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Relevant bibliographies by topics / Raw data

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Academic literature on the topic 'Raw data'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 June 2025

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Journal articles on the topic "Raw data"

1

ALLEN, G. "Raw data." Nature 339, no. 6225 (1989): 500. http://dx.doi.org/10.1038/339500c0.

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Boliek, Carol A. "Raw Data." Perspectives on Speech Science and Orofacial Disorders 19, no. 2 (2009): 99–101. http://dx.doi.org/10.1044/ssod19.2.99.

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Peña, Ernesto, and Kedrick James. "Raw Harmonies: Transmediation through Raw Data." Leonardo 53, no. 2 (2020): 183–88. http://dx.doi.org/10.1162/leon_a_01635.

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In this paper, the authors present the initial findings from explorations on transformation patterns of data in raw format when crossing or transmediating directly (i.e. unaffected by any other form of codification) between audio and visual media. These patterns have allowed the authors to engage in the production of transmediatic artifacts with some degree of control and agency, facilitating purposeful applications of transmediation. The products of such practices will enable a form of literacy, an aesthetic means to identify in visual media artifacts those patterns that could transmediate in

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Lepore, Paul D. "Pathology Raw Data." Toxicologic Pathology 24, no. 1 (1996): 147. http://dx.doi.org/10.1177/019262339602400120.

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Goh, Yeong-Jin, Ji-Seon Kim, Buhm Lee, and Kyoung-Min Kim. "A Study on Diagnosis of UAV Propeller based on Current Raw-Data." Journal of Institute of Control, Robotics and Systems 28, no. 5 (2022): 477–82. http://dx.doi.org/10.5302/j.icros.2022.22.0027.

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Coles, Simon, Loes Kroon-Batenburg, John Helliwell, and James Hester. "Raw Data Letters - describing and publishing raw diffraction data in a FAIR way." Structural Dynamics 12, no. 2_Supplement (2025): A37. https://doi.org/10.1063/4.0000346.

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IUCrData, the peer-reviewed open-access data publication from the International Union of Crystallography (IUCr), has launched a new section: Raw Data Letters as a collaborative innovation of IUCr Journals with the IUCr Committee on Data. Future raw data sets will become increasingly large and no one group will be able to analyse all the scientific content in a timely manner and others will need access to raw crystallographic datasets for open-science-based research to proceed at a rapid pace. Further, there is an increasing need to better support validation of results in a more in-depth and tr

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Sivakolundhu, Rekha. "From Raw Data to Actionable Insights: How Data Pipelines Enable Effective Data Observability for Informed Decision Making." International Journal of Science and Research (IJSR) 13, no. 7 (2024): 273–76. http://dx.doi.org/10.21275/sr24705081043.

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Vaníček, J. "Data gathering for science and research." Agricultural Economics (Zemědělská ekonomika) 50, No. 1 (2012): 29–34. http://dx.doi.org/10.17221/5163-agricecon.

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Reasoning and argumentation in empirical sciences and research is based on row data and the intermediate and final structures, calculations etc., derived from the raw data. In this contribution, the short survey of the different techniques to gather raw data is given. The gain of the paper should consist in the manifestation of explicit limitation of usage this data for further utilization and deductions depending on the scaling type and validity problems during data gathering.

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Terwilliger, Thomas C. "Archiving raw crystallographic data." Acta Crystallographica Section D Biological Crystallography 70, no. 10 (2014): 2500–2501. http://dx.doi.org/10.1107/s139900471402118x.

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Van Horn, Royal. "Raw Data and News." Phi Delta Kappan 83, no. 9 (2002): 652–54. http://dx.doi.org/10.1177/003172170208300904.

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Dissertations / Theses on the topic "Raw data"

1

JULARDZIJA, MIRHET. "Processing RAW image data in mobile units." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-27724.

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Akbar, Adnan. "Extracting knowledge from raw IoT data streams." Thesis, University of Surrey, 2018. http://epubs.surrey.ac.uk/845475/.

