Open Science Practices: A Practical Guide for Life Scientists

Open science refers to a range of methods, tools, platforms, and practices that aim to make scientific research more accessible, transparent, reproducible, and reliable. These practices include sharing code, data, and research materials, embracing new publishing formats such as registered reports and preprints, pursuing replication studies and reanalyses, optimizing statistical approaches to improve evidence assessment, and re-evaluating institutional incentives. The ongoing shift toward open science practices is partly due to mounting evidence that studies across disciplines suffer from biases, underpowered designs, and irreproducible or non-replicable results. It also stems from a general desire amongst many researchers to reduce hyper-competitivity in science and instead promote collaborative research that benefits science and society. This guide provides life scientists with concrete practices they can implement in their own research, from preregistration and data sharing to preprints and open-source analysis tools.

At a Glance

The table below summarizes the core open science practices covered in this guide, the primary actions required, and the platforms or resources that support each practice.

Understanding Open Science in the Life Sciences

Open science is not a single policy or platform but a collection of practices that address different stages of the research lifecycle. For life scientists, these practices touch on how studies are designed, how data are collected and stored, how analyses are conducted, how results are shared, and how credit is assigned. The motivation for adopting these practices comes from evidence that studies across disciplines suffer from biases, underpowered designs, and irreproducible or non-replicable results. Open science practices are intended to counter these problems by making the research process more visible and verifiable.

The term open science encompasses sharing code, data, and research materials, embracing new publishing formats such as registered reports and preprints, pursuing replication studies and reanalyses, optimizing statistical approaches to improve evidence assessment, and re-evaluating institutional incentives. For life scientists, this means thinking about how each component of a research project can be made accessible to others, from the initial study plan to the final published article and the underlying datasets.

Preregistration of Study Designs and Analysis Plans

Preregistration is the a priori registration of study designs and analysis plans. It has long been established as standard practice in clinical human research and is increasingly taken up in other fields of science. Despite growing evidence suggesting that preregistration can mitigate questionable research practices, and the existence of two platforms targeting animal studies, preregistration has remained uncommon in animal research. A survey of Swiss animal researchers found that nearly half had never heard of preregistration, and the same survey identified multiple perceived barriers to adoption.

Why Preregistration Matters

Preregistration addresses a specific problem in scientific practice. When researchers analyze data before deciding what to report, they can unconsciously or consciously select analyses that produce favorable results. Preregistration creates a public record of the intended study design and analysis plan, making it possible for others to distinguish between confirmatory analyses that were planned in advance and exploratory analyses that were conducted after seeing the data.

In the context of animal research, preregistration represents a potentially promising step forward in light of a reproducibility crisis and calls for more transparency and rigor. However, implementing such policies without understanding their impact carries potential risks. It is essential to uncover animal researchers' perspectives on the strengths, weaknesses, opportunities, and threats of preregistration before advancing its implementation.

Barriers to Preregistration in Animal Research

A comprehensive feasibility project on preregistration of animal experiments in Switzerland assessed animal researchers' experiences with preregistration, examined their attitudes, subjective norms, perceived behavioral control, intentions, motivations, and perceived obstacles, and identified perceived facilitators and barriers. The survey was conducted among all registered study directors of ongoing animal experiments in Switzerland, with 1,385 invited study directors.

The findings revealed that lack of training was a major barrier. In a survey of applied ethology researchers, preregistration was uncommon, with lack of training cited as the main barrier. Of those who had published a preprint or preregistered study protocol, preregistration was mainly used for quality control and integrity. These findings suggest that increasing awareness and providing practical training could increase adoption.

How to Preregister a Life Science Study

To preregister a study, researchers should document the following elements before data collection begins:

1. Research question and hypotheses
2. Primary and secondary outcome measures
3. Sample size and power calculations
4. Inclusion and exclusion criteria
5. Data collection procedures
6. Analysis plan, including statistical tests and software
7. Criteria for interpreting results

The Open Science Framework provides a platform for creating and hosting preregistrations. For animal studies, specialized platforms exist that target the unique considerations of animal research. The Open Science Framework has the mission to increase openness, integrity, and reproducibility in research, and it recommends and offers a collection of practices intended to make scientific processes and results more transparent and available in a standardized way for reuse to people outside the research team.

