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86 results for “semantic data”
Figure 3. Graph created directly from the results.-Browsing Semantic Data in Slovakia
<p>Resulting graph is rather complex. There are 175 vertices and 201 edges, which were created directly, containing 158 persons and 17 companies. As we see on Figure 3, some filtering methods are required in order to create suitable overview of relations. Figure 4 thus shows the visualization of the same graph, but with tens of vertices merged. Now it contains 22 persons and 17 companies. A merging was performed for clarification and is built inside the visualization module. A simple condition says that a merging is performed if persons are unique, thus if a person is connected only to 1 firm. More formally, person vertices are merged, if their vertex degree equals 1 (each).</p>
Figure 2. Results for firm name "Váhostav" are in table. Each row defines a firm with its name, identification number and address. Then a connection is specified (whether it be a person or another firm).-Browsing Semantic Data in Slovakia
<p>We have searched for firm “Váhostav”, which is a rather big firm in Slovakia, with many press articles published about31. On Figure 2 there is a browsing window, for SBR data results, displaying tabular structure, which was refined from SBR dataset by continuous querying.</p>
Figure 1. Schema of clientside application for semantic browsing.-Browsing Semantic Data in Slovakia
<p>With the aim primary on unstructured information extraction and refining, relationship discovery and visualization, we propose our solution for SBR in the first place. The reason for this is, primary, that HTML formatted results of SBR are very jerky and uncertainty regarding the structure of information is very high. Readers can also be pointed by J. Suchal and P. Vojtek (2009), that care should be taken towards type errors. We discuss that later. In this work, we try to fill&up the gap of visualization and, somehow limited data access offered by SBR, adapting to the problems disclaimed above. We suggest a new client& side paradigm, which does not depend on a particular website like foaf.sk. Figure 1 describes the schema briefly and the key elements are parsers with other tools on the top and structured formats, for datastore, on the bottom.</p>
FAIRsFAIR Data of Survey on Semantics and interoperability solutions
<p>As part of the EOSC project family the FAIRsFAIR - Fostering Fair Data Practices in Europe - project aims to supply practical solutions for the use of the FAIR data principles throughout the research data life cycle. The work package "WP2 FAIR Practices: Semantics, Interoperability, and Services" will produce three reports on FAIR requirements for persistence and interoperability to identify domain-specific standards and practices in use. These will review and document commonalities and possible gaps regarding semantic interoperability, and the use of metadata and persistent identifiers across infrastructures. They will also look into differences in terms of standards, vocabularies and ontologies. The collected information will be updated during the course of the project in cooperation with other tasks and EOSC projects.</p> <p>This survey was done to complement and validate the information from desk research for the first of these reports. It was aimed at data managers and data support experts. We hoped to get information about tools and services we might have missed, but also some reflections on the thinking around identifiers and ontologies and other semantic artefacts. The information was also collected to support preparing workshops on semantics and interoperability that are forthcoming in the project, as well as the work on software and services. The survey covers questions about metadata, use of persistent identifiers, use of semantic artefacts and handling research software.</p> <p>The survey was conducted as a joint effort with WP3, FAIR Policy and Practice and its open consultation, and was disseminated on the fairsfair.eu web pages, social media channels and via email lists. We received 66 answers during the period the survey was open, that is between 15 July to 2 October 2019.</p>
A semantic segmentation dataset of Arctic sea ice from Operation IceBridge data
