Webinar on Linked Open Data in Cultural Heritage

Tuesday, February 15th, 2022

Welcome to the webinar titled: Semantic metadata enrichment and data augmentation of small museum collections following the FAIR principles

Abstract: The lecture will present the benefits and challenges of making the cultural heritage data of small regional museums findable, accessible, interoperable and reusable (FAIR). Using the Archaeological Museum of Tripoli, Greece, as a case study, it demonstrates how the employment of semantic methods, such as semantic enrichment and linking to Linked Data resources, and semantic technologies, such as the CIDOC-Conceptual Reference Model (CRM) and other standard ontologies, can help alleviate some of these challenges and help small museums make their data FAIR. It also discusses how a semantics-based approach can facilitate collaboration between Digital Humanities and Information Studies researchers, and cultural heritage institutions, by providing a common means of communication that means cultural heritage data can be reused, repurposed, and redeployed efficiently.



KvarkenSat Innovation Challenge 2022 on Sustainable Forestry

Thursday, February 10th, 2022

The DISA forestry group invites you to KvarkenSat Innovation Challenge 2022 on Sustainable Forestry that will start in next week with a pre-hackathon followed by the hackathon using space-based data helping to combat climate change!

Acceptance based on submission, the best submissions may be approved early.

The challenges

Climate change brings about major changes affecting us all. Extreme weather events become more frequent and especially the amount of rainfall increases in Northern Europe, one contributor being the warmer winters. New species of both vegetation and animals enter new areas while the existing species might have even major changes in their habitats. These lead to new challenges in the forestry industry. We are looking for ideas and solutions combining existing knowledge and datasets with space-based data and datasets based on satellite measurements, in four particular themes including soil moisture, spruce bark beetles, forest ground damage and the forest value chain.

Who can apply?

The hackathon is open to students, teachers, researchers and start-ups in teams of 3-5 persons. Relevant expertise to participate include: space and satellite data, machine learning and neural networks, computer science, positioning systems, automation, image processing/recognition, engineering, logistics, business/communications and forestry.


The three best proposals across all of the themes will be awarded a cash prize (over 100 000SEK) and possible continuation/acceleration within start-ups and innovation programs.

Pre-Hack Webinar

To get familiar with the hackathon, meet the mentors and partners, and participate in Q&A-session join our webinar on 15 February at 13.00 (14.00 Finnish time).

Link to the join the webinar:

More information about the hackathon:

DISA Lunch seminar – Industry – Novotek

Tuesday, November 30th, 2021

Welcome to a DISA Industry Seminar with Novotek. There is a genuine interest to find research collaboration related to water and wastewater management together with them and Kalmar Vatten.

  • When? December 9th 12-13
  • Where? Online – you will get a link when you register
  • Registration?

During the seminar you will meet Thomas Lundqvist

In Swedish water and wastewater management, there are great opportunities to create large savings and environmental benefits by starting to work systematically with advanced data analysis, which is often called “analytics” or “machine learning”. Examples of areas where this methodology has great potential are:

  • Leak detection and leak detection in water and sewage systems.
  • Reduction of chemical consumption in the water purification process
  • Improving water quality
  • Reduction of energy consumption
  • Improved control of flue gas production which leads to increased process yield. Simply more biogas from the same amount of feedstock.

Although the analysis technique has been known for a long time, there are some challenges when starting to work with this in, for example, a municipal treatment plant. It requires a certain type of competence that is usually not available in a simple way. Advanced software is also required for this purpose. Once you have acquired the skills and tools, you come to the problem that is usually even more difficult to solve. There is a lack of sufficient data. This may be because:

  • You do not have enough sensors that measure relevant things.
  • The sensors are not connected and the data is not stored.
  • If sensor values ​​are stored, the measurements are usually truncated incorrectly to save storage space and cost.
  • Large amounts of redundant information are often stored completely unnecessarily.

If you were to solve all this, you still have a big challenge as you lack other similar facilities to compare your analyzes with. There is a great potential to be able to share data between several municipalities and waterworks to learn from others and to simply get a much larger data base for their analyzes.
Novotek, together with Linnaeus University and Kalmar Vatten, wants to start a collaboration with the aim of finding a long-term solution together to the challenges that all waterworks and treatment plants face when society places increased demands on water treatment and water production, now and in the future!

