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Journal articles on the topic 'Rich Data Set'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Papoušek, Jan, Radek Pelánek, and Vít Stanislav. "Adaptive Geography Practice Data Set." Journal of Learning Analytics 3, no. 2 (2016): 317–21. http://dx.doi.org/10.18608/jla.2016.32.17.

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We present a data set on student learning of geography facts in an open online system slepemapy.cz. The data set has simple format with intuitive interpretation. At the same time it offers rich possibilities for modelling and analysis, for example of prior knowledge, forgetting or response times.

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Tabuenca, Bernardo, and Dirk Börner. "Noise in Classrooms Data Set." Journal of Learning Analytics 3, no. 2 (2016): 325–29. http://dx.doi.org/10.18608/jla.2016.32.19.

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We present a data set comprising noise samples collected during 26 sessions of the subject “Technology” of Secondary Education studies with a mobile device. The data set includes rich metadata with the aim to facilitate the correlation with further studies, namely, type of session (i.e. traditional face-to-face lecture, collaborative workshop session, individual computer session), the number of students participating in the session, the percentage of male/female students, the mean age of the students, timestamp when the sample was collected, language of the session, country, city and location

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Zhu, Sirui, Glareh Amirjamshidi, and Matthew J. Roorda. "Data Fusion of Commercial Vehicle GPS and Roadside Intercept Survey Data." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 44 (2018): 10–20. http://dx.doi.org/10.1177/0361198118768516.

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GPS tracking technology produces large amounts of data which represent samples of the commercial vehicle population that are much larger than conventional commercial travel surveys. However, passively collected GPS data lack behavioral detail that a conventional survey offers. This study develops a data fusion method to impute variables of interest for a large GPS data set, by establishing a link to a behaviorally rich commercial travel survey data set. As a case study, this study uses detailed information from the Ministry of Transportation of Ontario’s Commercial Vehicle Survey (CVS), a truc

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Roberto, Paiano, and Pasanisi Stefania. "BRAIN Journal - A New Challenge for Information Mining." BRAIN: Broad Research in Artificial Intelligence and Neuroscience 8, no. 2 (2017): 63–80. https://doi.org/10.5281/zenodo.1045363.

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ABSTRACT In the field of "Data Exploration" many approaches have been developed to solve the problem of management of big data that are also semantically rich. Nowadays, there is a strong need to support the discovery-oriented applications where data discovery is a highly ad hoc interactive process to support the users by assisting the navigation in the data to find interesting objects. In this work starting by a theoretical data exploration system, where we identified the main features that a data exploration system must have to an efficient exploratory experience, we propose a combination of

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O'Donoghue, Aileen A., Jean A. Eilek, and Frazer N. Owen. "Observations of Straight-angle Tailed Radio Galaxies in Rich Clusters of Galaxies." Symposium - International Astronomical Union 175 (1996): 349–50. http://dx.doi.org/10.1017/s0074180900081067.

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We have begun VLA observations of straight-angle tailed radio sources (SATs) at 1.5 and 4.8 GHz (L and C band) to achieve one arcsecond resolution at each frequency. This will provide a SAT data set similar to the O'Donoghue, Owen, and Eilek (1990) WAT data set with both total intensity and spectral index information. We will use these data to examine morphological and dynamical properties of straight-tailed radio sources in clusters of galaxies.

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Matney, Susan A., Theresa (Tess) Settergren, Jane M. Carrington, Rachel L. Richesson, Amy Sheide, and Bonnie L. Westra. "Standardizing Physiologic Assessment Data to Enable Big Data Analytics." Western Journal of Nursing Research 39, no. 1 (2016): 63–77. http://dx.doi.org/10.1177/0193945916659471.

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Disparate data must be represented in a common format to enable comparison across multiple institutions and facilitate Big Data science. Nursing assessments represent a rich source of information. However, a lack of agreement regarding essential concepts and standardized terminology prevent their use for Big Data science in the current state. The purpose of this study was to align a minimum set of physiological nursing assessment data elements with national standardized coding systems. Six institutions shared their 100 most common electronic health record nursing assessment data elements. From

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Yan, Su, W. Scott Spangler, and Ying Chen. "Chemical Name Extraction Based on Automatic Training Data Generation and Rich Feature Set." IEEE/ACM Transactions on Computational Biology and Bioinformatics 10, no. 5 (2013): 1218–33. http://dx.doi.org/10.1109/tcbb.2013.101.