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As sensors are adopted in almost every field of life, the Internet of Things (IoT) is triggering a massive influx of data. This large amount of data is of little value until it is processed intelligently to extract high-level knowledge which can be used to make decisions. The process of knowledge extraction from data streams is complex predominantly due to heterogeneous data sources, unreliable networks and real-time processing requirements. Different recent studies have showed that solutions based on complex event processing (CEP) have the potential to extract high-level knowledge from these

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Rothschedl, Christopher, Roland Ritt, Paul O'Leary, Matthew Harker, Michael Habacher, and Michael Brandner. "Real-time-data analytics in raw materials handling." Technische Universitaet Bergakademie Freiberg Universitaetsbibliothek "Georgius Agricola", 2018. http://nbn-resolving.de/urn:nbn:de:bsz:105-qucosa-231350.

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This paper proposes a system for the ingestion and analysis of real-time sensor and actor data of bulk materials handling plants and machinery. It references issues that concern mining sensor data in cyber physical systems (CPS) as addressed in O’Leary et al. [2015].

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Tse, Rebecca. "Three-dimensional building reconstruction from raw LIDAR data." Thesis, University of South Wales, 2008. https://pure.southwales.ac.uk/en/studentthesis/threedimensional-building-reconstruction-from-raw-lidar-data(d486c0a1-d4bd-4eb3-a81b-39ab0b23007e).html.

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Airborne Laser Scanning is an advanced surveying technology (also called Light Detection and Ranging - LIDAR) which mounts a laser scanner on an aircraft. The aircraft scans the Earth's surface and captures data by emitting and receiving light pulses transmitted onto the terrain objects. The captured data are in three dimensions (3D); however no extra information is provided to describe them. Additional algorithms are needed to extract meaningful and useful information from the data. The popularity of LIDAR has attracted attention as researchers try to develop algorithms for 3D building recons

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Rothschedl, Christopher, Roland Ritt, Paul O'Leary, Matthew Harker, Michael Habacher, and Michael Brandner. "Real-time-data analytics in raw materials handling." TU Bergakademie Freiberg, 2017. https://tubaf.qucosa.de/id/qucosa%3A23195.

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This paper proposes a system for the ingestion and analysis of real-time sensor and actor data of bulk materials handling plants and machinery. It references issues that concern mining sensor data in cyber physical systems (CPS) as addressed in O’Leary et al. [2015].

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Herzberg, Nico, and Mathias Weske. "Enriching raw events to enable process intelligence : research challenges." Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2013/6401/.

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Business processes are performed within a company’s daily business. Thereby, valuable data about the process execution is produced. The quantity and quality of this data is very dependent on the process execution environment that reaches from predominantly manual to fullautomated. Process improvement is one essential cornerstone of business process management to ensure companies’ competitiveness and relies on information about the process execution. Especially in manual process environments data directly related to the process execution is rather sparse and incomplete. In this paper, we presen

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Joakim, Aronsson. "I Want to Breathe You In : Data as Raw Commodity." Thesis, Konstfack, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:konstfack:diva-7977.

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In this paper, I look at the history of the internet and online advertising. The internet is inextricably linked to capitalism and is fueled by advertising. As a result, companies like Facebook, Apple, Microsoft, Google, and Amazon collect data in large volumes to improve targeted advertising. An investigation of new power structures has emerged with the internet and how they dominate its and our future. My creativity lies between art and technology. By merging new technologies like Artificial Intelligence with humor and graphic design, I try to shine a light on the subject.

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Saputra, Michael Wijaya. "Water and Fat Image Reconstruction from MRI Raw Multi Coil Data." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-372138.

