Meeting abstracts of the NursID Spring School 2025.

The interdisciplinary spring school “Language, music, and cognition: Organizing events in time” was held from February 26 to March 2, 2018 at the Institute of Musicology of the University of Cologne. Language, speech, and music as events in time were explored from different perspectives including evolutionary biology, social cognition, developmental psychology, cognitive neuroscience of speech, language, and communication, as well as computational and biological approaches to language and music. There were 10 lectures, 4 workshops, and 1 student poster session. Overall, the spring school investigated language and music as neurocognitive systems and focused on a mechanistic approach exploring the neural substrates underlying musical, linguistic, social, and emotional processes and behaviors. In particular, researchers approached questions concerning cognitive processes, computational procedures, and neural mechanisms underlying the temporal organization of language and music, mainly from two perspectives: one was concerned with syntax or structural representations of language and music as neurocognitive systems (i.e., an intrapersonal perspective), while the other emphasized social interaction and emotions in their communicative function (i.e., an interpersonal perspective). The spring school not only acted as a platform for knowledge transfer and exchange but also generated a number of important research questions as challenges for future investigations.

Background Human trafficking describes a new form of slavery and affects thousands of people annually, mostly women and children. A Spring School on the topic of ‘Human Trafficking, Public Health and the Law' was developed and performed by four European institutions: Technische Universitat Dresden, Germany; Universita degli Studi di Siena, Italy; Masarykova Univerzita, Brno, Czech Republic, Universitatea Romano-Americana, Bucharest, Romania and …

Since 2003 the University of Udine in Italy has organized FilmForum/MAGIS Spring School – an international film and media studies conference, supported and attended by scholars from around the glob...

Editorial: Spring School on Physics, Chemistry and Biology with Single Molecules, Hofgeismar, April 4th to 8th 2002

「春の学校」開催される(応用数理「春の学校」)

With the advent of faster and cheaper storage units, over the last decade it has become more and more common practice to collect large amounts of data. Market research firms, environmental monitoring stations, control systems for production chains, health care providers, bioinformatic companies, web page archives and other similar systems may easily record millions of observations/variables per day. This generates exponentially growing data sets [1, Chapter 1]. Because of their size many of such large data sets will likely contain precious information about the originating process in terms of its evolution and hidden patterns. Each feature of the system indeed may be more or less important to explore depending on the goal of the analysis and more or less easy to describe depending on the data. Appropriate data analysis tools are then required for each problem and for each set of data. Many data analysis techniques are available nowadays, each one with a different flavor. For example, Statistics, because of it’s branching from mathematics, is usually considered the most rigorous; while Neural Networks have gained the field as a “black-box” method. Some of the data analysis techniques were developed in physics especially for time series analysis; others simulate the evolutionary behavior of populations; some others concentrate on non-crisp decision processes. But all these strategies can be very effective and, in principle at least, there is a great potential for synergy. For each given problem, how do we choose the appropriate analysis method? And if different methods yield different results, may we use them in cooperation as to show different aspects of the same system? That is, are they complementary? Can we help the analysis with some of the background knowledge? In addition many of the previously cited methods might not be designed for large data sets. Is it then possible to reduce the data set dimensionality without loosing informative contents? Is there any pre-processing technique that can enhance the aspects of the system we want to investigate? How do we deal with sets of heterogeneous data? Those are some of the questions researchers deal with when starting a new investigation. Intelligent Data Analysis (IDA) represents an attempt to investigate and integrate different techniques, including Artificial Intelligence and Statistics, to enable an efficient analysis of large amounts of data.

The Conference and Spring School on Properties, Design and Applications of Upconversion Nanomaterials (UPCON) provides a new forum for all experts and newcomers in the field of upconversion research. On the occasion of the second UPCON 2018 in Valencia (Spain), we are pleased to present a collection of 12 reviews and research articles that reflect recent advances in upconversion materials, their unique luminescent properties and many applications spanning from nanoscale thermometry to biomedicine.

Black holes are among the most exciting phenomena predicted by General Relativity and play a key role in fundamental physics. Many interesting phenomena involve dynamical black hole configurations in the high curvature regime of gravity. In these lecture notes I will summarize the main numerical relativity techniques to explore highly dynamical phenomena, such as black hole collisions, in generic D-dimensional space–times. The present notes are based on my lectures given at the NR/HEP2 spring school at IST/Lisbon (Portugal) from March 11–14, 2013.

The AdS/CFT correspondence provides quantum theories of gravity in which spacetime and gravitational physics emerge from ordinary non-gravitational quantum systems with many degrees of freedom. Recent work in this context has uncovered fascinating connections between quantum information theory and quantum gravity, suggesting that spacetime geometry is directly related to the entanglement structure of the underlying quantum mechanical degrees of freedom and that aspects of spacetime dynamics (gravitation) can be understood from basic quantum information theoretic constraints. In these notes, we provide an elementary introduction to these developments, suitable for readers with some background in general relativity and quantum field theory. The notes are based on lectures given at the CERN Spring School 2014, the Jerusalem Winter School 2014, the TASI Summer School 2015, and the Trieste Spring School 2015.