<p>This dataset is a semantic segmentation dataset of Arctic sea ice based on deep learning method from Operation IceBridge images. It contains 29,372 labeled images, each of which corresponds to an image of Operation IceBridge and are stored in TIFF format. The dataset can be accessed using ArcGIS, ENVI, and the GDAL library in Python easily. Where label 1 represents melt ponds, label 2 represents sea ice/snow, label 3 represents submerged ice, and label 4 represents open ocean water, respectively.</p>
Taxonomies for Semantic Research Data Annotation
<p>This dataset contains 35 of 39 taxonomies that were the result of a systematic review. The systematic review was conducted with the goal of identifying taxonomies suitable for semantically annotating research data. A special focus was set on research data from the hybrid societies domain.</p> <p>The following taxonomies were identified as part of the systematic review:</p> <table> <tbody> <tr> <td> <p><strong>Filename</strong></p> </td> <td> <p><strong>Taxonomy Title</strong></p> </td> </tr> <tr> <td> <p>acm_ccs</p> </td> <td> <p>ACM Computing Classification System [1]</p> </td> </tr> <tr> <td> <p>amec</p> </td> <td> <p>A Taxonomy of Evaluation Towards Standards [2]</p> </td> </tr> <tr> <td> <p>bibo</p> </td> <td> <p>A BIBO Ontology Extension for Evaluation of Scientific Research Results [3]</p> </td> </tr> <tr> <td> <p>cdt</p> </td> <td> <p>Cross-Device Taxonomy [4]</p> </td> </tr> <tr> <td> <p>cso</p> </td> <td> <p>Computer Science Ontology [5]</p> </td> </tr> <tr> <td> <p>ddbm</p> </td> <td> <p>What Makes a Data-driven Business Model? A Consolidated Taxonomy [6]</p> </td> </tr> <tr> <td> <p>ddi_am</p> </td> <td> <p>DDI Aggregation Method [7]</p> </td> </tr> <tr> <td> <p>ddi_moc</p> </td> <td> <p>DDI Mode of Collection [8]</p> </td> </tr> <tr> <td> <p>n/a</p> </td> <td> <p>DemoVoc [9]</p> </td> </tr> <tr> <td> <p>discretization</p> </td> <td> <p>Building a New Taxonomy for Data Discretization Techniques [10]</p> </td> </tr> <tr> <td> <p>dp</p> </td> <td> <p>Demopaedia [11]</p> </td> </tr> <tr> <td> <p>dsg</p> </td> <td> <p>Data Science Glossary [12]</p> </td> </tr> <tr> <td> <p>ease</p> </td> <td> <p>A Taxonomy of Evaluation Approaches in Software Engineering [13]</p> </td> </tr> <tr> <td> <p>eco</p> </td> <td> <p>Evidence & Conclusion Ontology [14]</p> </td> </tr> <tr> <td> <p>edam</p> </td> <td> <p>EDAM: The Bioscientific Data Analysis Ontology [15]</p> </td> </tr> <tr> <td> <p>n/a</p> </td> <td> <p>European Language Social Science Thesaurus [16]</p> </td> </tr> <tr> <td> <p>et</p> </td> <td> <p>Evaluation Thesaurus [17]</p> </td> </tr> <tr> <td> <p>glos_hci</p> </td> <td> <p>The Glossary of Human Computer Interaction [18]</p> </td> </tr> <tr> <td> <p>n/a</p> </td> <td> <p>Humanities and Social Science Electronic Thesaurus [19]</p> </td> </tr> <tr> <td> <p>hcio</p> </td> <td> <p>A Core Ontology on the Human-Computer Interaction Phenomenon [20]</p> </td> </tr> <tr> <td> <p>hft</p> </td> <td> <p>Human-Factors Taxonomy [21]</p> </td> </tr> <tr> <td> <p>hri</p> </td> <td> <p>A Taxonomy to Structure and Analyze Human–Robot Interaction [22]</p> </td> </tr> <tr> <td> <p>iim</p> </td> <td> <p>A Taxonomy of Interaction for Instructional Multimedia [23]</p> </td> </tr> <tr> <td> <p>interrogation</p> </td> <td> <p>A Taxonomy of Interrogation Methods [24]</p> </td> </tr> <tr> <td> <p>iot</p> </td> <td> <p>Design Vocabulary for Human–IoT Systems Communication [25]</p> </td> </tr> <tr> <td> <p>kinect</p> </td> <td> <p>Understanding Movement and Interaction: An Ontology for Kinect-Based 3D Depth Sensors [26]</p> </td> </tr> <tr> <td> <p>maco</p> </td> <td> <p>Thesaurus Mass Communication [27]</p> </td> </tr> <tr> <td> <p>n/a</p> </td> <td> <p>Thesaurus Cognitive Psychology of Human Memory [28]</p> </td> </tr> <tr> <td> <p>mixed_initiative</p> </td> <td> <p>Mixed-Initiative Human-Robot Interaction: Definition, Taxonomy, and Survey [29]</p> </td> </tr> <tr> <td> <p>qos_qoe</p> </td> <td> <p>A Taxonomy of Quality of Service and Quality of Experience of Multimodal Human-Machine Interaction [30]</p> </td> </tr> <tr> <td> <p>ro</p> </td> <td> <p>The Research Object Ontology [31]</p> </td> </tr> <tr> <td> <p>senses_sensors</p> </td> <td> <p>A