The future of publishing is not open access

The Future of Publishing Is Not Open Access. It Is Open Science.

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Table of Contents

Introduction

For more than two decades, open access has dominated conversations about the future of scholarly publishing. Publishers debated it. Librarians championed it. Funders mandated it. Researchers argued over it. Entire business models were redesigned around it.

The basic premise was simple. Research should be freely available to anyone who wants to read it.

In many ways, open access succeeded. Millions of scholarly articles that once sat behind expensive subscription paywalls are now freely accessible. Governments have introduced open access mandates. Funding agencies increasingly require publicly funded research to be openly available. Even the largest commercial publishers, once viewed as opponents of the movement, have adapted their strategies to accommodate a world where openness is becoming the norm rather than the exception.

Yet there is a growing sense that the publishing industry is still focused on yesterday’s revolution.

Much of the current debate revolves around familiar topics such as article processing charges (APCs), transformative agreements, repository mandates, and subscription replacements. These issues remain important, but they increasingly feel like discussions about implementation rather than direction. The larger transformation is happening elsewhere.

A new vision is emerging across the global research ecosystem. Researchers are no longer being asked merely to share published papers. They are being encouraged, and in some cases required, to share research data, software code, methodologies, peer review reports, laboratory workflows, preprints, and a wide range of research outputs that were traditionally hidden from public view.

This broader movement is known as open science.

Open science represents a fundamental shift in how knowledge is created, shared, evaluated, and reused. It seeks to open not only the final product of research but the entire research process itself. In this model, a journal article is no longer the sole or even primary output of scientific work. Instead, it becomes one component within a much larger ecosystem of interconnected knowledge assets.

The implications for publishing are enormous.

Publishers built their businesses around documents. Open science is increasingly organized around data, infrastructure, interoperability, and collaboration. The institutions that shape the future of research may not be those that control journals, but those that manage repositories, research platforms, metadata networks, and scientific data ecosystems.

What makes this transformation particularly fascinating is that some of the most ambitious experiments are occurring not in Europe or North America, but across Asia. Countries such as Japan, China, India, Malaysia, Indonesia, Singapore, and South Korea are investing heavily in repositories, open science platforms, research data infrastructures, and national strategies that extend far beyond traditional open access publishing.

The future of publishing may still involve journals. But it is becoming increasingly clear that journals alone are no longer enough.

The future of publishing is not open access.

It is open science.

Open Access Solved Only One Piece of the Puzzle

The rise of open access was a necessary correction to a deeply flawed publishing system. For decades, publicly funded research often disappeared behind subscription barriers that limited access to wealthy universities and institutions. Researchers in developing countries, independent scholars, policymakers, journalists, and members of the public frequently found themselves locked out of knowledge that their taxes had helped fund.

Open access challenged this arrangement by focusing on a single objective: removing barriers to reading research.

That objective remains important. Access matters. Knowledge hidden behind paywalls cannot contribute fully to scientific progress, innovation, education, or public understanding. The growth of open access has undoubtedly expanded the global reach of scholarship and increased opportunities for collaboration.

However, access to papers and access to science are not the same thing.

A published article represents only the visible tip of a much larger research iceberg. Beneath every article lies a complex ecosystem of datasets, experimental procedures, analytical methods, software tools, peer review interactions, and research workflows. Traditionally, most of these components remained inaccessible to outsiders. Readers could see the conclusions, but they often could not examine the processes that produced them.

This limitation has become increasingly problematic in an era where reproducibility and research integrity are under intense scrutiny. A paper may describe a finding, but without access to the underlying data or methodology, other researchers may struggle to verify the results. In some cases, important scientific claims cannot be reproduced because the supporting materials remain unavailable.

Open science emerged as a response to this challenge.

Rather than asking whether people can read research papers, open science asks whether people can understand, evaluate, replicate, and build upon research. It expands the scope of openness beyond publication and into the entire lifecycle of knowledge creation.

This distinction is subtle but profound.

Open access focuses primarily on dissemination. Open science focuses on participation.

A researcher who downloads an open access article gains access to information. A researcher who gains access to the associated data, code, protocols, and research materials gains the ability to validate findings, conduct new analyses, and generate entirely new discoveries.

In other words, open access increases visibility. Open science increases usability.

That difference may define the next stage of scholarly communication.

The Journal Article Is Losing Its Monopoly

For centuries, the journal article occupied a privileged position within academia. It was the primary vehicle through which researchers communicated discoveries, established priority, and earned professional recognition. Entire academic careers were built around publishing articles in prestigious journals.

