4 Research data management storage has become a strategic issue for universities and research institutes. Instruments, simulations, sequencing, imaging and digitization projects produce data faster than ever, funders require data to be managed and shared, and researchers expect storage to be available, safe and affordable throughout a project and long after it ends. Many institutions still rely on a patchwork of departmental servers, personal drives and cloud accounts, which leaves data at risk and makes compliance with funder policies difficult. This article explains what research data management (RDM) asks of storage, the tiers universities commonly provide, how funder requirements in different regions shape them and how to make central storage sustainable. It is written for research IT architects, RDM teams and library services. For long-term preservation specifically, see our hub on digital preservation storage. The research data lifecycle Research data passes through stages, each with different storage needs: Active: data is being collected, processed and analyzed. It needs performance, collaboration and frequent access, often close to compute. Shared: data is shared with collaborators inside and outside the institution during the project. Published: data underpinning publications is deposited in a repository with identifiers and metadata so others can find and reuse it. Archived: data is retained after the project for verification, reuse or compliance, often for years. Disposed: data is deleted when retention ends, unless it has long-term value. A university storage service usually needs to support all of these, with clear handoffs between them. Funder and policy requirements by region Funders increasingly require data management plans and data sharing: Europe: Horizon Europe requires data management plans for projects generating data and promotes the FAIR principles (findable, accessible, interoperable, reusable), with open access to data where possible. National funders across Europe have similar expectations. United Kingdom: UK research councils expect publicly funded research data to be made available with as few restrictions as possible and to be preserved for reuse. United States: The National Institutes of Health Data Management and Sharing Policy, effective January 2023, requires NIH-funded researchers to submit and follow data management and sharing plans. Other federal agencies have related requirements. Asia-Pacific: Japan and other countries have adopted open science policies that encourage or require data management and sharing for publicly funded research. Institutional policies often set minimum retention periods for research data after project completion, frequently several years and sometimes longer for clinical or long-term studies. Data involving human subjects is also subject to data protection law, such as GDPR in Europe, which constrains where and how it is stored. The storage tiers universities provide Active research storage High-performance or general-purpose storage for data in use, often mounted on research computing clusters and lab workstations. Researchers need shared access, snapshots for recovery and good performance. Parallel file systems or high-performance NAS are common for compute-heavy workloads. Collaboration and sync-and-share Services that let researchers share data with external collaborators, sometimes built on object storage behind a sync-and-share or data transfer platform. Research data archive Large, cost-efficient storage for data that is no longer active but must be retained. Object storage is a natural fit: it scales to petabytes, costs less per terabyte than active storage, supports immutability and offers S3 access for tools and repositories. See object storage vs traditional storage. Data repository storage Storage behind the institutional data repository, where published datasets are deposited with persistent identifiers. Repository platforms increasingly support S3-compatible storage. Preservation storage For data with long-term value, preservation storage with multiple copies and fixity checking. See how many copies digital preservation needs. Why central storage matters Data safety: departmental servers and personal drives are often unbacked and unmonitored. Compliance: central services can enforce retention, access control and data protection requirements. Cost efficiency: pooling capacity improves utilization and buying power. Discoverability: central archives and repositories make data findable. Continuity: data survives when researchers or students leave. Making storage sustainable Funding models Universities fund research storage in different ways: a free allocation per researcher or project, with charges beyond it; cost recovery from grants, where funders permit storage costs; central funding as research infrastructure; or a mix. Long-term archive storage is hard to fund from grants that end, so many institutions fund archive tiers centrally or charge a one-off fee for a defined retention period. Cost per terabyte Archive tiers should be priced low enough that researchers choose them over keeping data on expensive active storage or unmanaged drives. Object storage on standard hardware helps. See storage cost per terabyte. Lifecycle automation Policies that move data from active to archive tiers after projects end, and prompt review at the end of retention, prevent active storage from filling with forgotten data. See data lifecycle management. Growth planning Research data growth is uneven: a new instrument or large project can add petabytes quickly. Storage that grows by adding nodes avoids disruptive upgrades. See storage capacity planning. Sensitive research data Health data, personal data and commercially sensitive data need extra protection: access controls, encryption, audit logging and sometimes dedicated secure environments. Data protection rules may restrict storage locations, such as keeping data within the EU or national borders. Plan secure tiers with these requirements in mind, and avoid placing sensitive data on services that cannot meet them. See GDPR data storage requirements. Integrating with research computing Active data often needs to be close to compute. Object storage increasingly serves as the capacity tier behind high-performance file systems, with data staged to fast storage for processing and returned afterward. Workflow tools and data movers that speak S3 make this practical. Common research data patterns Different disciplines stress storage in different ways, and a university service has to cope with all of them: Genomics and life sciences produce large sequencing outputs and intermediate files, with heavy compute and long retention for raw data. See genomics data storage. Imaging and microscopy generate very large image stacks from electron and light microscopes, often terabytes per experiment. Physics and engineering simulations produce huge output files that must be staged from fast scratch to archive. Social sciences and humanities often have smaller volumes but more sensitive or rights-restricted data, plus digitization projects that grow steadily. Environmental and satellite data arrive continuously and must be retained for long-term analysis. Designing tiers around these patterns, rather than a single average researcher, avoids both overprovisioning and painful surprises when a new instrument arrives. Supporting researchers, not just storage Technology alone does not produce good research data management. Researchers need help writing data management plans, choosing formats, describing data with metadata and deciding what to keep. Libraries and research IT teams often run joint RDM support services. Storage teams contribute by offering clear service tiers with simple request processes, publishing costs and retention terms, and making it easy to move data between tiers. When the managed option is easier than an external hard drive, researchers use it. Handling project end The end of a project is the moment data is most at risk. Students graduate, postdocs move on and grant funding stops. A good service prompts project owners to decide what to archive, what to deposit in a repository and what to delete, and moves data off active storage accordingly. Automated reminders tied to project end dates, plus a default archive tier for anything not explicitly handled, prevent data from being lost or abandoned on expensive storage. Checklist: research data management storage Map storage tiers to the research data lifecycle. Identify funder and regulatory requirements by region. Provide active, collaboration, archive, repository and preservation tiers. Price archive storage to encourage its use. Automate movement from active to archive tiers. Plan sustainable funding beyond grant lifetimes. Protect sensitive data with secure tiers and location controls. Integrate storage with research computing and repositories. Plan for uneven, rapid data growth. Putting it together Research data management storage works when it follows the research lifecycle: fast, collaborative storage for active work, affordable archive storage for retention, repository storage for publication and preservation storage for long-term value. Funders across Europe, the UK, the US and Asia-Pacific increasingly require data to be managed and shared, which makes central, well-governed storage essential. Object storage provides a scalable, affordable foundation for archive, repository and preservation tiers, while sustainable funding and lifecycle automation keep the service viable. Frequently asked questions What storage do universities use for research data? Typically a mix: high-performance storage for active data, collaboration services, object storage for archives and repositories, and preservation storage for long-term value. Do funders require research data management? Many do. Horizon Europe, UK research councils, the NIH and others require data management plans and encourage or require data sharing. How long must research data be kept? It depends on institutional policy, funder requirements and the type of research, often several years after project completion and longer for some studies. Why use object storage for research data archives? It scales to petabytes, costs less per terabyte than active storage, supports immutability and offers S3 access for tools and repositories. How do universities fund long-term research data storage? Through combinations of central funding, grant cost recovery, free allocations and one-off fees for defined retention periods. Further reading See digital preservation storage, the OAIS model and storage, digital preservation copies, fixity checks at scale and data lifecycle management.
