Relevant bibliographies by topics / Big Data Research

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Big Data Research'

Author: Grafiati
Published: 4 June 2021
Last updated: 28 January 2023

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Journal articles on the topic "Big Data Research"

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Anoushiravani, Afshin A., Jason Patton, Zain Sayeed, Mouhanad M. El-Othmani, and Khaled J. Saleh. "Big Data, Big Research." Orthopedic Clinics of North America 47, no. 4 (2016): 717–24. http://dx.doi.org/10.1016/j.ocl.2016.05.008.

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Balazinska, Magdalena. "Big data research." Proceedings of the VLDB Endowment 8, no. 12 (2015): 2053–56. http://dx.doi.org/10.14778/2824032.2824140.

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Mathias, Dr Elton, Dr Roveena Goveas, and Manish Rajak. "Clinical Research - A Big Data Science Approach." International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (2018): 1075–78. http://dx.doi.org/10.31142/ijtsrd9547.

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Zhu, Haibin. "Big Data and Collaboration Research: Creating Real Big Data." IEEE Systems, Man, and Cybernetics Magazine 4, no. 1 (2018): 16–19. http://dx.doi.org/10.1109/msmc.2017.2703590.

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Lee, Hae-Young. "Big Challenge in Big Data Research: Continual Dispute on Big Data Analysis." Korean Circulation Journal 50, no. 1 (2020): 69. http://dx.doi.org/10.4070/kcj.2019.0349.

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Lytras, Miltiadis D., Vijay Raghavan, and Ernesto Damiani. "Big Data and Data Analytics Research." International Journal on Semantic Web and Information Systems 13, no. 1 (2017): 1–10. http://dx.doi.org/10.4018/ijswis.2017010101.

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The Big Data and Data Analytics is a brand new paradigm, for the integration of Internet Technology in the human and machine context. For the first time in the history of the human mankind we are able to transforming raw data that are massively produced by humans and machines in to knowledge and wisdom capable of supporting smart decision making, innovative services, new business models, innovation, and entrepreneurship. For the Web Science research, this is a new methodological and technological spectrum of advanced methods, frameworks and functionalities never experienced in the past. At the
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Martin, Joseph. "Big data, big future." BioTechniques 68, no. 4 (2020): 166–68. http://dx.doi.org/10.2144/btn-2020-0027.

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Kim, Ki Hong. "Cultural Research and Big Data." Humanities Contens, no. 41 (June 30, 2016): 193. http://dx.doi.org/10.18658/humancon.2016.06.41.193.

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Cho, William C. "Big Data for Cancer Research." Clinical Medicine Insights: Oncology 9 (January 2015): CMO.S38279. http://dx.doi.org/10.4137/cmo.s38279.

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Kim, Se-Hong. "Clinical Research Using Big Data." Korean Journal of Family Practice 10, no. 2 (2020): 75–76. http://dx.doi.org/10.21215/kjfp.2020.10.2.75.

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Dissertations / Theses on the topic "Big Data Research"

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Sturt, Bradley Eli. "Dynamic optimization in the age of big data." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127292.

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Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, May, 2020<br>Cataloged from the official PDF of thesis.<br>Includes bibliographical references (pages 241-249).<br>This thesis revisits a fundamental class of dynamic optimization problems introduced by Dantzig (1955). These decision problems remain widely studied in many applications domains (e.g., inventory management, finance, energy planning) but require access to probability distributions that are rarely known in practice. First, we propose a new data-driven approach for addressi
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Sánchez, Villaseñor Omar. "HERRAMIENTAS, RETOS, OPORTUNIDADES, SEGURIDAD Y TENDENCIAS DEL BIG DATA." Tesis de Licenciatura, Universidad Autónoma del Estado de México, 2019. http://hdl.handle.net/20.500.11799/100155.

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La generación masiva de datos es un fenómeno que se encuentra en constante aumento, debido a que la adquisición y el almacenamiento de datos son cada vez más accesibles, sencillos y baratos, además gracias a la constante participación de las personas en el uso de redes sociales, transacciones bancarias, compras en línea, expedientes médicos, entre otras actividades. En consecuencia, se generan cada vez más datos lo que provocó el surgimiento del concepto Big Data. El incremento en el volumen de adquisición de datos, la variedad de ellos y la velocidad a la cual estos se generan trajo como c
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Kim, Jinju. "ENJOYING SOCIAL TV Re-discovering the social process and big data research." Doctoral thesis, Universitat Autònoma de Barcelona, 2019. http://hdl.handle.net/10803/668003.