The journal article became so dominant that many people came to equate scientific knowledge with published papers themselves.

Today, that assumption is becoming increasingly difficult to sustain.

Modern research generates a growing range of outputs that extend far beyond traditional manuscripts. Large datasets, software code, computational models, digital archives, multimedia content, interactive visualizations, and machine-readable metadata are now essential components of many research projects. In some disciplines, these outputs may hold as much value as the article itself.

Consider fields such as genomics, climate science, astronomy, or artificial intelligence. The datasets generated in these disciplines often contain immense scientific value that extends well beyond the conclusions presented in a single paper. Researchers may continue extracting insights from the same dataset for years or even decades. New analytical techniques can produce discoveries that were impossible when the data was first collected.

In such cases, the article functions less as the final product and more as a summary of a much larger body of knowledge.

The growing popularity of preprints further illustrates this shift. Researchers increasingly share findings before formal peer review, allowing ideas to circulate rapidly through the scientific community. The traditional sequence of research, submission, peer review, publication, and dissemination is being replaced by a more dynamic and continuous process of communication.

This evolution raises difficult questions for publishers.

If researchers can distribute preprints independently, share datasets through repositories, publish code through open platforms, and collaborate through digital infrastructures, where exactly does the journal fit within the modern research ecosystem?

The answer is not that journals are disappearing. Their role remains important. Peer review, editorial oversight, quality assurance, and scholarly curation continue to provide significant value.

What is changing is their position within the ecosystem.

The journal article is no longer the sole gateway to knowledge. It is becoming one node within a much larger network of interconnected research outputs.

This shift challenges some of the most fundamental assumptions that have guided scholarly publishing for generations. Publishers that continue to view articles as the exclusive center of scholarly communication may find themselves increasingly disconnected from the realities of modern research.

Data Is Becoming More Valuable Than Papers

Perhaps the clearest indication that open science is transforming publishing can be found in the growing importance of research data.

Historically, data existed primarily to support publications. Researchers collected information, analyzed results, wrote papers, and moved on to the next project. Once the article was published, the underlying data often remained stored on personal computers, institutional servers, or physical storage devices where few people could access it.

Today, governments, funders, and research institutions are beginning to view data very differently.

Rather than treating data as a byproduct of research, they increasingly regard it as a strategic asset.

This shift is evident across Asia. Countries are investing heavily in repositories, national research data platforms, and infrastructure designed to make scientific data discoverable, accessible, interoperable, and reusable. Malaysia’s Open Science Platform, China’s expanding scientific data initiatives, and various national repository projects across the region all reflect a growing recognition that future scientific progress depends on effective data stewardship.

The logic is straightforward.

A research article communicates a specific interpretation of evidence. A dataset preserves the evidence itself.

Future researchers may revisit that evidence using new methods, answer entirely different questions, or combine it with other datasets to generate insights that the original investigators never imagined. The long-term value of a dataset can therefore exceed the value of any individual paper derived from it.

Artificial intelligence further strengthens this trend.

Large-scale AI systems depend on structured, high-quality, machine-readable information. Data repositories, standardized metadata, and interoperable infrastructures are becoming essential components of future research ecosystems. Scientific information that remains locked within static PDF documents is far less useful to advanced computational systems than information organized within accessible and reusable data frameworks.

This creates a fascinating possibility.

For centuries, publishers have been in the business of distributing documents.

The future may require them to become stewards of knowledge infrastructure.

If that happens, the most valuable assets in scholarly communication may no longer be journals or articles. They may be the platforms, repositories, metadata systems, and data ecosystems that enable science itself to function more effectively.

That would represent a far bigger transformation than open access ever achieved.

Asia Is Building the Infrastructure of Open Science

For much of the modern publishing era, Asia was often portrayed as a consumer of scholarly communication systems developed elsewhere. Major journals were headquartered in Europe and North America. Citation databases were controlled by Western organizations. Research assessment systems frequently rewarded publication in foreign journals. Even the infrastructure supporting scholarly communication was largely designed outside the region.

That reality is changing.

One of the most important developments in contemporary publishing is the emergence of Asia as a builder rather than simply a user of research infrastructure. Across the region, governments, universities, and research organizations are investing heavily in repositories, national data platforms, metadata systems, and open science frameworks that are designed to support entire research ecosystems rather than individual publications.

