers search in one language while the work they need is described in another. A record from a Spanish-language repository might never surface for someone searching in English, or vice versa, because traditional keyword search can only match the exact words on the page. Component A set out to close that gap with multilingual semantic search, which finds results by matching meaning instead of exact wording. To figure out the best way to do this, the team ran a rigorous, reproducible evaluation, comparing roughly thirty different AI models across two test collections of 30,000 to 50,000 documents, with thousands of test searches in as many as 10 languages. This work resulted in promising outcomes: in the pilot, semantic and blended search surfaced roughly twice as many relevant results in the top 10 (up from an average of 4.5 to 9.1) compared with keyword search alone, with the biggest gains for languages that keyword matching tends to handle poorly, like Chinese, Japanese, and Arabic. The team evaluated different models and chose one that balanced high-quality results with efficiency and speed to run on millions of records. 

That evaluation has now been built into a beta working system on LA Referencia's search platform. When someone runs a search, they can choose the traditional keyword match, a "meaning-based" match that works across languages, or a blended mode that combines both. Reindexing becomes substantially faster when previously generated embeddings are reused. Rebuilding the full index for a test set of about 28,000 records took roughly 5 hours the first time, but only about 10 minutes once the meaning-based data was already stored, making regular updates practical as the collection keeps growing. Alongside this, the team added a open WAF that screens out automated bots and scrapers, plus a way to apply that same protection across multiple LA Referencia sites at once.

You can also dive into the technical details by reading LA Referencia’s full Component A report.

What's next

LA Referencia continues to review and add repositories from additional member countries and new countries beyond their network. The team plans to expand its multilingual semantic search evaluation across the full regional collection and consolidate the semantic search beta ahead of broader rollout.

Component B: Decentralized ARK Identifiers (dARK)  and Metadata Preservation

Component B involves building dARK 2.0, the next generation of LA Referencia's infrastructure for assigning, publishing, and resolving persistent ARK identifiers.  This component accounts for regional, sovereign, and sustainable infrastructure built specifically for the region served by LA Referencia.

Progress to date

Persistent identifiers are what keep a piece of research findable and citable for the long term by using a permanent link that still leads to the right paper, dataset, or report years later, even if the file moves or the system hosting it changes. Today, most of that infrastructure is run by a handful of external providers. dARK 2.0 is LA Referencia's vision for an infrastructure the region can run, govern, and trust for itself without relying on one single organization as a locus of control.

To do that, the team split dARK 2.0 into separate components that can each be checked, updated, or scaled independently. A shared, tamper-evident ledger (a type of blockchain) maintains the authoritative record of which identifiers exist and what they point to, while the descriptive metadata for each record is stored separately, allowing anyone to verify it hasn't been altered. That split keeps the "proof" layer simple and trustworthy, while the descriptive data can keep growing without needing to touch the "proof" layer. The team also built the full pipeline this system requires: reserving new identifiers, preparing and storing their metadata, publishing the reference to the ledger, and allowing anyone to look up an identifier and resolve it to the correct record.

The team stress-tested this workflow against millions of records and ran hundreds of automated checks to confirm it holds up, and built administrative tools so staff can monitor identifiers, activity, and lookups. The dARK pipeline is now ready to be deployed and connected to LA Referencia's regional harvesting system, so the harvesting workflow is being integrated with the dARK pipeline to support identifier assignment as records are processed, even across large, constantly changing collections. 

Dive into the details in the full Component B report.

What’s next

The next step is spreading the dARK infrastructure across institutions rather than running it from one place. A matching setup is being prepared at RNP in Brazil, and partner institution IBICT is preparing to migrate its identifiers over from the original 1.0 version of dARK. With multiple institutions using the system, LA Referencia plans to validate cross-site operation and formalize the security and governance model for this federated regional infrastructure.

Component C: Regional Orphan Data Repository & Dataverse Support

Component C pairs a new Dataverse-based regional repository with a federated training and curation program, aimed at giving research data lacking institutional infrastructure a home while spreading the skills needed to run data services sustainably across the region.

Progress to date

The team began with a regional diagnostic that identified roughly 96 registered data repositories and around 30 Dataverse installations across Latin America, concentrated in Brazil, Argentina, Mexico, and Colombia, drawing on questionnaires and interviews covering governance, infrastructure, curation, preservation, support, and interoperability. That diagnostic confirmed the region already has significant experience and expertise, which has shaped a peer-collaboration strategy treating the repository as a socio-technical service: software and infrastructure paired with policy, multidisciplinary teams, researcher support, and ongoing training.