Human-Centered Taxonomy of Interaction Modalities and Devices [32]</p> </td> </tr> <tr> <td> <p>sipat</p> </td> <td> <p>A Taxonomy of Spatial Interaction Patterns and Techniques [33]</p> </td> </tr> <tr> <td> <p>social_errors</p> </td> <td> <p>A Taxonomy of Social Errors in Human-Robot Interaction [34]</p> </td> </tr> <tr> <td> <p>sosa</p> </td> <td> <p>Semantic Sensor Network Ontology [35]</p> </td> </tr> <tr> <td> <p>swo</p> </td> <td> <p>The Software Ontology [36]</p> </td> </tr> <tr> <td> <p>tadirah</p> </td> <td> <p>Taxonomy of Digital Research Activities in the Humanities [37]</p> </td> </tr> <tr> <td> <p>vrs</p> </td> <td> <p>Virtual Reality and the CAVE: Taxonomy, Interaction Challenges and Research Directions [38]</p> </td> </tr> <tr> <td> <p>xdi</p> </td> <td> <p>Cross-Device Interaction [39]</p> </td> </tr> </tbody> </table> <p><br> We converted the taxonomies into SKOS (Simple Knowledge Organisation System) representation. The following 4 taxonomies were not converted as they were already available in SKOS and were for this reason excluded from this dataset:</p> <p>1) DemoVoc, cf. <a href="http://thesaurus.web.ined.fr/navigateur/">http://thesaurus.web.ined.fr/navigateur/</a><br> available at <a href="https://thesaurus.web.ined.fr/exports/demovoc/demovoc.rdf">https://thesaurus.web.ined.fr/exports/demovoc/demovoc.rdf</a></p> <p>2) European Language Social Science Thesaurus, cf. <a href="https://thesauri.cessda.eu/elsst/en/">https://thesauri.cessda.eu/elsst/en/</a><br> available at <a href="https://zenodo.org/record/5506929">https://zenodo.org/record/5506929</a></p> <p>3) Humanities and Social Science Electronic Thesaurus, cf. <a href="https://hasset.ukdataservice.ac.uk/hasset/en/">https://hasset.ukdataservice.ac.uk/hasset/en/</a><br> available at <a href="https://zenodo.org/record/7568355">https://zenodo.org/record/7568355</a></p> <p>4) Thesaurus Cognitive Psychology of Human Memory, cf. <a href="https://www.loterre.fr/presentation/">https://www.loterre.fr/presentation/</a><br> available at <a href="https://skosmos.loterre.fr/P66/en/">https://skosmos.loterre.fr/P66/en/</a></p> <p> </p> <p><strong>References</strong></p> <p>[1] “The 2012 ACM Computing Classification System,” <em>ACM Digital Library</em>, 2012. <a href="https://dl.acm.org/ccs">https://dl.acm.org/ccs</a> (accessed May 08, 2023).</p> <p>[2] AMEC, “A Taxonomy of Evaluation Towards Standards.” Aug. 31, 2016. Accessed: May 08, 2023. [Online]. Available: <a href="https://amecorg.com/amecframework/home/supporting-material/taxonomy/">https://amecorg.com/amecframework/home/supporting-material/taxonomy/</a></p> <p>[3] B. Dimić Surla, M. Segedinac, and D. Ivanović, “A BIBO ontology extension for evaluation of scientific research results,” in <em>Proceedings of the Fifth Balkan Conference in Informatics</em>, in BCI ’12. New York, NY, USA: Association for Computing Machinery, Sep. 2012, pp. 275–278. doi: <a href="https://doi.org/10.1145/2371316.2371376">10.1145/2371316.2371376</a>.</p> <p>[4] F. Brudy <em>et al.</em>, “Cross-Device Taxonomy: Survey, Opportunities and Challenges of Interactions Spanning Across Multiple Devices,” in <em>Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems</em>, in CHI ’19. New York, NY, USA: Association for Computing Machinery, Mai 2019, pp. 1–28. doi: <a href="https://doi.org/10.1145/3290605.3300792">10.1145/3290605.3300792</a>.</p> <p>[5] A. A. Salatino, T. Thanapalasingam, A. Mannocci, F. Osborne, and E. Motta, “The Computer Science Ontology: A Large-Scale Taxonomy of Research Areas,” in <em>Lecture Notes in Computer Science 1137</em>, D. Vrandečić, K. Bontcheva, M. C. Suárez-Figueroa, V. Presutti, I. Celino, M. Sabou, L.-A. Kaffee, and E. Simperl, Eds., Monterey, California, USA: Springer, Oct. 2018, pp. 187–205. Accessed: May 08, 2023. [Online]. Available: <a href="http://oro.open.ac.uk/55484/">http://oro.open.ac.uk/55484/</a></p> <p>[6] M. Dehnert, A. Gleiss, and F. Reiss, “What makes a data-driven business model? A consolidated taxonomy,” presented at the European Conference on Information Systems, 2021.</p> <p>[7] DDI Alliance, “DDI Controlled Vocabulary for Aggregation Method,” 2014. <a href="https://ddialliance.org/Specification/DDI-CV/AggregationMethod_1.0.html">https://ddialliance.org/Specification/DDI-CV/AggregationMethod_1.0.html</a> (accessed May 08, 2023).