Category: Use case

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Interactive HPC Research Supercomputing UCloud Use case

Combining AI and satellite data to explore the upper atmosphere

For researcher Lotte Ansgaard Thomsen at Aalborg University, UCloud has played a central role in the research project UpperAtmosphere, where she combines artificial intelligence, satellite data and physical models to better understand conditions in the upper atmosphere.

Many satellites operate in this part of the atmosphere, where conditions can change quickly due to solar activity. However, traditional physical models do not always capture these rapid changes accurately. To address this, Lotte uses AI to learn patterns from different data sources and improve the physical model.

Lotte Ansgaard Thomsen is an Associate Professor in the Department of Sustainability and Planning at Aalborg University. In her current research project, she investigates how AI can be used to improve the understanding of key variables in the upper atmosphere. This can contribute to more accurate models of the space environment where satellites operate and provide better insight into the conditions that may affect satellite performance, communication systems, and space-based infrastructure.

Combining AI and physical models

A key part of the project is the combination of several different data sources. Satellite measurements, solar activity data and ionospheric data each contribute with important information. When these data sources are used together, the AI model can improve the physical model significantly. According to Lotte, this combination is one of the central insights of the project so far:

“A key insight is combining different data sources really matters. Satellite measurements, solar activity, and ionospheric data each add something unique. When I use them together in AI on top of the physical model, the model improves significantly.”

Using UCloud as the primary platform

Lotte has used UCloud as the primary platform for running the computational work in the project. A large part of this involves training AI models, which requires substantial computing power because the datasets are large and the models need many iterations to find the right architecture. However, UCloud has not only been used for the heavy computations at the end of the process. It has also supported the development and testing of code along the way.

“It gives me a good environment to build the workflow, try things out, and adjust as I go.”

Lotte also uses UCloud in another research project, where large language models are used to work with environmental data. Across both projects, access to computing power has been essential.

“In both projects, we simply could not have done the work without access to this kind of computing power or a similar alternative.”

Access to computing power made results possible faster

When Lotte moved from industry into academia, she was concerned about whether she would have access to enough computing power for her research. Discovering that UCloud existed was therefore an important advantage.

“It was really nice to discover that UCloud existed. Having access to that number of resources has been a big advantage for the project.”

With UCloud, Lotte could access the computing power needed without first having to apply for separate funding. This made it possible to move faster from development to results.

“If I did not have access to UCloud or another free service, and first had to apply for funding just to get compute, it would have been impossible to have results so fast.”

A user-friendly platform for research

According to Lotte, one of the main benefits of UCloud is its user-friendly setup for research and development work.

“First of all, UCloud is really well-suited for development work. It gives me a good environment to build and test my workflow, and for research work, I have found that it has a very user-friendly interface.”

She has used Visual Studio Code, a code editor used for writing, testing and developing code, on UCloud, which gives her a setup similar to the one she uses locally. This makes it easier to move between local development and cloud-based work. Another important advantage has been the possibility to scale up and access more compute when needed.

“It has been very easy to scale up and get more compute when I need it.”

Fast support makes a difference

For Lotte, the support around UCloud has also been an important part of the experience. When working with complex research projects, technical issues can slow down the research process. In her experience, the support team has responded quickly when challenges have occurred.

“If I run into any issues, the team helps almost immediately. That makes a big difference when working on complex projects.”

Lotte has received support from Aalborg University’s local front office, which helps researchers get started with UCloud and provides support when questions or technical issues arise. All Danish universities have a local front office where researchers can get support.

Overall, UCloud has provided Lotte with access to the computing resources, development environment and support needed to work with large datasets, AI models and complex research workflows.

“I think UCloud is a really good solution overall. When people talk about the need for more European cloud alternatives, this is the kind of thing I think of.”

This work was supported by DeiC National HPC (g.a. DeiC-KU-L1-291125). Read more about DeiC’s calls for resources on HPC-platforms: Grants og funding | DeiC.

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Research Supercomputing UCloud Use case

Danish language model project enters a new phase with stronger national computing power

How do we ensure that the artificial intelligence of the future understands the Danish language, Danish institutions, and Danish society – while handling data within frameworks that we control ourselves?

This question lies at the heart of the research project Danish Foundation Models (DFM), where the University of Southern Denmark, Aarhus University, the University of Copenhagen, and the Alexandra Institute are collaborating to develop open Danish language models. The project has been underway for some time, and researchers have already released the first models, established benchmarks, and initiated collaborations with external partners. Now, the project is entering its next phase.

With access to the new national AI supercomputing facility BITTEN, inaugurated in Sønderborg in May, and accessible through the research platform UCloud, the pace of development can increase significantly.

