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Littérature scientifique sur le sujet « Discovery from data »

Auteur : Grafiati

Publié le 10 mars 2023

Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Discovery from data"

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Mak, H. Craig. "Discovery from data repositories." Nature Biotechnology 29, no. 1 (2011): 46–47. http://dx.doi.org/10.1038/nbt0111-46.

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Pazzani, M. J. "Knowledge discovery from data?" IEEE Intelligent Systems 15, no. 2 (2000): 10–12. http://dx.doi.org/10.1109/5254.850821.

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Gama, João, and Jesus Aguilar-Ruiz. "Knowledge discovery from data streams." Intelligent Data Analysis 11, no. 1 (2007): 1–2. http://dx.doi.org/10.3233/ida-2007-11101.

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Gama, João, Jesus Aguilar-Ruiz, and Ralf Klinkenberg. "Knowledge discovery from data streams." Intelligent Data Analysis 12, no. 3 (2008): 251–52. http://dx.doi.org/10.3233/ida-2008-12301.

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Gama, João, Auroop Ganguly, Olufemi Omitaomu, Raju Vatsavai, and Mohamed Gaber. "Knowledge discovery from data streams." Intelligent Data Analysis 13, no. 3 (2009): 403–4. http://dx.doi.org/10.3233/ida-2009-0372.

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Morita, Chie, and Hiroshi Tsukimoto. "Knowledge discovery from numerical data." Knowledge-Based Systems 10, no. 7 (1998): 413–19. http://dx.doi.org/10.1016/s0950-7051(98)00040-9.

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Cook, Diane J., Lawrence B. Holder, and Surnjani Djoko. "Knowledge discovery from structural data." Journal of Intelligent Information Systems 5, no. 3 (1995): 229–48. http://dx.doi.org/10.1007/bf00962235.

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Kalenkova, Anna, Andrea Burattin, Massimiliano de Leoni, Wil van der Aalst, and Alessandro Sperduti. "Discovering high-level BPMN process models from event data." Business Process Management Journal 25, no. 5 (2019): 995–1019. http://dx.doi.org/10.1108/bpmj-02-2018-0051.

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Purpose The purpose of this paper is to demonstrate that process mining techniques can help to discover process models from event logs, using conventional high-level process modeling languages, such as Business Process Model and Notation (BPMN), leveraging their representational bias. Design/methodology/approach The integrated discovery approach presented in this work is aimed to mine: control, data and resource perspectives within one process diagram, and, if possible, construct a hierarchy of subprocesses improving the model readability. The proposed approach is defined as a sequence of step

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Gottlob, Georg, and Pierre Senellart. "Schema mapping discovery from data instances." Journal of the ACM 57, no. 2 (2010): 1–37. http://dx.doi.org/10.1145/1667053.1667055.

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Vatsavai, Ranga Raju, Olufemi A. Omitaomu, Joao Gama, Nitesh V. Chawla, Mohamed Medhat Gaber, and Auroop R. Ganguly. "Knowledge discovery from sensor data (SensorKDD)." ACM SIGKDD Explorations Newsletter 10, no. 2 (2008): 68–73. http://dx.doi.org/10.1145/1540276.1540297.

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Thèses sur le sujet "Discovery from data"

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Höppner, Frank. "Knowledge discovery from sequential data." [S.l. : s.n.], 2003. http://deposit.ddb.de/cgi-bin/dokserv?idn=96728421X.

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Cao, Huiping. "Pattern discovery from spatiotemporal data." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B37381520.

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Cao, Huiping, and 曹會萍. "Pattern discovery from spatiotemporal data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B37381520.

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Chau, Tom. "Event level pattern discovery in multivariate continuous data." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape15/PQDD_0003/NQ30594.pdf.

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El, Sayed Ahmed. "Contributions in knowledge discovery from textual data." Lyon 2, 2008. http://theses.univ-lyon2.fr/documents/lyon2/2008/el-sayed_a.

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This dissertation focuses on two key issues in text mining, namely unsupervised learning and knowledge acquisition. In spite of their relative maturity, both issues still present some major challenges that need to be addressed. First, for unsupervised learning, a well-known, unresolved challenge is to perform clustering with minimal input parameters. One natural way to reach this is to involve validity indices in the clustering process. Although of great interest, validity indices were not extensively explored in the literature, especially when dealing with high-dimensional data like text. Hen

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El, Sayed Ahmed Zighed Djamel Abdelkader. "Contributions in knowledge discovery from textual data." Lyon : Université Lumière Lyon 2, 2008. http://theses.univ-lyon2.fr/sdx/theses/lyon2/2008/el-sayed_a.