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Zeynali, Ali, Bo Sun, Mohammad Hajiesmaili, and Adam Wierman. "Data-driven Competitive Algorithms for Online Knapsack and Set Cover." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (2021): 10833–41. http://dx.doi.org/10.1609/aaai.v35i12.17294.

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The design of online algorithms has tended to focus on algorithms with worst-case guarantees, e.g., bounds on the competitive ratio. However, it is well-known that such algorithms are often overly pessimistic, performing sub-optimally on non-worst-case inputs. In this paper, we develop an approach for data-driven design of online algorithms that maintain near-optimal worst-case guarantees while also performing learning in order to perform well for typical inputs. Our approach is to identify policy classes that admit global worst-case guarantees, and then perform learning using historical data

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Arslan, Ruben C. "How to Automatically Document Data With the codebook Package to Facilitate Data Reuse." Advances in Methods and Practices in Psychological Science 2, no. 2 (2019): 169–87. http://dx.doi.org/10.1177/2515245919838783.

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Data documentation in psychology lags behind not only many other disciplines, but also basic standards of usefulness. Psychological scientists often prefer to invest the time and effort that would be necessary to document existing data well in other duties, such as writing and collecting more data. Codebooks therefore tend to be unstandardized and stored in proprietary formats, and they are rarely properly indexed in search engines. This means that rich data sets are sometimes used only once—by their creators—and left to disappear into oblivion. Even if they can find an existing data set, rese

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Zhao, Wayne Xin, Gaole He, Kunlin Yang, et al. "KB4Rec: A Data Set for Linking Knowledge Bases with Recommender Systems." Data Intelligence 1, no. 2 (2019): 121–36. http://dx.doi.org/10.1162/dint_a_00008.

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To develop a knowledge-aware recommender system, a key issue is how to obtain rich and structured knowledge base (KB) information for recommender system (RS) items. Existing data sets or methods either use side information from original RSs (containing very few kinds of useful information) or utilize a private KB. In this paper, we present KB4Rec v1.0, a data set linking KB information for RSs. It has linked three widely used RS data sets with two popular KBs, namely Freebase and YAGO. Based on our linked data set, we first preform qualitative analysis experiments, and then we discuss the effe

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Debie, P., M. E. Verhulst, J. M. E. Pennings, et al. "The Analysis of High-Frequency Finance Data using ROOT." Journal of Physics: Conference Series 2438, no. 1 (2023): 012068. http://dx.doi.org/10.1088/1742-6596/2438/1/012068.

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Abstract High-frequency financial market data is conceptually distinct from high energy physics (HEP) data. Market data is a time series generated by market participants, while HEP data is a set of independent events generated by collisions between particles. However, there are similarities within the data structure and required tools for data analysis, and both fields share a similar set of problems facing the increasing size of data generated. This paper describes some of the core concepts of financial markets, discusses the data similarities and differences with HEP, and provides an impleme

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Marongiu, M., C. Guidorzi, G. Stratta, et al. "Radio data challenge the broadband modelling of GRB 160131A afterglow." Astronomy & Astrophysics 658 (January 27, 2022): A11. http://dx.doi.org/10.1051/0004-6361/202140403.

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Context. Gamma-ray burst (GRB) afterglows originate from the interaction between the relativistic ejecta and the surrounding medium. Consequently, their properties depend on several aspects: radiation mechanisms, relativistic shock micro-physics, circumburst environment, and the structure and geometry of the relativistic jet. While the standard afterglow model accounts for the overall spectral and temporal evolution for a number of GRBs, its validity limits emerge when the data set is particularly rich and constraining, especially in the radio band. Aims. We aimed to model the afterglow of the

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Uchida, Craig D., and William R. King. "Police Employee Data: Elements and Validity." Justice Research and Policy 4, no. 1-2 (2002): 11–19. http://dx.doi.org/10.3818/jrp.4.1.2002.11.

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The Federal Bureau of Investigation's (FBI) Uniform Crime Reports (UCR) Police Employee data, collected from U.S. police agencies annually since 1930, provide information on various aspects of police organizations (such as the number of employees and assaults on officers). Such data, spanning 72 years, offer researchers a potentially rich data set. This article provides a brief history of the Police Employee data, describes the various data elements, and tentatively addresses the validity and reliability of these data. Finally, suggested improvements (as well as possible uses) for these data a

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Amosu, Adewale, and Yuefeng Sun. "Identification of thermally mature total organic carbon-rich layers in shale formations using an effective machine-learning approach." Interpretation 9, no. 3 (2021): T735—T745. http://dx.doi.org/10.1190/int-2020-0184.1.