Come and discuss with us and see how we can solve these challenges together!

DISA Seminar December 6th: Machine Learning for Astroparticle Physics

Monday, November 22nd, 2021

Astroparticle physics is a sub-branch of Physics dealing with the detection of gamma-rays, neutrinos, gravitational waves and cosmic rays from the Universe. In this seminar, we will focus on the field of extragalactic gamma-ray astronomy.

The two main types of datasets in the field are those produced via simulations and those acquired via detector equipments (“real”), leading to a large amount of data to be calibrated, prepared, filtered, reconstructed and analysed. Simulations are especially needed in order to understand the sensitivity of the given equipment to a given searched physics “signal” and to prepare the data analysis procedure before looking at the real data. Decades of experience in data analysis in the field lead to the ability to publish solid results.

In this context, machine learning is giving a big boost in speeding up the data analysis procedures. The first successful applications of supervised Machine Learning in the field date back to the years 2010-2013 and concern classification and regression methods. Nowadays, there is a huge effort to exploit Deep Learning methods to achieve faster simulations and to improve the current data analysis methods, where physicists cannot “see” or “predict” any significant features describing the datasets.

I will present the current activities and future challenges of my research groups at Linnaeus University and at the University of Paris and, more generally, the challenges of the field.

Keywords: gamma-ray astronomy, supervised machine learning, event classification, feature regression


  1. Y. Becherini et al., 2011 (Astrop. Phys., Vol 34, 12, 2011, 858-870)
  2. Y. Becherini et al., 2012 (Gamma 2012)
  3. Y. Becherini et al., 2012 (arXiv:1211.5997) 
  4. M. Senniappan et al
  5. T. Bylund et al., 

ICT with Industry workshop – Artificial Intelligence for Text Simplification (17-21 January 2022)

Wednesday, October 20th, 2021

Are you a young scientist with a background in ICT and do you have a creative and inquisitive mind? Do you like to think outside-the-box? Would you like to get into contact with industrial partners such as KB, RTL, Axini, SIG or Philips and solve a case together? Then apply for the “ICT with Industry 2022” Lorentz Workshop.

Every year, the Lorentz Center and NWO together organize an ICT with Industry workshop. During five days a group of about 50 researchers from IT and Computer Science from a wide range of universities (within the Netherlands and Europe) will work together extensively on challenging problems proposed by companies.

This year the KB has also provided a case: ARTificial Intelligence for Simplified Texts (ARTIST). During the ICT with Industry workshop we aim to explore the possibilities to make news articles, books and other publications more accessible to people with low literacy by applying AI techniques to automatically rewrite publications.

More info

Important dates:
– application deadline: 22 November 2021
– notification: early December 2021
– workshop: 17-21 January 2022


In the Netherlands, about 2.5 million citizens between 16 and 65 years old find it hard to read. This means they face challenges to fully participate in today’s society. Recently we have seen this problem when people with low-level literacy received invitations for the COVID- 19 vaccines that were too complicated for them. But also understanding the news by reading news articles in the newspaper or websites can be difficult making it hard to understand current issues.

The KB, national library of the Netherlands, aims to make all publications available to all Dutch citizens, including people who have reading disabilities. In this use case we propose to explore the possibilities to make news articles, books and other publications more accessible to people with low literacy by applying AI techniques to automatically rewrite publications. In the Netherlands, several initiatives have been undertaken to manually make books or news articles more accessible. However, this is very labour intensive and only makes a small selection of publications available for illiterates. During the ICT with Industry workshop we aim to explore several methods to automatically rewrite news articles/book, making them available for all Dutch citizens.