Japan offers a particularly interesting example. Rather than pursuing an aggressive APC-driven publishing strategy, Japan has invested heavily in repository infrastructure and Green open access compliance. Under its new policies, publicly funded research must be deposited into institutional repositories, ensuring long-term accessibility while avoiding many of the costs associated with commercial publishing models. This approach reflects a broader recognition that sustainable open science depends not only on publishing papers but also on maintaining robust systems for preserving and sharing knowledge.

China has pursued a different but equally ambitious path. The country has invested heavily in scientific data platforms, preprint repositories, national data-sharing initiatives, and large-scale digital infrastructure projects. Rather than focusing exclusively on publishing outcomes, policymakers increasingly view scientific infrastructure as a strategic national asset. The result is a research ecosystem in which data management, repository development, and platform construction are becoming central components of scientific policy.

India’s One Nation One Subscription initiative demonstrates yet another model. While often discussed as a massive access agreement, ONOS is fundamentally an infrastructure project. It seeks to create a national research environment where access to scientific knowledge is no longer determined by the financial capacity of individual institutions. Instead, scholarly resources become part of a shared national foundation that supports research across the entire country.

Malaysia provides another glimpse into the future through the Malaysia Open Science Platform (MOSP). Rather than focusing solely on publications, MOSP emphasizes FAIR data principles, persistent identifiers, standardized metadata, and long-term data stewardship. The platform reflects a growing understanding that future research ecosystems will depend upon the ability to connect data, researchers, institutions, and technologies through interoperable systems.

What unites these diverse initiatives is a shared recognition that publishing is increasingly becoming an infrastructure business.

For decades, discussions about scholarly communication focused primarily on content. Today, infrastructure is becoming equally important. Repositories, data platforms, authentication systems, metadata standards, and cloud-based research environments may ultimately shape the future of knowledge production more profoundly than any individual journal.

This represents a significant shift in perspective.

The future of scholarly communication may depend less on who publishes research and more on who builds the systems through which research flows.

The Rise of Research Data Sovereignty

As research data becomes more valuable, questions of ownership and control become increasingly important:

  • Who should store national research data?
  • Who should control access to it?
  • Who should determine how it is reused?
  • Who benefits when that data is incorporated into artificial intelligence systems?

These questions are moving rapidly from technical discussions to matters of national policy.

The concept of data sovereignty has become increasingly influential across many sectors, and scientific research is no exception. Governments are beginning to recognize that research data represents more than academic output. It can support economic development, technological innovation, industrial competitiveness, healthcare improvements, and national security objectives.

In this context, research data is no longer viewed simply as information. It is increasingly viewed as infrastructure.

Historically, many countries relied heavily on international publishers, commercial databases, and foreign technology providers to store and distribute scholarly knowledge. While these arrangements offered convenience and global visibility, they also created dependencies. Valuable research outputs often became embedded within systems controlled by organizations operating outside national jurisdictions.

Open science offers an alternative vision.

By investing in domestic repositories, national data platforms, and sovereign research infrastructures, countries can maintain greater control over how knowledge is stored, accessed, and reused. This does not necessarily imply isolation or protectionism. In fact, many open science initiatives are explicitly designed to facilitate international collaboration. The difference is that participation occurs from a position of ownership rather than dependency.

This issue becomes even more significant in the age of artificial intelligence.

AI systems derive value from access to large quantities of high-quality information. Research datasets, publications, and metadata represent valuable resources for training future AI models. As governments become more aware of the economic and strategic significance of AI, they are paying closer attention to where research data resides and who controls access to it.

A decade ago, discussions about scholarly publishing focused heavily on subscription costs and APCs.

A decade from now, discussions may focus on data governance, AI training rights, infrastructure ownership, and digital sovereignty.

That would fundamentally transform how we think about publishing.

The industry has traditionally viewed itself as a distributor of knowledge. Increasingly, it may need to view itself as a steward of strategic information assets.

Why AI Accelerates the Open Science Movement

Artificial intelligence may become the single most powerful force accelerating the transition from open access to open science.

The reason is surprisingly simple.

AI systems do not consume knowledge in the same way humans do.

A researcher can read a journal article and extract meaning from a narrative description of methods, results, and conclusions. Machines operate differently. They require structured data, standardized metadata, interoperable formats, and accessible repositories. In many respects, the ideal environment for advanced AI is the same environment envisioned by open science advocates.