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We have developed a support vector machine (SVM) method that relies on core-measured data as well as gamma-ray, deep resistivity, sonic, and density wireline well-log data in identifying thermally mature total organic carbon (TOC)-rich layers at depth intervals with missing geochemical data in unconventional resource plays. We first test the SVM method using the Duvernay Shale Formation data. The SVM method successfully classifies the TOC data set into TOC-rich and TOC-poor classes and the [Formula: see text] data set into thermally mature and thermally immature classes when the optimal featur

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Markovikj, Dejan, Sonja Gievska, Michal Kosinski, and David Stillwell. "Mining Facebook Data for Predictive Personality Modeling." Proceedings of the International AAAI Conference on Web and Social Media 7, no. 2 (2021): 23–26. http://dx.doi.org/10.1609/icwsm.v7i2.14466.

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Beyond being facilitators of human interactions, social networks have become an interesting target of research, providing rich information for studying and modeling user’s behavior. Identification of personality-related indicators encrypted in Facebook profiles and activities are of special concern in our current research efforts. This paper explores the feasibility of modeling user personality based on a proposed set of features extracted from the Facebook data. The encouraging results of our study, exploring the suitability and performance of several classification techniques, will also be p

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Michel, Thibauld, Yu-Hseng Tseng, Hannah Wilson, Kuo-Fang Chung, and Catherine Kidner. "A HYBRID CAPTURE BAIT SET FOR BEGONIA." Edinburgh Journal of Botany 79 (August 18, 2022): 1–33. http://dx.doi.org/10.24823/ejb.2022.409.

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Hybrid capture with baits has proven to be a rich source of genetic data for many genera. The depth of information provided allows resolution of rapid radiations and of deep phylogenetic patterns. Retrieved data can also be used for population genetic studies and analysis of functional genetic diversity. To gain a better understanding of the evolutionary patterns across this large, diverse and fascinating genus through phylogenetics, population genetics and sequence analysis, we have designed and tested a set of 1239 baits covering low copy number and functionally annotated genes involved in s

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Ahyerre, M., F. O. Henry, F. Gogien, M. Chabanel, M. Zug, and D. Renaudet. "Test of the efficiency of three storm water quality models with a rich set of data." Water Science and Technology 51, no. 2 (2005): 171–77. http://dx.doi.org/10.2166/wst.2005.0045.

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The objective of this article is to test the efficiency of three different Storm Water Quality Model (SWQM) on the same data set (34 rain events, SS measurements) sampled on a 42 ha watershed in the center of Paris. The models have been calibrated at the scale of the rain event. Considering the mass of pollution calculated per event, the results on the models are satisfactory but that they are in the same order of magnitude as the simple hydraulic approach associated to a constant concentration. In a second time, the mass of pollutant at the outlet of the catchment at the global scale of the 3

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Bhatia, Sudeep. "Predicting Risk Perception: New Insights from Data Science." Management Science 65, no. 8 (2019): 3800–3823. http://dx.doi.org/10.1287/mnsc.2018.3121.

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We outline computational techniques for predicting perceptions of risk. Our approach uses the structure of word distribution in natural language data to uncover rich representations for a very large set of naturalistic risk sources. With the application of standard machine learning techniques, we are able to accurately map these representations onto participant risk ratings. Unlike existing methods in risk perception research, our approach does not require any specialized participant data and is capable of generalizing its learned mappings to make quantitative predictions for novel (out-of-sam

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Andry, Johanes Fernandes, Henny Hartono, Honni, Aziza Chakir, and Rafael. "Data Set Analysis Using Rapid Miner to Predict Cost Insurance Forecast with Data Mining Methods." Journal of Hunan University Natural Sciences 49, no. 6 (2022): 167–75. http://dx.doi.org/10.55463/issn.1674-2974.49.6.17.

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The insurance protection program cannot be separated from everyday human life because there will always be risks in every human activity. Most people have entered into insurance agreements with state-owned and national private-owned insurance companies. The information system is one of the resources to increase competitive advantage. Information systems can be used to obtain, process, and disseminate information to support day-to-day operations and support strategic decision-making activities. The rapid growth of data accumulation has created data-rich but insufficient information conditions.