DISA Seminar November 1st on Visualization Perspectives in Explainable AI

Thursday, October 14th, 2021
  • When? November 1st, 2021 at 12-13
  • Where? Online, links will be sent to those registered
  • Registration via this link

This talk with Professor Andreas Kerren, will overview interactive data visualization research with a focus on the development and use of visualization techniques for explainable artificial intelligence. The field of Information Visualization (InfoVis) uses interactive visualization techniques to help people understand and analyze data. It centers on abstract data without spatial correspondences; that is, usually it is not possible to map this information directly to the physical world. This data is typically inherently discrete. The related field of Visual Analytics (VA) focuses on the analytical reasoning of typically large and complex (often heterogeneous) data sets and combines techniques from interactive visualizations with computational analysis methods. I will show how these two fields belong together and highlight their potential to efficiently analyze data and Machine Learning (ML) models with diverse applications in the context of data-intensive sciences. As ML models are considered as complex and their internal operations are mostly hidden in black boxes, it becomes difficult for model developers but also for analysts to assess and trust their results. Moreover, choosing appropriate ML algorithms or setting hyperparameters are further challenges where the human in the loop is necessary. I will exemplify solutions of some of these challenges with the help of a selection of visualization showcases recently developed by my research groups. These visual analytics examples range from the visual exploration of the most performant and most diverse models for the creation of stacking ensembles (i.e., multiple classifier systems) to ideas of making the black boxes of complex dimensionality reduction techniques more transparent in order to increase the trust into their results.

Did you miss it? If so you can watch it here:

information visualization, visual analytics, explainable AI, interaction, machine learning models, trust, explorative analysis, dimensionality reduction, high-dimensional data analysis

Further reading:


Workshop “Critical perspectives on cultural heritage: Re-visiting digitisation” 26 October, 9-12hrs

Tuesday, September 28th, 2021

Organizers: The workshop is co-organized by Linnaeus University (Centre for Applied Heritage and iInstitute) and Swedish National Heritage Board


About: Today, the Semantic Web and Linked Open Data are creating new value for the descriptive information in the cultural heritage sector. Libraries, museums, heritage management and archives are seeing new possibilities in sharing by turning their catalogues into open datasets that can be directly accessed, allowing cultural heritage data to be circulated, navigated, analyzed and re-arranged at unprecedented levels. This is supported by research funding bodies, governments and EU policies and numerous political interests, resulting in enormous investment in digitization projects which make cultural heritage information openly available and machine readable. But before deploying this data, one must ask: is this data fit for deployment?

Libraries, museums, heritage management and archives have long histories. Both the collections they house and the language they use(d) to describe said collections are products of that historical legacy, shaped by, amongst others, institutionalized colonialism, racism and patriarchy. Yet descriptive information is now being digitized and shared as if that legacy is not inherent to the collections. Instead, existing units of information are being distributed through new Web 3.0 technologies, bringing with it an outdated knowledge-base. Besides the risk of progressive techniques being applied to regressive content, we may also sacrifice the development of new knowledge in libraries, museums, heritage management and archives aimed at facilitating socially sustainable futures, remediating exploitative historical legacies.

For this workshop, we have invited researchers and practitioners to discuss ways in which digitisation approaches may be set up to change the nature and legacy of cultural collection prior to digital dissemination.


iInstitute / Digital Humanities webinar: The Ethics of Datafication and AI by Geoffrey Rockwell

Tuesday, May 18th, 2021

Summary – We all want artificial intelligence to be responsible, trustworthy, and good… the question is how to get beyond principles and check lists. In this paper I will argue for the importance of the data used in training machines, especially when it comes to avoiding bias. Further, I will argue that there is a role for humanists and others who have been concerned with the datafication of the cultural record for some time. Not only have we traditionally been concerned with social, political and ethical issues, but we have developed practices around the curation of the cultural record. We need to ask about the ethics around big data and the creation of training sets. We need to advocate for an ethic of care and repair when it comes to digital archives that can have cascading impact.

About the speaker – Geoffrey Rockwell is a Professor of Philosophy and Digital Humanities, Director of the Kule Institute for Advanced Study and Associate Director of AI for Society signature area at the University of Alberta. He publishes on textual visualization, text analysis, ethics of technology and on digital humanities including a co-authored book Hermeneutica from MIT Press (2016). He is co-developer of Voyant Tools (, an award winning suite of text analysis tools. He is currently the President of the Canadian Society for Digital Humanities.