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Wang, Yang. "High-order pattern discovery and analysis of discrete-valued data sets." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq22245.pdf.

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Amado, Vanessa. "Knowledge discovery and data mining from freeway section traffic data." Diss., Columbia, Mo. : University of Missouri-Columbia, 2008. http://hdl.handle.net/10355/5591.

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Thesis (Ph. D.)--University of Missouri-Columbia, 2008.<br>The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on June 8, 2009) Vita. Includes bibliographical references.

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Caruccio, Loredana. "Relaxed functional dependencies: definition, discovery and applications." Doctoral thesis, Universita degli studi di Salerno, 2018. http://hdl.handle.net/10556/3051.

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2016 - 2017<br>Functional dependencies (FDs) were conceived in the early '70s, and were mainly used to verify database design and assess data quality. However, to solve several issues in emerging application domains, such as the identification of data inconsistencies, patterns of semantically related data, query rewriting, and so forth, it has been necessary to extend the FD definition... [edited by author]<br>XVI n.s.

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PaÌirceÌir, RoÌnaÌn. "Knowledge discovery from distributed aggregate data in data warehouses and statistical databases." Thesis, University of Ulster, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.274398.

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Livres sur le sujet "Discovery from data"

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Gaber, Mohamed Medhat, Ranga Raju Vatsavai, Olufemi A. Omitaomu, João Gama, Nitesh V. Chawla, and Auroop R. Ganguly, eds. Knowledge Discovery from Sensor Data. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12519-5.

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R, Ganguly Auroop, ed. Knowledge discovery from sensor data. Taylor & Francis, 2009.

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Ye, Chen, Hongzhi Wang, and Guojun Dai. Knowledge Discovery from Multi-Sourced Data. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1879-7.

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Bandyopadhyay, Sanghamitra, Ujjwal Maulik, Lawrence B. Holder, and Diane J. Cook. Advanced Methods for Knowledge Discovery from Complex Data. Springer London, 2005. http://dx.doi.org/10.1007/1-84628-284-5.

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Páircéir, Rónán. Knowledge discovery from distributed aggregate data in data warehouses and statistical databases. The Author], 2002.

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Read, J. F. CTD data from the north east Atlantic, April 1989, collected on RRS Discovery Cruise 181. Institute of Oceanographic Sciences, Deacon Laboratory, 1991.

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Marchese, Francis T. Knowledge Visualization Currents: From Text to Art to Culture. Springer London, 2013.

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Suit, William T. Lateral and longitudinal stability and control parameters for the space shuttle Discovery as determined from flight test data. National Aeronautics and Space Administration, Langley Research Center, 1988.

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Agarwalla, Sobhesh Kumar. Impact of introduction of call auction on price--discovery: Evidence from Indian stock market using high--frequency data. Indian Institute of Management, Ahmedabad, 2012.

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Gaber, Mohamed Medhat. Knowledge Discovery from Sensor Data: Second International Workshop, Sensor-KDD 2008, Las Vegas, NV, USA, August 24-27, 2008, Revised Selected Papers. Springer-Verlag Berlin Heidelberg, 2010.

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Chapitres de livres sur le sujet "Discovery from data"

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Ikonomovska, Elena, and Joao Gama. "Learning Model Trees from Data Streams." In Discovery Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88411-8_8.

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Hasegawa, Hiroshi H., Takashi Washio, and Yukari Ishimiya. "“Thermodynamics” from Time Series Data Analysis." In Discovery Science. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-46846-3_33.

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Riddle, Pat, Roman Fresnedo, and David Newman. "Framework for a Generic Knowledge Discovery Toolkit." In Learning from Data. Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-2404-4_33.

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George, Dileep, Kazumi Saito, Pat Langley, Stephen Bay, and Kevin R. Arrigo. "Discovering Ecosystem Models from Time-Series Data." In Discovery Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39644-4_13.

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Adhikari, Prem Raj, and Jaakko Hollmén. "Mixture Models from Multiresolution 0-1 Data." In Discovery Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40897-7_1.

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Hasegawa, H. H., T. Washio, Y. Ishimiya, and T. Saito. "Nonequilibrium Thermodynamics from Time Series Data Analysis." In Discovery Science. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44418-1_35.

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Trabold, Daniel, and Tamás Horváth. "Mining Strongly Closed Itemsets from Data Streams." In Discovery Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67786-6_18.