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n MRI, water and fat signal separation with robust techniques are often helpful in the diagnosis using MRI. Reliable separation of water and fat will help the doctor to get accurate diagnoses such as the size of a tumour. Moreover, fat images can also help in diagnosing the liver and heart condition. To perform water and fat separation, multiple echoes, i.e. measurements of the raw MR signal at different time points, are required. By utilizing the knowledge of the expected signal evolution, it is possible to perform the separation. A main magnetic field is used in MRI. This field is not perfec

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Moruzzi, Davide <1997&gt. "Cyber data intelligence tool: from raw data breaches to structured storing, performant searching and user-friendly visualisation." Master's Degree Thesis, Università Ca' Foscari Venezia, 2021. http://hdl.handle.net/10579/19916.

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The thesis aims to summarise and motivate the cyber intelligence project carried out during the internship period in a local company. The scenario in which the developed software runs will be presented. The technologies and criticalities found will be highlighted and the final product that can be used by the company for its own purposes will be shown. It is possible to find everything you need by surfing the web, also a lot of stolen credentials. Everyday people upload on the web data which came from a breach due to lack of security in the network infrastructures, malware attacks or human err

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Verner, Alexander. "LSTM Networks for Detection and Classification of Anomalies in Raw Sensor Data." Diss., NSUWorks, 2019. https://nsuworks.nova.edu/gscis_etd/1074.

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In order to ensure the validity of sensor data, it must be thoroughly analyzed for various types of anomalies. Traditional machine learning methods of anomaly detections in sensor data are based on domain-specific feature engineering. A typical approach is to use domain knowledge to analyze sensor data and manually create statistics-based features, which are then used to train the machine learning models to detect and classify the anomalies. Although this methodology is used in practice, it has a significant drawback due to the fact that feature extraction is usually labor intensive and requir

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Books on the topic "Raw data"

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Rørth, Pernille. Raw Data. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23974-3.

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National Institute of Statistics (Cambodia), ed. Socio-economic survey 2007: Raw data. Royal Government of Cambodia, Ministry of Planning, National Institute of Statistics, 2009.

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van Leeuwen, Floor, ed. Hipparcos, the New Reduction of the Raw Data. Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-6342-8.

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1952-, Leeuwen Floor van, ed. Hipparcos, the new reduction of the raw data. Springer Verlag, 2007.

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1952-, Leeuwen Floor van, ed. Hipparcos, the new reduction of the raw data. Springer Verlag, 2007.

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(Firm), Novus, ed. Raw material compendium: A compilation of worldwide data sources. NOVUS, 1992.

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Mercer, Dede D. The Mercer files: [raw research data, research yr. 1993. D.D. Mercer, 1993.

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(Firm), SAS Institute, ed. SAS/TUTOR: Reading raw data and formatting values with the DATA step : course guide. SAS Institute, 1994.

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Fujii, Noriyuki. Data book of ceramic raw materials of selected areas in Turkey. Maden Tetkik ve Arama Genel Müdürlüğü, 1995.

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Workshop, on Raw Data Archive (1995 Gaborone Botswana). Report of the Workshop on Raw Data Archive: 16 May 1995, Gaborone, Botswana. National Institute of Development Research and Documentation, University of Botswana, 1995.

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Book chapters on the topic "Raw data"

1

Rørth, Pernille. "Raw Data." In Raw Data. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23974-3_1.

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Rørth, Pernille. "Q and A With Pernille Rørth, Author of Raw Data." In Raw Data. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23974-3_2.

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Nahler, Gerhard. "raw data." In Dictionary of Pharmaceutical Medicine. Springer Vienna, 2009. http://dx.doi.org/10.1007/978-3-211-89836-9_1188.

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Weik, Martin H. "raw data." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_15513.

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Kenny, Peter. "The Raw Data." In Better Business Decisions from Data. Apress, 2014. http://dx.doi.org/10.1007/978-1-4842-0184-8_5.

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Schwartz, Richard. "The raw data." In Mathematical Surveys and Monographs. American Mathematical Society, 2014. http://dx.doi.org/10.1090/surv/197/28.