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Selmer, Sarah J., Johnna J. Bolyard, and James A. Rye. "Statistical Reasoning over Lunch." Mathematics Teaching in the Middle School 17, no. 5 (2011): 274–81. http://dx.doi.org/10.5951/mathteacmiddscho.17.5.0274.

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Nicolás, Robinson-García, Torres-Salinas Daniel, Zahedi Zohreh, and Costas Rodrigo. "New data, new possibilities: exploring the insides of Altmetric.com." El Profesional de la Información 23, no. 4 (2014): 359–66. https://doi.org/10.3145/epi.2014.jul.03.

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This paper analyzes Altmetric.com, one of the most important altmetric data providers currently used. We have analyzed a set of publications with doi number indexed in the Web of Science during the period 2011-2013 and collected their data with the Altmetric API. 19% of the original set of papers was retrieved from Altmetric.com including some altmetric data. We identified 16 different social media sources from which Altmetric.com retrieves data. However five of them cover 95.5% of the total set. Twitter (87.1%) and Mendeley (64.8%) have the highest coverage. We conclude that Altmetric.com is

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Bentley, Nokome. "Data and time poverty in fisheries estimation: potential approaches and solutions." ICES Journal of Marine Science 72, no. 1 (2014): 186–93. http://dx.doi.org/10.1093/icesjms/fsu023.

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Abstract The increasingly sophisticated methods developed for stock assessment are not always suited to data-poor fisheries. Data-poor fisheries are often low in value, so the researcher time available for their assessment is also small. The dual constraints of reduced data and reduced time make stock assessments for low-value stocks particularly challenging. Prior probability distributions are useful for transferring knowledge from data-rich to data-poor fisheries. When data are limited, it is important to make the most of what few data is available. However, fully understanding potential bia

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Fernández-Trincado, José G., Leonardo Chaves-Velasquez, Angeles Pérez-Villegas, et al. "Dynamical orbital classification of selected N-rich stars with Gaia Data Release 2 astrometry." Monthly Notices of the Royal Astronomical Society 495, no. 4 (2020): 4113–23. http://dx.doi.org/10.1093/mnras/staa1386.

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ABSTRACT We have used the galaxy modelling algorithm gravpot16, to explore the most probable orbital elements of a sample of 64 selected N-rich stars across the Milky Way. We use the newly measured proper motions from Gaia Data Release 2 with existing line-of-sight velocities from the second generation of the Apache Point Observatory Galactic Evolution Experiment (APOGEE-2) and spectrophotometric distance estimations from starhorse. We adopted a set of high-resolution particle simulations evolved in the same steady-state Galactic potential model with a bar, in order to identify the groups of N

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Kang, Hyunmo, Catherine Plaisant, Bongshin Lee, and Benjamin B. Bederson. "NetLens: Iterative Exploration of Content-Actor Network Data." Information Visualization 6, no. 1 (2007): 18–31. http://dx.doi.org/10.1057/palgrave.ivs.9500143.

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Networks have remained a challenge for information retrieval and visualization because of the rich set of tasks that users want to accomplish. This paper offers an abstract Content-Actor network data model, a classification of tasks, and a tool to support them. The NetLens interface was designed around the abstract Content-Actor network data model to allow users to pose a series of elementary queries and iteratively refine visual overviews and sorted lists. This enables the support of complex queries that are traditionally hard to specify. NetLens is general and scalable in that it applies to

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Sundström, Malin. "Climate of Data-driven Innovation Within E-business Retail Actors." FIIB Business Review 8, no. 2 (2019): 79–87. http://dx.doi.org/10.1177/2319714519845777.

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Even though contemporary practice within e-business retailing demonstrates knowledge on how to collect and process big data, e-businesses have a hard time with the organizational mind-set to focus on data-driven innovation. In this perspective paper the author analyses an action-research study from a Swedish e-business retail actor, and explores how to change the mind-set to focus on data-driven innovation, and identifies the results from such a change. With a rich empirical data, the author demystifies the rather complex view of data-driven innovation within e-business firms, and describes ho

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Farkas, Laszlo, Beda H. Bolzenius, and Georg Will. "Powder Diffraction Data and Unit Cell of Kutnohorite." Powder Diffraction 3, no. 3 (1988): 172–74. http://dx.doi.org/10.1017/s0885715600032930.