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Rinzivillo, S., F. Turini, V. Bogorny, C. Körner, B. Kuijpers, and M. May. "Knowledge Discovery from Geographical Data." In Mobility, Data Mining and Privacy. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75177-9_10.

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Pazouki, Ehsan. "Knowledge Discovery from Agricultural Data." In Encyclopedia of Digital Agricultural Technologies. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-24861-0_263.

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Pazouki, Ehsan. "Knowledge Discovery from Agricultural Data." In Encyclopedia of Smart Agriculture Technologies. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-89123-7_263-1.

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Actes de conférences sur le sujet "Discovery from data"

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Hu, Bing, Ashish Saragadam, Anita Layton, and Helen Chen. "Synthetic Data from Diffusion Models Improves Drug Discovery Prediction." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822459.

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Courain, Margaret Eileen. "PERSPECTIVES AND FUTURE DIRECTIONS FROM CODATA '90." In Data For Discovery. Begellhouse, 2023. http://dx.doi.org/10.1615/1-56700-002-9.500.

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Gong, Chang, Di Yao, Chuzhe Zhang, et al. "Causal Discovery from Temporal Data." In KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2023. http://dx.doi.org/10.1145/3580305.3599552.

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Wan, Mengting, Xiangyu Chen, Lance Kaplan, Jiawei Han, Jing Gao, and Bo Zhao. "From Truth Discovery to Trustworthy Opinion Discovery." In KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016. http://dx.doi.org/10.1145/2939672.2939837.

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Metwally, Ahmed, Jia-Yu Pan, Minh Doan, and Christos Faloutsos. "Scalable community discovery from multi-faceted graphs." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363859.

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Carabedian, Michel, and Jacques-Emile Dubois. "EXTRACTING GENERIC KNOWLEDGE FROM SPECIFIC STRUCTURE/ SPECTRUM DATA BANKS. CASE OF A 13C NMR DATA BANK." In Data For Discovery. Begellhouse, 2023. http://dx.doi.org/10.1615/1-56700-002-9.220.

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Paun, S., M. Fisher, P. Mckee, M. Poesio, and U. Kruschwitz. "Pattern discovery in big data streams." In IET Seminar on Data Analytics 2013: Deriving Intelligence and Value from Big Data. Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/ic.2013.0233.

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Guo, Zhen, Zhongfei Zhang, Shenghuo Zhu, Yun Chi, and Yihong Gong. "Knowledge Discovery from Citation Networks." In 2009 Ninth IEEE International Conference on Data Mining (ICDM). IEEE, 2009. http://dx.doi.org/10.1109/icdm.2009.137.

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Yu, Kui, Xindong Wu, Hao Wang, and Wei Ding. "Causal Discovery from Streaming Features." In 2010 IEEE 10th International Conference on Data Mining (ICDM). IEEE, 2010. http://dx.doi.org/10.1109/icdm.2010.82.

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Burl, M. C., D. DeCoste, B. L. Enke, D. Mazzoni, W. J. Merline, and L. Scharenbroich. "Automated Knowledge Discovery from Simulators." In Proceedings of the 2006 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2006. http://dx.doi.org/10.1137/1.9781611972764.8.

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Rapports d'organisations sur le sujet "Discovery from data"

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Ng, Andrew Y., and Christopher D. Manning. Discovery of Deep Structure from Unlabeled Data. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada614158.

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Capraro, Gerard T., and Gerald B. Berdan. Intensive Knowledge Discovery from Heterogeneous Distributed Data and Knowledge. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada397959.

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Peterka, Tom, Deborah Bard, Janine Bennett, et al. ASCR Workshop on In Situ Data Management: Enabling Scientific Discovery from Diverse Data Sources. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1493245.

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Barnes, Heather, Brandy Farlow, Mary Hannah Marshall, et al. From Competition to Collaboration: Fostering a Culture of Data Sharing in the NIH HEAL Data Ecosystem. RTI Press, 2025. https://doi.org/10.3768/rtipress.2025.op.0095.2501.

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Open, accessible research data provides a foundation for scientific discovery. Despite clear data sharing benefits and increased data sharing expectations, hesitancy to share data is common in many scientific research domains. This case study highlights work underway in the National Institutes of Health (NIH)–sponsored project Helping to End Addiction Long-term® (HEAL) Data Ecosystem (HDE). HDE has initiated a suite of activities designed to engage HEAL-funded investigators and promote a data sharing culture. In this paper, we present results of a landscape analysis highlighting (1) common bar

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Dassanayake, Wajira, Xiaoming Li, and Klaus Buhr. A Revisit of Price Discovery Dynamics Across Australia and New Zealand. Unitec ePress, 2015. http://dx.doi.org/10.34074/rsrp.039.

