Research data(interner Link) provides an essential basis for academic work. The diversity of such data reflects the range of different academic disciplines, research interests and research methods. Research data includes measurement data, laboratory values, audiovisual information, texts, survey or observation data, methodological test procedures and questionnaires. Compilations and simulations can likewise constitute a key outcome of academic research and are therefore also included under the term research data.

Research data in some subject areas is based on the analysis of objects (such as tissue, material, rock, water and soil samples, test specimens, installations, artefacts and art objects): such items as well as the data require careful handling.

For the purposes of the “Information Infrastructures for Research Data” funding programme, the term “research data” does not include research software.

The aim of DFG research funding is to support projects developed within the research community itself in the field of knowledge-driven research. By contrast, DFG infrastructure funding aims to develop (or advance) technologies, systems and tools which, for example, enable scientifically relevant data to be collected, analysed, disseminated or secured in the long term.

In infrastructure funding, the focus is on establishing and further developing high-performance information infrastructures for science and the humanities.

Basic research cannot be funded under infrastructure funding, which includes all LIS funding programmes. For this reason, the questions addressed in infrastructure projects should not be described as “research questions”. Under this programme, this applies in particular to eligible studies, which likewise do not fall under the basic research funded by the DFG. Rather, these studies serve to clarify questions relating to research data management that are relevant to the establishment and further development of research data infrastructures.

Infrastructure funding cannot be used to advance academic qualifications.

“Information infrastructures for research data” or “research data infrastructures” comprise both technical and organisational structures that enable researchers to work with research data.

One aspect of the technical foundations of information infrastructures is information infrastructure software. This is predominantly based on a software stack. The individual hierarchical components of this stack are often reusable and established software, such as operating systems, web server technology, runtime environments, programming languages, compilers, databases, frameworks, libraries and interfaces.

The term “information infrastructure software” indicates clearly that, on both the provider and operator side, information infrastructures are software systems whose development, implementation and functional advancement are eligible for funding under the funding programme. 

Vertical integration means the user-oriented technical and, where applicable, organisational integration of an information infrastructure, beginning at the researcher’s workplace and extending from there through local structures and processes, the regional level (e.g. state-level research data management initiatives), and the national level (e.g. the National Research Data Infrastructure (NFDI)), through to the international level (e.g. the European Open Science Cloud (EOSC)).

Applicants are advised to engage with and network with national and international initiatives at an early stage.

Interoperability and the use of synergies should be actively pursued, especially in the interests of vertical integration, but also with a view to harmonising standards, organisational measures etc.

In the context of the funding programme, implementation means converting a prototype or software design into a reliable service.

Functional further development means expanding the range of functions and services offered by an existing information infrastructure. Further developments are aligned with changing needs in science and the humanities.

A “purely technical innovation” means the integration of new or updated hardware or software components (such as software updates). This falls under the maintenance of an information infrastructure and is usually the responsibility of the operator. Purely technical innovations are not eligible for funding.

Digital services are software and its organisational environment for use in research. The use of a service does not necessarily incur costs.

Depending on their focus, digital services can be categorised as follows:

• Digital scientific service: Information technology service that provides such things as environments, tools and solution components to support the academic work pursued by researchers and research groups.
• Digital generic service: Infrastructure service, e.g. for the purpose of identity and authorisation management and for the transfer, storage, processing, sharing, archiving and retrieval of data and information.

The organisational form of an information infrastructure comprises the design of all workflows involved in the establishment, expansion and operation of an information infrastructure for research data. When an organisational form is designed, responsibilities, rights and obligations relating to the design and use of the information infrastructure are defined.

Networking forms concern the integration of the information infrastructure for research data under development in the subject-specific research communities and/or in the developer or operator communities. Various formats can be used to support the establishment of the information infrastructure (e.g. conference contributions, workshops, round-table discussions, journal articles and public relations activities).