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Esta tesis estudia el comportamiento de las audiencias de lo que hoy se conoce como la televisión social: producciones audiovisuales a las que se accede a través de las redes sociales y el televidente puede disfrutar de su proyección y al tiempo compartir su experiencia audiovisual con otras personas. A diferencia de las investigaciones tradicionales que han analizado el consumo transnacional de las producciones culturales (principalmente desde los Estados Unidos al resto del mundo), esta tesis estudia el consumo que hacen las audiencias occidentales (de habla inglesa, española y francesa) d
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Schintler, Laurie A., and Manfred M. Fischer. "Big Data and Regional Science: Opportunities, Challenges, and Directions for Future Research." WU Vienna University of Economics and Business, 2018. http://epub.wu.ac.at/6122/1/Fischer_etal_2018_Big%2Ddata.pdf.

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Recent technological, social, and economic trends and transformations are contributing to the production of what is usually referred to as Big Data. Big Data, which is typically defined by four dimensions -- Volume, Velocity, Veracity, and Variety -- changes the methods and tactics for using, analyzing, and interpreting data, requiring new approaches for data provenance, data processing, data analysis and modeling, and knowledge representation. The use and analysis of Big Data involves several distinct stages from "data acquisition and recording" over "information extraction" and "data integra
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Lacey, Arron S. "Using novel data types for Big Data research in epilepsy : patient records, clinic letters and genetic mutation." Thesis, Swansea University, 2019. https://cronfa.swan.ac.uk/Record/cronfa48905.

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Introduction: The aims of this thesis was to explore novel data types in healthcare that could enhance epidemiology studies in epilepsy and to develop novel methods of analysing routinely collected linked healthcare data, unstructured free text in hospital clinic letters and genetic variation. Method: The SAIL Databank was used to source linked healthcare data for people with epilepsy across Wales to study the effects of epilepsy and social deprivation, coding of epilepsy in GP records and the educational attainment of children born to mothers with epilepsy. Hospital clinic letters from Morris
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Jayapandian, Catherine Praveena. "Cloudwave: A Cloud Computing Framework for Multimodal Electrophysiological Big Data." Case Western Reserve University School of Graduate Studies / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1405516626.

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Poorthuis, Ate. "Social Space and Social Media: Analyzing Urban Space with Big Data." UKnowledge, 2015. http://uknowledge.uky.edu/geography_etds/41.

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This dissertation focuses on the key role that big data can play in minimizing the perceived disconnect between social theory and quantitative methods in the discipline of geography. It takes as its starting point the geographic concept of space, which is conceptualized very differently in social theory versus quantitative methodology. Contrary to this disparity, an examination of the disciplinary history reveals a number of historic precedents and potential pathways for a rapprochement, especially when combined with some of the new possibilities of big data. This dissertation also proposes so
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Mortenson, Michael J. "Is operational research in UK universities fit-for-purpose for the growing field of analytics?" Thesis, Loughborough University, 2018. https://dspace.lboro.ac.uk/2134/33851.

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Over the last decade considerable interest has been generated into the use of analytical methods in organisations. Along with this, many have reported a significant gap between organisational demand for analytical-trained staff, and the number of potential recruits qualified for such roles. This interest is of high relevance to the operational research discipline, both in terms of raising the profile of the field, as well as in the teaching and training of graduates to fill these roles. However, what is less clear, is the extent to which operational research teaching in universities, or indeed
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Schobel, Seth Adam Micah. "The viral genomics revolution| Big data approaches to basic viral research, surveillance, and vaccine development." Thesis, University of Maryland, College Park, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10011480.

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<p> Since the decoding of the first RNA virus in 1976, the field of viral genomics has exploded, first through the use of Sanger sequencing technologies and later with the use next-generation sequencing approaches. With the development of these sequencing technologies, viral genomics has entered an era of big data. New challenges for analyzing these data are now apparent. Here, we describe novel methods to extend the current capabilities of viral comparative genomics. Through the use of antigenic distancing techniques, we have examined the relationship between the antigenic phenotype and the g
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Dhar, Samir. "Addressing Challenges with Big Data for Maritime Navigation: AIS Data within the Great Lakes System." University of Toledo / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1479391944819196.