</p> <p>[8] DDI Alliance, “DDI Controlled Vocabulary for Mode Of Collection,” 2015. <a href="https://ddialliance.org/Specification/DDI-CV/ModeOfCollection_2.0.html">https://ddialliance.org/Specification/DDI-CV/ModeOfCollection_2.0.html</a> (accessed May 08, 2023).</p> <p>[9] INED - French Institute for Demographic Studies, “Thésaurus DemoVoc,” Feb. 26, 2020. <a href="https://thesaurus.web.ined.fr/navigateur/en/about">https://thesaurus.web.ined.fr/navigateur/en/about</a> (accessed May 08, 2023).</p> <p>[10] A. A. Bakar, Z. A. Othman, and N. L. M. Shuib, “Building a new taxonomy for data discretization techniques,” in <em>2009 2nd Conference on Data Mining and Optimization</em>, Oct. 2009, pp. 132–140. doi: <a href="https://doi.org/10.1109/DMO.2009.5341896">10.1109/DMO.2009.5341896</a>.</p> <p>[11] N. Brouard and C. Giudici, “Unified second edition of the Multilingual Demographic Dictionary (Demopaedia.org project),” presented at the 2017 International Population Conference, IUSSP, Oct. 2017. Accessed: May 08, 2023. [Online]. Available: <a href="https://iussp.confex.com/iussp/ipc2017/meetingapp.cgi/Paper/5713">https://iussp.confex.com/iussp/ipc2017/meetingapp.cgi/Paper/5713</a></p> <p>[12] DuCharme, Bob, “Data Science Glossary.” https://www.datascienceglossary.org/ (accessed May 08, 2023).</p> <p>[13] A. Chatzigeorgiou, T. Chaikalis, G. Paschalidou, N. Vesyropoulos, C. K. Georgiadis, and E. Stiakakis, “A Taxonomy of Evaluation Approaches in Software Engineering,” in <em>Proceedings of the 7th Balkan Conference on Informatics Conference</em>, in BCI ’15. New York, NY, USA: Association for Computing Machinery, Sep. 2015, pp. 1–8. doi: <a href="https://doi.org/10.1145/2801081.2801084">10.1145/2801081.2801084</a>.</p> <p>[14] M. C. Chibucos, D. A. Siegele, J. C. Hu, and M. Giglio, “The Evidence and Conclusion Ontology (ECO): Supporting GO Annotations,” in <em>The Gene Ontology Handbook</em>, C. Dessimoz and N. Škunca, Eds., in Methods in Molecular Biology. New York, NY: Springer, 2017, pp. 245–259. doi: <a href="https://doi.org/10.1007/978-1-4939-3743-1_18">10.1007/978-1-4939-3743-1_18</a>.</p> <p>[15] M. Black <em>et al.</em>, “EDAM: the bioscientific data analysis ontology,” <em>F1000Research</em>, vol. 11, Jan. 2021, doi: <a href="https://doi.org/10.7490/f1000research.1118900.1">10.7490/f1000research.1118900.1</a>.</p> <p>[16] Council of European Social Science Data Archives (CESSDA), “European Language Social Science Thesaurus ELSST,” 2021. <a href="https://thesauri.cessda.eu/en/">https://thesauri.cessda.eu/en/</a> (accessed May 08, 2023).</p> <p>[17] M. Scriven, <em>Evaluation Thesaurus</em>, 3rd Edition. Edgepress, 1981. Accessed: May 08, 2023. [Online]. Available: <a href="https://us.sagepub.com/en-us/nam/evaluation-thesaurus/book3562">https://us.sagepub.com/en-us/nam/evaluation-thesaurus/book3562</a></p> <p>[18] Papantoniou, Bill <em>et al.</em>, <em>The Glossary of Human Computer Interaction</em>. Interaction Design Foundation. Accessed: May 08, 2023. [Online]. Available: <a href="https://www.interaction-design.org/literature/book/the-glossary-of-human-computer-interaction">https://www.interaction-design.org/literature/book/the-glossary-of-human-computer-interaction</a></p> <p>[19] “UK Data Service Vocabularies: HASSET Thesaurus.” <a href="https://hasset.ukdataservice.ac.uk/hasset/en/">https://hasset.ukdataservice.ac.uk/hasset/en/</a> (accessed May 08, 2023).</p> <p>[20] S. D. Costa, M. P. Barcellos, R. de A. Falbo, T. Conte, and K. M. de Oliveira, “A core ontology on the Human–Computer Interaction phenomenon,” <em>Data Knowl. Eng.</em>, vol. 138, p. 101977, Mar. 2022, doi: <a href="https://doi.org/10.1016/j.datak.2021.101977">10.1016/j.datak.2021.101977</a>.</p> <p>[21] V. J. Gawron <em>et al.</em>, “Human Factors Taxonomy,” <em>Proc. Hum. Factors Soc. Annu. Meet.</em>, vol. 35, no. 18, pp. 1284–1287, Sep. 1991, doi: <a href="https://doi.org/10.1177/154193129103501807">10.1177/154193129103501807</a>.</p> <p>[22] L. Onnasch and E. Roesler, “A Taxonomy to Structure and Analyze Human–Robot Interaction,” <em>Int. J. Soc. Robot.</em>, vol. 13, no. 4, pp. 833–849, Jul. 2021, doi: <a href="https://doi.org/10.1007/s12369-020-00666-5">10.1007/s12369-020-00666-5</a>.