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AbstractX-ray powder diffraction data, unit cell parameters and chemical data published previously for kutnohorite, the manganese-rich double carbonate mineral, are critically evaluated and compared with new measurements on a specimen from Chvaletice, Czechoslovakia with a composition Ca (Mn0.64Mg0.23Fe0.13)(CO3)2. Data were collected with a computer controlled diffractometer and analysed with profile analysis techniques. The new powder diffraction data set yields much better data and unit cell parameters than the earlier ones given for kutnohorite on PDF 11-345 (Powder Diffraction File, 1987)

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Feng, Chao, Lyn Bartram, and Diane Gromala. "Beyond Data: Abstract Motionscapes as Affective Visualization." Leonardo 50, no. 2 (2017): 205–6. http://dx.doi.org/10.1162/leon_a_01229.

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Motionscapes—the compositions of visual forms in motion—have often been used for the evocation of affects in recent interactive artifacts and environments. While the motionscape aesthetic can be informed by art theory and history, previous empirical work investigating the affective affordances of motionscapes brings new perspectives to the design language of motionscapes. The authors argue that motionscapes that are commonly employed in artistic contexts can be appropriated for the design space of human-computer interaction (HCI) as a rich modality for affective visualization. The authors prop

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Progonov, Dmytro. "Detection of Stego Images with Adaptively Embedded Data by Component Analysis Methods." Advances in Cyber-Physical Systems 6, no. 2 (2021): 146–54. http://dx.doi.org/10.23939/acps2021.02.146.

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Ensuring the effective protection of personal and corporate sensitive data is topical task today. The special interest is taken at sensitive data leakage prevention during files transmission in communication systems. In most cases, these leakages are conducted by usage of advance adaptive steganographic methods. These methods are aimed at minimizing distortions of cover files, such as digital images, during data hiding that negatively impact on detection accuracy of formed stego images. For overcoming this shortcoming, it was proposed to pre-process (calibrate) analyzed images for increasing s

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Gao, Yizhu, Xiaoming Zhai, Okan Bulut, Ying Cui, and Xiaojian Sun. "Examining Humans’ Problem-Solving Styles in Technology-Rich Environments Using Log File Data." Journal of Intelligence 10, no. 3 (2022): 38. http://dx.doi.org/10.3390/jintelligence10030038.

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This study investigated how one’s problem-solving style impacts his/her problem-solving performance in technology-rich environments. Drawing upon experiential learning theory, we extracted two behavioral indicators (i.e., planning duration for problem solving and human–computer interaction frequency) to model problem-solving styles in technology-rich environments. We employed an existing data set in which 7516 participants responded to 14 technology-based tasks of the Programme for the International Assessment of Adult Competencies (PIAAC) 2012. Clustering analyses revealed three problem-solvi

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Chanen, Cassandra, and Robert Soden. "Data Practices and Double-Binds of Toronto's Climate Governance." Proceedings of the ACM on Human-Computer Interaction 9, no. 2 (2025): 1–25. https://doi.org/10.1145/3710955.

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Contemporary urban climate adaptation and mitigation strategies both draw upon and produce diverse forms of data. As the tenets of data-driven governance are increasingly integrated into city governments' climate policy, careful attention to this intersection is necessary to evaluate how to best leverage data as part of municipal climate action. This project applies a practice-based approach to research, employing a combination of document analysis and semi-structured interviews to identify a set of climate data practices that offer a rich understanding of the role of data in climate politics

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Eliyahu Sason, Yackov Lubarsky, Alexei Gaissinski, Eli Kravchik, and Pavel Kisilev. "Oracle-based data generation for highly efficient digital twin network training." ITU Journal on Future and Evolving Technologies 4, no. 3 (2023): 472–84. http://dx.doi.org/10.52953/aweu6345.

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Recent advances in Graph Neural Networks (GNNs) has opened new capabilities to analyze complex communication systems. However, little work has been done to study the effects of limited data samples on the performance of GNN-based systems. In this paper, we present a novel solution to the problem of finding an optimal training set for efficient training of a RouteNet-Fermi GNN model. The proposed solution ensures good model generalization to large previously unseen networks under strict limitations on the training data budget and training topology sizes. Specifically, we generate an initial dat

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Guo, Hong Tao, Zhi Guo Chang, and Shan Wei He. "Geostationary Meteorological Satellite Data Processing System." Advanced Materials Research 181-182 (January 2011): 257–60. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.257.

