Live Demonstration of the PITHIA e-Science Centre

PITHIA-NRF (Plasmasphere Ionosphere Thermosphere Integrated Research Environment and Access services: a Network of Research Facilities) is a four-year project funded by the European Commission’s H2020 programme to integrate data, models and physical observing facilities for further advancing European research capacity in this area. A central point of PITHIA-NRF is the PITHIA e-Science Centre (PeSC), a science gateway that provides access to distributed data sources and prediction models to support scientific discovery. As the project reached its half-way point in March 2023, the first official prototype of the e-Science Centre was released. This live demonstration will provide an overview of the current status and capabilities of the PeSC, highlighting the underlying ontology and metadata structure, the registration process for models and datasets, the ontology-based search functionalities and the interaction methods for executing models and processing data. One of the main objectives of the PeSC is to enable scientists to register their Data Collections, that can be both raw or higher-level datasets and prediction models, using a standard metadata format and a domain ontology. For these purposes, PITHIA builds on the results of the ESPAS FP7 project by adopting and modifying its ontology and metadata specification. The project utilises the ISO 19156 standard on Observations and Measurements (O&M) to describe Data Collections in an XML format that is widely used within the research community. Following the standard, Data Collections are referring to other XML documents, such as Computations that a model used to derive the results, Acquisitions describing how the data was collected, Instruments that were used during the data collection process, or Projects that were responsible for the data/model. Within the XML documents, specific keywords of the Space Physics ontology can be used to describe the various elements. For example, Observed Property can be Field, Particle, Wave, or Mixed, at the top level. When preparing the XML metadata file, only these values are accepted for validation. Once described in XML format, Data Collections can be published in the PeSC and searched using the ontology-based search engine. Besides large and typically changing/growing Data Collections, PeSC also supports the registration of Catalogues. These are smaller sets of data, originating from a Data Collection and related to specific events, e.g. volcano eruptions. Catalogue Data Subsets can be assigned DOIs to be referenced in publications and provide a permanent set of data for reproducibility. Additionally, to publication and search, the PeSC also provides several mechanisms for interacting with Data Collections, e.g. executing a model or downloading subsets of the data. In the current version two of the four planned interaction methods are implemented: accessing the Data Collection by a direct link and interacting with it via an API and an automatically generated GUI. Data Collections can either be hosted by the local provider or can be deployed on EGI cloud computing resources. The development of the PeSC is still work in progress. Authentication and authorisation are currently being implemented using EGI Checkin and the PERUN Attribute Management System. Further interaction mechanisms enabling local execution and dynamic deployment in the cloud will also be added in the near future. The main screen of the PeSC is illustrated on Figure 1. The source code is open and available in GitHub

Changing memories by interference: the effect of emotional dimensions in reconsolidation of episodic memories

Episodes with an emotional component preoccupy memory formation and this advantage facilitates their preservation and mitigates the impact of interfering episodes. The present study examined the relation of the emotional dimensions of original and interfering episodes to the memory outcome, using a reconsolidation paradigm. In a between-subjects design, 102 healthy young adults were presented with an emotional or neutral image and learned either an emotional or neutral story, respectively (day 1). On day 2, experimental groups were presented with an image of the opposite emotionality, reactivated the original story, and learned a story of the opposite emotionality. On day 3, experimental and control groups were tested for their memory on target and filler clues of the original story and rated both stories for arousal and valence. Overall, there was evidence of interference on the long-term retention of target clues only for the neutral story (i.e. when the interfering story was emotional), and of filler clues for both types of stories. Moreover, individual target clue retention rates correlated with the arousal ratings for both the original neutral story and the interfering emotional story, while they were not related to arousal ratings for the original emotional story or the interfering neutral one

Towards a Cloud Native Big Data Platform using MiCADO

In the big data era, creating self-managing scalable platforms for running big data applications is a fundamental task. Such self-managing and self-healing platforms involve a proper reaction to hardware (e.g., cluster nodes) and software (e.g., big data tools) failures, besides a dynamic resizing of the allocated resources based on overload and underload situations and scaling policies. The distributed and stateful nature of big data platforms (e.g., Hadoop-based cluster) makes the management of these platforms a challenging task. This paper aims to design and implement a scalable cloud native Hadoop-based big data platform using MiCADO, an open-source, and a highly customisable multi-cloud orchestration and auto-scaling framework for Docker containers, orchestrated by Kubernetes. The proposed MiCADO-based big data platform automates the deployment and enables an automatic horizontal scaling (in and out) of the underlying cloud infrastructure. The empirical evaluation of the MiCADO-based big data platform demonstrates how easy, efficient, and fast it is to deploy and undeploy Hadoop clusters of different sizes. Additionally, it shows how the platform can automatically be scaled based on user-defined policies (such as CPU-based scaling)

