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Open Science Monitor

Drivers and barriers of open science

Many factors can be involved in promoting or inhibiting open science, from awareness of open science among researchers to the availability of infrastructure, training and career incentives. This section presents a high-level overview of some of the important drivers and barriers for open science.

They are grouped into five categories:

  • Values and norms in science
  • Social and environmental factors
  • Policies and rules
  • Costs and funding
  • Technology and Infrastructure

The factors were identified on the basis of results from an online consultation conducted as part of the development of this monitor and a review of recent articles and reports on open science. Some factors may be more relevant for some disciplines or research environments, depending on the degree to which open science practices have already been established.


Click on the links below to see the drivers and barriers related to each category.

Values and norms in science


Belief in importance of transparency for science and that it brings:

  • Better replicability
  • Less publication bias
  • A more reliable scientific record

Moral imperative to make outputs of publicly funded research publicly accessible.

Idea that sharing methods, materials and data can make research more efficient (and that this is critical for tackling major societal challenges).

Established open science practices within disciplines, such as:

  • Data sharing in genomics and crystallography
  • The use of preprints in physics


Professional disincentives against sharing:

  • Fear of ideas being stolen
  • Fear of not being given credit for ideas
  • Fear of making public errors when sharing early work

Fear of reputational risks from trying new things, especially for early-career researchers.

Concern that scientific quality will suffer if publishing frameworks are less regulated and more outputs are generated.

  • Information overload is a related concern
  • Concern that emphasis on social media could mean public appeal overshadows scientific rigour

Lack of trust in citizen science:

  • Perception that data quality is lower
  • Concern that rigour and objectivity could be undermined in citizen science by private or external influences

Social and enviromental factors


Dissatisfaction with traditional peer review, leading to interest in new forms of scholarly communication and assessment processes (e.g. preprints).

Objections to reliance on publication metrics for assessing research performance, as in e.g. the San Francisco Declaration on Research Assessment (DORA).

Increased demand for public accountability of research.

Globalised world facing major challenges (e.g. climate change, infectious disease) that science may help solve.

Career benefits, such as:

  • Recognition of the value of shared data
  • Increased visibility of outputs

Research community increasingly made up of people who have grown up using digital technology and the Internet.


Researchers lack awareness about:

  • Open science practices
  • Citizen science projects relevant to their field

Cultural inertia in science.

  • Misunderstandings about what is meant by, e.g. ‘open access’
  • Established researchers are less likely to change
  • Notion that early career researchers must adhere to status quo to advance and follow decisions made by supervisors

Perceived or actual lack of professional rewards or other individual benefits from sharing data or making papers openly accessible.

Concern about science becoming more vulnerable to influence by those pushing specific agendas, or that greater public involvement would mean more short-sighted funding decisions.

Policies and rules


Journals use of incentives and enablers, e.g.

  • Use of badges to acknowledge open science practices
  • Accepting preprints
  • Use of Transparency and Openness Promotion (TOP) guidelines

Funders and institutions introducing policies that promote or mandate open science practices, including:

  • Making publications available through open access
  • Placing publications in open access repositories (e.g. PubMed Central and institutional repositories)
  • Developing a data management plan

Efforts to harmonise policies and share information across countries through projects such as PASTEUR4OA and OpenAIRE in Europe.

Availability and use of tools to track open science practices, e.g.:

  • Altmetrics and other non-traditional metrics
  • Permanent digital object identifiers that facilitate data citation


Lack of professional incentives or rewards, such as hiring decisions, promotion decisions and grant proposal reviews taking into account preprints or other open science activities.

  • Lack of clarity and guidance about how to assess such outputs and activities
  • Wanting to capitalise on data collected before sharing it

Constraints and fragmentation in existing EU copyright legislation discourage researchers from exploiting text and data mining.

Costs and funding


Institutions and funders providing support for open science in the form of funding and support for infrastructures like OpenAIRE, SSOAR and Zenodo.

Funders withholding grant payments from researchers who do not abide by their open access publishing rules.

Funding for open science-related projects, e.g.:

  • EU-funded projects through FP-7 and Horizon 2020
  • NWO replication studies pilot (in the Netherlands)
  • The Leamer-Rosenthal Prizes for Open Social Science (in the U.S.)


Perception that open science activities, such as preparing data for sharing, are time-consuming.

Perceived and/or actual costs associated with open access publishing, taking time to prepare data for sharing, and operating data storage infrastructure.

Commercial publishers may make decisions about fees and policies that are driven by private interest rather than public good.

Technology and infrastructure


Rise in the use of social media and digital technology in society more widely.

  • This technology enables interaction and information sharing, and has led to a general democratisation of knowledge
  • This expands the scope for citizen involvement in science to data-processing projects, e.g. Zooniverse

Increased data storage capacity and processing speeds. More data being generated and collected through, e.g. text mining and the use of electronic patient records.

Belief and evidence that exploitation of data can bring socioeconomic benefits.

Increased availability of open science journals, platforms and tools, including:

  • Open access journals
  • Repositories for data and publications (e.g. PubMed Central, Zenodo, FigShare and institutional repositories)
  • Preprint servers
  • Search engines finding preprints


Lack of skills and training for data management and curation.

Lack of long-term investment in digital infrastructure for storing data and publications.

Lack of data-sharing standards (on formats, documentation, etc.).

Insufficient attention to different data sharing and data infrastructure needs across disciplines and organisations.


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