Abstract
As science evolves, so must the systems that support it and evaluate it. Inspired by UNESCO's open science definition, this paper explores how funders and institutions can move beyond the traditional metrics to define scientific impact and to create a more inclusive, transparent and collaborative research ecosystem. We demonstrate through practical examples how the academic landscape is moving towards more openness as the new standard. We conclude with a discussion of challenges in implementing open science practices and recommendations for funders and institutions that will help to create a more unified framework of recognition and reward of scientific work.
Plain language summary
Funders and research institutions have traditionally used particular metrics to assess impact, primarily the number of papers published and the reputation of journals that publish them. Those metrics create incentives for researchers which impact how they choose to conduct and share their research. The open science movement--which under the UNESCO definition includes the sharing of scientific knowledge, infrastructure for sharing research openly, engagement with societal actors, and dialogue with other knowledge systems--challenges these traditional practices. It encourages sharing all research outputs, supporting the infrastructure needed to share them, and engaging with those not traditionally included in research or policy design. This paper explains traditional incentive structures and open science, discusses practical examples of how some funders and institutions are changing their incentive structures, presents some of the challenges to adopting open science, and presents recommendations.
Keywords
Introduction
The institutions that house science and the funders that support it all aim to impact the world. Their goals differ. Some funders seek scientific breakthroughs in health, climate, or social equity, while institutions may prioritize knowledge generation, education, or service delivery. Yet, they share a common purpose: to drive meaningful change.
To achieve impact, both funders and institutions create incentive structures and metrics to steer research in desired directions. These systems often rely on tracking traditional outputs like papers, patents, and funding as proxies for impact. Researchers, in response, shape their research activities to fit into the context created by these incentives.
Open Science (OS) practices challenge the adequacy of these traditional systems. When openly shared software, reagents, or datasets can rival or exceed the impact of a publication or patent, and where key insights can be gained from outside of traditional academic stakeholders, it raises a critical question: are current incentive and measurement systems aligned with the full range of impactful scientific activities?
This paper explores how existing systems can evolve to better support the diverse and growing pathways to impact enabled by the OS movement. We draw on examples from the American and Dutch contexts, which present distinct landscapes for OS. Differences include the massive scale of US federal and philanthropic funding compared to the more centralized, state-led Dutch initiatives. Furthermore, we acknowledge the advanced maturity of OS incentive structures within Dutch institutions and the differing influence of private versus public actors across these jurisdictions. The analysis offered in this manuscript provides valuable insights and recommendations for broad application of OS.
In the first part of the paper we examine the goals of funders and institutions, the conventional proxies for impact, and the ways in which OS both challenges and enriches this framework, ultimately opening up new approaches to defining and evaluating scientific success.
The second section elaborates on UNESCO's framing on OS, which is developed in view of the potential for OS to further its Sustainable Development Goals, narrow knowledge gaps between countries, and accelerate innovation by responsible research practices. We then present illustrative practical examples of OS practices organized by the four main themes found within the UNESCO definition. Finally, we conclude with recommendations for how funders and institutions can adapt their systems to acknowledge and reward the open practices in those categories in a value-driven manner.
Funders and institutions - incentivizing impact
Understanding how incentives in science work, and how they could change, requires looking at the dynamic interplay between the perspectives of researchers, funders, and institutions.1–4 A researcher's choice of research topics and approaches reflects a mix of intrinsic motivations, such as curiosity or a commitment to societal benefit, and extrinsic pressures like funding, career progression, or degree attainment. Of the external factors, funders and institutions exert the greatest influence because they provide the resources and spaces that enable research to happen. Because of the primacy of this influence, our analysis focuses on them.
The Michael J. Fox Foundation (MJFF) highlights in its
Public funders such as the
This preference for papers is a critical point of convergence between private and public funders, even though it is not visible by their high-level reporting. Funders rarely use the number of papers as an explicit metric in their own impact reports, which focus instead on strategic goals like disease eradication. However, they rely heavily on these same papers as the primary input for assessing a proposal potential during the grant-making process. By using publication history as the chief proxy for scientific merit, funders inadvertently reinforce the same paper-centric incentive structure used by institutions for hiring and promotion. This creates a unified external pressure on researchers. While the stated mission is societal impact, the practical currency for securing the resources to achieve that same impact remains the traditional scholarly article.