Science Gateways with Embedded Ontology-based E-learning Support

Science gateways are widely utilised in a range of scientific disciplines to provide user-friendly access to complex distributed computing infrastructures. The traditional approach in science gateway development is to concentrate on this simplified resource access and provide scientists with a graphical user interface to conduct their experiments and visualise the results. However, as user communities behind these gateways are growing and opening their doors to less experienced scientists or even to the general public as “citizen scientists”, there is an emerging need to extend these gateways with training and learning support capabilities. This paper describes a novel approach showing how science gateways can be extended with embedded e-learning support using an ontology-based learning environment called Knowledge Repository Exchange and Learning (KREL). The paper also presents a prototype implementation of a science gateway for analysing earthquake data and demonstrates how the KREL can extend this gateway with ontology-based embedded e-learning support

Sharing Data Collections and Models for Ionosphere, Thermosphere and Plasmasphere Research

PITHIA-NRF (Plasmasphere Ionosphere Thermosphere Integrated Research Environment and Access services: a Network of Research Facilities) is a project funded by the European Commission’s H2020 programme to build a distributed network of observing facilities, data processing tools and prediction models dedicated to ionosphere, thermosphere and plasmasphere research. One of the core components of PITHIA-NRF is the PITHIA e-Science Centre that supports access to distributed data resources and facilitates the execution of various models on local infrastructures and remote cloud computing resources. There are two major types of resources to be registered with the e-Science Centre: Data Collections and Models. Data Collections are either generated as direct outcome of an observation facility (e.g. radars, radio telescopes, meteor cameras, etc.) or can also be generated by various scientific Models. Models are scientific applications that take either raw or cleaned data from observation facilities and produce higher level datasets with predicted characteristics to facilitate further scientific research. Both Data Collections and Models are registered with the PITHIA e-Science Centre using a rich set of metadata that is based on the ISO 19156 standard on Observations and Measurements (O&M), and specifically augmented and tailored for the requirements of space physics. The metadata structure and the related ontology were originally developed in the FP7 ESPAS project [1] and are currently being modified for the specific requirements of PITHIA. PITHIA-NRF decided to describe and register data collections only, instead of the central registration of every individual data granule, as in previous projects such as ESPAS. Such simplification enables easier management of the e-Science Centre and can lead to longer term sustainability with feasible amount of maintenance effort required. On the other hand, local searchability of individual data pieces still remains, not restricting the scientists to access the required details at the necessary granularity. When it comes to the execution of models, the PITHIA e-Science Centre supports three types of model execution and access scenarios, all provided from a single entry-point. Models can be executed on local resources of the various PITHIA nodes (institutions sharing Data Collections and Models). Additionally, some Models can be deployed and executed on cloud computing resources on-demand. Finally, nodes can also offer Models to be downloaded and executed on the users’ own resources. Model providers can select the most suitable execution mechanism, based on the specific characteristics of the models and the resources (both human and computational resources) they have. The implementation of the PITHIA e-Science Centre is work in progress. This presentation will report on the current state of this development work. The ESPAS metadata structure and ontology, tailored for the specific requirements of the project, have already been demonstrated to the research community on the example of some Data Collections and Models. Based on this metadata structure, work is currently ongoing to enable the registration and the ontology-based search facility of both Models and Data Collections. Proof of concept implementations [2] of the various Model access and execution mechanisms have also been implemented and demonstrated to the research community. Acknowledgement This work was funded by the PITHIA-NRF - Plasmasphere Ionosphere Thermosphere Integrated Research Environment and Access services: a Network of Research Facilities (No. 101007599) EU H2020 project. Keywords – e-Science Centre, ontology, metadata, Data Collection, Model execution. REFERENCES [1] Anna Belehaki, Sarah James, Mike Hapgood, Spiros Ventouras, Ivan Galkin, Antonis Lembesis, Ioanna Tsagouri, Anna Charisi, Luca Spogli, Jens Berdermann, Ingemar Häggström, The ESPAS e-infrastructure: Access to data from near-Earth space, Advances in Space Research, Volume 58, Issue 7, 2016, Pages 1177-1200, ISSN 0273-1177, https://doi.org/10.1016/j.asr.2016.06.014. [2] Gabriele Pierantoni, Tamas Kiss, Alexander Bolotov, Dimitrios Kagialis, James DesLauriers, Amjad Ullah, Huankai Chen, David Chan You Fee, Hai-Van Dang, Jozsef Kovacs, Anna Belehaki, Themistocles Herekakis, Ioanna Tsagouri, Sandra Gesing: Towards a Reference Architecture based Science Gateway Framework with Embedded E-Learning Support, Concurrency and Computation, Practice and Experience, Wiley, 2022, https://doi.org/10.1002/cpe.687

