Linnaeus University Centre for Data Intensive Sciences and Applications

Seed projects

At DISA, we encourage and support seed projects. Seed projects are intended to promote and nurture excellence research, development, and innovation in data intensive sciences and applications with cross-discipline collaboration. The seed project should belong to one of the research areas within DISA. Please define which core DISA research area/group the seed project belongs to in the proposal, and make sure that the research coordinator of the area is aware of the proposal.

Financial support

DISA can finance up to SEK 100 000 to initiate research cooperation with a connection to data intensive sciences and applications. External partners can, if required, be funded up to a maximum level of 50 % of DISA’s project funding. They need to co-finance the project with the same amount.

During 2022, there is SEK 700 000 in total available to apply for seed funding.

Objectives for seed projects

The seed project should increase the chances of applications for external funding and/or of publications in top tier journals. Seed project proposals should argue that the planned activities are decisive for achieving the(se) objective(s). Activities may include, but are not limited to:

• Building new cross-disciplinary teams of researchers and/or with industry, aiming for a long-term collaboration.
• Exploring novel research questions in a pre-study.
• Collecting large amounts of data and running initial experiments and analyses.
• Implementing research demonstrators or infrastructure.

Prerequisites and evaluation criteria

The project consortium should consist of one or more researches from DISA. The researchers in the consortium should come from different research disciplines, in order to build a strong cross-discipline collaboration. It is important that all members in the consortium have an active role in the seed project. Please describe the different roles in the proposal. Industry/public sector collaboration is a plus. Please define clearly the added value that the seed project will give to the consortium.

If any member of the consortium is active in another ongoing seed project, please explain the relation between the projects/research.

The evaluation criteria include relevance of the proposal for the operational and strategic goals of DISA, feasibility of the project activity, and chances to eventually succeed with an application for external funding and/or of publications in top tier journals.

Read about the application process in this pdf file. Applications should be submitted no later than the last day in each month to be handled during the following DISA coordinator meeting.

For more information about the seed project concept, please contact Diana Unander.

Ongoing seed projects

• Using Artificial Intelligence to Detect Acanthamoeba Keratitis in the eye - the AIDAK study Applicants
  Applicants Linnaeus University: Patrick Bergman, Jenny Roth, Antonio Macedo, Alisa Lincke and Welf Löwe
  External collaboration: Neil Lagali, Linköpings University

Previous seed projects

• Data intensive analysis for identification and prediction of risk medications and side effects in Kalmar region (DIIPRIM)
  Applicants: Tora Hammar, Olof Björneld, Alisa Lincke, Rafael Messias Martins, Thomas Holgersson, Linnaeus University; Björn Wettermark, Uppsala University; Ylva Askfors, Hanna Justad, Region Stockholm
• A platform to collect and analyze canoe/kayak training data, and produce motivational feedback to athletes
  Applicants: Giangiacomo Bravo, Åsa Eklund, Anders Gerestrand, Ilir Jusufi, Susanne Linner, Anna Melin, Haris Pojskic
• User performance data from a video-based application/platform to enhance mobility and integrated learning in physical activities of daily living amongst older adults
  Applicants: Sofia Backåberg, Larry Katz, Mirjam Ekstedt, Welf Löwe, Niklas Backåberg
• IoT for ships – an untapped data resource
  Applicants: Fredrik Ahlgren, Björn Pundars, Håkan Lundström (external)
• Analyzing state-of-the-practice for self-adaptive systems in industry using data analytics
  Applicants: Nadeem Abbas, Danny Weyns, Ilir Jusufi and Bengt Larsson
• Digitized ancient remains detection using computer vision and artificial intelligence
  Applicants: Peter Skoglund, Ludvig Papmehl-Dufay, Welf Löwe
• Vibration-based strength grading of sawn timber using piezoceramic transducers and one-dimensional convolutional neural networks
  Applicants: Osama Abdeljaber, Anders Olsson and Welf Löwe
• Towards a data-driven approach to ground-fault location
  Applicants: Mauro Caporuscio, Pieternella Cijvat, Hans Ottosson
• European spruce bark beetles; advanced predictive forecasting by means of machine learning
  Applicants: Johan Bergh, Johan Hagelbäck, Björn Lundsten (Softwerk AB)
• Data-intensive tools for effective carbon mitigation in forestry
  Applicants: Jorge Zapico, Rafael Martins, Johan Bergh
• Developing the Skeleton Avatar camera Technique (SAT) as a rapid, valid and sensitive measurement of mobility in elderly persons
  Applicants: Cecilia Fagerström, Anders Halling, Linda Askenäs, Olof Björneld, Amanda Hellström, Mirjam Ekstedt
• Exploring data and establishing routines for collaboration on energy experiments
  Applicants: Mike Farjam, Krushna Mahapatra, Giangiacomo Bravo
• An Exploration of the Challenges and Possibilities of Multidimensional Visualization in the Context of Visual Learning Analytics
  Applicants: Rafael Martins, Marcelo Milrad, Italo Masiello
• Smart-Troubleshooting in the Connected Society
  Applicants: Francesco Flammini, Welf Löwe, Shiyan Hu, Mauro Caporuscio, Morgan Ericsson, Narges Khakpour, Diego Perez-Palacin
• ODXVR x NTS: Exploring the Nordic Tweet Stream in Virtual Reality
  Applicants: Aris Alissandrakis, Mikko Laitinen, Jukka Tyrkkö