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In order to design a set of geostationary meteorological satellite data processing system，which have common data processing，practical remote sensing products and rich visual stylet，used VC++6.0 MFC and dynamic link library and the mature remote sensing products processing algorithms, to design it. Multi-satellite source geostationary meteorological satellite data are integrated, remote sensing products are generated, for example, cloud detection, cloud classification, cloud top height. The original cloud image, remote sensing and geographic information products are displayed vividly. The three

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Timmer, Stephane, and Lutz Kaufmann. "Conflict minerals traceability – a fuzzy set analysis." International Journal of Physical Distribution & Logistics Management 47, no. 5 (2017): 344–67. http://dx.doi.org/10.1108/ijpdlm-01-2016-0026.

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Purpose The purpose of this paper is to investigate legal effects on social sustainability practices at buying firms. The US Dodd-Frank Act has forced listed companies to determine the degree to which their products contain conflict minerals (CM). The research question this study seeks to answer is the following: which factors influence a company’s ability to determine the provenance of its inputs? Design/methodology/approach The study examines secondary data in the form of CM reports of 50 US listed firms for two consecutive years using a fuzzy set qualitative comparative analysis (fsQCA) app

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Kim, Dongwoo. "Partial identification in nonseparable count data instrumental variable models." Econometrics Journal 23, no. 2 (2019): 232–50. http://dx.doi.org/10.1093/ectj/utz025.

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Summary This paper investigates undesirable limitations of widely used count data instrumental variable models. To overcome the limitations, I propose a partially identifying single-equation model that requires neither strong separability of unobserved heterogeneity nor a triangular system. Sharp bounds (identified sets) of structural features are characterised by conditional moment inequalities. Numerical examples show that the size of an identified set can be very small when the support of an outcome is rich or instruments are strong. An algorithm for estimation and inference is presented. I

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Entani, Tomoe. "Interval Data Envelopment Analysis for Inter-Group Data Usage." Journal of Advanced Computational Intelligence and Intelligent Informatics 24, no. 1 (2020): 113–22. http://dx.doi.org/10.20965/jaciii.2020.p0113.

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Organizations are interested in exploiting the data from the other organizations for better analyses. Therefore, the data related policies of organizations should be sensitive to the data privacy issue, which has been widely discussed recently. The present study is focused on inter-group data usage for a relative evaluation. This research is based on the data envelopment analysis (DEA), which is used to measure the efficiency of a decision making unit (DMU) relatively employed within a group. In DEA, establishing an efficient frontier consisting of efficient DMUs is essential. We can obtain th

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Kim, Jihyun. "An analysis of data paper templates and guidelines: types of contextual information described by data journals." Science Editing 7, no. 1 (2020): 16–23. http://dx.doi.org/10.6087/kcse.185.

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Purpose: Data papers are a promising genre of scholarly communication, in which research data are described, shared, and published. Rich documentation of data, including adequate contextual information, enhances the potential of data reuse. This study investigated the extent to which the components of data papers specified by journals represented the types of contextual information necessary for data reuse.Methods: A content analysis of 15 data paper templates/guidelines from 24 data journals indexed by the Web of Science was performed. A coding scheme was developed based on previous studies,

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Thibodeau, Jay C., L. Tyler Williams, and Annie L. Witte. "Point and Click Data: An Assessment of Editorial Perceptions and Recommendations for the Peer-Review Process in the New Data Frontier." Journal of Information Systems 33, no. 1 (2017): 129–44. http://dx.doi.org/10.2308/isys-51980.

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ABSTRACT In the new research frontier of data availability, this study develops guidelines to aid accounting academicians as they seek to evidence data integrity proactively in the peer-review process. To that end, we explore data integrity issues associated with two emerging data streams that are gaining prominence in the accounting literature: online labor markets and social media sources. We provide rich detail surrounding academic thought about these data platforms through interview data collected from a sample of former senior journal editors and survey data collected from a sample of pee

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Dhinesh Kumar, P., and S. Anusuya. "Identifying User Personality in Facebook Using their Activity Data." Asian Journal of Computer Science and Technology 8, S1 (2019): 89–93. http://dx.doi.org/10.51983/ajcst-2019.8.s1.1943.

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The facilitators of human interactions, social networks have become an interesting target of research, providing rich information for studying and modelling user’s behaviour. Identification of personality-related indicators encrypted in Facebook profiles and activities are of special concern in our current research efforts. This paper explores the feasibility of modelling user personality based on a proposed set of features extracted from the Facebook data. The encouraging results of our study, exploring the suitability and performance of several classification techniques, will also be present

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Rychwalska, Agnieszka, Geoffrey Goodell, and Magdalena Roszczynska-Kurasinska. "Data Management for Platform-Mediated Public Services: Challenges and Best Practices." Surveillance & Society 19, no. 1 (2021): 22–36. http://dx.doi.org/10.24908/ss.v19i1.13986.