</p> <p>[23] R. A. Schwier, “A Taxonomy of Interaction for Instructional Multimedia.” Sep. 28, 1992. Accessed: May 09, 2023. [Online]. Available: <a href="https://eric.ed.gov/?id=ED352044">https://eric.ed.gov/?id=ED352044</a></p> <p>[24] C. Kelly, J. Miller, A. Redlich, and S. Kleinman, “A Taxonomy of Interrogation Methods,” <em>Psychol. Public Policy Law</em>, vol. 19, p. 165, May 2013, doi: <a href="https://doi.org/10.1037/a0030310">10.1037/a0030310</a>.</p> <p>[25] Y. Chuang, L.-L. Chen, and Y. Liu, “Design Vocabulary for Human-IoT Systems Communication,” in <em>Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems</em>, Montreal QC Canada: ACM, Apr. 2018, pp. 1–11. doi: <a href="https://doi.org/10.1145/3173574.3173848">10.1145/3173574.3173848</a>.</p> <p>[26] N. Díaz Rodríguez, R. Wikström, J. Lilius, M. P. Cuéllar, and M. Delgado Calvo Flores, “Understanding Movement and Interaction: An Ontology for Kinect-Based 3D Depth Sensors,” in <em>Ubiquitous Computing and Ambient Intelligence. Context-Awareness and Context-Driven Interaction</em>, G. Urzaiz, S. F. Ochoa, J. Bravo, L. L. Chen, and J. Oliveira, Eds., in Lecture Notes in Computer Science, vol. 8276. Cham: Springer International Publishing, 2013, pp. 254–261. doi: <a href="https://doi.org/10.1007/978-3-319-03176-7_33">10.1007/978-3-319-03176-7_33</a>.</p> <p>[27] “Thesaurus: mass communication - UNESCO Digital Library.” <a href="https://unesdoc.unesco.org/ark:/48223/pf0000015031">https://unesdoc.unesco.org/ark:/48223/pf0000015031</a> (accessed May 08, 2023).</p> <p>[28] Institute for Scientific and Technical Information, <em>Thesaurus Cognitive Psychology of Human Memory</em>, Version 2.0. 2021. Accessed: May 08, 2023. [Online]. Available: <a href="https://fairsharing.org/FAIRsharing.LcyXdU">https://fairsharing.org/FAIRsharing.LcyXdU</a></p> <p>[29] S. Jiang and R. C. Arkin, “Mixed-Initiative Human-Robot Interaction: Definition, Taxonomy, and Survey,” in <em>2015 IEEE International Conference on Systems, Man, and Cybernetics</em>, Oct. 2015, pp. 954–961. doi: <a href="https://doi.org/10.1109/SMC.2015.174">10.1109/SMC.2015.174</a>.</p> <p>[30] S. Moller, K.-P. Engelbrecht, C. Kuhnel, I. Wechsung, and B. Weiss, “A taxonomy of quality of service and Quality of Experience of multimodal human-machine interaction,” in <em>2009 International Workshop on Quality of Multimedia Experience</em>, Jul. 2009, pp. 7–12. doi: <a href="https://doi.org/10.1109/QOMEX.2009.5246986">10.1109/QOMEX.2009.5246986</a>.</p> <p>[31] K. Belhajjame <em>et al.</em>, “Using a suite of ontologies for preserving workflow-centric research objects,” <em>J. Web Semant.</em>, vol. 32, pp. 16–42, May 2015, doi: <a href="https://doi.org/10.1016/j.websem.2015.01.003">10.1016/j.websem.2015.01.003</a>.</p> <p>[32] M. Augstein and T. Neumayr, “A Human-Centered Taxonomy of Interaction Modalities and Devices,” <em>Interact. Comput.</em>, vol. 31, no. 1, pp. 27–58, Jan. 2019, doi: <a href="https://doi.org/10.1093/iwc/iwz003">10.1093/iwc/iwz003</a>.</p> <p>[33] J. Jerald, “A Taxonomy of Spatial Interaction Patterns and Techniques,” <em>IEEE Comput. Graph. Appl.</em>, vol. 38, no. 1, pp. 11–19, Jan. 2018, doi: <a href="https://doi.org/10.1109/MCG.2018.011461524">10.1109/MCG.2018.011461524</a>.</p> <p>[34] L. Tian and S. Oviatt, “A Taxonomy of Social Errors in Human-Robot Interaction,” <em>ACM Trans. Hum.-Robot Interact.</em>, vol. 10, no. 2, pp. 1–32, Jun. 2021, doi: <a href="https://doi.org/10.1145/3439720">10.1145/3439720</a>.</p> <p>[35] A. Haller, K. Janowicz, S. Cox, D. Phuoc, K. Taylor, and M. Lefrançois, <em>Semantic Sensor Network Ontology</em>. 2017.</p> <p>[36] J. Malone <em>et al.</em>, “The Software Ontology (SWO): a resource for reproducibility in biomedical data analysis, curation and digital preservation,” <em>J. Biomed. Semant.</em>, vol. 5, no. 1, p. 25, Jun. 2014, doi: <a href="https://doi.org/10.1186/2041-1480-5-25">10.1186/2041-1480-5-25</a>.</p> <p>[37] L. Borek, Q. Dombrowski, J. Perkins, and C. Schöch, “TaDiRAH: a Case Study in Pragmatic Classification,” <em>Digit. Humanit. Q.</em>, vol. 010, no. 1, Feb. 2016.</p> <p>[38] M. A. Muhanna, “Virtual reality and the CAVE: Taxonomy, interaction challenges and research directions,” <em>J. King Saud Univ. - Comput. Inf. Sci.</em>, vol. 27, no. 3, pp. 344–361, Jul. 2015, doi: <a href="https://doi.org/10.1016/j.jksuci.2014.03.023">10.1016/j.jksuci.2014.03.023</a>.</p> <p>[39] F. Scharf, C. Wolters, M. Herczeg, and J. Cassens, “Cross-Device Interaction: Definition, Taxonomy and Application,” presented at the AMBIENT 2013 : The Third International Conference on Ambient Computing, Applications, Services and Technologies, Porto, Portugal: IARIA, 2013, pp. 35–41. Accessed: May 08, 2023. [Online]. Available: <a href="https://www.imis.uni-luebeck.de/de/forschung/publikationen/6380">https://www.imis.uni-luebeck.de/de/forschung/publikationen/6380</a></p>
