Integration Of Heterogeneous Data Research Articles

Research on the Exposure Risk Analysis of Wildfires with a Spatiotemporal Knowledge Graph

This study focuses on constructions that are vulnerable to fire hazards during wildfire events, and these constructions are known as ‘exposures’, which are an increasingly significant area of disaster research. A key challenge lies in estimating dynamically and comprehensively the risk that individuals are exposed to during wildfire spread. Here, ‘exposure risk’ denotes the potential threat to exposed constructions from fires within a future timeframe. This paper introduces a novel method that integrates a spatiotemporal knowledge graph with wildfire spread data and an exposure risk analysis model to address this issue. This approach enables the semantic integration of varied and heterogeneous spatiotemporal data, capturing the dynamic nature of wildfire propagation for precise risk analysis. Empirical tests are employed for the study area of Xichang, Sichuan Province, using real-world data to validate the method’s efficacy in merging multiple data sources and enhancing the accuracy of exposure risk analysis. Notably, this approach also reduces the time complexity from O (m×n×p) to O (m×n).

Unveiling Prognostic RNA Biomarkers through a Multi-Cohort Study in Colorectal Cancer.

Because cancer is a leading cause of death and is thought to be caused by genetic errors or genomic instability in many circumstances, there have been studies exploring cancer's genetic basis using microarray and RNA-seq methods, linking gene expression data to patient survival. This research introduces a methodological framework, combining heterogeneous gene expression data, random forest selection, and pathway analysis, alongside clinical information and Cox regression analysis, to discover prognostic biomarkers. Heterogeneous gene expression data for colorectal cancer were collected from TCGA-COAD (RNA-seq), and GSE17536 and GSE39582 (microarray), and were integrated with Entrez Gene IDs. Using Cox regression analysis and random forest, genes with consistent hazard ratios and significantly affecting patient survivability were chosen. Predictive accuracy was evaluated using ROC curves. Pathway analysis identified potential RNA biomarkers. The authors identified 28 RNA biomarkers. Pathway analysis revealed enrichment in cancer-related pathways, notably EGFR downstream signaling and IGF1R signaling. Three RNA biomarkers (ZEB1-AS1, PI4K2A, and ITGB8-AS1) and two clinical biomarkers (stage and age) were chosen for a prognostic model, improving predictive performance compared to using clinical biomarkers alone. Despite biomarker identification challenges, this study underscores integration of heterogenous gene expression data for discovery.

Machine Learning Approaches in Spatial Data Mining

This review paper surveys the integration of machine learning techniques in spatial data mining, a crucial intersection of geographic information systems and data mining. It examines the application of various machine learning algorithms such as classification, regression, clustering, and deep learning in spatial data analysis. The paper discusses challenges like data preprocessing, feature selection, and model interpretability, alongside recent advancements including spatial-temporal analysis and heterogeneous data integration. Through critical analysis of existing literature, it identifies trends, methodologies, and future research directions. Practical implications and applications across domains like urban planning, environmental monitoring, and epidemiology are explored. As a comprehensive resource, this review facilitates understanding and utilization of machine learning approaches for extracting insights from spatial data, benefiting researchers, practitioners, and policymakers alike.

ARCHITECTURAL HERITAGE DIGITAL REPRESENTATIONS FOR CONSERVATION STRATEGIES

Abstract. Digital technologies enable a thorough analysis of Architectural Heritage, offering various representations that provide different levels of documentation and knowledge-based conservation tools. This paper presents a methodological approach that leverages three digital representations related to three levels of documentation: Virtual Tour Informational Modelling, Point Cloud Informational Modelling, and Historical Building Information Modelling. Their characteristics and potential are compared, focusing on the effectiveness in supporting the analysis process, and the management of future conservation works. Furthermore, the dichotomy between 2D and 3D representations of AH is tackled, emphasizing the need to align with the conservation strategies, timeline, and budget constraints. It also discusses the integration of heterogeneous data and various digital representations to address the challenges in AH preventive conservation and management, resulting in the proposal of a sustainable documentation workflow to support the conservation process.