Preregistration in Human Electrophysiology

Open science practices are gaining momentum in psychophysiological research, but at the nascent stage of a special issue in the International Journal of Psychophysiology there was no systematic collection of resources to support the adoption of open science practices specific to studies of human electrophysiology. The purpose of that special issue was to gather and provide resources that identify the idiosyncratic considerations and implications of open science practices specifically for studies of human EEG and event-related potentials. Papers also show the importance of promoting good scientific practices in the application of open science principles to EEG and ERPs. The introduction to the special issue provides a roadmap for identifying the resources necessary to begin and improve the application of open science principles to EEG and ERP research. Open science practices are expected to help increase the robustness, rigor, and replicability of EEG and ERP research and ultimately become the norm in studies of EEG and ERPs.

For researchers working with human electrophysiology data, preregistration requires specifying the preprocessing pipeline, artifact rejection criteria, and analysis parameters before data collection. This is particularly important because EEG data can be analyzed in many different ways, and the choice of analysis parameters can substantially affect results.

Data Sharing and the FAIR Principles

Data sharing is a core open science practice. In a survey of applied ethology researchers, 27% reported always sharing their data, 57% shared sometimes, and 15% never shared their data. Sharing data as supplementary material was most common, and only a minority used data repositories. Only 21% of respondents had heard of the FAIR principles.

What FAIR Means for Life Science Data

FAIR stands for Findable, Accessible, Interoperable, and Reusable. These principles provide a framework for making data useful to others. For life science data, this means:

1. Findable: Data have unique identifiers and are described with rich metadata
2. Accessible: Data can be retrieved using standard protocols
3. Interoperable: Data use standard formats and vocabularies
4. Reusable: Data have clear usage licenses and provenance information

The Research Data Framework from the National Institute of Standards and Technology provides guidance on managing research data throughout its lifecycle. This framework addresses the planning, creation, storage, sharing, and preservation of research data.

Choosing a Data Repository

When sharing life science data, researchers should choose a repository that:

1. Assigns persistent identifiers such as DOIs
2. Supports discipline-specific file formats
3. Provides metadata templates
4. Offers access controls when needed
5. Preserves data for the long term

General repositories and discipline-specific repositories both have roles. The key is to deposit data in a repository that others can find and access, instead of only providing data as supplementary material attached to a journal article.

Data Sharing in Spatial Transcriptomics

The Xenium In Situ platform is a spatial transcriptomics product capable of mapping hundreds of genes in situ at subcellular resolution. Given the multitude of commercially available spatial transcriptomics technologies, recommendations in choice of platform and analysis guidelines are increasingly important. A study exploring 25 Xenium datasets generated from multiple tissues and species compared scalability, resolution, data quality, capacities, and limitations with eight other spatially resolved transcriptomics technologies and commercial platforms. The study benchmarked the performance of multiple open-source computational tools when applied to Xenium datasets in tasks including preprocessing, cell segmentation, selection of spatially variable features, and domain identification. This study serves as an independent analysis of the performance of Xenium and provides best practices and recommendations for analysis of such datasets.

For researchers generating spatial transcriptomics data, sharing raw and processed data is essential because the analysis workflows are complex and evolving. Open-source computational tools enable other researchers to reanalyze data with updated methods as they become available.

Preprints and Open Access Publishing

Preprints are manuscripts posted on public servers before or alongside journal submission. In a survey of applied ethology researchers, preprints were uncommon, with only 25% having posted one. Of those who had published a preprint, preprints were mainly used for result dissemination. The main barrier to preprint use was lack of training.

Benefits of Preprints for Life Scientists

Preprints provide several benefits:

1. Rapid dissemination of results before journal review
2. Open access to research findings for readers without journal subscriptions
3. Timestamps that establish priority of discovery
4. Feedback from the community before formal peer review

The National Center for Biotechnology Information provides literature resources that include PubMed, a database of biomedical literature. PubMed indexes many preprints and provides a way for researchers to discover recent work in their field.