Echo from noise: synthetically generated cardiac ultrasound data using semantic diffusion models
<p>This is the data repository for the paper: "Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation", available at: https://arxiv.org/abs/2305.05424. The corresponding code is available at: https://github.com/david-stojanovski/echo_from_noise</p> <p> </p> <p>This is the first work to utilize Denoising Diffusion Probabilistic Models (DDPMs) for generating medical images using semantic label maps as a source image for conditioning the generated image.</p> <p>Each of the 400+50 CAMUS patients contributes with 4 labelled frames (ED and ES for 2 chamber and 4 chamber), totalling 1800 initial semantic maps, to which we added the sector label. These semantic maps then had five random deformations applied (a combination of random affine and elastic deformation) to produce, 9000 transformed semantic maps (8000 for training and 1000 for validation). </p> <p>Affine transformation ranges for rotation degrees, translate, scale and shear were: (-5, 5), (0, 0.05), (0.8, 1.05) and 5 respectively. This was implemented using the torchvision python package. Elastic deformation was implemented using the TorchIO package. The settings for number of control points and max displacement were (10, 10, 4) and (0, 30, 30) respectively.</p> <p>Using these 9000 semantic maps as input to the generative models, we produced 9000 synthetic ultrasound images.</p> <p>Each echo view folder contains 3 folders:</p> <p>1) annotations: augmented labels, with no sector label and no clipping due to sector</p> <p>2) images: semantic diffusion model inferenced images</p> <p>3) sector_annotations: label maps which contain ultrasound cone sector, which were used to generate corresponding semantic diffusion model images</p> <p>ema_0.9999_050000_2ch_ed_256.pt and ema_0.9999_050000_4ch_ed_256.pt are the saved checkpoints for the 2 and 4 chamber diffusion models respectively.</p> <p>The pretrained segmentation networks are provided within the <a href="https://zenodo.org/api/files/0af4e6a3-234d-40a3-8351-c91261628982/final_models.zip">final_models.zip</a> file.</p> <p>A diagram of image numbers is shown in <a href="https://zenodo.org/api/files/0af4e6a3-234d-40a3-8351-c91261628982/Data%20diagram.png">Data diagram.png</a></p>
Supplementary code and data for the paper: 'The fall of genres that did not happen: formalising history of the "universal" semantics of Russian iambic tetrameter'
<p>The dataset provides preprocessed data and the full code used in the paper 'The fall of genres that did not happen: formalising history of the "universal" semantics of Russian iambic tetrameter'. The code can be also be accessed as rendered notebooks on Github.</p><p>The dataset is structured as follows:</p><ul><li><i>data/ </i>: This folder contains preprocessed data,including a sampled corpus of periodicals and a document-term matrix used for topic modelling; </li><li><i>scr/</i> : The code used for the analysis, with separate scripts for figures; </li><li><i>plots/</i> : The figures used in the paper, which correspond to the aforementioned code.</li></ul>
A Simple Semantic-based Data Storage Layout for Querying Point Clouds
<p>Dataset contains three datasets: SMALL, MEDIUM and LARGE point cloud.</p> <p>Contains Python code which is made up of two files pc_new_semantics.py and paperutils.py</p> <p> </p>
Semantic links between selected CSV datasets harvested by the European Data Portal and the DBpedia knowledge graph