Culture, norms, and societal understanding are lost

Within just a few years, language models have become a strategic technology. They are already used for text generation, search, decision support, automation, and analysis. However, the most widespread models have been developed by global companies and trained primarily on English and other major languages.

This creates limitations when such models are expected to operate within a Danish context.

“If you look closely at the details, many international models are actually poor at Danish. The way they formulate themselves often resembles English translated into Danish. That is not how we speak or write,” says Professor Peter Schneider-Kamp from the University of Southern Denmark, who leads DFM on behalf of SDU.

The challenge extends beyond language. It also concerns culture, social norms, and an understanding of society.
DFM has, among other things, developed Danish benchmarks that test models on knowledge of Danish culture. Here, even the largest international models often perform poorly.

“They lack an understanding of how Denmark works – our literature, our public institutions, our healthcare system, and our cultural points of reference,” Schneider-Kamp explains.

Denmark cannot remain on the sidelines

The development of AI is moving so rapidly that access to domestic expertise and infrastructure is becoming increasingly important. According to Schneider-Kamp, it is risky to assume that other countries will continue to provide the services and models that Europe needs indefinitely.

“We cannot simply rely on American or Chinese companies to provide the right solutions for us forever. We need to take part in the development ourselves,” he says.

This is not about replicating Silicon Valley at the same scale, but about being able to develop solutions tailored to Danish needs – and doing so on a transparent and responsible foundation.

“We want models where we know what they have been trained on, that comply with GDPR and the AI Act, and that understand the Danish language, Danish culture, and Danish norms,” he says.

UCloud is the backbone

Behind the project lies a less visible, but crucial part of the story: research infrastructure.

DFM is developed to a large extent using UCloud, the national platform for interactive high-performance computing developed by SDU eScience Center together with partners. It provides researchers with access to storage capacity, GPUs, software, and collaboration tools in one integrated environment.

For Peter Schneider-Kamp, UCloud is absolutely central.

“UCloud is our secure environment where we develop models, train models, store data, and evaluate them. It is absolutely central to everything we do,” he says.

The security perspective is essential. When researchers work with large amounts of data – and in some projects also sensitive data – it is critical that the data can be handled within a controlled environment.

“If we receive new datasets from, for example, libraries, media organisations, or other sources, we can store them securely, work directly on them, and maintain control over the data,” he says.

The alternative is often more complex and fragmented solutions where data has to be moved between systems and countries.

New national supercomputer BITTEN

On 5 May 2026, a new national supercomputer was inaugurated in Sønderborg: BITTEN. The facility was established by the University of Southern Denmark in collaboration with Danfoss and Hewlett Packard Enterprise (HPE) and forms part of the Danish research infrastructure for artificial intelligence, advanced computing, and data-intensive research.

The supercomputer is made available through UCloud, allowing researchers and students at universities across Denmark to access it through their existing systems and workflows.

The collaboration combines SDU’s experience in research infrastructure, Danfoss’ expertise in energy-efficient cooling and heating solutions, and HPE’s knowledge of supercomputing and data centre technology.

The facility has also been designed with a strong focus on energy-efficient operation and the reuse of excess heat.

Lack of computing capacity slows down research

For AI research, access to computing power is not a luxury – it is a prerequisite.

Previously, the DFM group regularly experienced bottlenecks when training models.

“Sometimes we have had to wait two, three, four, or even five days to get access to a GPU. Meanwhile, PhD students and postdoctoral researchers are sitting ready with ideas and code but are held back by a lack of resources,” says Peter Schneider-Kamp.

This is precisely where the new capacity can make a difference.

More computing power means faster experiments, larger models, more iterations, and a shorter path from idea to result.

“We hope it will allow us to turn ideas into research results and concrete use cases much faster. We are incredibly excited to gain access to the many additional GPUs,” he says.

More than technology

DFM is therefore about more than software and hardware. The project illustrates how research, digital sovereignty, data security, and innovation are closely connected.

If Denmark wants to use AI in healthcare, the public sector, education, and industry, it requires solutions that can be understood, adapted, and trusted.

With UCloud as its operational backbone and the new national supercomputing capacity of BITTEN, Danish Foundation Models now stands at a point where the work can move from a promising development phase to broader implementation.

The question is no longer only whether Denmark can develop its own language models.

The question is whether we can afford not to.

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Interactive HPC Research Supercomputing UCloud Use case

How UCloud supported large-scale research combining time-use and consumption data to understand socio-economic differences 

 UCloud played an important role in Sofia Topcu Madsen’s research by helping her analyse large-scale time-use and consumption data across seven countries faster and more efficiently, supporting her work to gain insights into how people spend their time and money in everyday life. 

Sofia Topcu Madsen is a former PhD fellow at Aalborg University, Department of Sustainability and Planning. As part of her PhD project, Getting the Data Right, she investigates how people across seven countries in low- and middle-income countries spend time and money on different everyday activities — and how socio-economic factors shape what people are able to do. 