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Palz, Wolfgang, and Jürgen Greif. "Sources of raw data." In European Solar Radiation Atlas. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-80237-9_4.

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Noack, Marcus M., and Kevin G. Yager. "Analysis of Raw Data." In Methods and Applications of Autonomous Experimentation. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003359593-20.

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Chen, Xinjun. "Raw Data Processing Method." In Application of Gray System Theory in Fishery Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0635-2_2.

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Zhu, Xiaoming, Bingying Song, Yingzi Ni, Yifan Ren, and Rui Li. "Big Data—From Raw Data to Big Data." In Business Trends in the Digital Era. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1079-8_1.

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Conference papers on the topic "Raw data"

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Pilikos, Georgios, Mario Azcueta, Roberto Camarero, and Nicolas Floury. "Raw SAR Data Compression with Deep Learning." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10641432.

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Sun, Junzhen, and Lizhi Lou. "Smartphone velocity estimation with raw GNSS data." In 4th International Conference on Signal Image Processing and Communication, edited by Xianye Ben and Lei Chen. SPIE, 2024. http://dx.doi.org/10.1117/12.3041282.

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Sarkar, Prithu, Yadavalli Devi Priya, Pravina B. Patel, Prasanta Chatterjee Biswas, Sreenivasulu Arigela, and Sravanthi Sallaram. "Data Visualization in Transforming Raw Data into Compelling Visual Narratives." In 2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT). IEEE, 2024. http://dx.doi.org/10.1109/tqcebt59414.2024.10545256.

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Ramos, Jose Francisco, and Sergi Trilles. "FROM RAW DATA TO A DATA STORY BY USING LEGO PIECES." In 17th annual International Conference of Education, Research and Innovation. IATED, 2024. https://doi.org/10.21125/iceri.2024.2717.

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Monnier, Goulven, Benjamin Camus, Yann-Hervé Hellouvry, Pierre Dubois, and Erik De Witte. "Synthetic RAW data generator for ESA HARMONY mission." In 2024 International Radar Conference (RADAR). IEEE, 2024. https://doi.org/10.1109/radar58436.2024.10994021.

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Ou, MengLing, Zenghua Zhao, Jianyu Yang, Jiankun Wang, and Jiafan Lu. "Data-Driven Vehicle Positioning with Smartphone Raw GNSS Data in Urban Areas." In 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA). IEEE, 2024. https://doi.org/10.1109/ispa63168.2024.00232.

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Gawde, Ameya, Gresha Bhatia, Daishik Mehta, Hitesh Rohira, and Aniket Phatak. "Aligning Raw Image to Real-time Coordinate System." In 2010 International Conference on Data Storage and Data Engineering (DSDE). IEEE, 2010. http://dx.doi.org/10.1109/dsde.2010.21.

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Baunsgaard, Sebastian, Matthias Boehm, Ankit Chaudhary, et al. "ExDRa: Exploratory Data Science on Federated Raw Data." In SIGMOD/PODS '21: International Conference on Management of Data. ACM, 2021. http://dx.doi.org/10.1145/3448016.3457549.

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Cazzanti, Luca, Antonio Davoli, and Leonardo M. Millefiori. "Automated port traffic statistics: From raw data to visualisation." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840765.

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Cai, Tao, Yueming Ma, Dejiao Niu, Pengfei Gao, Tianle Lei, and Jianfei Dai. "A New IoT Storage System Based on Raw NVM." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020847.

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Reports on the topic "Raw data"

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Lakhina, Anukool, Konstantina Papagiannaki, Mark Crovella, Christophe Diot, Eric D. Kolaczyk, and Nina Taft. Analysis of Origin Destination Flows (Raw Data). Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada466318.

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Cullinan, Valerie, Lenna Mahoney, Thomas Brouns, et al. BY-110 Headspace Sampling Comparison Test: Raw Data. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/2007073.

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Hudson, Corey. Exploiting BWA to Manipulate Raw Genomic Data In-Place. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1762309.