<p>These dataset contains the results of the interlinking process between selected csv datasets harvested by the European DAta Portal and the DBpedia knowledge graph. </p> <p>We aim at answering the following questions:<br> What are the more popular column types? This will provide hindsight about what the datasets hold and how they can be joined. It will also provide hindsight on what specific linking schemes could be applied in future elements.<br> What datasets have columns of the same type? This will suggest datasets that may be similar or related.<br> What entities appear in most datasets (co-referent entities)? This will suggest entities for which more data is published.<br> What datasets share a particular entity? This will suggest datasets that may be joined, or are related through that particular entity</p> <p>Results are provided as augmented tables, that contain the columns of the original csv, plus a metadata file in JSON-LD format. The metadata files can be loaded in an RDF-store and queried.</p> <p>Refer to the accompanying report of activities for more details on the methodolog and how to query the dataset.</p> <p><br> </p>
From father Busa to Linked Data. What does Thomas Aquinas have to do with the Semantic Web
<p>5<sup>th</sup> Lecture</p>
GERBIL evaluation data of Robust and Collective Entity Disambiguation through Semantic Embeddings
<p>A table containing the SIGIR 2016 experiments performed with GERBIL in context of the SIGIR 2016 work "Robust and Collective Entity Disambiguation through Semantic Embeddings" by Stefan Zwicklbauer, Christin Seifert and Michael Granitzer</p> <p>It also contains the original URL to the GERBIL website</p> <p>Corresponding GitHub Repository:</p> <p>https://github.com/quhfus/</p> <p> </p>
Raw data for the creation of a maturity model for Catalogues of Semantic Artefacts
<p>This dataset includes two data collections (in two different formats, i.e. CSV and XLSX) with the raw data used for creating the <a href="https://doi.org/10.5281/zenodo.10618105">Maturity Dimensions and Sub-Criteria for Catalogues of Semantic Artefacts</a>. In particular:</p> <p>1. <em>Dimension identification in literature</em> includes the list of relevant materials gathered involving all the members of the EOSC Task Force on Semantic Interoperability that include (1) definitions of semantic artefact catalogues and (2) dimensions that can be used to measure the maturity of such catalogues;</p> <p>2. <em>Catalogue assessment</em> is the result of the analysis of 26 different catalogues of semantic artefacts against the dimensions and sub-criteria described in the maturity model.</p>
Data, code, models for "Weakly Supervised Semantic Segmentation for Joint Key Local Structure Localization and Classification of Aurora Image"
<p>Data, code and models for https://ieeexplore.ieee.org/document/8410588/</p>
SeSaMe: A Data Set of Semantically Similar Java Methods
<p>This is the data set presented in the paper</p> <p>Kamp, M., Kreutzer P., Philippsen M.: SeSaMe: A Data Set of Semantically<br> Similar Java Methods. 16th International Conference on Mining Software<br> Repositories (MSR 2019), Montreal, QC, Canada. 2019</p>
Semantic Triples from "A Collaborative, Realism-Based, Electronic Healthcare Graph: Public Data, Common Data Models, and Practical Instantiation"
<p>These RDF triples (<a href="https://zenodo.org/api/files/3d3308cb-8221-4a17-abc5-0ae32bb33f26/synthea_graph_exportable.nq.zip?versionId=7aff7c4a-bb0c-46ae-b006-7a80bcec0925">synthea_graph_exportable.nq.zip</a>) are the result of modeling electronic health records (<a href="https://zenodo.org/api/files/3d3308cb-8221-4a17-abc5-0ae32bb33f26/synthea_csv_output_turbo_cannonical.zip">synthea_csv_output_turbo_cannonical.zip), </a>that were synthesized with the Synthea software (https://github.com/synthetichealth/synthea). Anyone who loads them into a triplestore database is encouraged to provide feedback at https://github.com/PennTURBO/EhrGraphCollab/issues. The following abstract comes from a paper, describing the semantic instantiation process, and presented to the ICBO 2019 conference (https://drive.google.com/file/d/1eYXTBl75Wx3XPMmCIOZba-8Cv0DIhlRq/view).