91ÁÔÆæ

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Open science is built on more than open access—it depends on the transparency, accessibility and reusability of the underlying research components.

Open data, code, protocols and methods are central to this, enabling researchers to validate findings, reproduce results and accelerate discovery across disciplines. By sharing not just outcomes but the full research process—from datasets and analytical workflows to detailed methodologies—research becomes more robust, collaborative and impactful. Explore how opening these key elements supports integrity, innovation and trust in science and discover practical guidance on how to put these principles into action. 

Practical open science tools and innovations

Open science is about innovation. We are committed to building on our open science policies with practical tools that help researchers share data, code, protocols and methods when they publish with us.

Our approach focuses on:  

  • Guided support to help authors understand what to share  
  • Integrated tools embedded in submission and review systems  
  • Trusted platforms to ensure outputs are accessible and reusable  

Here we outline some of the solutions that have been piloted on selected journals and are now being progressively integrated more widely across 91ÁÔÆæ portfolios:

Supporting open data sharing

Integrated data sharing with Figshare

Authors can upload and publish datasets directly during manuscript submission, without leaving the submission system.

  • Simplifies the process of depositing and linking data &²Ô²ú²õ±è;
  • Improves compliance with journal and funder requirements &²Ô²ú²õ±è;
  • Increases visibility and reuse of datasets 

Impact

  • Increased use of repositories and measurable growth in data sharing following integration (12% increase in published data at piloted journals) &²Ô²ú²õ±è;

  • Evidence of higher citation impact for articles with linked datasets in some fields 

Supporting open code sharing

Code Ocean integration

Authors can share code in an executable environment linked to the publication, enabling reviewers and readers to access and run the code easily. 

  • Ensures long‑term preservation of the version of code used 
  • Supports more effective peer review of code 
  • Improves reuse and transparency

Impact

  • Strong author uptake where available (over 30% opt‑in reported in pilot journals 
  • Increased availability of verified, reproducible code alongside publications 

Supporting open protocols and methods

protocols.io platform

Researchers can share detailed, step‑by‑step methods in a structured, versioned format, linked directly to publications. 

  • Enables clearer documentation of experimental and analytical workflows 
  • Supports replication and reuse 
  • ²Ñ±ð³Ù³ó´Ç»å²õ a°ù±ð&²Ô²ú²õ±è;±è±ð°ù³¾²¹²Ô±ð²Ô³Ù±ô²â&²Ô²ú²õ±è;²¹±¹²¹¾±±ô²¹²ú±ô±ð
    ²¹²Ô»å&²Ô²ú²õ±è;³¦¾±³Ù±ð²¹²ú±ô±ð, w³ó¾±±ô±ð s³Ù¾±±ô±ô&²Ô²ú²õ±è;²ú±ð¾±²Ô²µâ€¯
    updateable through versioning
  • Facilitates adaptation of protocols to expedite science via forking
  • Provides credit for methodological contributions

Impact

  • High adoption in early pilots, with around one third of authors opting to share protocols when offered 
  • Improved availability of complete, reusable methods

Innovation: embedding support and automation

Alongside platform integrations, we are developing new ways to reduce effort and improve consistency in open science practices.

  • Automated guidance for authors: Open Science Assistant beta  
    An automated tool that analyses manuscripts and provides tailored recommendations on what data to share and how to link and declare these in the paper 
  • Workflow‑level automation &²Ô²ú²õ±è;
    Automated checks to improve the quality and completeness of data sharing information, speeding QC processes and supplementing editorial expertise 
  • Proactive approaches to data sharing &²Ô²ú²õ±è;
    New workflows that identify valuable but unshared research outputs and support authors in making them openly available via data specific publications and journals, for example Scientific Data, BMC Research Notes, BMC Genomic Data and the Discover s±ð°ù¾±±ð²õ

These initiatives are designed to:

  • Move support earlier in the research and submission process 
  • Reduce friction for authors 
  • Improve the quality, consistency and reuse of shared research outputs

A growing ecosystem

Together, these tools reflect a shift from guidance alone to embedded, practical support for open science. 

We are iteratively developing, testing with researchers, relevant community bodies and societies and editors, with the aim of making it easier to share high‑quality, reproducible research as a standard part of publishing.

Community involvement

We work with the scientific community in a variety of ways to improve data management and publication. We also support a number of education and best practice initiatives.

  • We support a number of education and best practice initiatives. These include: ; ; Love Data Week; ; ; .
  • Our free and series support researchers in understanding the benefits, challenges and practicalities of managing and publishing research data.

Maria Hodges, Executive Editor, BMC 

Graham Smith, Research Data Year group representative


Graham Smith,  (FAIRsharing Registry: Connecting data policies, standards and databases RDA working group)

Chris Graf, Stakeholder Advisor 

Sowmya Swaminathan, 

Data publishing

Through data publishing, we aim to make scientific data more reusable, discoverable, interpretable, and citable.

We facilitate data publishing in open access journals Scientific Data and BMC Research Notes; through Registered Reports in BMC journals; and pilot projects on data citation and data badges.

91ÁÔÆæ’s flagship open data journal, , offers authors the opportunity to describe and share their research data with a wide audience. 

publishes dark data - single observations,
short null results, orphan data and confirmatory results. The
‘data note’ article type offers authors a peer-reviewed, citeable
summary of their data.

Our Research Data Helpdesk helps authors and editors find out how best to deal with their data and understand funder and institute mandates. With journal-level Research Data Policies, we ensure that the data at the heart of scholarly research are appropriately archived and, where possible, widely accessible.

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