The problem of implementing the concept of maximum integration of heterogeneous data in practical navigation

The topic of the work is the research of the problem of the practical realization of opportunistic navigation, which actually consists in the fact that the “signals of opportunity” are not optimized for global navigation according to their original purpose. A hypothesis about the possibility of strategic application of the navigation concept based on the principles of data integration with the objective existence of tactically surmountable difficulties is put forward. In the conditions of vulnerability of the global satellite system, an approach to use an additional source of combined signals in order to continuously ensure accurate geolocation of water transport vehicles is proposed. The comparative assessment confirms the comparability of the positioning error of alternative navigation with the data of classical satellite observations of mobile objects. An assumption about the effectiveness of spline modeling of the cartographic standard of informativeness for the practical organization of the basic principle of opportunistic positioning by analogy with the paradigm of correlation-extreme navigation is made. A computer visualization of a fixed fragment of the ocean gravitational field based on spline approximation is performed in order to optimize the problem of simultaneous localization and three-dimensional representation of the ship movement in a graphical environment unaffiliated with foreign software. A hybrid algorithm for mathematical synthesis of the navigational isosurface has been tested as a harmonized support for the navigation staff to implement the “terrain-referenced procedure” in the landscape coordinate space. The variants of the “ambiguity resolution” problem implementation in the issue of maximum integration of heterogeneous navigation data are considered. The probability of practical use of the opportunistic navigation postulates in marine applications, taking into account the “navigation gap” factor for leveling local satellite incapacity with a real possibility of countering malicious interception of ship control, is estimated. The expediency of using the factor of observational reckoning of the vessel path as an additional condition for navigation safety in the aspect of packet processing of the “signals of opportunity” slot is substantiated.

A Robust Chinese Named Entity Recognition Method Based on Integrating Dual-Layer Features and CSBERT

In the rapidly evolving field of cybersecurity, the integration of multi-source, heterogeneous, and fragmented data into a coherent knowledge graph has garnered considerable attention. Such a graph elucidates semantic interconnections, thereby facilitating sophisticated analytical decision support. Central to the construction of a cybersecurity knowledge graph is Named Entity Recognition (NER), a critical technology that converts unstructured text into structured data. The efficacy of NER is pivotal, as it directly influences the integrity of the knowledge graph. The task of NER in cybersecurity, particularly within the Chinese linguistic context, presents distinct challenges. Chinese text lacks explicit space delimiters and features complex contextual dependencies, exacerbating the difficulty in discerning and categorizing named entities. These linguistic characteristics contribute to errors in word segmentation and semantic ambiguities, impeding NER accuracy. This paper introduces a novel NER methodology tailored for the Chinese cybersecurity corpus, termed CSBERT-IDCNN-BiLSTM-CRF. This approach harnesses Iterative Dilated Convolutional Neural Networks (IDCNN) for extracting local features, and Bi-directional Long Short-Term Memory networks (BiLSTM) for contextual understanding. It incorporates CSBERT, a pre-trained model adept at processing few-shot data, to derive input feature representations. The process culminates with Conditional Random Fields (CRF) for precise sequence labeling. To compensate for the scarcity of publicly accessible Chinese cybersecurity datasets, this paper synthesizes a bespoke dataset, authenticated by data from the China National Vulnerability Database, processed via the YEDDA annotation tool. Empirical analysis affirms that the proposed CSBERT-IDCNN-BiLSTM-CRF model surpasses existing Chinese NER frameworks, with an F1-score of 87.30% and a precision rate of 85.89%. This marks a significant advancement in the accurate identification of cybersecurity entities in Chinese text, reflecting the model’s robust capability to address the unique challenges presented by the language’s structural intricacies.

KD SENSO-MERGER: An architecture for semantic integration of heterogeneous data

This paper presents KD SENSO-MERGER, a novel Knowledge Discovery (KD) architecture that is capable of semantically integrating heterogeneous data from various sources of structured and unstructured data (i.e. geolocations, demographic, socio-economic, user reviews, and comments). This goal drives the main design approach of the architecture. It works by building internal representations that adapt and merge knowledge across multiple domains, ensuring that the knowledge base is continuously updated. To deal with the challenge of integrating heterogeneous data, this proposal puts forward the corresponding solutions: (i) knowledge extraction, addressed via a plugin-based architecture of knowledge sensors; (ii) data integrity, tackled by an architecture designed to deal with uncertain or noisy information; (iii) scalability, this is also supported by the plugin-based architecture as only relevant knowledge to the scenario is integrated by switching-off non-relevant sensors. Also, we minimize the expert knowledge required, which may pose a bottleneck when integrating a fast-paced stream of new sources. As proof of concept, we developed a case study that deploys the architecture to integrate population census and economic data, municipal cartography, and Google Reviews to analyze the socio-economic contexts of educational institutions. The knowledge discovered enables us to answer questions that are not possible through individual sources. Thus, companies or public entities can discover patterns of behavior or relationships that would otherwise not be visible and this would allow extracting valuable information for the decision-making process.