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This study re-investigates the price discovery dynamics of selected stocks cross-listed on the Australian Stock Exchange (ASX) and the New Zealand Stock Exchange (NZX) during a bear trading phase from January 2008 to December 2011. A differing price discovery dynamic in a bear market versus a bull market may occur because of variations in investor sentiments and disparities in the role of the stock prices. Using intraday data, we employ the vector error correction mechanism, Hasbrouck’s (1995) information share and Grammig et al.’s (2005) conditional information share methods. Consistent with

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Dassanayake, Wajira, Xiaoming Li, and Klaus Buhr. A Revisit of Price Discovery Dynamics Across Australia and New Zealand. Unitec ePress, 2015. http://dx.doi.org/10.34074/rsrp.039.

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This study re-investigates the price discovery dynamics of selected stocks cross-listed on the Australian Stock Exchange (ASX) and the New Zealand Stock Exchange (NZX) during a bear trading phase from January 2008 to December 2011. A differing price discovery dynamic in a bear market versus a bull market may occur because of variations in investor sentiments and disparities in the role of the stock prices. Using intraday data, we employ the vector error correction mechanism, Hasbrouck’s (1995) information share and Grammig et al.’s (2005) conditional information share methods. Consistent with

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Cole, Christian, Laura Ward, Judith Fisher, et al. Lived Experience Co-Production Within the Alleviate Pain Data Hub. University of Dundee, 2025. https://doi.org/10.20933/100001363.

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Chronic Pain affects up to 28 million people in the UK. The aim of the Alleviate Pain Data Hub is to improve the visibility and accessibility of pain data, leading to improvements in patient quality of life. The 2024 UK National Pain Survey was run by Alleviate, in collaboration with Pain UK, the Advanced Pain Discovery Platform (APDP) and Chronic Pain Australia. The survey was based on the annual National Pain Survey developed by Chronic Pain Australia and has been further revised for the UK pain community, based on feedback from the UK National Pain Survey 2023.

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Kaufmann, Joachim, Peter Kaufmann, and Simone Maria Grabner. Assessment of completed BRIDGE Discovery projects Synthesis at programme level. BMK - Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology, 2023. http://dx.doi.org/10.22163/fteval.2023.640.

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The aim of this short evaluation was to systematically collect information from the completed projects of the BRIDGE Discovery programme as of June 2023. This will be used for strategic optimisation and decision making for the funding period 2025-2028. BRIDGE Discovery is an open-topic funding programme at the interface between basic and applied research, which is jointly funded and implemented by the Swiss National Science Foundation (SNSF) and Innosuisse - Swiss Agency for Innovation Promotion. The study used a mixed-methods approach to gather information about the programme context and the

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Sloan, Steven, Shelby Peterie, Richard Miller, Julian Ivanov, J. Schwenk, and Jason McKenna. Detecting clandestine tunnels by using near-surface seismic techniques. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40419.

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Geophysical detection of clandestine tunnels is a complex problem that has been met with limited success. Multiple methods have been applied spanning several decades, but a reliable solution has yet to be found. This report presents shallow seismic data collected at a tunnel test site representative of geologic settings found along the southwestern U.S. border. Results demonstrate the capability of using compressional wave diffraction and surface-wave backscatter techniques to detect a purpose-built subterranean tunnel. Near-surface seismic data were also collected at multiple sites in Afghani

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Borgeaud, Maurice, Jonathan Bamber, Anny Cazenave, et al. Earth Observation Groundbreaking Science Discoveries. ESA, 2025. https://doi.org/10.5270/essc-esa-eo-groundbreaking-science-2025.

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This document was prepared by the Earth Sciences Panel of the European Space Sciences Committee in response to the ESA contract 500104016 entitled “Ground-breaking science discoveries and successes enabled by ESA Earth Observation satellites”. This brochure showcases 12 ground-breaking scientific discoveries and successes enabled by the programme. These are divided into 3 cases for each of the 4 main thematic domains of the Earth sciences: atmosphere, ocean, land, and polar regions. The criteria used to define a “groundbreaking science discovery” include a clear “elevator pitch”, the “degree o

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