The headline technical result is a live, working test version of the repository (a "Dataverse Alpha") running on LA Referencia's own infrastructure. The repository works in English, Spanish, and Portuguese, includes fields to tag a dataset's country of origin and which of the UN's 17 Sustainable Development Goals it relates to, and uses open licenses (CC0 and CC BY) so the data can be freely reused. Sample datasets are already loaded so the team can test uploading, describing, searching, and accessing files before real institutional deposits start coming in.

Alongside the technical build, the team is developing a 16-hour asynchronous MOOC, Introduction to Data Management and Curation in Dataverse, covering foundations and publication, metadata and documentation, technical verification and formats, and ethics, licensing, and approval. This is one of three planned training products; the others include hands-on curation training and direct support for pilot institutions. The course will be built and installed on a production Moodle environment with materials in Spanish and Portuguese.

We encourage readers to explore the full technical details in the full Component C report.

What’s next

The team will finish course content, validate repository workflows end-to-end, select the first pilot institutions, and turn the lessons from the Alpha into a stable model for the regional service.

LA Referencia’s Project: Supporting the Goals of the IOI Fund for Network Adoption

Six months in, LA Referencia has moved from design and diagnostic work to functioning systems: a measurably better multilingual discovery experience, a working architecture for verifiable, sovereign persistent identifiers, and a live regional data repository paired with a training pipeline built to outlast any single grant. None of this was built for one institution; every improvement contributes to shared, federated infrastructure that LA Referencia's member repositories across Latin America already rely on. 

That is the premise of the IOI Fund for Network Adoption: that the fastest, most durable way to grow open infrastructure adoption is to invest in networks that already have the governance, trust, and reach to put new capacity to work across many institutions at once. LA Referencia's first six months show that thesis in motion. The technical foundation that they are building will enable the project's next phase, expanding to new countries and languages, validating dARK across institutions, and bringing its first pilot institutions onto the regional Dataverse.

Read more about the project on LA Referencia's project reporting site, including the full executive report this post is based on, or learn more about IOI's work with LA Referencia and the IOI Fund for Network Adoption. 

Duration: April 2026 to December 2026
Team: Emmy Tsang, Emma Green, Sarah Lippincott
Funder: TDCC-NES
Skillset: Strategic Planning, Convening / Facilitation, Fiscal Assessment & Planning

Overview

TDCC-NES is the Thematic Digital Competence Centre for the Natural & Engineering Sciences in the Netherlands, a network organisation that helps Dutch researchers and digital support experts explore digital competency challenges and find solutions through connection and collaboration, reducing fragmentation and duplication across the domain. Funded by NWO as part of its investment in digital research infrastructure, TDCC-NES engaged IOI to develop a credible long-term sustainability model ahead of the end of the NWO funding window.

What we will do

Through a phased engagement, IOI is building a shared, evidence-based understanding of TDCC-NES's value proposition, stakeholder landscape, and sustainability options, through stakeholder interviews, document review, and a discovery findings presentation to the governing board. In late 2026, we will convene key stakeholders in a sustainability workshop to co-develop the foundations of a co-investment model and build co-ownership of the path to 2030.

Outcomes

Phase 1 delivered a discovery findings report and board presentation that established a shared evidence base for the board's sustainability conversation. Further outcomes will be added as the engagement progresses.

Facing a funding transition? Let's design your next model together.

Many publicly funded infrastructure and network organisations face the end of an initial grant or funding window. IOI helps organisations like yours understand your value to stakeholders and design sustainable co-investment models. Book a free consult.

Data resources that researchers rely on are critical to advancing science and discovery. That research often starts with data that research funders, such as Wellcome, did not create and does not maintain, including biodata repositories, population cohort databases, and environmental and climate data resources, built and sustained by organizations that often operate out of public view. At a moment when the research community is paying closer attention than ever to the resilience of the data infrastructure it depends on, we are supporting Wellcome to gain a clearer, deeper picture of that landscape. In turn, this will enable collective conversations within the research community about how we sustain these resources.

IOI is leading that effort, drawing on our track record of working alongside infrastructure providers, not just evaluating them from the outside. We'll be engaging directly with the teams running these resources to understand their governance, funding models, technical dependencies, open policies, sustainability outlooks and the broader ecosystem they operate within, the kind of insight that only comes from sustained, trust based engagement.

The foundation for this work is Infra Finder, IOI's open discovery platform for research infrastructure. We'll build verified, publicly accessible profiles for each resource in scope, using our established research and diligence process.This is also a significant expansion for Infra Finder, which has grown to include nearly 150 profiles of open infrastructure since its 2024 launch. Infra Finder is more than a directory. It's the foundation of our annual State of Open Infrastructure report, which tracks how the field is evolving year over year. Adding this cohort brings these data resources into that same yearly monitoring and analysis, giving Wellcome and the broader community longitudinal visibility into how they change and how they're funded over time.

We're looking forward to the work ahead. Stay tuned for more in the coming months!