Enhancing Accident Risk Prediction with Novel Data and Findings from Heterogeneous Sparse Sources

Accurate accident risk prediction is paramount for proactive safety measures and resource allocation. This paper introduces an innovative approach to enhance accident risk prediction by leveraging novel data sources and uncovering insights from heterogeneous sparse data. Traditional models often suffer from limitations in data diversity and scope, hindering their predictive capabilities. In response, our study integrates a wide range of heterogeneous data, including traffic flow data, weather conditions, road infrastructure, and historical accident records. To address the challenges of working with sparse data, we employ advanced data science techniques, including feature engineering, imputation, and machine learning. The paper presents a new dataset that combines diverse data types, providing a comprehensive foundation for our predictive model. Through rigorous analysis, we extract valuable insights from these heterogeneous sources to improve accident risk assessment. The proposed approach offers several advantages, including the ability to predict accidents in previously underrepresented areas and under varying conditions. We evaluate the model's performance through extensive experimentation and validate its accuracy against real-world accident data. Our findings demonstrate significant enhancements in prediction accuracy compared to conventional models. This research contributes to the field of accident risk prediction by showcasing the potential of heterogeneous sparse data integration and advanced data science techniques. It highlights the importance of utilizing novel data sources and the value of uncovering hidden patterns and insights to foster safer environments and more efficient resource allocation in accident-prone areas.

Genotype andphenotype data standardization, utilization andintegration inthe big data era foragricultural sciences.

Large-scale genotype and phenotype data have been increasingly generated to identify genetic markers, understand gene function and evolution and facilitate genomic selection. These datasets hold immense value for both current and future studies, as they are vital for crop breeding, yield improvement and overall agricultural sustainability. However, integrating these datasets from heterogeneous sources presents significant challenges and hinders their effective utilization. We established the Genotype-Phenotype Working Group in November 2021 as a part of the AgBioData Consortium (https://www.agbiodata.org) to review current data types and resources that support archiving, analysis and visualization of genotype and phenotype data to understand the needs and challenges of the plant genomic research community. For 2021-22, we identified different types of datasets and examined metadata annotations related to experimental design/methods/sample collection, etc. Furthermore, we thoroughly reviewed publicly funded repositories for raw and processed data as well as secondary databases and knowledgebases that enable the integration of heterogeneous data in the context of the genome browser, pathway networks and tissue-specific gene expression. Based on our survey, we recommend a need for (i) additional infrastructural support for archiving many new data types, (ii) development of community standards for data annotation and formatting, (iii) resources for biocuration and (iv) analysis and visualization tools to connect genotype data with phenotype data to enhance knowledge synthesis and to foster translational research. Although this paper only covers the data and resources relevant to the plant research community, we expect that similar issues and needs are shared by researchers working on animals. Database URL: https://www.agbiodata.org.

A knowledge graph-based data harmonization framework for secondary data reuse

Background and objectiveThe adoption of new technologies in clinical care systems has propitiated the availability of a great amount of valuable data. However, this data is usually heterogeneous, requiring its harmonization to be integrated and analysed. We propose a semantic-driven harmonization framework that (1) enables the meaningful sharing and integration of healthcare data across institutions and (2) facilitates the analysis and exploitation of the shared data. MethodsThe framework includes an ontology-based common data model (i.e. SCDM), a data transformation pipeline and a semantic query system. Heterogeneous datasets, mapped to different terminologies, are integrated by using an ontology-based infrastructure rooted in a top-level ontology. A graph database is generated by using these mappings, and web-based semantic query system facilitates data exploration. ResultsSeveral datasets from different European institutions have been integrated by using the framework in the context of the European H2020 Precise4Q project. Through the query system, data scientists were able to explore data and use it for building machine learning models. ConclusionsThe flexible data representation using RDF, together with the formal semantic underpinning provided by the SCDM, have enabled the semantic integration, query and advanced exploitation of heterogeneous data in the context of the Precise4Q project.