The project looks at countries including Uganda, Kenya, Tanzania, India, Sri Lanka, Argentina and Mongolia. Based on large-scale time-use and consumption data, Sofia analyses activities such as transport and household work and explores how time and money spent on everyday activities vary across different socio-economic groups. 

Working with large datasets 

Sofia’s analyses are based on different types of detailed data. One example is time-use diaries, where participants report what they do throughout the day in 10-minute intervals. For some countries, the datasets are very large. In the Indian dataset alone, Sofia works with around half a million observations. 

This makes the computational work demanding. At first, Sofia tried running the analyses on her own computer, but the datasets were too large and the analyses too time-consuming. 

“On my own computer, the analyses could take several days to run. With UCloud, it became much faster and more manageable,” Sofia explains. 

UCloud gave her access to more computing power, making it possible to run large analyses more efficiently and rerun them when corrections or adjustments were needed. This became important throughout the project, as even small changes in the data setup or model specification could require the analyses to be run again. 

“UCloud has been a huge help. I do not think I would have been able to complete this part of the project in the same way without access to it,” Sofia says. 

Using Stata and R on UCloud 

To conduct the analysis, Sofia used Stata and R on UCloud. Stata and R are tools that researchers use to work with data and carry out statistical analyses. 

In Sofia’s project, the tools were used to run regression analyses, where she examines how different factors may be connected — for example how education, income or gender may relate to the amount of time and money people spend on transport, household work or other everyday activities. 

She used SUR methods, which make it possible to run several related analyses at the same time. This was relevant because the project looks at activities across a full 24-hour day, where time spent on one activity can be connected to time spent on another. Similarly, money spent on products supporting one activity limits money for other products. 

Running several analyses at the same time with large datasets requires a lot of computing power and would have been very time-consuming to do on a normal computer. By using UCloud, Sofia could run the analyses faster and more efficiently, saving her a lot of time. 

Contributing to research on sustainable development 

Sofia’s research is connected to the UN Sustainable Development Goals by exploring how everyday activities can be used as indicators of broader social and economic conditions. 

How people spend their time can tell us something important about everyday life, inequality and opportunities. For example, a joint perspective on time and money spent on transport, household work, or leisure can reveal how resources, responsibilities, and opportunities are distributed across different population groups. 

“Time can also be understood as a resource. Looking at how people spend their time gives us another way to understand poverty, inequality and sustainable development,” Sofia explains. 

A platform that was easy to get started with 

Sofia describes UCloud as easy to access and use, especially once the workflow was in place. She also highlights the support as an important part of the experience. 

“The support has been very effective. I have received quick answers to my questions, and that has been a big help,” she says. 

Sofia received support from Aalborg University’s local Front Office. Each Danish university has its own Front Office, where researchers can get help with access, use and questions related to UCloud. 

Would use UCloud again 

Sofia is now employed at the University of Copenhagen, Department of Food and Resource Economics, and she can see herself using UCloud again in future research. 

“I would definitely use UCloud again,” she says. 

Because UCloud is available to researchers affiliated with Danish universities, Sofia can also continue to use the platform in future research projects at the University of Copenhagen. 

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Interactive HPC Research Supercomputing Teaching UCloud Use case

UCloud Provides Student Access to Advanced NLP in Teaching 

In the Master’s programme in Cognitive Science at Aarhus University, UCloud plays a central role in teaching Natural Language Processing (NLP). For instructor and PhD student Mina Almasi, the platform is essential in enabling students to work hands-on with complex models – regardless of the limitations of their own computers.

From Theory to Hands-On Learning 

In a white classroom in Nobelparken, Mina stands in front of 15 students. On the screen behind her, lines of Python code appear in neat, symmetrical rows as she explains which code libraries the students need to access.

In her teaching, she uses the Coder Python application in UCloud because the course is based on Python programming. But the choice of platform is not just about software – it is about giving students the opportunity to translate theory into practice.

According to Mina, NLP teaching previously tended to remain at a more theoretical level, due to limited access to both models and the computing power needed to test theories in practice – especially when it came to large language models. With UCloud, students can now work directly with language models (LLMs) and make use of powerful GPUs and CPUs. This allows them to test theories themselves and experiment hands-on with the tools they are learning about.

“We still teach the theory, but now we can also have students use the tools in practice. They can code on their own and gain insight into how a large language model works by working directly with it through UCloud,” she explains.

A Standardised Setup that Democratises the Classroom 

Another advantage of using UCloud in NLP teaching is that the platform ensures equal access for all students, regardless of the computer they own.

“There is a kind of democratisation of the classroom, because you don’t need the latest computer. You can use a five-year-old machine to run very heavy tasks that the newest tools in Natural Language Processing require,” she explains.