This creates a powerful alignment of interests.

Open science seeks to make research outputs discoverable, reusable, and interconnected. AI systems thrive when information possesses precisely those characteristics.

Consider the limitations of the traditional scholarly article. Most articles are distributed as PDFs, a format optimized for human reading rather than machine interpretation. Valuable information is often embedded within tables, figures, supplementary files, or narrative text that may be difficult for machines to process efficiently.

Open science attempts to overcome these limitations by encouraging structured metadata, open datasets, persistent identifiers, machine-readable formats, and standardized workflows.

In effect, open science transforms research from a collection of isolated documents into a connected knowledge network.

This distinction becomes increasingly important as AI tools become integrated into research workflows. Researchers already use AI to search literature, identify trends, summarize findings, generate hypotheses, and analyze data. Future systems may operate on an even larger scale, continuously synthesizing information across millions of publications and datasets.

To function effectively, however, these systems require access to far more than journal articles. These include:

  • The underlying data
  • Metadata
  • Software code
  • Interoperable repositories
  • Open infrastructures

In this sense, open science is not merely a philosophical movement. It is becoming a practical requirement for the next generation of scientific discovery.

This creates both opportunities and challenges for publishers.

Organizations that continue to focus exclusively on documents may find themselves marginalized within an increasingly data-driven ecosystem. Meanwhile, publishers that invest in metadata services, data repositories, AI-ready infrastructure, research integrity tools, and interoperability frameworks may discover entirely new sources of value.

The transition will not happen overnight. Journals remain central to scholarly communication, and peer review continues to play a critical role in validating knowledge.

Nevertheless, AI is changing the economics of information.

Open access helped humans reach research.

Open science helps machines work with research.

As artificial intelligence becomes more deeply embedded within academia, that distinction will become increasingly difficult to ignore.

Preprints Are Changing the Speed of Science

For centuries, scholarly publishing operated according to a relatively predictable timeline. Researchers completed their work, submitted manuscripts to journals, waited for peer review, responded to reviewer comments, revised their papers, and eventually saw their findings published months or even years later.

The process was slow, but the slowness was often viewed as a feature rather than a flaw. Careful review was supposed to improve quality and protect the integrity of the scientific record.

That logic still holds merit. Yet the modern research environment increasingly values speed alongside rigor.

Scientific challenges such as pandemics, climate change, food security, energy transitions, and artificial intelligence evolve far more rapidly than traditional publishing cycles. Researchers often need access to emerging findings immediately rather than after lengthy editorial processes have concluded.

This reality has fueled the rise of preprints.

A preprint allows researchers to share findings publicly before formal peer review. Instead of waiting months for publication, scientists can communicate results within days. Other researchers can review the work, offer feedback, identify errors, suggest improvements, and build upon new ideas almost immediately.

What began as a niche practice in a few scientific disciplines has evolved into a global movement.

In Asia, the growth of preprints reflects a broader shift toward open science. Initiatives such as Indonesia’s RINarxiv and other regional platforms are helping normalize preprint culture throughout Southeast Asia. Researchers increasingly view early sharing not as a threat to publication but as a way to accelerate scientific progress.

The implications for publishing are profound.

Traditionally, journals served two major functions simultaneously: dissemination and validation. They distributed knowledge and certified quality through peer review. Preprints separate those functions.

Dissemination can now occur almost instantly.

Validation may happen later.

This distinction changes the role of journals within the research ecosystem. If researchers can communicate findings directly to the global community, journals no longer possess a monopoly over scientific visibility. Their value increasingly resides in evaluation, curation, quality assurance, and trust rather than simple distribution.

This evolution does not diminish the importance of journals. If anything, it highlights their most valuable contributions. In a world flooded with information, credible validation may become even more important than publication itself.

However, it does suggest that publishers must rethink their role.

The future may belong to organizations that facilitate continuous scientific communication rather than those that simply manage periodic publication events.

The Business Model Crisis Nobody Is Talking About

The publishing industry spent much of the past decade debating how to replace subscription revenue.

The assumption was that open access represented the primary economic challenge facing scholarly communication. Publishers experimented with APCs, transformative agreements, read-and-publish deals, and various hybrid models to adapt.

Yet open science introduces a far more fundamental question.

What exactly are publishers selling?

For generations, the answer was straightforward.

Publishers sold access.

Libraries paid subscription fees.

Individuals purchased books and journals.

Revenue flowed from controlling distribution.

