Scope of the call

Main goals of the call: Leverage potential in the academic research data landscape

Dealing with data in research contexts is as important as it is challenging. In many cases, good data practices encounter missing processes, lacking awareness and cultures to be sustainable, viable and impactful. While there have been some substantial public investments in digitalization and data infrastructures in the last years, there are often the human resources missing to fully capitalize on these investments. However, in many cases, good ideas cannot be realized because money and attention are missing. Most universities and research institutions have also developed digitalization and data strategies which need to be implemented via concrete projects and undertakings.

This call, thus, also aims to support institutions in their strategic capacity. In this context, it is crucial that applications developed are not one-offs, but need to be maintained and developed beyond the project duration. 

• Projects should engage with current needs in the promotion of good data practices in a defined context as "executable applications" for data in research. "Applications" should be understood widely as, e.g., transferable use cases, software prototypes, modelling, information/data models (metadata, schema definitions and ontologies), data environments.
  "Executable" means that the applications developed should be easily integrated into code or software environments in due course (either in existing or future data environments or workflows). They should be transferable and scalable.
• Projects should provide applications delivering useful services to research, either by rendering existing data processes more efficient or by providing tools that allow for new approaches. The outcomes of the projects shall support data collection for research and the further use of research data, however, should not be tied to a specific research project but be of wider relevance, also supporting interoperability.     
• Projects can tackle research data related challenges in any scientific area, i.e., are not limited to computer science. However, the nature of the projects requires the integration of IT competences into the projects. 

What is the purpose of the applications developed?

The purpose of the applications should be directed towards supporting research activities with/for data in a broad sense. It may connect to other tasks such as teaching, administration or third mission, but the focus should be on the development of applications for research activities. 

What should be the output of the project?

WWTF asks for "code" rather than text, training or talking: The output should be concrete applications in the form of use cases, best practices, data sandboxes etc. and should include code, (information) models, ontologies, application prototypes and alike. These applications - based on their code-like nature - should have the potential to be implemented in data environments/workflows in a seamless manner. The potential for transferability and scalability is an important criterion for selection. 

Activities that do not count as output

Activities and outputs listed below are not eligible for funding: 

• The production of primary research data
• The preparation of existing data sets for secondary / further use by other researchers. However, concrete data sets may serve as use cases for broader applications
• The creation of a network as a sole output
• Training and education activities as the sole output
• The procurement and installation of hardware 
• Purely text-based outputs such as strategy papers or guidelines

Defining (a) potential user group(s)

Projects need to define (a) potential user group(s) of the application developed. Projects should have benefits for wider user groups beyond the context of the project. These user groups can be located within the own institution or across institutions. Applications can also target disciplinary communities independent of institutional settings.  

Sustainability and impact

Proposals need to define how the applications developed can be consolidated/maintained and further developed beyond the lifespan of the project itself. Projects should also define how their project links to institutional data / digitalization strategies as well as (potentially) to inter-university activities. 

Dissemination and exploitation strategies

Proposals should define in what ways their outputs should be shared, be it either open or in form of licensing (monetarization). Keep in mind that WWTF prefers open science / open data approaches according to the FAIR principles, however, monetarization strategies might be justified in the context of specific projects.

Applicants need to present a dissemination plan and strategy. 

Project team

The project team may include academic researchers, and/or data stewards, IT- or administrative personnel, and/or relevant user groups. The PI&C must not necessarily be a scientist.