</p> <p>ABSTRACT: There is ample literature on the semantic modeling of biomedical data in general, but less has been published on realism-based, semantic instantiation of electronic health records (EHR). Reasons include difficult design decisions and issues of data governance. A collaborative approach can address design and technology utilization issues, but is especially constrained by limited access to the data at hand: protected health information.</p> <p>Effective collaboration can be facilitated by public EHR-like data sets, which would ideally include a large variety of datatypes mirroring actual EHRs and enough records to drive a performance assessment. An investment into reading public EHR-like data from a popular common data model (CDM) is preferable over reading each public data set’s native format.</p> <p>In addition to identifying suitable public EHR-like data sets and CDMs, this paper addresses instantiation via relational-to-RDF mapping. The completed instantiation is available for download, and a competency question demonstrates fidelity across all discussed formats.</p>
Langmark: annotations for scenes with semantic inconsistencies connecting distributional semantic models to vision science – data and code
<p>Data (including object annotations) and code from the following manuscript:</p> <p><em>Langmark: annotations for scenes with semantic inconsistencies connecting distributional semantic models to vision science</em>.</p>
Demo-Dataset for publication "FAIR workflows in Earth system modelling: a use case with semantic data management"
<p>This demodataset is intended to be used to test the workflow described in the publication by Lennartz & Schlemmer "FAIR workflows in Earth System modelling: a use case with semantic data management". It contains example model output for an arbitrary biogeochemical model tracer (here: dissolved organic carbon, DOC) from an ocean model as a 4-dimensional dataset (latitude, longitude, depth, time), the corresponding grid point locations as well as a textfile specifying parameter inputs for the model. The file structure is adapted for seamless integration into the workflow described in Lennartz & Schlemmer, which builds on the open source semantic research data management system LinkAhead. The dataset contains the following structure: The folder DataAnalysis stores data required for data analysis, such as the grid point locations in the file TMM_grid_v2018a.mat. The folder SimulationData stores model output in the folder 2022_TMM, containing the parameter input file nl_in.txt and the model output TR_monthly.mat. Related instructions can be accessed here: https://gitlab.com/salexan/fairworkflows-demodataset .</p>
Data companion to Coussé & Bouma (2021) Semantic scope restrictions in complex verb constructions in Dutch
<p>The material in this archive accompanies the paper</p> <p> Evie Coussé and Gerlof Bouma,<br> Semantic scope restrictions in complex verb constructions in Dutch,</p> <p>accepted for publication in Linguistics, An Interdisciplinary Journal<br> of the Language Sciences, De Gruyter Mouton.</p> <p>The archive contains:</p> <ul> <li>the annotated data of the paper</li> <li>the code for extraction of the data from the Spoken Dutch Corpus and the Lassy Small Corpus</li> <li>documentation of the selection and annotation process that were too detailed to be included in the paper</li> </ul>
The NGI Forward semantic social network data
<p>The <a href="https://research.ngi.eu/">NGI Forward project</a> is part of the European Union's <a href="https://www.ngi.eu">Next Generation Internet Initiative</a>. It is meant to provide European institutions with policy advice for how to shape the future, human-centric Internet. As part of it, a team of ethnographers coded a specially convened online conversation, then arranged its results into a semantic social network. This dataset encodes that conversation, as well as the results of the coding exercise, in raw data form for further exploration and replication purposes. The dataset is pseudonymized.</p> <ul> <li><a href="https://exchange.ngi.eu/">Funnel website</a> of the project.</li> <li><a href="https://journals.sagepub.com/doi/10.1177/1525822X20908236">About semantic social networks</a>.</li> <li><a href="https://edgeryders.eu/t/long-term-ssna-data-storage-documentation-manual/12786">Data export and documentation process</a> (contains links to the code used to export the data)</li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.