An operating model describes the structures and processes that enable the sustainable operation of an information infrastructure. Accordingly, an operating model may include provisions governing the use of the information infrastructure (scope of use, possibly for different user groups, rights, obligations, possibly costs, data clearing house, etc.). Organisational processes and responsibilities may also be defined (e.g. concerning applications for storage capacity, maintenance cycles, the allocation of tasks between the institutions involved in the project, etc.).

An operating model may also include a business model covering financial aspects such as usage fees.

The development of an operating model is eligible for funding under the “Information Infrastructures for Research Data” programme; here it should be noted that all project results must be made available for reuse under an open licence, including those relating to the operating models to be developed.

The range of services and functions must always be aligned with research needs. The examples below illustrate various aspects and can serve as guidance when planning a research data infrastructure. The list is not exhaustive.

Information and planning for the management of research data before, during and as a follow-up to research projects (e.g. data management planning, recording the data lifecycle, compliance with funding guidelines, information services, etc.)

Organisation and preparation of research data (e.g. definition of a data curation profile, rights and access management, etc.)

Description and documentation of research data including structured information (e.g. use of metadata and metadata standards, controlled vocabularies, authority data, ontologies, etc.)

Storage and compatibility of research data for analysis (e.g. selection and configuration of storage locations, authentication and authorisation infrastructure, strategies and measures for data security and backup, planning of interfaces to analysis and visualisation software, and scientific computing, etc.)

Publication and archiving of research data (e.g. publication in accordance with FAIR, CARE or FACT principles in a suitable repository, use of persistent identifiers, measures for long-term archiving, selection of open and machine-readable file formats, etc.)

Findability and reuse of research data (e.g. directories of repositories, metadata services and indexes, data citation, etc.)

Rights and obligations relating to the handling of research data (e.g. clarification and definition of copyright and data protection specifications for reuse, access and publication of data, definition of usage licences, etc.)

Ethics and good research practice (e.g. clarification and compliance with ethical guidelines throughout the data lifecycle, development and implementation of discipline-specific guidelines for handling research data, etc.)

The sustainability concept should be aligned with the development phase of the information infrastructure. The more mature a project is, the more binding the technical, financial and organisational arrangements for ensuring the long-term operation of the infrastructure must be.

• The results from each development phase must comply with the FAIR principles (findable, accessible, interoperable, reusable). When developing prototypes, ensuring reusability may itself be recognised as a form of sustainability. It should be noted that even if a prototype receives a negative evaluation, there remains an obligation to preserve and make available the project results in a well-documented form.
• If a project leads to the operation of an information infrastructure for research data, the organisation of long-term operation becomes the focus of the sustainability concept. A key element here is a viable long-term concept that ensures the ongoing operation of the infrastructure. Various organisational forms are possible. In addition to the responsibility of the applicant organisations, responsibility may also be transferred to other institutions or organisations (such as NFDI consortia), providing they commit to ensuring long-term operation on a binding basis.
• The establishment of a developer community may be envisaged for the continuous further development of an information infrastructure.
• If it becomes apparent during the course of a project that a service will only be operated for a limited period of time, an end-of-life concept must be developed.

In order to ensure sustainability, so-called lock-in effects should be avoided, i.e. dependencies on commercial providers.

The licensing of information infrastructure software is governed by the programme guidelines. Open-source licences must be used that allow free reuse by third parties. For research data, it is recommended that licensing be as open and unrestricted as possible.

The aim of data curation is to compile coherent, reproducible and reusable datasets. A data curation profile sets out the criteria for selecting datasets (e.g. quality, content, format, metadata, markup depth, etc.) and establishes a standard for describing this data (metadata schema, authority data, persistent identifiers, markup formats, etc.).

Proposals for projects in the Scientific Library Services and Information Systems area must be structured in accordance with the relevant proposal preparation instructions.

Please submit your proposal in English.

The eligibility criteria are set out in section(interner Link) 2.1 of the programme guidelines. These stipulate that both researchers and institutions are eligible to apply.