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Johra, Hicham. Data of a Full-Scale Experimental Study on the Onset of Internal Natural Convection inside a Horizontal Insulation Layer. Aalborg University, 2025. https://doi.org/10.54337/aau772472626.

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This report is supplementary material describing the experimental setup used for generating results gathered in the open-access dataset “Data of a Full-Scale Experimental Study on the Onset of Internal Natural Convection inside a Horizontal Insulation Layer“ (including both raw experimental data and summary results) stored on a dedicated zenodo.org online repository. This open-access dataset is supplementary material to the peer-reviewed scientific journal article entitled “Full-scale experimental study on the critical Rayleigh number for the onset of internal natural convection inside a horiz

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McCarthy, James. PR-312-12208-Z04 Summary of NO2 - NOx Ratio Test Data for Reciprocating Engines and Turbines. Pipeline Research Council International, Inc. (PRCI), 2016. http://dx.doi.org/10.55274/r0010929.

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Ismay, Chester. Exploratory Data Analysis in R with Tidyverse. Instats Inc., 2025. https://doi.org/10.61700/9ig8c167iq5b91556.

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This workshop provides a comprehensive introduction to Exploratory Data Analysis (EDA) using R and the tidyverse suite, focusing on practical data manipulation and visualization techniques essential for robust research. Participants will gain expertise in handling complex datasets and creating publication-ready visualizations, enabling them to transform raw data into actionable insights. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers ECTS Equivalent points.

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Truong, Vuong, James Chafouleas, Thomas Mosdal, Sophia Bragdon, Megan Bishop, and Jay Clausen. Thermography conversion for optimal noise reduction. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49414.

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Computer vision applications in terms of raw thermal radiance are limited by byte size. Normalizing the raw imagery reduces functional complexities that could otherwise aide a computer processing algorithm. This work explores a method to normalize 16-bit signed integer (I16) into unsigned 8-bit (U8) while maintaining the integrity of the correlation coefficients between the raw data sets and the environmental parameters that affects thermal anomaly detectability.

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Saldivar-Carranza, Enrique D., and Darcy M. Bullock. Deriving a Purdue Probe Diagram from Connected Vehicle Waypoint Data. Purdue University, 2024. http://dx.doi.org/10.5703/1288284317763.

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The provided video shows the transition from raw connected vehicle (CV) geospatial trajectory data to a Purdue Probe Diagram (PPD). PPDs allow for the estimation of delay and operational traffic signal performance measures, such as arrivals on green, split failures, and downstream blockage. These performance measures can be used to identify locations where retiming or capital investment could improve operations.

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Urlaub, Morelia, Semeidi Husrin, Michel Kühn, Cord Papenberg, and Janine Berndt. Technical Report for Raw 2D MCS Reflection Data, R/V Sonne Cruise 299, Leg 2, Singapore (Singapore) – Port Louis (Mauritius), 15/08/23 – 02/09/23. GEOMAR Helmholtz Centre for Ocean Research Kiel, 2024. https://doi.org/10.3289/tr_2d-mcs_so299_2.

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The raw 2D multichannel seismic reflection data in this project were acquired during expedition SO299-2 offshore Krakatau using a single GI Gun. The data were recorded with a 48-channel streamer. The Krakatau survey aimed to shed light on the erupted volumes of the disastrous 1883 eruption and decipher the processes leading to a damaging tsunami. The data comprise 381-line kilometers and are provided in raw format (SEG-D) and associated standardized metadata. Detailed information on the acquisition can be found in the SO299-2 cruise report (https://doi.org/10.48433/cr_so299_2). This technical

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Gutjahr, A., F. Phillips, P. W. Kubik, and D. Elmore. An improved method for statistical analysis of raw accelerator mass spectrometry data. Office of Scientific and Technical Information (OSTI), 1987. http://dx.doi.org/10.2172/6329593.

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