Open access disrupted this arrangement by making content freely available. The industry’s response was to shift costs elsewhere, most commonly through APCs paid by authors or funders.

But open science may challenge even that model.

When researchers increasingly share preprints, datasets, software code, methodologies, and other outputs through repositories and open platforms, the traditional publication transaction becomes less central to the research process. Knowledge begins flowing through a distributed network of infrastructures rather than through a single publication channel.

In such an environment, access itself becomes less valuable as a commercial product.

Researchers already expect search engines to be free.

They expect repositories to be accessible.

They expect collaborative platforms to enable rapid sharing.

Future generations may regard restrictions on scientific information as increasingly unnatural.

This creates a difficult strategic dilemma for publishers.

If access is free and dissemination is decentralized, publishers must identify new sources of value.

Some organizations are already moving in this direction.

Rather than focusing exclusively on content, they are investing in workflow tools, research analytics, data management services, peer review platforms, integrity monitoring systems, institutional dashboards, and AI-powered research environments.

These developments offer an important clue about where publishing may be heading.

The future business of scholarly communication may revolve less around documents and more around services.

Researchers will still need help organizing knowledge.

Institutions will still need tools to measure impact.

Funders will still need systems to track compliance.

Universities will still need infrastructure to manage research outputs.

Publishers possess expertise, networks, and resources that position them well to provide such services.

For generations, publishers viewed journals as their primary products. In an open science environment, journals may become one service among many. The organizations that recognize this transition early may thrive.

Those that remain tied exclusively to publication-centric business models may struggle to remain relevant.

The Open Science Revolution Still Faces Major Obstacles

It is tempting to describe open science as an inevitable future. The reality is more complicated. Despite its growing momentum, open science faces significant technical, cultural, financial, and political barriers that cannot be ignored.

One of the most persistent challenges involves incentives.

Researchers generally respond to the reward systems that govern their careers. Hiring committees, promotion panels, grant agencies, and university rankings continue to place enormous emphasis on publications and citation metrics. Sharing datasets, documenting workflows, releasing software code, and participating in open peer review often require substantial additional effort while delivering relatively limited professional recognition.

As long as traditional evaluation systems remain dominant, many researchers will understandably prioritize activities that advance their careers.

This challenge is particularly important because open science demands more than technological change. It requires behavioral change.

Researchers must be willing to share work earlier, expose methods to scrutiny, document processes more thoroughly, and embrace greater transparency. These expectations represent significant cultural shifts within disciplines that have long operated under different norms.

Financial sustainability presents another obstacle:

  • Open science infrastructure is not free
  • Repositories require maintenance
  • Data platforms require storage capacity
  • Persistent identifier systems require governance
  • Metadata standards require ongoing coordination

Someone must pay for these activities.

The question becomes especially difficult for developing countries and smaller institutions with limited resources. While open science promises greater equity, maintaining the infrastructure necessary to support openness can be expensive.

There are also legitimate concerns surrounding privacy and security.

Not all research data can or should be openly shared. Medical records, sensitive social science data, proprietary industrial research, and information with national security implications require careful governance. Policymakers must balance openness with ethical responsibilities, legal obligations, and public trust.

Finally, there is the challenge of quality.

The promise of open science rests partly on the assumption that greater transparency improves research integrity. In many cases, that is true. Open datasets and transparent methodologies make it easier to identify errors and verify results.

However, openness alone does not guarantee quality.

Poorly documented datasets remain problematic even when openly available. Incomplete metadata can limit reuse. Low-quality repositories can create confusion rather than clarity.

The ffuture, therefore,depends not simply on making information available but on making it useful.

That distinction matters.

The success of open science will not be measured by the volume of data shared or the number of repositories created. It will be measured by whether researchers can effectively discover, understand, trust, and reuse knowledge.

Achieving that goal will require sustained investment, thoughtful governance, and significant cultural change.

Open science offers tremendous opportunities. But like every major transformation in scholarly communication, its success is far from guaranteed.

The Future Publisher May Look Very Different

If the open science movement continues along its current trajectory, the scholarly publisher of 2035 may look very different from the publisher of 2015.

This is not because journals will disappear.

Predictions about the death of journals have surfaced repeatedly over the past several decades, and they have consistently proven exaggerated. Researchers still need trusted venues. Institutions still need quality assurance mechanisms. Peer review, editorial oversight, and scholarly curation remain essential functions within the research ecosystem.