At the same time, the standardised setup makes teaching more seamless. All students work with the same standard configuration in UCloud, so any issues that arise are the same for everyone. This creates a shared sense of problem-solving, as challenges can be addressed collectively rather than handled individually by students on their own. As Mina puts it:

“Instead of stopping the lesson to solve individual problems, the problems become collective and an opportunity for learning for everyone. If we have a software issue – for example, a Python library version that is outdated or incompatible – it affects everyone, and we can solve it together.”

Preparing Students for Working Life

For Mina, using UCloud also helps prepare students for the reality that awaits them after graduation. According to her, many of the students who go on to IT positions will likely use cloud computing platforms rather than coding on local machines. In this way, the teaching becomes direct preparation for future job tasks and gives students experience with the technologies they will encounter in practice.

Advice for Other Instructors 

Mina has used UCloud since her bachelor’s degree and finds that the platform makes teaching both smoother and more engaging.

“I recommend that other instructors make use of the platform. You just have to get started – but feel free to ask colleagues for advice on how they use it. Get some inspiration, because UCloud is a fantastic tool. It can do a great many things, but like other systems, it can feel a bit overwhelming at first, so it’s a good idea to get some guidance along the way before you begin.”

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Interactive HPC Research UCloud Use case

DeiC Interactive HPC Crucial for Danish AI Language Models

By Jasper Riis-Hansen and Line Ejby Sørensen, Center for Humanities Computing (CHC), Aarhus University

DeiC Interactive HPC – UCloud plays a central role in the Danish Foundation Models (DFM) project, which forms part of the Danish government’s strategic initiative for artificial intelligence.

Danish Foundation Models (DFM) is supported by the Ministry of Digital Affairs as part of the national AI strategy, which aims to ensure that Denmark has access to advanced and tailored language models. These models are intended for use across a wide range of sectors, including healthcare, public administration, education, and private enterprise.

A shared digital environment

The DFM project brings together Danish universities, research institutions, and industry partners in a joint effort to establish new standards for ethically responsible and inclusive AI language technologies.

The project is a collaboration between Aarhus University, the University of Copenhagen, the University of Southern Denmark, and the Alexandra Institute. DeiC Interactive HPC – UCloud plays a vital role in this work by providing high data security, scalable computing power, and, not least, an accessible, secure, national cloud platform that enables collaboration among project partners.

“UCloud forms the foundation for an important step in research digitalisation, as the platform provides easy access to computing power, enabling scalable data analysis and modelling, while also offering a secure environment for handling sensitive data. The platform also facilitates collaboration across institutions and allows us to manage data access as needed. This is particularly relevant in the DFM project, which includes many partners participating at different levels.”
Postdoc Kenneth Enevoldsen

Data security and computing power

Because AI models are often trained on sensitive data, it is crucial that data processing complies with both GDPR and Danish security standards. UCloud is ISO27001-certified and specifically designed to meet both Danish and EU requirements for secure data handling.

“In the DFM project, we work with very large amounts of data from a variety of sources – including sensitive data that the models are trained on – and this places high demands on data security. That is why UCloud is such a valuable tool for the project – precisely because of its high level of data security and access to scalable computing power.”
Postdoc Kenneth Enevoldsen

Although DFM also makes use of European supercomputers such as LUMI in Finland and Leonardo in Italy, the day-to-day operations of the project are heavily reliant on UCloud. In addition to being a springboard for high-performance computing, UCloud also provides a secure and user-friendly platform with a wide range of accessible applications – all essential for daily research, collaboration, data processing, and innovation across the project’s interdisciplinary team.

Critical infrastructure for Danish AI development

DFM’s principal investigators, Kristoffer Nielbo and Peter Schneider-Kamp, emphasise that the robust digital research environment provided by DeiC Interactive HPC – UCloud constitutes critical infrastructure. It streamlines workflows, enhances collaboration, and accelerates the development of both language and AI technologies.

“Without UCloud, the DFM project would have had to develop this type of digital infrastructure itself – with significant time and financial costs. The platform’s role in the project clearly demonstrates how robust, collaborative digital research environments are essential to Denmark’s AI strategies.”

FACTS: Danish Foundation Models (DFM)

Danish Foundation Models (DFM) is a collaborative project involving Aarhus University, the University of Copenhagen, the University of Southern Denmark, and the Alexandra Institute.

The project is supported by the Ministry of Digital Affairs with a grant of DKK 30.7 million and aims to develop advanced language models with open access and transparent development processes.

The models are specifically tailored to Danish and other Scandinavian languages and cultures and are intended for use across sectors such as healthcare, public administration, education, and business.

DFM seeks to establish a new standard for ethically responsible, inclusive, and transparent AI language technology – for the benefit of both Danish society and the research community.