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Data harvesting and profiling have become a de facto business model for many businesses in the digital economy. The surveillance of individual persons through their use of private sector platforms has a well-understood effect on personal autonomy and democratic institutions. In this article, we explore the consequences of implementing data-rich services in the public sector and, specifically, the dangers inherent to undermining the universality of the reach of public services, the implicit endorsement of the platform operators by the government, and the inability of members of the public to av

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Elmqvist, Niklas, John Stasko, and Philippas Tsigas. "DataMeadow: A Visual Canvas for Analysis of Large-Scale Multivariate Data." Information Visualization 7, no. 1 (2008): 18–33. http://dx.doi.org/10.1057/palgrave.ivs.9500170.

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Supporting visual analytics of multiple large-scale multidimensional data sets requires a high degree of interactivity and user control beyond the conventional challenges of visualizing such data sets. We present the DataMeadow, a visual canvas providing rich interaction for constructing visual queries using graphical set representations called DataRoses. A DataRose is essentially a starplot of selected columns in a data set displayed as multivariate visualizations with dynamic query sliders integrated into each axis. The purpose of the DataMeadow is to allow users to create advanced visual qu

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Bhattacharya, Rajorshi, Ylva M. Pihlström, and Loránt O. Sjouwerman. "Estimating distances to AGB stars using IR data." Proceedings of the International Astronomical Union 18, S380 (2022): 116–18. http://dx.doi.org/10.1017/s1743921323002648.

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AbstractWe present a method to estimate distances to AGB stars, utilizing the rich infrared data sets available for these infrared-bright targets. The method is based on the assumption that stars with intrinsically similar properties (metallicity, initial mass, etc.) produce similar spectral energy distributions (SEDs) and similar luminosities. We here discuss the results for AGB stars belonging to the BAaDE survey sample whose distances were calibrated using the template SEDs of stars with their VLBI parallaxes. As VLBI parallaxes are only known for a handful of sources, the resulting templat

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Saier, Tarek, and Michael Färber. "unarXive: a large scholarly data set with publications’ full-text, annotated in-text citations, and links to metadata." Scientometrics 125, no. 3 (2020): 3085–108. http://dx.doi.org/10.1007/s11192-020-03382-z.

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AbstractIn recent years, scholarly data sets have been used for various purposes, such as paper recommendation, citation recommendation, citation context analysis, and citation context-based document summarization. The evaluation of approaches to such tasks and their applicability in real-world scenarios heavily depend on the used data set. However, existing scholarly data sets are limited in several regards. In this paper, we propose a new data set based on all publications from all scientific disciplines available on arXiv.org. Apart from providing the papers’ plain text, in-text citations w

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EskandariNasab, MohammadReza, Shah Muhammad Hamdi, and Soukaina Filali Boubrahimi. "Impacts of Data Preprocessing and Sampling Techniques on Solar Flare Prediction from Multivariate Time Series Data of Photospheric Magnetic Field Parameters." Astrophysical Journal Supplement Series 275, no. 1 (2024): 6. http://dx.doi.org/10.3847/1538-4365/ad7c4a.

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Abstract The accurate prediction of solar flares is crucial due to their risks to astronauts, space equipment, and satellite communication systems. Our research enhances solar flare prediction by employing sophisticated data preprocessing and sampling techniques for the Space Weather Analytics for Solar Flares (SWAN-SF) data set, a rich source of multivariate time series data of solar active regions. Our study adopts a multifaceted approach encompassing four key methodologies. Initially, we address over 10 million missing values in the SWAN-SF data set through our innovative imputation techniq

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Gillen, Benjamin J., Sergio Montero, Hyungsik Roger Moon, and Matthew Shum. "BLP-2LASSO for aggregate discrete choice models with rich covariates." Econometrics Journal 22, no. 3 (2019): 262–81. http://dx.doi.org/10.1093/ectj/utz010.

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Summary We introduce the BLP-2LASSO model, which augments the classic BLP (Berry, Levinsohn, and Pakes, 1995) random-coefficients logit model to allow for data-driven selection among a high-dimensional set of control variables using the 'double-LASSO' procedure proposed by Belloni, Chernozhukov, and Hansen (2013). Economists often study consumers’ aggregate behaviour across markets choosing from a menu of differentiated products. In this analysis, local demographic characteristics can serve as controls for market-specific preference heterogeneity. Given rich demographic data, implementing thes

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San Nicolás Romera, César, Miguel Ángel Nicolás Ojeda, and Josefa Ros Velasco. "Video Games Set in the Middle Ages." Games and Culture 13, no. 5 (2016): 521–42. http://dx.doi.org/10.1177/1555412015627068.