Academic and healthcare institutions engaged in PD research use a range of incentives tied to career advancement, such as hiring, promotion, tenure, privileged access to resources, and degree granting. While the mechanisms differ, they often rely on similar metrics -papers, grants obtained, translational activities- as proxies for academic, clinical, and economic impact. These metrics reflect how institutions attempt to quantify scholarly and professional achievement, thus reinforcing a system where recognition is tied closely to specific and established measurable outputs.
Funder and institutional frameworks for evaluating impact consistently revolve around the use of traditionally recognized outputs as key metrics, such as participation in or initiation of clinical trials, the number and prestige of publications, and new patents, companies, and partnerships for economic impact. While these metrics offer tangible means of assessment, a broader OS (OS) perspective forces us to reconsider how impact is defined and incentivized.
Open science - revealing gaps in the traditional model
PD research stands out for its commitment to OS practices. Funders like
Yet, even in PDs research, the metrics funders and institutions use do not reflect the full spectrum of impact the research they fund can have through open practices. Using (1) open scientific knowledge, (2) OS infrastructures, (3) open engagement of societal actors and (4) open dialogue with other knowledge systems (Figure 1).
Thematic areas of the UNESCO recommendation on OS. 6 this figure illustrates the key pillars and interconnected components of the UNESCO framework for OS, including open scientific knowledge, OS infrastructures, open engagement with societal actors and open dialogue with other knowledge systems. It is based on the official 2021 document that was unanimously adopted by 193 Member States. Source: UNESCO’s four pillars of Open Science by UNESCO (doi.org/10.54677/MNMH8546) licensed under CC-BY-SA 3.0 IGO.
Open scientific knowledge includes open publications, open data, open educational resources, open-source code, and open hardware, to which we would add open materials (e.g., plasmids, samples, cell lines). Open infrastructure encompasses the technical systems, governance models, standards, and best practices that make it possible to safely and effectively share, access, and reuse scientific knowledge. Open engagement of societal actors includes trans- and multidisciplinary collaborations, engaging with individuals and groups and outside of the traditional academic or private research community - e.g., patients, external experts, citizens - in the design, running, and dissemination of knowledge. Finally, open dialogue with other knowledge systems means ensuring that the norms and ideas from other sources, like indigenous knowledge systems, marginalized groups, and local communities, are used to inform how research is conducted.
Envisioning grant applications or promotion reviews under this framework paints a radically expanded picture of impact. These processes would value shared data and null results, contributions to the running of resource sharing platforms, engagement with non-researchers, and incorporation of community norms from outside of academia. While implementing such a vision demands significant shifts in mindset and policy, as we discuss in the next section some funders and institutions are already making progress.
Open scientific knowledge
The “research paper” has long held a privileged position as the primary output of scientific work. Journals centralize the sharing of findings, facilitate peer review, and curate knowledge. Yet the paper was always a compromise: a compressed representation of data, methods, and materials that couldn’t feasibly be communicated in full. This matters because when we talk about open scientific knowledge, we mean the accessibility and usability of all forms of scientific content, including underlying evidence, tools, and processes that make conclusions possible.
Today, technologies have removed many practical constraints that once justified the paper's dominance. OS embraces this shift, urging researchers to share the full spectrum of research outputs. It also challenges outdated publishing models, where journals require researchers to pay to publish publicly funded work or readers pay to access it. A system often criticized for its inequity and for restricting access to research, especially for institutions and individuals with limited funds.
A growing ecosystem of open platforms is reshaping how research is shared. Preprint servers like
A funder that exemplifies a comprehensive approach in the PD domain is Aligning Science Across Parkinson's (ASAP).
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ASAP's
It should be noted that ASAP is somewhat unique in that it was founded with OS policies and practices at its core, meaning OS was easier to implement than at established funders where OS needs to be integrated into existing policies and operations. Moreover, during the revisions for this paper MJFF released their OS Policy Handbook, which closely parallels ASAP's approach and shows that large, established funders can and do make major strides in instituting best practices in OS.9,26
Institutional progress has been slower. The research paper remains the dominant metric for admissions, hiring, and promotion. While
These examples show progress, but also demonstrate that isolated efforts are not enough. A well-designed incentive should specify expectations, define how compliance will be measured, and clarify consequences. This requires a collaborative effort among funders, institutions and journals to ensure their policies are aligned and mutually supportive.