What is likely to change is the relative importance of those functions compared to everything else happening around them.

For most of modern publishing history, the journal article sat at the center of the value chain. Publishers managed submissions, coordinated peer review, produced final articles, and distributed content to readers. Nearly every activity revolved around the publication itself.

Open science challenges that model by expanding the scope of scholarly communication.

Research is no longer confined to manuscripts. It includes datasets, software code, preprints, protocols, multimedia outputs, metadata, machine-readable records, and collaborative digital environments. The article increasingly becomes one component within a broader knowledge ecosystem.

As a result, future publishers may spend less time managing documents and more time managing connections.

They may operate large-scale data repositories where researchers deposit and preserve datasets. They may provide infrastructure for reproducibility checks, allowing readers to verify analyses and replicate findings. They may develop sophisticated metadata systems that connect articles, datasets, grants, institutions, and researchers into integrated knowledge networks.

Some may become specialists in research integrity.

The rapid growth of scientific output has made it increasingly difficult to monitor quality at scale. Publishers may deploy advanced tools to identify manipulated images, detect fraudulent peer review, verify datasets, and assess methodological transparency. In an era of information abundance, trust itself may become one of the most valuable services publishers can offer.

Artificial intelligence will likely accelerate this transformation.

As researchers increasingly rely on AI tools to navigate scientific literature, publishers may shift their attention toward structuring information in ways that are useful not only for human readers but also for machines. Metadata quality, interoperability standards, persistent identifiers, and machine-readable content may become just as important as traditional editorial processes.

The winners in this environment may not necessarily be the organizations that publish the most content.

They may be the organizations that make knowledge easier to discover, connect, verify, and reuse. This distinction is important because it changes how we think about publishing itself.

Historically, publishing was often defined as the distribution of information. In the future, publishing may be defined as the orchestration of knowledge.

That is a much larger responsibility.

It requires managing relationships between people, data, technologies, institutions, and ideas. It requires building systems rather than simply producing outputs. It requires supporting the entire lifecycle of research rather than focusing exclusively on the final publication stage.

Many publishers have already begun moving in this direction, although perhaps not always consciously. Investments in research analytics, repository services, workflow tools, research intelligence platforms, and AI-powered discovery systems suggest that the industry recognizes the changing landscape.

The question is not whether publishing will evolve.

The question is whether publishers can evolve quickly enough.

Conclusion: Open Access Was the First Chapter

The history of scholarly publishing is often described as a struggle over access.

For decades, researchers, librarians, and policymakers debated who should be allowed to read scientific knowledge and how that knowledge should be funded. Open access emerged from those debates and fundamentally reshaped the publishing landscape.

Its achievements should not be underestimated.

Millions of articles that were once inaccessible are now freely available. Researchers in developing nations enjoy greater visibility. Publicly funded research is increasingly available to the public. Open access has transformed scholarly communication in ways that would have seemed unimaginable at the beginning of the century.

Yet open access now appears less like a destination and more like a starting point.

The emerging frontier is open science.

Unlike open access, which focuses primarily on published outputs, open science seeks to open the entire research process. It emphasizes transparency, collaboration, reproducibility, interoperability, and reuse. It expands the conversation from articles to data, from publications to infrastructure, and from dissemination to participation.

This shift carries profound implications for publishers.

The future may belong less to organizations that control content and more to those that enable knowledge ecosystems. Repositories, research data platforms, AI-ready infrastructures, interoperability frameworks, and scientific networks may become just as important as journals themselves.

Perhaps the most interesting aspect of this transformation is where much of it is occurring.

Across Asia, governments and institutions are investing in repositories, national research platforms, open science policies, data-sharing infrastructures, and large-scale collaborative networks. Countries that were once viewed primarily as consumers of global publishing systems are increasingly becoming architects of new ones.

Japan is expanding repository-driven openness. China is investing heavily in scientific infrastructure. India is experimenting with national-scale access models. Malaysia is building open science platforms. Indonesia is demonstrating the potential of community-driven publishing ecosystems.

Together, these developments suggest that the future of scholarly communication will not be designed in a single country or controlled by a handful of organizations. It will emerge from a complex network of infrastructures, policies, technologies, and communities spread across the world.

The publishing industry’s greatest challenge over the next decade may not be deciding how to fund open access. It may be deciding what role it wants to play in an open science world. Because the future of publishing is no longer simply about making research available. It is about making research usable.

And that is a far bigger ambition than open access ever promised.

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