How to Post a Preprint

To post a preprint, researchers should:

1. Check journal policies on preprint posting
2. Choose a preprint server appropriate for the discipline
3. Prepare the manuscript according to the server's requirements
4. Post the manuscript and obtain a DOI
5. Link the preprint to the final published article when available

Preprints are particularly valuable in fast-moving fields where delays in publication can slow progress. The COVID-19 pandemic demonstrated how open science practices, including preprint sharing, can accelerate research and save lives.

Open-Source Tools for Life Science Research

Open-source software plays a central role in life science research. These tools are freely available, and their source code can be inspected, modified, and shared. This transparency supports reproducibility because other researchers can examine exactly how analyses were conducted.

Fiji for Biological Image Analysis

Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. Fiji is proposed as a platform for productive collaboration between computer science and biology research communities.

For life scientists who work with microscopy images, Fiji provides a complete environment for image processing and analysis. The integrated update system means that plugins can be shared and updated easily, and the scripting languages enable automation of repetitive analysis tasks.

Open-Source Tools for Spatial Transcriptomics

The benchmarking study of Xenium datasets evaluated the performance of multiple open-source computational tools for preprocessing, cell segmentation, selection of spatially variable features, and domain identification. These tools enable researchers to analyze spatial transcriptomics data without relying on proprietary software, and the open-source nature of the tools means that the analysis methods are transparent and can be improved by the community.

Choosing Open-Source Tools

When selecting open-source tools for life science research, consider:

1. Whether the tool is actively maintained
2. Whether documentation and tutorials are available
3. Whether the tool has been validated in published studies
4. Whether the tool supports the file formats used in your research
5. Whether the tool can be integrated into your existing workflow

Reporting Guidelines and Experimental Design Support

Reporting guidelines provide checklists of information that should be included in research reports. These guidelines improve the completeness and transparency of research reports, making it easier for others to understand and reproduce the work.

The EQUATOR Network

The EQUATOR Network is an international initiative that provides resources for reporting health research. The network maintains a comprehensive database of reporting guidelines for different study types, including randomized trials, observational studies, systematic reviews, and diagnostic accuracy studies. Life scientists can use the EQUATOR Network to identify the appropriate reporting guideline for their study type and to access templates and examples.

The NC3Rs Experimental Design Assistant

The NC3Rs Experimental Design Assistant is a free online tool that guides researchers through the design of animal experiments. The tool helps researchers think through important design considerations, including randomization, blinding, sample size calculation, and statistical analysis. By using the tool, researchers can improve the quality of their experimental designs and avoid common pitfalls that lead to irreproducible results.

Reporting Standards for Heart Rate Variability Research

A systematic review of heart rate variability analysis in clinical research assessed the current use of HRV analysis, focusing on the diseases studied, the common indices used, and the physiological insights gained. The review included 99 articles published between 2019 and 2025. HRV was most frequently applied in mental, behavioral, or neurodevelopmental disorders, diseases of the nervous system, and diseases of the circulatory system. Approximately 58% of articles justified a targeted selection of HRV indices aligned with their clinical hypotheses, whereas the rest relied on batteries of indices. The review highlighted the diverse use and interpretation of HRV across clinical contexts, pointing to a lack of standardized protocols. The heterogeneity of recording durations, maneuver use, and spectral units provided, together with under-reporting of nonlinear prerequisites, all limit cross-study comparability and clinical translation.

The review outlined a tentative roadmap for clinically oriented HRV work that emphasizes measurement standardization, analytic rigor and transparency, and clinical translation, ideally supported by preregistration, harmonized reporting, and open, validated resources. Future research should focus on developing standardized and updated guidelines for HRV analysis to improve its clinical utility and to encourage interdisciplinary collaboration.

Open Science Badges and Journal Policies

Open Science Badges are visual icons placed on publications that certify that an open practice was followed. The badges signal to readers that an author has shared the corresponding research evidence, thus allowing an independent researcher to understand how to reproduce the procedure.