For more information, visit: Danish Foundation Models, Ministry of Digital Affairs press release

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Interactive HPC Supercomputing UCloud Use case

Supercomputing for computational linguistics and (social) media data

Supercomputing has long been associated with areas such as physics, engineering, and data science. However, researchers in humanities at Aarhus University are increasingly turning to supercomputing allowing them to delve into unexplored territories and discover new insights.
From analysing historical archives to simulating ancient civilizations to analysing social media data, supercomputing offers unique opportunities to generate insights and advance knowledge in humanities.

In this article series, we highlight three cases with humanities researchers from Aarhus University that illustrate the varied ways in which supercomputing is being used in humanities research.

While many studies are based on historical data, the research of Rebekah Baglini, Associate Professor in Linguistics at Interacting Minds Centre, Aarhus University is an excellent example of supercomputing applied to recent data in the humanities.   

She employs supercomputing in her current projects involving the collection, processing, and annotation of large-scale media data from traditional and social media sources. By examining this diverse range of data, Rebekah Baglini investigates causal inference and causal reasoning from a linguistic perspective. Her research involves the application of semantic model theory and computational methods to uncover insights in linguistics.

“I aim to develop computationally assisted methods to identify trends in the discursive and informational landscape around topics concerning media dynamics, public health and science communication, crisis and risk messaging, as well as the emergence of mis- and dis-information”. 

Rebekah Baglini, Associate Professor in Linguistics, Aarhus University

In addition to her linguistic investigations, Rebekah Baglini also strives to enhance the existing computational language models for multilingual natural language processing (NLP), with a particular focus on under-resourced languages.   

Humanities researchers should know the affordances of High-Performance Computing  

Rebekah’s pursuits demonstrate the continuous progress of digital humanities and the ongoing efforts to enhance existing language models, ultimately leading to a deeper understanding in the field of humanities.   

“My earlier work involved smaller language corpora and didn’t require HPC resources. However, as my projects grew in scale, involving large corpus creation, the relevance of supercomputing increased. I recognise that not all projects require HPC. However, it is useful for researchers to gain training in the affordances of HPC, parallel compute, and large models so they know what’s possible, and can potentially take on projects of larger scale or make use of state-of-the-art resources for data processing, modelling, and simulation.”  

Rebekah Baglini, Associate Professor in Linguistics, Aarhus University

This explains why NLP and Computational Linguistics have become integral to Rebekah Baglini’s teaching, enabling her to offer students practical exposure to working with extensive datasets and large language models, fostering hands-on learning opportunities. She emphasises that there is a significant learning curve when delving into the realm of supercomputing. 

“There has definitely been a learning curve involved in the transition from locally maintained clusters to the cloud based Interactive HPC platform, particularly because it is also a somewhat new service without comprehensive documentation, and my affiliation with Center for Humanities Computing at Aarhus University has been a valuable resource as there is a great deal of collective experience and knowledge to draw on in the community”.  

Rebekah Baglini, Associate Professor in Linguistics, Aarhus University

Rebekah has used the DeiC Interactive HPC system for storing and analysing news and social media in the national research project HOPE that monitored Scandinavian user behaviour during Covid-19.

Today she uses the system in her own AUFF Starting Grant Project CROSS: Causal Reasoning and Online Science Scepticism to train language models to identify and analyse emerging narratives that undermine or counteract verified messaging on scientific findings and public health recommendations.

You have just read the third and final case in our series on Interactive HPC usage in humanities.
Through these compelling cases it becomes evident that supercomputing in humanities research is transforming traditional approaches, empowering researchers to uncover new insights and deepen our understanding of the field.  It opens doors to interdisciplinary collaborations and expands the possibilities for data analysis and modelling, ultimately shaping the future of digital humanities. 

Check out the other two cases featuring Katrine Frøkjær Baunvig and the case of creating a Grundtvig-artificial intelligence using HPC and Iza Romanowska and the case of Utilizing agent-based models in archaeological data.

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Interactive HPC Research Supercomputing Uncategorized Use case

Utilizing agent-based models in archaeological data   

Supercomputing has long been associated with areas such as physics, engineering, and data science. However, researchers in humanities at Aarhus University are increasingly turning to supercomputing allowing them to delve into unexplored territories and discover new insights.
From analysing historical archives to simulating ancient civilizations to analysing social media data, supercomputing offers unique opportunities to generate insights and advance knowledge in humanities.

In this article series, we highlight three cases with humanities researchers from Aarhus University that illustrate the varied ways in which supercomputing is being used in humanities research.

Iza Romanowska is assistant professor at Aarhus University working at the Aarhus Insitute of Advanced Studies where she studies complex ancient societies.