Open science infrastructures
Despite its importance, infrastructure has traditionally received limited recognition and funding.11,12 This disconnect is paradoxically most visible in biomedical research, where reproducibility, transparency, and collaboration depend on robust, interoperable, and inclusive infrastructure, to support data sharing, quality, and usability.
We speculate that this funding gap is driven by several competing pressures. First, traditional funding models prioritize high-visibility, personal, time-bound research projects over the ‘invisible’ and long-term costs of operational maintenance and community oriented work.2,13 Since the benefits of a strong infrastructure are long term and hard to attribute to individual actors, they often fail to meet traditional return on investment criteria. 13 And even if funders do manage to fund such an initiative, researchers usually underuse those platforms, partly because incentives to share data, code, and materials remain weak. 1 Another reason is that the rapid AI revolution has raised the bar for what infrastructure must do, requiring it to be not only technically advanced, but also ethically inclusive and environmentally sustainable.
Historically, infrastructure development was largely bottom-up and voluntary, leaving funders with little incentive to prioritize it. However, this landscape is shifting towards a top down, mandatory framework. In this new regulatory climate, infrastructure has recently started to gain recognition among policymakers and funders. A prominent example is the forthcoming of the
At a regional level, the
Going beyond technical infrastructure and data, HRDS aims to operate across four different layers: 1) Identifying local health challenges and related knowledge gaps and prioritizing research accordingly, 2) Supporting researchers in addressing those challenges, 3) Ensuring stakeholder organization and community engagement and finally 4) supporting translation, implementation and continuous evaluation of research results (Figure 2).
Health Research Data Space - South West Netherlands with it's different focus layers. (1) Community conscious research prioritization, (2) Responsible Research Enablers, (3) Community engagement and (4) Implementation and continuous evaluation.
HRDS research support spans the entire research workflow including providing research
With the upcoming European Health Data Space (EHDS) and the existence of dedicated support initiatives, setting up data spaces is no longer an insurmountable challenge. Tools such as the
What is needed now is for funders and institutions to prioritize and incentivize the use of these infrastructures, ensuring that they are not only created but also sustained and integrated into research practice. Compliance mechanisms, such as infrastructure usage reports and sustainability plans, can ensure that investments in infrastructure are not only made but maintained and evaluated over time.
Open engagement of societal actors and open dialogue with other knowledge systems
OS under the UNESCO definition requires more than sharing outputs and supporting infrastructure. It calls for inclusive inputs; e.g., research processes that actively engage societal actors, experts from other disciplines, patients, advocates, and marginalized communities, in shaping scientific agendas and outcomes.
Achieving this level of openness requires strategic collaboration. People must come together to define common goals, build trust, and agree on how to work collectively. Here, the Netherlands’
Frameworks like Participatory Research, Responsible Research and Innovation (RRI), Citizen Science, and Transdisciplinary Research support collaboration across disciplines and knowledge systems. Among these, the Research for Impact (R4I) framework offers a particularly robust model for integrating diverse perspectives and fostering societal relevance. 15 R4I requires defining a common goal and specifying the pathways that lead to positive societal impact using a Theory of Change. It also encourages dialogue across scientific, ethical, experiential, and cultural domains, aiming to generate transdisciplinary knowledge that is both academically rigorous and socially meaningful.
The DNA Dialogen project illustrates how the R4I framework can be applied in practice. This transdisciplinary project engages citizens in dialogue about the question whether or not to allow the use of human germline genome editing (HGGE). Its envisioned impact is to ensure societal decision-readiness. However, engaging diverse stakeholders in dialogue is not easy. Over half of the budget of DNA dialogen is to invest in relationships of trust with citizen representatives of communities and develop methods of dialogue and engagement that motivate citizens to want to engage in dialogue with scientists. Transdisciplinary collaboration is key: scientists contribute technical expertise; artists help translate complex ideas into accessible formats; citizens share experiential and cultural perspectives and ethicists, and legal scholars support the translation of societal input into societally aligned governance of HGGE. DNA dialogen reestablishes a connection of trust with communities that are underrepresented in science and serves to improve the equity of research investments.