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Within the study of video games, there is a burgeoning interest in the phenomenon of historical representation; nonetheless, few studies have centered on the reflection of particular eras of History, such as the Middle Ages and the effect of this on interpretations of culture and potential pedagogical applications with respect to this specific period of time. In this study, we present and discuss the compilation and content of a database of over 600 medieval titles released between 1980 and 2013, demonstrating the growing popularity, with producers and consumers, of what we could now refer to

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Dong, Limei, Xinyu Zuo, and Yiping Xiong. "Prediction of hydrological and water quality data based on granular-ball rough set and k-nearest neighbor analysis." PLOS ONE 19, no. 2 (2024): e0298664. http://dx.doi.org/10.1371/journal.pone.0298664.

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Hydrological and water quality datasets usually encompass a large number of characteristic variables, but not all of these significantly influence analytical outcomes. Therefore, by wisely selecting feature variables with rich information content and removing redundant features, it not only can the analysis efficiency be improved, but the model complexity can also be simplified. This paper considers introducing the granular-ball rough set algorithm for feature variable selection and combining it with the k-nearest neighbor method and back propagation network to analyze hydrological and water q

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Zhou, Bo, Huizhen Wu, Baoshu Wu, and Zhenjiang Song. "Valuable Data “Gain” and “Loss”: The Quantitative Impact of Information Choice on Consumers’ Decision to Buy Selenium-Rich Agricultural Products." Foods 13, no. 20 (2024): 3256. http://dx.doi.org/10.3390/foods13203256.

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Biotechnology assumes a paramount role in addressing micronutrient deficiencies. The promotion thereof and the augmentation of public awareness are indispensable for implementation. The advancement of big data presents challenges due to the plethora of information and the constrained processing capacity, thereby inducing difficulties in consumer decision-making. The study is obliged to intensify information dissemination to empower consumers to apprehend the value of selenium-enriched products as an integral constituent of positive nutrition guidance. The study undertook an experiment related

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Moore, Jimmy, Pascal Goffin, Jason Wiese, and Miriah Meyer. "An Interview Method for Engaging Personal Data." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 4 (2021): 1–28. http://dx.doi.org/10.1145/3494964.

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Whether investigating research questions or designing systems, many researchers and designers need to engage users with their personal data. However, it is difficult to successfully design user-facing tools for interacting with personal data without first understanding what users want to do with their data. Techniques for raw data exploration, sketching, or physicalization can avoid the perils of tool development, but prevent direct analytical access to users' rich personal data. We present a new method that directly tackles this challenge: the data engagement interview. This interview method

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Eost-Telling, Charlotte L., Paul Kingston, Louise Taylor, and Jan Bailey. "THE METHODOLOGICAL RELEVANCE OF MASS OBSERVATION DATA." Innovation in Aging 3, Supplement_1 (2019): S757. http://dx.doi.org/10.1093/geroni/igz038.2778.

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Abstract The Mass Observation Project, established in 1937, documents the lives of ordinary people living in the UK, and explores a wide range of social issues. The Project distributes a set of written questions (“Directives”) to a panel of 500 members of the British public (“Observers”) three times each year; “Observers” respond in writing. From the initial commissioning of a “Directive” to data becoming available for analysis takes between four to six months. This approach offers researchers an opportunity to capture in-depth qualitative data from individuals with a range of demographic back

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Shah, Syed Ateeb Akhter, Muhammad Ishtiaq, Sumbal Qureshi, and Kaneez Fatima. "Inflation Forecasting for Pakistan in a Data-rich Environment." Pakistan Development Review, April 10, 2023, 643–58. http://dx.doi.org/10.30541/v61i4pp.643-658.

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This paper uses machine learning methods to forecast the year-on-year CPI inflation of Pakistan and compare their forecasting performance against the comprehensive traditional forecasting suite contained in Hanif and Malik (2015). It also augments the comprehensive forecasting suite with the dynamic factor model which is able to handle a large amount of information and put all of these models in competition against the latest machine learning models. A set of 117 predictors covering a period of July 1995 to June 2020 is used for this purpose. We set the naïve mean model as the benchmark and co

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