To overcome the challenges of limited data from these ancient societies, researchers have started utilizing Agent-based model (ABM) sometimes enabled by supercomputing. ABMs are computational models that simulate the behaviour and interactions of individual entities, known as agents, within a specified environment or system. Each agent in the model is typically programmed with a set of rules or algorithms that control its behaviour, decision-making processes, and interactions with other agents and the environment.

ABM is a valuable tool in archaeology that allows us to simulate and analyse the behaviours and interactions of individuals or groups in past societies, and the use of ABM allows comparison of the model against real archaeological data.

Assistant Professor Iza Romanowska

In one of Iza Romanowska’s studies, agent-based modelling (ABM) made it possible for her and her colleagues to explore the Roman economy in the context of long-distance trade, using ceramic tableware to understand the distribution patterns and buying strategies of traders in the Eastern Mediterranean between 200 BC and AD 300.  

The potential of supercomputing in humanities becomes particularly evident when studying such societies with only limited data as experienced by archaeologists and historians. Iza Romanowska explains that the availability of data is limited in her field compared to other disciplines, stating that while social scientists studying more contemporary populations have access to abundant amounts of data such as the number of traders, transactions, and values, “we have none of this information.” Therefore, the use of HPC has been essential for her research.  

ABM as methodological tool necessitates running the simulation many times, and by many, I mean eight hundred thousand times, and that is possible with a laptop… if one plans to be doing their Ph.D. for 500 years. Supercomputing is bigger, faster, better without any qualitative change in terms of the research.

Assistant Professor Iza Romanowska

Using a high-performance computer like the DeiC Interactive HPC system enhances the scalability and speed of ABMs, allowing researchers to gain deeper insights into the behavior and outcomes of complex systems. The DeiC Interactive HPC facility hosts out-of-the-box tools, like NetLogo, for working with ABM. Researchers can also use ABM frameworks for Python or R in one of the many development apps like JupyterLab or Coder.  

Supercomputing and coding as research tools advance humanities research 

While humanities data in general is plentiful and can be analysed effectively, Iza Romanowska finds that there is a gap in understanding the underlying processes that generate the observed patterns, resulting in underdeveloped explanatory frameworks. Her point is that the lack of formal tools for theory building and testing remains a major disciplinary issue. 

“Within humanities including archaeology and history, data analysis is well-established. However, there’s a kind of fundamental disciplinary problem with that we don’t have or use many computational tools for theory building and theory testing. Supercomputing as a tool for the humanities can contribute to fill this gap and strengthen theory building and ultimately it can advance the field of humanities research.”  

Assistant Professor Iza Romanowsk

Iza Romanowska believes that more people in humanities should learn to code to take advantage of the possibilities offered by their data. She suggests that supercomputing can be a natural progression from this. While many humanities researchers may not feel like they need supercomputing, perhaps they are simply not asking questions that could benefit from high-performance computing (HPC). 

I would especially encourage junior researchers in the humanities to embrace supercomputing. It never hurts to acquire a skill, and many of these tools are becoming so easily available that it’s almost a shame to not use them.

You have just read the second of three cases in our series on Interactive HPC usage in humanities.
Through these compelling cases it becomes evident that supercomputing in humanities research is transforming traditional approaches, empowering researchers to uncover new insights and deepen our understanding of the field.  It opens doors to interdisciplinary collaborations and expands the possibilities for data analysis and modelling, ultimately shaping the future of digital humanities. 

Stay tuned for our third case featuring Rebekah Baglini representing her field of linguistics and check out the first case featuring Katrine Frøkjær Baunvig and the case of creating a Grundtvig-artificial intelligence using HPC

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Interactive HPC Research Supercomputing UCloud Use case

Creating a Grundtvig-artificial intelligence using HPC

Beyond Tradition
Unveiling the Uses of Supercomputing in Humanities. 

Supercomputing has long been associated with areas such as physics, engineering, and data science. However, researchers in humanities at Aarhus University are increasingly turning to supercomputing allowing them to delve into unexplored territories and discover new insights.
From analysing historical archives to simulating ancient civilizations to analysing social media data, supercomputing offers unique opportunities to generate insights and advance knowledge in humanities.

In this article series, we highlight three cases with humanities researchers from Aarhus University that illustrate the varied ways in which supercomputing is being used in humanities research. 

Katrine Frøkjær Baunvig, head of the Grundtvig Center at Aarhus University has used supercomputing as a methodological approach, and it has led her to non-trivial conclusions that significantly impact our understanding of of 19th-century nation builder and prominent pastor N.F.S. Grundtvig ‘s vast body of works and his immense influence on Danish culture.  

In order to conduct a certain type of text mining, so-called word embeddings, she has created an artificial intelligence of Grundtvig, enabling a comprehensive analysis of his over 1000 works and 8 million words, resulting in unprecedented insights.