Building relationships of trust is labor and time intensive and is therefore costly. To scale and sustain such practices, funders and institutions must align incentives with the principles of open engagement. This includes recognizing and rewarding collaborative and transdisciplinary work, embedding societal impact criteria in funding calls and evaluation frameworks, and supporting capacity-building for inclusive research design. Compliance mechanisms, such as requirements for stakeholder involvement, ethical co-creation processes, and transparent impact planning, can help institutionalize these practices. Some PD funders themselves actively engage in these kinds of initiatives. MJFF, for example, has set up an advisory
Discussion, concerns, and recommendations
This paper explored how funders and institutions traditionally defined scientific impact and the movement towards rethinking reward systems in academia to make it more open. The examples discussed demonstrate how the research ecosystem can gradually shift towards more openness, both in terms of inclusive outputs as well as inclusive inputs. In this section, we reflect on the importance of OS and conclude with recommendations for funders and institutions.
Why is open science important? and why is it difficult to implement?
OS contributes to the right of everyone to share in scientific advancement and its benefits (United Nations 1948), 16 by increasing research accessibility, transparency, collaboration and inclusivity, and making research more equitable. Although the full scientific and societal impact of OS is still being studied, studies suggest that OS practices including, open access, open data, FAIR data and collaborations can improve the quality of the generated knowledge, foster public trust and accelerate innovation. 13 It also enhances societal impact by providing methods for public engagement, creating more engaged communities, informed policy, and broader educational access.
While OS developments are laudable, they still fall short of OS as conceived by UNESCO. Where policies and incentives do exist, they are generally limited to sharing scientific knowledge, ignoring infrastructure and engagement.
A major reason for these shortcomings is the absence of clear expectations, metrics, and guidance from funders and institutions, which leaves OS underdeveloped. Terminology itself adds to the challenge: “OS”, “RRI”, “Citizen Science”, “FAIR data” and related concepts are often used interchangeably, leaving researchers unclear on expectations and standards.
From the researcher's point of view, the practical demands of collaboration, inclusivity and transparency can be challenging to deal with, even when funders and institutions advocate for OS. Consistently implementing OS practices across projects requires leadership and managerial skills that many researchers may not have had the opportunity to develop during their training, limiting the effective realization of OS. This is compounded by a culture where the fear of being “scooped” by competitors can disincentivize early sharing of work, and where the economic barrier of Article Processing Charges (APCs) can exclude researchers from less-funded institutions. This problem is exacerbated by the conflict between early-OS-adopting funders, who mandate immediate open access for the scientific articles they fund, and journal policies, which often charge exorbitant fees for the same, and enforce restrictive embargoes on repository uploads.
Furthermore, these challenges aren't limited to researchers alone; a significant systemic barrier is the failure of institutions and funders to adequately recognize the vital role played by essential team members. The expertise of vital players like the Lab Technicians, Project Managers, Data Stewards, Community Managers, and Research Software Engineers -The Champions- is indispensible. 17 When the contributions of these crucial community members is undervalued, meaningfully engaging with other societal actors and non-traditional knowledge systems remains at best an aspiration.
The path to OS is challenging and requires more than simple policy changes. The primary obstacles include cultural resistance, where traditional academic metrics still dominate and discourage open practices, and a fragmented infrastructure that lacks the capacity for truly open workflows. Furthermore, there's the problem of uneven scalability, leaving many researchers without the necessary skills or resources to participate effectively. These issues are magnified by vague rules and regulations, particularly concerning intellectual property, which create uncertainty. Another significant concern is the potential for data misuse, even when open data seeks democratization of knowledge. Finally, the APCs create a significant barrier for less-resourced researchers. Beyond these individual hurdles, widespread adoption of OS practices presents a collective action problem, as few funders or institutions are willing to be “a first mover” in changing the systems. Therefore, adopting OS demands a fundamental shift in academic culture, infrastructure and incentive systems to ensure its benefits are realized fairly. Central to this transformation is a redefinition of the relationship with journals, advocating for models that promote equity and inclusivity.18–21 Organizations like the Higher Education Leadership for Open Science (HELIOS Open) now driving this transformation by creating a coalition of institutions that are willing to implement these OS practices simultaneously.
While these challenges may seem daunting, it's important to recognize that changing research incentive structures is an inherently slow process, and there is virtue to that slowness. This deliberate pace allows for an approach that is both thoughtful and sensitive to context, acknowledging that not all OS practices are necessarily relevant to all kinds of PD research, and even those that are will need to be implemented in different ways. Despite the complexities, the crucial work of funders and institutions that are already ‘moving the OS needle’ must be replicated and extended if we want to maximize research impact.
This paper should not be taken as a call that everything must be open immediately in all instances. Instead, we want to encourage funders and institutions to deeply consider all of these topics, when they design their strategies for creating impact. Traditional metrics fail to accurately model real-world impact. We believe this approach is no longer adequate, especially given the current technological and cultural context.