Proposals submitted jointly by infrastructure providers and users are expressly encouraged in order to ensure that projects are consistently aligned with researchers’ needs at an early stage.

Individuals or institutions involved in the NFDI, EOSC, Discipline-Specific Information Services (FID) or other initiatives may of course submit proposals under the “Information Infrastructures for Research Data” funding programme, providing the formal requirements are met. However, duplicate funding of the same project is not permitted.

In addition to the content specified in the template(interner Link), the proposal should include the following aspects:

• Needs analysis
• Environment analysis
• Risk analysis

With regard to these three analyses, the proposal should be self-explanatory. If necessary, however, the analyses may also be submitted as separate documents accompanying the proposal.

All projects must be demand-driven, in other words they must meet the specific needs of relevant communities in science and the humanities or at research infrastructure institutions. The needs analysis aims to determine these needs.

The needs analysis can be based on the results of workshops, surveys, letters of support, etc. Existing information can be reused.

The environment analysis shows that there is not yet an adequate or sufficient solution available to meet the needs identified. This justifies the project proposal.

With regard to the objectives of the project, the environment analysis shows which technical and organisational solutions might be reused and how new solutions differ from existing ones.

At the same time, the environment analysis shows the horizontal and vertical structures in which a project can or should be embedded.

A risk analysis describes potential deviations from the project plan that may occur during implementation. It also outlines strategies for minimising risks and for responding appropriately to deviations.

A risk analysis may address risks relating to personnel, technology, organisation, content or other aspects.

Projects or work packages are not eligible for funding if they focus solely on the integration of new datasets in an information infrastructure or are dedicated exclusively to the curation of data, without being directly linked to the (further) development of the research data infrastructure.

The development of a prototype demonstrates the technical feasibility of a planned research data infrastructure. One of the project’s objectives is to assess whether the prototype can and should be developed into a reliable service and subsequently operated.

For this reason, proposals for prototypes must set out the criteria by which the suitability of the prototype will be assessed. Such criteria may include technical functionality (scalability, etc.) or user benefit (with regard to usability – ease of use, accuracy of fit, etc.). Based on these criteria, the prototype can be evaluated during the course of the project and at its conclusion; this is documented in the interim report or the final report.

The transition to a further development phase requires a renewal proposal, which must include an interim report on the evaluation of the prototype. If the prototype and the subsequent proposal are favourably reviewed, further development may be funded.

There is neither a minimum nor a maximum limit on the amount of funding that may be requested. The level of funding requested depends on the specific project idea and must be justified by the planned work programme.

Funding for hardware purchases is only possible where a clear and compelling project-related need is demonstrated. Items such as desktop computers, laptops and permanent storage must be financed through core support, for example.

If external services are required for the project, a quotation must be submitted along with the proposal. It is generally recommended that the technical expertise required to establish or further develop information infrastructures should be integrated within the applicant institutions. If work is to be outsourced to third parties, please note that service contracts require approval by the DFG. Further details are set out in the funding guidelines.

The level of the applicants’ own financial contribution is not specified under the “Information Infrastructures for Research Data” programme. However, reviewers will consider whether the proposed contribution appears reasonable in relation to the funds requested and the project objectives.

Here it is possible to state the share of staff employed on the project, for example, or the funding for direct project costs for items to be used specifically in connection with the project. General tasks performed by applicant institutions are not considered financial contributions.

The financial contribution may be presented in quantitative, tabular, qualitative or narrative form. Please note that project management and work package management tasks do not constitute a financial contribution: these are an underlying requirement for the feasibility of the project.

The general review criteria(interner Link) for LIS programmes apply as published on the DFG website.

As a rule, proposals are reviewed both by experts from the relevant subject area(s) and by individuals with expertise in the areas of infrastructure/IT and digital systems.

The final report follows the Guidelines for Project Reports in the Area of Scientific Library Services and Information Systems(interner Link). Please address the topic of sustainability in particular.