Food4healthKG: Knowledge graphs for food recommendations based on gut microbiota and mental health

Food is increasingly acknowledged as a powerful means to promote and maintain mental health. The introduction of the gut-brain axis has been instrumental in understanding the impact of food on mental health. It is widely reported that food can significantly influence gut microbiota metabolism, thereby playing a pivotal role in maintaining mental health. However, the vast amount of heterogeneous data published in recent research lacks systematic integration and application development. To remedy this, we construct a comprehensive knowledge graph, named Food4healthKG, focusing on food, gut microbiota, and mental diseases. The constructed workflow includes the integration of numerous heterogeneous data, entity linking to a normalized format, and the well-designed representation of the acquired knowledge. To illustrate the availability of Food4healthKG, we design two case studies: the knowledge query and the food recommendation based on Food4healthKG. Furthermore, we propose two evaluation methods to validate the quality of the results obtained from Food4healthKG. The results demonstrate the system’s effectiveness in practical applications, particularly in providing convincing food recommendations based on gut microbiota and mental health. Food4healthKG is accessible at https://github.com/ccszbd/Food4healthKG.

A semantic framework for condition monitoring in Industry 4.0 based on evolving knowledge bases

In Industry 4.0, factory assets and machines are equipped with sensors that collect data for effective condition monitoring. This is a difficult task since it requires the integration and processing of heterogeneous data from different sources, with different temporal resolutions and underlying meanings. Ontologies have emerged as a pertinent method to deal with data integration and to represent manufacturing knowledge in a machine-interpretable way through the construction of semantic models. Ontologies are used to structure knowledge in knowledge bases, which also contain instances and information about these data. Thus, a knowledge base provides a sort of virtual representation of the different elements involved in a manufacturing process. Moreover, the monitoring of industrial processes depends on the dynamic context of their execution. Under these circumstances, the semantic model must provide a way to represent this evolution in order to represent in which situation(s) a resource is in during the execution of its tasks to support decision making. This paper proposes a semantic framework to address the evolution of knowledge bases for condition monitoring in Industry 4.0. To this end, firstly we propose a semantic model (the COInd4 ontology) for the manufacturing domain that represents the resources and processes that are part of a factory, with special emphasis on the context of these resources and processes. Relevant situations that combine sensor observations with domain knowledge are also represented in the model. Secondly, an approach that uses stream reasoning to detect these situations that lead to potential failures is introduced. This approach enriches data collected from sensors with contextual information using the proposed semantic model. The use of stream reasoning facilitates the integration of data from different data sources, different temporal resolutions as well as the processing of these data in real time. This allows to derive high-level situations from lower-level context and sensor information. Detecting situations can trigger actions to adapt the process behavior, and in turn, this change in behavior can lead to the generation of new contexts leading to new situations. These situations can have different levels of severity, and can be nested in different ways. Dealing with the rich relations among situations requires an efficient approach to organize them. Therefore, we propose a method to build a lattice, ordering those situations depending on the constraints they rely on. This lattice represents a road-map of all the situations that can be reached from a given one, normal or abnormal. This helps in decision support, by allowing the identification of the actions that can be taken to correct the abnormality avoiding in this way the interruption of the manufacturing processes. Finally, an industrial application scenario for the proposed approach is described.

A Collaborative Emergency Drill System for Urban Tunnels Using BIM and an Agent-Based Model

With the rapid development of smart cities, the refined management of urban highway tunnels has put forward higher requirements for the emergency disposal ability of operation and maintenance personnel. This paper proposed a collaborative emergency drill system for urban tunnels using building information modeling (BIM) and an agent-based model. The objectives of this paper are as follows: (1) To help address the challenge of multi-person collaborative intelligent drills in complex emergency scenarios, this system constructed an emergency collaborative drill model and a virtual emergency scenario description method based on trait-based objects (TBOs). (2) To help address the challenge of the organization and integration of multi-source heterogeneous data in complex emergency scenarios, the system established an emergency scenario generation method through lightweight BIM data, standard emergency plan documents, and virtual emergency scenario data. The system was successfully applied to the Hongmei South Road Tunnel in Shanghai, China. The feasibility of the proposed system provided practical help for tunnel emergency management and was extended to other urban tunnels in Shanghai.