Presenting any workshop brings a level of anxiety and challenge (Mills, 2006), planning the anticipated outcomes, method of delivery and all the ancillary decisions that come because of this can often lead presenters to remain within their comfort zone and stick with the types of workshops that we as attendees have come to expect to be delivered to us at conferences (Chung, 2018). Deciding to do something radically different in a workshop involves a level of risk and adventure on the part of the presenters but also on the part of the attendees, who sign up for something that is going to be done differently. As doctoral students on the Professional Doctorate in Systemic Practice (PDSP) we are all invited to participate in the Annual Systemic Spring School, hosted by Brathay Hall in the lake district. This year the spring school was entitled "Making NewSense of Practice and Research" and this article acts as a reflexive account of the workshop presented by us during this event.

Digitalization in healthcare and the increasing availability of data demand data literacy competences of nurses and other healthcare professionals including technical, ethical and communication skills. The international Spring School 2023 "Information in Healthcare - From Date to Knowledge" aimed at these competences covering interoperability, data protection and security, data analytics and ethical issues. These topics were embedded in the overall case of data-driven quality improvement for diabetes patients in a region. The curriculum includes an online preparation-phase and a five-days attendance week, incorporating problem-based and group work approaches. According to the studentt's evaluation, the awareness of the importance of the topics was raised and theoretical as well as practical application skills were improved. The Spring School enhanced data literacy competences, critical thinking, problem-solving, interprofessional und intercultural skills among healthcare professionals. Such course offering can contribute to meeting the increasing challenges of digitalization in healthcare.

Lectures presented at the Spring School, Trieste, Italy 1993.

In this paper we present a proposal for the creation of a standardized evaluation model for participatory/public/community archaeology and heritage initiatives. The proposal is the result of discussions during a Spring School that the University of Padua and the MAG Museum of Alto Garda, Italy, organized between 9 and 15 April 2018. The Spring School brought together international practitioners to compare experiences and share visions for the sustainable future of archaeology and heritage management. We reflect on the process of bringing together our thoughts and views into one place, and on the theoretical and practitioner contexts that inform our proposed model. The model is intended for researchers and practitioners planning to carry out archaeological and heritage projects with communities. We invite readers to try using some or all of the sections of the model in their own work and to contribute to further refinement of the approach.

Before the COVID-19 pandemic, a need for a uniform approach to health information (HI) knowledge in population health analysis across Europe was evident. The Population Health Information Research Infrastructure (PHIRI) emerged as a proactive initiative to strengthen European HI capacities. This article describes the achievements of PHIRI, highlighting its capacity-building activities and their contribution towards a sustainable strategy for the implementation of the European Health Data Space (EHDS). PHIRI collaboration established a work package for skill-building activities in population health in partnership with other organizations. Activities included webinars, workshops, sessions, training schools and courses for researchers and public administration workers from Europe and beyond. The primary goal of the activities was to examine the impact of COVID-19 on European health systems at both local and national levels, including healthcare facilities and policymaking entities. Twelve activities were organized between October 2020 and the summer of 2023. In March 2023, the Spring School on Health Information was organized to share the knowledge achieved from PHIRI and other European Union-related projects. This event also validated the European Health Information Training Programme. PHIRI's findings emphasized the importance of equipping the workforce with core HI skills to improve health systems' preparedness and resilience. Through this research, it is possible to propose a strategy for building capacity that emphasizes the importance of providing training in human-machine dynamics. This approach will contribute to the sustainable implementation of the EHDS.

<p>The provision of climate services to users is a fast developing field. In support of this development, the IS-ENES3 project, funded within the EC Horizon2020 program, organized three schools on “Climate data for impact assessments” in 2020 and 2021. In an Autumn school, a Spring school and a Summer school, climate scientists and impact scientists were brought together. An important aim of the schools was to enhance interaction between Vulnerability-Impact-Adaptation (VIA) researchers, climate services providers and climate researchers. Another aim was to provide an overview of information on climate modeling, climate data, impact modelling and climate services based on the work of the IS-ENE3 project.</p><p>In the first three weeks a series of lectures was given, covering topics such as climate data and modelling, impact models, portals for accessing and processing climate data, setting-up impact assessments, and communication of results to stakeholders. In the last three weeks the participants worked in small groups of one climate scientist with one impact scientist on a case study under the guidance of the course lecturers. Impact and climate researchers were combined on purpose to let them experience how they could help each other.</p><p>Originally the schools were planned to take place on-site (e.g. in Prague) during one week; however, due to COVID-19 the schools had to be transformed to virtual schools with two weekly sessions during six weeks. Although the virtual set-up had some disadvantages (e.g. less possibilities for networking), there were also some advantages (e.g. the possibility to record the lectures and make them available to a broader audience; more time to explore and work with climate data in between the sessions, no CO<sub>2</sub> emissions for travelling). During this presentation we will present the set-up of the schools and the conversion to a virtual school. We will focus on the lessons learnt and the evaluation of the virtual schools by the participants and give some recommendations for similar schools and how to link the climate and VIA research communities .</p>