How Open Science Badges Work

The Journal of Neurochemistry became a signatory of the Transparency and Openness guidelines in 2016, which provides eight modular standards with increasing levels of stringency. These standards cover citation standards, data transparency, analytic methods and code transparency, research materials transparency, design and analysis transparency, study preregistration, analysis plan transparency, and replication. The Open Science Badges are maintained by the Open Science Badges Committee and by the Center for Open Science.

The badges are awarded when authors demonstrate that they have followed specific open practices. For example, a badge might be awarded for sharing data, for sharing analysis code, or for preregistering the study. The badges provide a visible signal to readers that the authors have taken steps to make their research more transparent.

Choosing Journals That Support Open Science

When selecting a journal for publication, life scientists should consider:

1. Whether the journal supports preprint posting
2. Whether the journal offers Open Science Badges
3. Whether the journal requires data availability statements
4. Whether the journal supports registered reports
5. Whether the journal has signed the Transparency and Openness guidelines

Practical Implementation Steps

Implementing open science practices requires planning and organization. The following steps provide a practical workflow for incorporating open science into a life science research project.

Step 1: Plan for Open Science at Project Initiation

At the start of a research project, decide which open science practices you will follow. Document these decisions in your lab notebook or project management system. Consider preregistering the study design and analysis plan, particularly if the study involves confirmatory hypothesis testing.

Step 2: Use Experimental Design Tools

Before collecting data, use tools such as the NC3Rs Experimental Design Assistant to review your study design. This tool can help identify weaknesses in randomization, blinding, and sample size that could compromise the validity of your results.

Step 3: Document Data Collection and Processing

During data collection, maintain detailed records of all procedures. For image analysis, use tools like Fiji that support scripting and automation, and save the scripts along with the data. For electrophysiology, document the preprocessing pipeline and artifact rejection criteria.

Step 4: Deposit Data in a Repository

After data collection, deposit the raw and processed data in a repository that assigns persistent identifiers. Include metadata that describes the data collection procedures, file formats, and any processing steps. Follow the FAIR principles to make the data findable, accessible, interoperable, and reusable.

Step 5: Write and Share Analysis Code

Prepare analysis code that is well documented and can be run by others. Share the code in a public repository or as supplementary material. For image analysis workflows, share the Fiji scripts or plugins used.

Step 6: Post a Preprint

When the manuscript is ready, post a preprint to make the results available before journal review. Check the journal's preprint policy to ensure compliance.

Step 7: Submit to a Journal with Open Science Policies

Select a journal that supports open science practices. Consider journals that offer Open Science Badges or that have signed the Transparency and Openness guidelines.

Step 8: Link All Research Outputs

When the article is published, link the preprint, data, code, and preregistration to the final article. This creates a complete record of the research that others can follow.

Records and Measurements for Open Science Compliance

Maintaining records of open science practices is important for demonstrating compliance and for evaluating the impact of these practices. The following records should be maintained:

1. Preregistration documents with dates and version numbers
2. Data deposition records with DOIs and repository names
3. Analysis code with version control history
4. Preprint posting records with dates and server names
5. Journal submission records showing compliance with open science policies

These records serve multiple purposes. They provide evidence of open science practices for grant reports and promotion packages. They also enable researchers to track their own adoption of open science practices over time and to identify areas where they could improve.

Common Failure Patterns in Open Science Implementation

Researchers attempting to implement open science practices often encounter common challenges. Understanding these failure patterns can help researchers avoid them.

Failure Pattern 1: Preregistering Too Late

Preregistration is only effective if it occurs before data collection and analysis. Researchers who preregister after collecting data or after seeing results have not gained the benefits of preregistration. The preregistration should be time-stamped and should occur before any data are collected.

Failure Pattern 2: Sharing Data Without Metadata

Data that are shared without adequate metadata are not reusable. Other researchers cannot understand the data structure, the meaning of variables, or the procedures used to collect the data. Data sharing should include comprehensive metadata that follows community standards.

Failure Pattern 3: Sharing Code Without Documentation

Analysis code that is shared without documentation is difficult or impossible for others to run. Code should include comments explaining the purpose of each step, and the code should be tested to ensure it runs without errors.