Recommendations for funders and institutions
To ease the adoption of OS enabling incentives, below is a list of recommendations and resources for funders and institutions. We offer these recommendations we identify as ‘low hanging fruits’which are relatively easy actions to promote OS in the context of PD research. As such, they do not cover the full range of challenges that we have analyzed in this manuscript.
Tailor your Strategy
Prioritize key practices. Focus on OS practices that align with your mission. If your goal is new biomarker discovery, prioritize data sharing like MJFF did with the Develop and co-create policies with non-traditional stakeholders. Engage relevant stakeholder groups such as patients and caregivers to help prioritize OS practices and define pathways for their continues involvement in funded research. A good example here is the Toolkit for Patient Partners
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Assess current policies. Use existing tools like Define clear expectations
Set measurable goals. Once you have identified your priorities, create a clear policy that specifies what is expected, how it will be measured (e.g., data or software citation metrics, downloads, PIDs in papers), and the consequences of compliance. Centralize information. Consolidate all policy information into a single, easily accessible document. Address usage and access. Ensure your policies clearly define where and how an output can be accessed and specify the types of licenses and contracts that permit its use. Approach metrics with caution. While metrics are important, avoid making them the sole target. The value of a metric can change over time. Consider using qualitative approaches, such as narrative CVs,
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for activities that are not well-suited for quantitative measurement. Support and incentivize researchers
Acknowledge concerns: Recognize and address researchers’ legitimate needs and concerns about OS. Encourage early planning. Integrate open data policies into a broader Use positive and negative consequences for compliance. Positive consequences include listing open activities in funding requests, job applications, or promotion and tenure packages combined with clear statements that they will be favorably considered. Negative consequences can include restrictions on dispersing current or future funding or unfavorable consideration in applications and promotion packages. Monitor for compliance and enforce policies. To ensure policies are more than just aspirations, monitor for compliance. Reduce administrative burden by requiring machine-readable persistent identifiers to outputs (e.g., Incentivize inputs and engagement. A full OS incentive framework should include expectations around the inputs to research and engagement with external stakeholders. Inputs can be listed in research plans. Engagement plans can be included in a special section in grant or promotion applications and assessed during regular reviews. This is one place where qualitative approaches are particularly appropriate. Think carefully about infrastructure. Providing infrastructure in the form of governance models and repositories is essential to making incentives actionable by researchers. Examples
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like NIH's embrace of the Uniform Biological Material Transfer Agreement or MJFF's creation of the PPMI repository show how funders can provide clear governance and a central location for outputs. If setting up a repository is not possible, a simpler catalog, such as ASAP's Provide resources and foster a culture of openness
Prioritize education. Help researchers comply with OS policies by providing webinars, guides, and workshops. Make these resources easy to find online and link them directly to your policy. Support long-term institutional capacity by creating Help offset costs. For funders, you should include costs associated with output sharing or engagement as a line item in funding applications. Institutions can help by forming agreements with repositories and platforms and setting up special funds, like the Open Access Funds created at many institutions to support open access publishing. Promote knowledge mobilization. Shift the focus from a narrow view of technology transfer to a broader view of knowledge mobilization. Emphasize that impact occurs when the right information reaches the right people. Engage with stakeholders and consider contributions outside of the traditional categories. Many of the OS practices we see today began with the efforts of individual researchers and labs. Engage with researchers to understand how they are sharing their work and engaging with non-traditional stakeholders. Celebrate and highlight success. Use impact reports and other communications to highlight “champions” and demonstrate the positive impact of their activities.
Footnotes
Acknowledgments
The authors are grateful to
- Lotte van der Heijden, MSc - Drs. Marlise Schouten - Prof. dr. Gerjo van Osch
for their insightful comments and critical appraisal, which significantly improved the quality of this work.
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
Declaration of conflicting interests
Some paragraphs were improved with the assistance of Microsoft Copilot, and Gemini, which supported the structuring and articulation of key concepts. The authors retain full responsibility for content curation, interpretation, scholarly integrity, alignment with the research context interpretation, and final editing.
Some examples discussed in this paper are based on work conducted by the authors in their professional capacity. The inclusion of these examples is for the purpose of illustrating the concepts and arguments presented and is not intended to promote or endorse any specific commercial service, or organization.