Grundtvig’s worldview: analysed by Katrine Frøkjær Baunvig in the upcoming paper ”Benign Structures. The Worldview of Danish National Poet, Pastor, and Politician N.F.S. Grundtvig”.

This approach has ushered in a completely new era in Grundtvig research, according to Katrine Frøkjær Baunvig. She dismisses the criticism of digital humanities sceptics who argue that word embedding fails to consider the surrounding context of words. 

“This type of rejection is prevalent only among researchers who have not taken the time to understand or familiarize themselves with the current state and level of the research. When creating a word embedding, I obtain a vast mapping of a given word’s extensive association structure. Therefore, I can clearly discern different semantic focal points and contexts where the word appears in Grundtvig’s body of work. This is precisely what allows me to gain an overview.” 

Katrine Frøkjær Baunvig, Head of the Grundtvig Center at Aarhus University

Katrine Frøkjær Baunvig opted to form a research partnership with the Center for Humanities Computing at Aarhus University. Her best advice for other researchers going into supercomputing in the humanities is to team up with the right people.  

“Stepping into the world of supercomputing requires an approach to work processes that, in my opinion, represents a new trend in the humanities, namely, interdisciplinary collaborations and team-based publishing. Someone takes care of what is typically called the domain expert area – in this case, knowledge of Grundtvig’s authorship – while others handle the more technical aspects of execution.”

Katrine Frøkjær Baunvig, Head of the Grundtvig Center at Aarhus University

She also emphasises the importance of comprehending the workings of the tools to better harness the power of supercomputing.  

“Even if you may not be able to train your algorithm yourself, it can be very practical to devote time and energy to obtain an operational understanding of the steps involved in creating a Grundtvig-artificial intelligence and the various types of applications such an intelligence can be used for.”

Katrine Frøkjær Baunvig, Head of the Grundtvig Center at Aarhus University
Grundtvig’s use of colour terms confirming his claim written to Ingemann: That one cannot paint Christ with colour. A point unfolded in another upcoming paper ”Med Farver kan man ingen Christus male” En komputationel udforskning af farvebrugen i Grundtvigs forfatterskab” by Katrine Frøkjær Baunvig.

With years of experience in using supercomputing in her research, Katrine plans to continue using it and encourages others to do so when it seems fit. Especially in times where humanities research is often dismissed as lacking scientific rigor, Katrine Frøkjær Baunvig sees an opportunity to make an impact.  With a keen sense of responsibility to bring her field forward, she is determined to prove that humanities research can be just as methodical and rigorous as research in any other discipline.  

“Researchers who have pioneering eagerness should explore supercomputing as it can give them a head start by venturing into “blue ocean” territory.” 

Katrine Frøkjær Baunvig, Head of the Grundtvig Center at Aarhus University

Katrine Frøkjær Baunvig has used the DeiC Interactive HPC system for a range of NLP tasks such as linguistic normalisation of historical Danish, semantic representation learning and inference, and finally, historical chat bot development based on custom Large Language Model for Danish. 

You have just read the first of three cases in our series on Interactive HPC usage in humanities.
Through these compelling cases it becomes evident that supercomputing in humanities research is transforming traditional approaches, empowering researchers to uncover new insights and deepen our understanding of the field.  It opens doors to interdisciplinary collaborations and expands the possibilities for data analysis and modelling, ultimately shaping the future of digital humanities. 

Stay tuned for our second and third case featuring Iza Romanowska and Rebekah Baglini representing their fields of archaeology and linguistics .

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Interactive HPC Research Supercomputing UCloud Use case

UCloud as a complementary HPC tool within theoretical particle physics

Though supercomputers form the key basis of his research, UCloud has been a valuable, complementary tool for Tobias and his colleagues and will most likely continue to be so in future work as well.

Post.doc. Tobias Tsang works within the broader research field of theoretical particle physics. As part of the Centre for Cosmology and Particle Physics Phenomenology (CP3-Origins) at University of Southern Denmark, his research more specifically concerns quantum field theory and quantum chromodynamics (QCD), i.a. how fundamental particles, protons and neutrons, interact with each other:

My research aims to provide high precision predictions based solely on the theory of the Standard Model – the best-known understanding of the interaction of fundamental (i.e. not containing ‘smaller constituents’) particles. This is done via very large-scale numerical simulations using the most powerful supercomputers around the world.