Failure Pattern 4: Treating Preregistration as a Box-Checking Exercise

Preregistration is only valuable if the registered plan is actually followed. Researchers who preregister a plan and then deviate from it without documenting the deviations have not gained the benefits of preregistration. Deviations should be documented and explained in the final report.

Failure Pattern 5: Ignoring Field-Specific Considerations

Open science practices need to be adapted to the specific requirements of different research fields. For example, human electrophysiology research has idiosyncratic considerations that differ from those in other fields. Researchers should seek guidance that is specific to their field instead of applying generic templates without modification.

Limitations and Tradeoffs of Open Science Practices

Open science practices have limitations and tradeoffs that researchers should understand.

Time and Effort Costs

Implementing open science practices requires time and effort. Preregistration requires writing a detailed study plan before data collection. Data sharing requires preparing data and metadata for deposition. Code sharing requires documenting and testing code. These activities add to the workload of research projects.

Concerns About Being Scooped

Some researchers worry that sharing data or posting preprints increases the risk of being scooped. However, preprints establish priority through time-stamped posting, and data sharing with appropriate licenses can protect the rights of the data producers.

Data Sensitivity and Privacy

Some life science data involve human subjects or sensitive information that cannot be freely shared. Researchers must balance open science principles with ethical and legal obligations to protect participant privacy. Anonymization and controlled access mechanisms can help address these concerns.

Quality Concerns

Some researchers worry that open science practices, particularly preprints, may lead to the dissemination of low-quality research. However, preprints are typically posted alongside journal submission, and the final published version undergoes peer review.

Institutional Incentives

The adoption of open science practices is influenced by institutional incentives. Researchers who are evaluated primarily on the number of publications in high-impact journals may have less motivation to invest in open science practices. Re-evaluating institutional incentives is an important part of the open science movement.

Welfare and Safety Context

Open science practices have implications for animal welfare and research safety. In animal research, preregistration can improve the quality of experimental design, which can reduce the number of animals needed and improve the reliability of results. The NC3Rs Experimental Design Assistant is specifically designed to help researchers design animal experiments that are rigorous and that minimize animal use.

In human research, open science practices can improve the transparency of research procedures, which can help protect participant welfare. Reporting guidelines help ensure that researchers report all relevant information about their methods, including any adverse events or complications.

For research involving hazardous materials or procedures, open science practices must be balanced with safety considerations. Data and code sharing should not compromise safety protocols or expose researchers or the public to unnecessary risks.

Professional Escalation Criteria

Researchers should seek professional guidance when they encounter situations that exceed their expertise. The following situations warrant escalation:

1. When designing a study that involves complex statistical analysis, consult a statistician before finalizing the preregistration
2. When sharing data that involves human subjects, consult your institutional review board or ethics committee
3. When sharing data that involves proprietary or confidential information, consult your institution's technology transfer office
4. When depositing data in a repository, consult your institution's library or data management service for guidance on repository selection and metadata standards
5. When implementing open science practices in a new field, consult researchers in that field who have experience with these practices

Frequently Asked Questions

What is the difference between preregistration and a registered report?

Preregistration involves registering the study design and analysis plan before data collection. A registered report is a publishing format in which the study design and analysis plan are reviewed and accepted before data collection, and the final article is published regardless of the results. Registered reports provide stronger guarantees against publication bias because the decision to publish is made before the results are known.

How do I choose a data repository for my life science data?

Choose a repository that assigns persistent identifiers such as DOIs, supports the file formats used in your research, provides metadata templates, and preserves data for the long term. Consider whether a general repository or a discipline-specific repository is more appropriate for your data. Your institution's library or data management service can provide guidance on repository selection.

Can I share data that involves human subjects?

Human subjects data can be shared if appropriate safeguards are in place. Anonymization or de-identification of data can enable sharing in many cases. For data that cannot be anonymized, controlled access mechanisms can allow qualified researchers to access the data under specific conditions. Consult your institutional review board or ethics committee before sharing human subjects data.

How do preprints affect journal publication?

Most journals allow preprint posting, but policies vary. Check the journal's policy before posting a preprint. Some journals have specific requirements about when preprints can be posted and how they should be linked to the final published article. The final published version of the article is the version of record, and the preprint should be updated to link to the published version.

What are Open Science Badges and how do I earn them?

Open Science Badges are visual icons placed on publications that certify that an open practice was followed. Badges are available for practices such as sharing data, sharing analysis code, and preregistering the study. To earn a badge, authors must demonstrate that they have followed the specific practice and provide evidence of compliance. The badges are maintained by the Open Science Badges Committee and the Center for Open Science.

How much time does preregistration take?

The time required for preregistration depends on the complexity of the study. A simple preregistration can be completed in a few hours, while a complex study with detailed analysis plans may require several days. The time invested in preregistration can save time later by clarifying the study design and analysis plan before data collection begins.

What should I do if I need to deviate from my preregistered plan?

Deviations from a preregistered plan are sometimes necessary and acceptable, but they should be documented and explained. In the final report, describe the deviations and the reasons for them. Distinguish between confirmatory analyses that followed the preregistered plan and exploratory analyses that were conducted after seeing the data.

How can I encourage my lab or department to adopt open science practices?

Start by implementing open science practices in your own research and sharing your experiences with colleagues. Organize training sessions on preregistration, data sharing, and other practices. Advocate for institutional policies that support open science, such as recognizing open science practices in promotion and tenure evaluations. The Open Science Framework provides resources that can support these efforts.

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• Biomedical Science Research Building

References and Further Reading

• Research Data Framework. National Institute of Standards and Technology.
• EQUATOR Network. EQUATOR Network.
• Experimental Design Assistant. NC3Rs.
• NCBI Literature Resources. National Center for Biotechnology Information.
• PubMed. National Library of Medicine.
• Fiji: an open-source platform for biological-image analysis.. Nature methods, 2012.
• Open science in human electrophysiology.. International journal of psychophysiology : official journal of the International Organization of Psychophysiology, 2022.
• Open science.. Current biology : CB, 2023.
• Taste.. Current biology : CB, 2023.
• Optimizing Xenium In Situ data utility by quality assessment and best-practice analysis workflows.. Nature methods, 2025.
• Open Science Badges in the Journal of Neurochemistry.. Journal of neurochemistry, 2018.
• Very-low-calorie ketogenic diet (VLCKD) in the management of metabolic diseases: systematic review and consensus statement from the Italian Society of Endocrinology (SIE).. Journal of endocrinological investigation, 2019.
• Prospects for rice in 2050.. Plant, cell & environment, 2023.
• A survey of researchers' attitudes to preregistration in animal research reveals multiple perceived barriers to adoption.. 2026.
• The long horizon of animal study preregistration: Attitudes, barriers, and myths.. 2026.
• Delivery of preregistration palliative care nurse education: a scoping review protocol.. 2026.
• A call for preregistration of in vitro research : Pregistration as a tool to improve reproducibility and transparency.. 2026.
• Application of heart rate variability in clinical practice and research: A systematic review of recent years.. 2026.
• Exploring open science in applied ethology: practice and attitudes among researchers.. 2026.
• ECLIPSE v2.0: A Systematic Falsification Framework with Quantitative Integrity Metrics. 2025.
• ECLIPSE v2.0: A Systematic Falsification Framework with Quantitative Integrity Metrics. 2025.
• Barriers to Full Participation in the Open Science Life Cycle among Early Career Researchers. Data Science Journal, 2022.
• Open Genetic Code: on open source in the life sciences. Life Sciences Society and Policy, 2014.
• Assessing Open Science Practices in Phytolith Research. Open Quaternary, 2022.
• Towards a Framework Based on Open Science Practices for Promoting Reproducibility of Software Engineering Controlled Experiments. Cibse 2023 Xxvi Ibero American Conference on Software Engineering, 2023.
• Open science saves lives: lessons from the COVID-19 pandemic. BMC Medical Research Methodology, 2021.

This article is educational and does not replace institutional policy, professional advice, or applicable safety and regulatory requirements.