Gathering, integration, and interpretation of heterogeneous data for the virtual reconstruction of the Notre Dame de Paris roof structure

The objective of this case study is to present the issues, methods and experiments underway to enable the acquisition of data and the creation of digital models of the timber structures, also known as the Forest, of Notre-Dame de Paris at different temporal states. Among the different models created, a restitution was developed from all the data collected and produced. This interpretation is the result of collecting, collating, and synthesizing heterogeneous data on pre- and post-fire structures. Indeed, the constitution of this model is also based on new data, obtained after the accident, which required the development of ad-hoc solutions to meet the various constraints of the site. These different resources have made it possible to produce the first univocal and three-dimensional restitution of the frameworks before - and after - the fire and the way to gather in particular all the partial data having been used to create it. This experimentation proposes the necessary qualities to enrich the future ecosystem of analysis and multidimensional memorization for the restoration and conservation of Notre-Dame.

Semantic integration of heterogeneous healthcare data based on hybrid root linked health record ontology

Semantic integration of heterogeneous healthcare data based on hybrid root linked health record ontology

Efficient Integration of Heterogeneous Mobility-Pollution Big Data for Joint Analytics at Scale with QoS Guarantees

Numerous real-life smart city application scenarios require joint analytics on unified views of georeferenced mobility data with environment contextual data including pollution and meteorological data. particularly, future urban planning requires restricting vehicle access to specific areas of a city to reduce the adverse effect of their engine combustion emissions on the health of dwellers and cyclers. Current editions of big spatial data management systems do not come with over-the-counter support for similar scenarios. To close this gap, in this paper, we show the design and prototyping of a novel system we term as EMDI for the enrichment of human and vehicle mobility data with pollution information, thus enabling integrated analytics on a unified view. Our system supports a variety of queries including single geo-statistics, such as ‘mean’, and Top-N queries, in addition to geo-visualization on the combined view. We have tested our system with real big georeferenced mobility and environmental data coming from the city of Bologna in Italy. Our testing results show that our system can be efficiently utilized for advanced combined pollution-mobility analytics at a scale with QoS guarantees. Specifically, a reduction in latency that equals roughly 65%, on average, is obtained by using EMDI as opposed to the plain baseline, we also obtain statistically significant accuracy results for Top-N queries ranging roughly from 0.84 to 1 for both Spearman and Pearson correlation coefficients depending on the geo-encoding configurations, in addition to significant single geo-statistics accuracy values expressed using Mean Absolute Percentage Error on the range from 0.00392 to 0.000195.

A pathway model of glucose-stimulated insulin secretion in the pancreatic β-cell.

The pancreas plays a critical role in maintaining glucose homeostasis through the secretion of hormones from the islets of Langerhans. Glucose-stimulated insulin secretion (GSIS) by the pancreatic β-cell is the main mechanism for reducing elevated plasma glucose. Here we present a systematic modeling workflow for the development of kinetic pathway models using the Systems Biology Markup Language (SBML). Steps include retrieval of information from databases, curation of experimental and clinical data for model calibration and validation, integration of heterogeneous data including absolute and relative measurements, unit normalization, data normalization, and model annotation. An important factor was the reproducibility and exchangeability of the model, which allowed the use of various existing tools. The workflow was applied to construct a novel data-driven kinetic model of GSIS in the pancreatic β-cell based on experimental and clinical data from 39 studies spanning 50 years of pancreatic, islet, and β-cell research in humans, rats, mice, and cell lines. The model consists of detailed glycolysis and phenomenological equations for insulin secretion coupled to cellular energy state, ATP dynamics and (ATP/ADP ratio). Key findings of our work are that in GSIS there is a glucose-dependent increase in almost all intermediates of glycolysis. This increase in glycolytic metabolites is accompanied by an increase in energy metabolites, especially ATP and NADH. One of the few decreasing metabolites is ADP, which, in combination with the increase in ATP, results in a large increase in ATP/ADP ratios in the β-cell with increasing glucose. Insulin secretion is dependent on ATP/ADP, resulting in glucose-stimulated insulin secretion. The observed glucose-dependent increase in glycolytic intermediates and the resulting change in ATP/ADP ratios and insulin secretion is a robust phenomenon observed across data sets, experimental systems and species. Model predictions of the glucose-dependent response of glycolytic intermediates and biphasic insulin secretion are in good agreement with experimental measurements. Our model predicts that factors affecting ATP consumption, ATP formation, hexokinase, phosphofructokinase, and ATP/ADP-dependent insulin secretion have a major effect on GSIS. In conclusion, we have developed and applied a systematic modeling workflow for pathway models that allowed us to gain insight into key mechanisms in GSIS in the pancreatic β-cell.

Deep-learning-enabled protein-protein interaction analysis for prediction of SARS-CoV-2 infectivity and variant evolution.