Toward a reference architecture based science gateway framework with embedded e‐learning support

Science gateways have been widely utilised by a large number of user communities to simplify access to complex distributed computing infrastructures. While science gateways are still becoming increasingly popular and the number of user communities is growing, the fast and efficient creation of new science gateways and the flexibility to deploy these gateways on-demand on heterogeneous computational resources, remain a challenge. Additionally, the increase in the number of users, especially with very different backgrounds, requires intuitive embedded e-learning tools that support all stakeholders to find related learning material and to guide the learning process. This paper introduces a novel science gateway framework that addresses these challenges. The framework supports the creation, publication, selection and deployment of cloud-based Reference Architectures that can be automatically instantiated and executed even by non-technical users. The framework also incorporates a Knowledge Repository Exchange and Learning module that provides embedded e-learning support. To demonstrate the feasibility of the proposed solution, two scientific case studies are presented based on the requirements of the plasmasphere, ionosphere and thermosphere research communities

Fears of compassion magnify the harmful effects of threat of COVID-19 on mental health and social safeness across 21 countries

Background: The COVID-19 pandemic is a massive global health crisis with damaging consequences to mental health and social relationships. Exploring factors that may heighten or buffer the risk of mental health problems in this context is thus critical. Whilst compassion may be a protective factor, in contrast fears of compassion increase vulnerability to psychosocial distress and may amplify the impact of the pandemic on mental health. This study explores the magnifying effects of fears of compassion on the impact of perceived threat of COVID-19 on depression, anxiety and stress, and social safeness. Methods: Adult participants from the general population (N = 4057) were recruited across 21 countries worldwide, and completed self-report measures of perceived threat of COVID-19, fears of compassion (for self, from others, for others), depression, anxiety, stress and social safeness. Results: Perceived threat of COVID-19 predicted increased depression, anxiety and stress. The three flows of fears of compassion predicted higher levels of depression, anxiety and stress and lower social safeness. All fears of compassion moderated (heightened) the impact of perceived threat of COVID-19 on psychological distress. Only fears of compassion from others moderated the effects of likelihood of contracting COVID-19 on social safeness. These effects were consistent across all countries. Conclusions: Fears of compassion have a universal magnifying effect on the damaging impact of the COVID-19 pandemic on mental health and social safeness. Compassion focused interventions and communications could be implemented to reduce resistances to compassion and promote mental wellbeing during and following the pandemic. © 2021 John Wiley & Sons, Ltd

Compassion Protects Mental Health and Social Safeness During the COVID-19 Pandemic Across 21 Countries

Objectives: The COVID-19 pandemic is having an unprecedented detrimental impact on mental health in people around the world. It is important therefore to explore factors that may buffer or accentuate the risk of mental health problems in this context. Given that compassion has numerous benefits for mental health, emotion regulation, and social relationships, this study examines the buffering effects of different flows of compassion (for self, for others, from others) against the impact of perceived threat of COVID-19 on depression, anxiety, and stress, and social safeness. Methods: The study was conducted in a sample of 4057 adult participants from the general community population, collected across 21 countries from Europe, Middle East, North America, South America, Asia, and Oceania. Participants completed self-report measures of perceived threat of COVID-19, compassion (for self, for others, from others), depression, anxiety, stress, and social safeness. Results: Perceived threat of COVID-19 was associated with higher scores in depression, anxiety, and stress, and lower scores in social safeness. Self-compassion and compassion from others were associated with lower psychological distress and higher social safeness. Compassion for others was associated with lower depressive symptoms. Self-compassion moderated the relationship between perceived threat of COVID-19 on depression, anxiety, and stress, whereas compassion from others moderated the effects of fears of contracting COVID-19 on social safeness. These effects were consistent across all countries. Conclusions: Our findings highlight the universal protective role of compassion, in particular self-compassion and compassion from others, in promoting resilience by buffering against the harmful effects of the COVID-19 pandemic on mental health and social safeness. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature