{"id":1391,"date":"2026-06-12T10:25:40","date_gmt":"2026-06-12T08:25:40","guid":{"rendered":"https:\/\/www.hsu-hh.de\/dataeng\/?page_id=1391"},"modified":"2026-07-20T09:54:30","modified_gmt":"2026-07-20T07:54:30","slug":"resources","status":"publish","type":"page","link":"https:\/\/www.hsu-hh.de\/dataeng\/en\/resources\/","title":{"rendered":"Resources"},"content":{"rendered":"\n

Here you will find all the resources published by our department that are available for download. We encourage you to use, adapt, and cite the materials provided in your research, teaching, and studies. We particularly welcome your feedback and look forward to seeing these materials actively used in academic papers and student projects.<\/p>\n\n\n\n

DiaData: A Multi-Modal, Integrated Time-Series Dataset for Type 1 Diabetes Research<\/h2>\n\n\n\n

„Unlock Type 1 diabetes insights with DiaData, the first large-scale integrated CGM dataset linking real-world glucose patterns to high-impact applications.<\/strong>„<\/em><\/strong><\/p>\n\n\n\n

DiaData is the first large-scale integrated CGM dataset for Type 1 diabetes research, combining 13 datasets and glucose measurements from 1720 individuals with T1D across diverse age groups. CGM values are recorded at 5-minute intervals, with additional 15-minute resampled versions provided. The main database (MDB) contains CGM measurements for all 1720 participants. From this, three subsets are extracted: Subdatabase I includes CGM data and demographics, Subdatabase II includes CGM and heart rate data, while Subdatabase III includes CGM data and personal features of sex, age, race, height, weight, age of diagnosis, and HbA1c values.<\/p>\n\n\n\n

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ARACHNE: An Open Multimodal Dataset of Physiological, Behavioral, and Subjective Anxiety Responses for Spider Phobia<\/h2>\n\n\n\n

„Understanding Fear Through Physiology, Behavior, and Virtual Reality.<\/strong><\/strong>„<\/em><\/strong><\/p>\n\n\n\n

This dataset contains multimodal physiological, behavioral, and subjective data collected from individuals with spider phobia during real-world and virtual reality Behavioral Avoidance Tasks (BATs). It includes continuous recordings of cardiac activity, electrodermal activity, temperature, and movement, as well as subjective anxiety ratings and contextual information. The dataset is suitable for research in anxiety assessment, affective computing, emotion recognition, digital biomarkers, and machine learning applications in mental health.<\/p>\n\n\n\n

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MontoFlow: A Semantic Framework for Maritime Sensor Data and Ontology-Based Diagnostics<\/h2>\n\n\n\n

„Semantic integration of maritime sensor data for intelligent diagnostics and real-time analytics.<\/strong><\/strong>„<\/em><\/strong><\/p>\n\n\n\n

MontoFlow is an open semantic framework for the integration and modeling of maritime sensor data. At its core is the SHIP Ontology<\/strong>, a domain-specific extension of the W3C SSN\/SOSA<\/strong> standards that formally models ship components, sensors, and their observations. The framework combines static ontology instantiation with dynamic Ontology-Based Data Access (OBDA), enabling real-time sensor data to be queried semantically without the need for costly RDF materialization. As a result, MontoFlow supports applications such as anomaly detection, condition monitoring, predictive maintenance, and explainable AI, while providing a reusable and extensible foundation for maritime diagnostics and other sensor-based domains.<\/p>\n\n\n\n

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AISHIP: A Maritime Ontology for Vessel Representation and Data Integration<\/h2>\n\n\n\n

„The semantic foundation for maritime vessel data, data integration, and intelligent analytics.<\/strong><\/strong>„<\/em><\/strong><\/p>\n\n\n\n

AISHIP is a maritime ontology for the semantic integration of heterogeneous vessel data. It extends the existing VesselAI ontology with detailed vessel characteristics, enhanced trajectory data, multimodal vessel representations, and a comprehensive model of propulsion systems and engine configurations. By unifying diverse data sources, AISHIP provides a shared semantic foundation for applications such as vessel behavior analysis, fleet management, search and rescue, and maritime digital twins, while supporting interoperable and reusable maritime knowledge models.<\/p>\n\n\n\n

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MQTT4SSN: An Ontology for Semantic MQTT Communication<\/em><\/h2>\n\n\n\n

\u201cSemantic modeling of MQTT communication in distributed production environments.\u201d<\/em><\/em><\/strong><\/p>\n\n\n\n

MQTT4SSN is an ontology for the semantic description of the MQTT message protocol and the data transmitted through it. The ontology models key elements such as brokers and clients, MQTT control packets and their payloads, topics, and the relationships among them. It also connects MQTT topic structures with SSN\/SOSA. MQTT4SSN thus provides an interoperable and reusable foundation for representing and semantically analyzing communication, sensor observations, and actuations within knowledge graphs.<\/em><\/p>\n\n\n\n

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MQTT2RDF: A Semantic Integration Framework for MQTT Data Streams<\/em><\/h2>\n\n\n\n

\u201cFrom live MQTT communication to RDF-based knowledge graphs and end-to-end semantic analytics.\u201d<\/em><\/em><\/strong><\/p>\n\n\n\n

MQTT2RDF is a reusable semantic integration framework that captures live MQTT traffic and transforms it into RDF-based knowledge graphs using the MQTT4SSN ontology. It supports the processing of heterogeneous payload formats and character encodings, enabling end-to-end semantic analysis from individual MQTT messages to the resulting knowledge graph. MQTT2RDF therefore provides a reusable, reproducible foundation for the semantic integration and analysis of MQTT data streams.<\/em><\/p>\n\n\n\n

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Download<\/a><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Here you will find all the resources published by our department that are available for download. We encourage you to use, adapt, and cite the materials provided in your research, […]<\/p>\n","protected":false},"author":4068,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"categories":[7],"tags":[],"class_list":["post-1391","page","type-page","status-publish","hentry","category-research"],"lang":"en","translations":{"en":1391,"de":1365},"pll_sync_post":{},"_links":{"self":[{"href":"https:\/\/www.hsu-hh.de\/dataeng\/wp-json\/wp\/v2\/pages\/1391","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.hsu-hh.de\/dataeng\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.hsu-hh.de\/dataeng\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.hsu-hh.de\/dataeng\/wp-json\/wp\/v2\/users\/4068"}],"replies":[{"embeddable":true,"href":"https:\/\/www.hsu-hh.de\/dataeng\/wp-json\/wp\/v2\/comments?post=1391"}],"version-history":[{"count":8,"href":"https:\/\/www.hsu-hh.de\/dataeng\/wp-json\/wp\/v2\/pages\/1391\/revisions"}],"predecessor-version":[{"id":1686,"href":"https:\/\/www.hsu-hh.de\/dataeng\/wp-json\/wp\/v2\/pages\/1391\/revisions\/1686"}],"wp:attachment":[{"href":"https:\/\/www.hsu-hh.de\/dataeng\/wp-json\/wp\/v2\/media?parent=1391"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.hsu-hh.de\/dataeng\/wp-json\/wp\/v2\/categories?post=1391"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.hsu-hh.de\/dataeng\/wp-json\/wp\/v2\/tags?post=1391"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}