What factors should you consider when choosing a translational research data sharing platform?

Translational research aims to bridge the gap between basic science and clinical practice by applying discoveries and innovations to real-world health problems. However, to achieve this goal, translational researchers need to share their data effectively and efficiently with other stakeholders, such as collaborators, funders, regulators, and patients. Choosing a suitable data sharing platform is a crucial decision that can affect the quality, reproducibility, and impact of translational research. In this article, we will discuss some factors that you should consider when selecting a data sharing platform for your translational research project.

1 Data types and formats

One of the first factors to consider is what types of data you need to share and in what formats. Translational research can involve a wide range of data, such as genomic, proteomic, imaging, clinical, behavioral, and environmental data. Each type of data may have different requirements for storage, processing, analysis, and visualization. Moreover, different data formats may have different standards, metadata, and interoperability. Therefore, you should look for a data sharing platform that can accommodate the diversity and complexity of your data types and formats, and that can support the tools and workflows that you use to generate and manage your data.

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Fedor Lipskerov

Founder @Synaptiflora | PhD Student in Molecular Biology at Technion | Science Communicator
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When managing diverse data in translational research, it’s crucial to choose platforms that align with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) to ensure data is not only well-organized but also easily sharable and reusable. Additionally, leveraging AI and machine learning can significantly enhance data analysis, fostering the development of predictive models that are vital for translating research into clinical outcomes.

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Thorben Seeger

Chief Business Development Officer | Lifebit
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To support research and innovation through the power of data, solutions are needed to enable diverse data access, standardisation, and analysis while maintaining security. Researchers will be limited in the novel insights they can gain if the data cannot be effectively combined to enhance its statistical power. Common Data Models (CDMs) are crucial to ensuring data is interoperable, including Observational Medical Outcomes Partnership (OMOP) in the case of clinical-genomic data Data sharing platforms with in-built extraction, transformation, loading pipelines (ETL) pipelines that can automate this work to process and convert raw data to analysis-ready data help further simplify this process for researchers.

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2 Data access and security

Another important factor to consider is how you want to control the access and security of your data. Depending on the nature and purpose of your translational research, you may want to share your data openly, selectively, or privately. For example, you may want to share your data openly to promote transparency, reproducibility, and collaboration; or you may want to share your data selectively to protect the privacy, confidentiality, and consent of your participants; or you may want to share your data privately to comply with the regulations, policies, and agreements of your funders, institutions, and partners. Therefore, you should look for a data sharing platform that can offer you the flexibility and functionality to set the appropriate levels of access and security for your data, and that can ensure the compliance and protection of your data.

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Thorben Seeger

Chief Business Development Officer | Lifebit
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Data federation has emerged as a solution to increase the useability of sensitive biomedical health data for translational research. Through data federation, researchers can be virtually linked to datasets of interest safely housed in highly secure computing environments known as Trusted Research Environments (TREs). The data is never physically moved or copied and the data controller or custodian maintains control. Combining these datasets securely via federation allows researchers to increase the statistical power of their research. Secure access to fully standardised and interoperable large datasets via federation can accelerate research by providing great power for clinical studies.

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3 Data discovery and reuse

A third factor to consider is how you want to facilitate the discovery and reuse of your data. One of the main benefits of sharing your data is to increase its visibility, availability, and utility for other researchers and stakeholders. However, to achieve this benefit, you need to make sure that your data can be easily found, accessed, understood, and integrated by others. Therefore, you should look for a data sharing platform that can provide you with the features and services to enhance the discoverability and reusability of your data, such as metadata standards, data citation, persistent identifiers, data quality assessment, data curation, and data linking.

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4 Data sustainability and impact

A fourth factor to consider is how you want to ensure the sustainability and impact of your data. Translational research can have long-term and far-reaching implications for health outcomes and policies. However, to realize this potential, you need to make sure that your data can be preserved, updated, and tracked over time and across domains. Therefore, you should look for a data sharing platform that can guarantee the sustainability and impact of your data, such as data archiving, data versioning, data provenance, data metrics, and data feedback.

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5 Data costs and benefits

A fifth factor to consider is how you want to balance the costs and benefits of sharing your data. Translational research can be costly and competitive, and sharing your data can entail additional expenses and risks. However, sharing your data can also bring significant rewards and opportunities, such as increased recognition, collaboration, funding, and innovation. Therefore, you should look for a data sharing platform that can help you optimize the costs and benefits of sharing your data, such as data incentives, data rewards, data support, and data value.

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6 Data culture and community

A sixth factor to consider is how you want to foster a culture and community of data sharing. Translational research can be challenging and multidisciplinary, and sharing your data can require a shift in mindset and behavior. However, sharing your data can also create a culture and community of trust, openness, and learning, which can enhance the quality and impact of translational research. Therefore, you should look for a data sharing platform that can promote a culture and community of data sharing, such as data education, data communication, data feedback, and data recognition.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Thorben Seeger

Chief Business Development Officer | Lifebit
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Even if researchers can securely access standardised, disparate datasets, these may not be provided on an easy to use, low code platform. Following FAIR principles - ensuring datasets are Findable, Accessible, Interoperable and Reusable - is essential to promoting democratisation of data and data quality, but does not entirely address all issues and challenges associated with deriving insights. In particular, researchers and clinicians without a data science background may be at a disadvantage to using analytical tools that require coding. End-to-end platforms take this one step further by providing researchers with the analytical tools they need to derive insight from biomedical data, regardless of background.

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What factors should you consider when choosing a translational research data sharing platform?

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1 Data types and formats

2 Data access and security

3 Data discovery and reuse

4 Data sustainability and impact

5 Data costs and benefits

6 Data culture and community

7 Here’s what else to consider

Research Management

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