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Over the centuries, libraries, archives, and museums have shown the practical and policy advantages of preserving sources of knowledge for society. Research and other types of data constitute a relatively new subject that requires our serious attention. Although some research data repositories were founded in the 1960s and even earlier, the data that are now being generated have resulted in the establishment of many new repositories and related infrastructure. Societies need such repositories to ensure that the most useful or unique data are preserved over the long term.

First, there are substantial and positive efficiency impacts, not only reducing the cost of conducting research, but also enabling more research to be done, to the benefit of researchers, research organisations, their funders, and society more widely

substantial additional reuse of the stored data, with between 44% and 58% of surveyed users across the studies saying they could neither have created the data for themselves nor obtained them elsewhere.

While these studies tend to provide a snapshot of the repository's value, which can be affected by the scale, age and prominence of the data repository concerned, it is important to note that in most cases, data archives are appreciating rather than depreciating assets. Most of the economic impact is cumulative and it grows in value over time, whereas most infrastructure (such as ships or buildings) has a declining value as it ages. Like libraries, data collections become more valuable as they grow and the longer one invests in them, provided that the data remain accessible, usable, and used.

Openness of public information strengthens freedom and democratic institutions by empowering citizens, and supporting transparency of political decision-making and trust in governance. It is no coincidence that the most repressive regimes have the most secretive institutions and activities (Uhlir, 2004). Open factual datasets also enhance public decision-making from the national to the local levels (Nelson, 2011), and open data policies demonstrate confidence of leadership and generally can broaden the influence of governments (Uhlir and Schröder, 2007). Countries that may be lagging behind socioeconomically frequently can benefit even more from access to public data resources (NRC, 2012b, 2002).

The survey of repositories undertaken for this and the previous RDA-WDS study classified the principal research data repository revenue sources as follows: • Structural funding (i.e. central funding or contract from a research or infrastructure funder that is in the form of a longer-term, multi-year contract). We use the term “structural” to underline the difference between this and project funding. The research data repository is considered as a form of research infrastructure or as providing an ongoing service. Although the funding may be regularly reviewed, it is a form of funding that is substantively different to project funding.

Host institution funding and support (i.e. direct or indirect support from a host institution). Some research data repositories are hosted by a research performing institution, e.g. a university, and receive direct funding or indirect (but costed) support from their host. • Data deposit fees (i.e. in the form of annual contracts with depositing institutions or per-deposit fees). As indicated, this can take the form of a period contract or a charge per deposit. In either case, the cost is borne by the entity that wishes to ensure that the data are preserved and curated for the long term. • Access charges (i.e. charging for access to standard data or to value-added services and facilities). This covers charges of various sorts (e.g. contract or per-access charges) and can be levied either for standard data or value-added services. In all cases, the cost is borne by the entity that wishes to access and use the data. • Contract services or project funding (i.e. charges for contract services to other parties or for research contracts). This covers short-term contracts and projects for various activities not covered above (i.e. these are not contracts to deposit or access data, but cover other services that may be provided). Similarly, this category of funding is distinct from structural funding because, although it may come from a research or infrastructure funder, it is for specific, time- and objective-limited projects, rather than for ongoing services or infrastructure.

The 47 data repositories analysed reported 95 revenue sources, an average of two per repository. Twenty-four repositories reported funding from more than one source, and seven reported more than three revenue sources. Combining revenue sources is an important element in developing a sustainable research data infrastructure.

A large majority (more than 80%), said they would not be considering any revenue sources that are incompatible with the open data principle.

The stage of development of a repository, its institutional or disciplinary context, its scale, and level of federation are also important determinants of what might be a sustainable business model. Referring to the dynamic of the evolution of firms, some economists draw a human parallel, talking of the phases as births, deaths, and marriages (and sometimes divorces). All phases are needed and should be accommodated. Indeed, sometimes it may not be desirable, effective, or efficient for a repository to be sustainable - provided that the data can continue to be hosted elsewhere.

This is the situation facing research data repositories. To be sustainable, data repositories need to generate sufficient revenue to cover their costs, but setting a price above the marginal cost of copying and distribution will reduce net welfare

Actions needed to develop a successful research data repository business model include: • Understanding the lifecycle phase of the repository's development (e.g. the need for investment funding, development funding, ongoing operational funding, or transitional funding) • Identifying who the stakeholders are (e.g. data depositors, data users, research institutions, research funders, and policy makers) • Developing the product/service mix (e.g. basic data, value-added data, value-added services and related facilities, and contract and research services) • Understanding the cost drivers and matching revenue sources (e.g. scaling with demand for data ingest, data use, the development and provision of value-adding services or related facilities, research priorities, and policy mandates) • Identifying revenue sources (e.g. structural funding, host institutional funding, deposit-side charges, access charges, and value-added services or facilities charges ) • Making the value proposition to stakeholders (e.g. measuring impacts and making the research case, measuring value and making the economic case, informing, and educating) (Figure 6).