Host-pathogen interactions and pathogen evolution are underpinned by protein-protein interactions between viral and host proteins. An understanding of how viral variants affect protein-protein binding is important for predicting viral-host interactions, such as the emergence of new pathogenic SARS-CoV-2 variants. Here we propose an artificial intelligence-based framework called UniBind, in which proteins are represented as a graph at the residue and atom levels. UniBind integrates protein three-dimensional structure and binding affinity and is capable of multi-task learning for heterogeneous biological data integration. In systematic tests on benchmark datasets and further experimental validation, UniBind effectively and scalably predicted the effects of SARS-CoV-2 spike protein variants on their binding affinities to the human ACE2 receptor, as well as to SARS-CoV-2 neutralizing monoclonal antibodies. Furthermore, in a cross-species analysis, UniBind could be applied to predict host susceptibility to SARS-CoV-2 variants and to predict future viral variant evolutionary trends. This in silico approach has the potential to serve as an early warning system for problematic emerging SARS-CoV-2 variants, as well as to facilitate research on protein-protein interactions in general.

AFEI: adaptive optimized vertical federated learning for heterogeneous multi-omics data integration.

Vertical federated learning has gained popularity as a means of enabling collaboration and information sharing between different entities while maintaining data privacy and security. This approach has potential applications in disease healthcare, cancer prognosis prediction, and other industries where data privacy is a major concern. Although using multi-omics data for cancer prognosis prediction provides more information for treatment selection, collecting different types of omics data can be challenging due to their production in various medical institutions. Data owners must comply with strict data protection regulations such as European Union (EU) General Data Protection Regulation. To share patient data across multiple institutions, privacy and security issues must be addressed. Therefore, we propose an adaptive optimized vertical federated-learning-based framework adaptive optimized vertical federated learning for heterogeneous multi-omics data integration (AFEI) to integrate multi-omics data collected from multiple institutions for cancer prognosis prediction. AFEI enables participating parties to build an accurate joint evaluation model for learning more information related to cancer patients from different perspectives, based on the distributed and encrypted multi-omics features shared by multiple institutions. The experimental results demonstrate that AFEI achieves higher prediction accuracy (6.5% on average) than using single omics data by utilizing the encrypted multi-omics data from different institutions, and it performs almost as well as prognosis prediction by directly integrating multi-omics data. Overall, AFEI can be seen as an efficient solution for breaking down barriers to multi-institutional collaboration and promoting the development of cancer prognosis prediction.

Информационно-вычислительные ресурсы ИРНОК: инфраструктура, данные, приложения

В статье представлены этапы развития информационно-вычислительной и телекоммуникационной инфраструктуры Иркутского научно-образовательного комплекса в период с 2008 по 2022 г. Показано развитие инфраструктуры: сети передачи данных, систем хранения данных, виртуализации, вычислительных ресурсов, в том числе оборудования Иркутского суперкомпьютерного центра. Abstract The Integrated information and computing network of the Irkutsk scientific and educational complex (IICN IREC) is the basic technological platform for most interdisciplinary research conducted by scientific organizations of the Irkutsk region. The article presents the stages of development of the information, computing and telecommunications infrastructure of the IICN IREC in the period from 2008 to 2022. The main directions of modernization of IREC were: the development of a communication network, the deployment of a virtualization platform, data storage system and GPU cluster. The Irkutsk supercomputer center (ISCC) of the SB RAS, which interacts with IREC, has also been developed. The most significant stage in the development of the ISCC was the implementation of the project to create a new generation computing cluster, named after the first organizing director of the institute, Academician V.M. Matrosov. The effectiveness of the created information and computing infrastructure is shown on the example of the implementation of two major integration projects. These are the projects “Fundamental research and breakthrough technologies as the basis for the advanced development of the Baikal region and its interregional relations” (2016–2019) and “Fundamental foundations, methods and technologies for digital monitoring and forecasting of the environmental situation in the Baikal natural territory (BNT)” (2020–2022). More than a dozen institutions have taken part in these projects. To support interdisciplinary research, tools that ensure effective interaction of researchers with the IREC information and computing infrastructure have been developed at the ISDCT SB RAS. Among them are Geoportal, Digital Platform for Monitoring of BNT and Orlando Tools. As part of the projects, a large array of various formats data on the state of the region was collected and computationally complex problems were solved. The integration of heterogeneous scientific data and the possibility of processing them with high-performance computing tools open up new opportunities for obtaining new breakthrough results.