Post.doc. Tobias Tsang, Centre for Cosmology and Particle Physics Phenomenology (CP3-Origins) at University of Southern Denmark

Experience and achievements

More traditional mathematical methods that can be written down with pen and paper do not apply for research on quantum chromodynamics. As such, Tobias’ research relies on a method called ‘Monte Carlo’ which is applied to compute statistical field theories of simple particle systems. Though this type of research is done using very large supercomputers, Tobias has recurrently applied UCloud for exploratory studies of smaller volumes of data:

When doing large scale simulations, we sometimes do it on something called ‘10,000 cores in parallel’, and clearly this is not something we can easily do on a resource like UCloud. But for the small exploratory studies, UCloud is a nice resource in the sense that it is available; you don’t have to sit here on a hot day and burn your laptop to death – you can send it to UCloud and run it there. I think this is kind of the point where I have used UCloud the most; for small exploratory studies and some of the projects that don’t need a huge amount of computer time but still a significant portion.

Post.doc. Tobias Tsang

Though UCloud has served as a supplemental rather than a key tool in Tobias’ work together with the CP3-Origins research centre, he describes it as a nice complement to other HPC resources:

“I don’t think UCloud will ever be the only resource we use. But this is also the design of it; UCloud is not meant to be a huge machine, it is meant to be an available resource that is easy to use and that gives you a playground to set up things really from scratch where you can test things out and run smaller jobs and analyses. In that sense, it is quite complementary to a lot of the things we normally work with. For exploratory studies and for code testing, UCloud will definitely remain very useful.”

Post.doc. Tobias Tsang

At one specific project done at SDU as a collaboration between CP3 and IMADA (Institute of Mathematics and Data Science) a few years back, the vast majority of samples were generated on UCloud, and a significant amount of data production and measurements were also carried out on there [1]. UCloud needs, however, to be considered a part of a whole, according to Tobias:

“It is not that one particular machine made it possible; we would otherwise have found another machine to run it on. But UCloud provided us with a nice set up where we could just use local resources without having to go through big grant applications to get computer time.”

Post.doc. Tobias Tsang

Pros and cons

In terms of time optimization, UCloud has also been a game changer for Tobias:

One of the nice things about UCloud compared to other machines is the wall clock time: quite often, for larger clusters, depending on the cluster though, you are very much restricted by the queue policies. So, there are some clusters where you have a maximum run time of 4 hours, and if you happen to run a small job that is longer than this, then you can’t – you have to always tailor your job to fit exactly and to make the maximum use of it. On UCloud you have a 200-hour wall clock. This is very helpful as for a lot of these things that have to run sequentially, you might not need a huge resource, you just need to have a long enough time span to actually do it.

Post.doc. Tobias Tsang

Though UCloud slowed the work process down a bit in the beginning as everything had to be installed and set up, this downside was quickly resolved and overshadowed by the benefits: 

“Once you get used to it, you can kind of equalize the work process to what you would have on a cluster where everything is just readily installed.”

Post.doc. Tobias Tsang

Despite pros and cons, Tobias describes UCloud as a flexible system:

The fact that UCloud is really just a virtual machine has both positive and negative sides. The positive side is that you are really free to do whatever you want to do; you can install everything and you don’t have any restrictions that you would have on larger clusters where you can’t easily install software, or you can’t install it into the parts where you want to install it. On larger clusters, you are typically limited by the compilers that are already there. So, from that point of view, UCloud, at least to me, seems like a more flexible system. The downside is that you have to install everything; you can’t just quickly run something, you kind of have to constantly install everything from scratch.

Post.doc. Tobias Tsang

Last but not least, Tobias stresses the interaction with the UCloud front office as a major benefit that has helped the research group significantly, especially compared to other clusters with a much longer response time:

One of the nice things with UCloud as a general system is that every time something didn’t work, we got a really quick email back. Any questions we raised were answered quickly, so it was never something that kept us stuck for weeks or months – typically things were resolved in a very timely time scale. And things that we actively suggested as nice features or things that we thought were missing on UCloud were likewise addressed.

Post.doc. Tobias Tsang

[1]  Della Morte, Jaeger, Sannino, Tsang and Ziegler, “One Flavour QCD as an analogue computer for SUSY”, PoS LATTICE2021 (2022) 225, https://doi.org/10.22323/1.396.0225

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Interactive HPC Research UCloud Use case

National Health Data Science Sandbox for Training and Research

UCloud is not just an ideal platform for the individual researcher who wants interactive access to HPC resources or an easy way to collaborate with national or international partners. It is also highly suitable for teaching. Jennifer Bartell and Samuele Soraggi, who are both working on the project National Health Data Science Sandbox for Training and Research, share their experiences with using UCloud.

National “sandbox” platform

The growing amounts of data in all research fields offer researchers new opportunities and possibilities for scientific breakthrough. In the case of health science, the use of large amounts of data has great potential to improve our health care – it can e.g. expand our ability to understand and diagnose diseases. One of the constraints of using health data is that many datasets (e.g. person-specific health records or genomics data) are sensitive from a patient privacy perspective and governed by strict access and usage guidelines. This can be a major challenge in particular for students or researchers who are just learning best practices in handling health data while also developing data science skills.

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