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Books on the topic "Big Data Research"

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Woo, Sang Eun, Louis Tay, and Robert W. Proctor, eds. Big data in psychological research. American Psychological Association, 2020. http://dx.doi.org/10.1037/0000193-000.

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Póvoa, Ana Paula Ferreira Dias Barbosa, and Joao Luis de Miranda, eds. Operations Research and Big Data. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24154-8.

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Mills, Kathy A. Big Data for Qualitative Research. Routledge, 2019. http://dx.doi.org/10.4324/9780429056413.

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Kishore Mishra, Brojo, Vivek Kumar, Sanjaya Kumar Panda, and Prayag Tiwari. Handbook of Research for Big Data. Apple Academic Press, 2021. http://dx.doi.org/10.1201/9781003144526.

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Prodromou, Theodosia, ed. Big Data in Education: Pedagogy and Research. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76841-6.

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Prodromou, Theodosia, ed. Big Data in Education: Pedagogy and Research. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76841-6.

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Blackburn, Damian. Big data for law firms. Ark Group, 2013.

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Handbook of research on organizational transformations through big data analytics. Business Science Reference, 2015.

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Wang, Baoying, Ruowang Li, and W. Perrizo. Big data analytics in bioinformatics and healthcare. Medical Information Science Reference, 2015.

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1983-, Milligan Ian, and Weingart, Scott (Scott B.), eds. Exploring big historical data: The historian's macroscope. Imperial College Press, 2015.

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Book chapters on the topic "Big Data Research"

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Lukošius, Vaidas, and Michael R. Hyman. "Big Data." In Quantitative Research Methods in Consumer Psychology. Routledge, 2018. http://dx.doi.org/10.4324/9781315641577-6.

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Bechmann, Anja, and Jiyoung Ydun Kim. "Big Data." In Handbook of Research Ethics and Scientific Integrity. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-76040-7_18-1.

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Bechmann, Anja, and Jiyoung Ydun Kim. "Big Data." In Handbook of Research Ethics and Scientific Integrity. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-16759-2_18.

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Poulovassilis, Alexandra. "Database Research Challenges and Opportunities of Big Graph Data." In Big Data. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39467-6_6.

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Edwards, Kieran Jay, and Mohamed Medhat Gaber. "Research Methodology." In Studies in Big Data. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06599-1_5.

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Li, Yong, Zhongying Zhang, Jingpeng Wu, and Qiang Zhang. "Research on User Identity Authentication Based on Online Behavior Similarity." In Big Data. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9709-8_18.

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Chen, Guanlin, Peipei Tang, Canghong Jin, and Zhuoyue Zhu. "Research on Urban Street Order Based on Data Mining Technology." In Big Data. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2922-7_15.

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Panda, Binay. "Big Data and Cancer Research." In Big Data Analytics. Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-3628-3_14.

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Akoka, Jacky, Isabelle Comyn-Wattiau, and Nabil Laoufi. "Research on Big Data." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25747-1_18.

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Bartalesi, Valentina, Carlo Meghini, and Costantino Thanos. "Big Research Data Integration." In Communications in Computer and Information Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30284-9_2.

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Conference papers on the topic "Big Data Research"

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Maemura, Emily, Christoph Becker, and Ian Milligan. "Understanding computational web archives research methods using research objects." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840982.

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Singh, Lisa, Colton Padden, Pamela Davis-Kean, et al. "Text Analytic Research Portals: Supporting Large-Scale Social Science Research." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671696.

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Matsubayashi, Mamiko, and Keiko Kurata. "Conceptual design for comprehensive research support platform: Successful research data management generating big data from little data." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258475.

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Thavasimani, Priyaa, and Paolo Missier. "Facilitating reproducible research by investigating computational metadata." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840958.

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Patton, Robert M., J. Travis Johnston, Steven R. Young, et al. "Exascale Deep Learning to Accelerate Cancer Research." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006467.

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Das, Manirupa, Renhao Cui, David R. Campbell, Gagan Agrawal, and Rajiv Ramnath. "Towards methods for systematic research on big data." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363989.

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Sukul, Adisak, Baskar Gopalakrishnan, Wallapak Tavanapong, and David A. M. Peterson. "Online video ad measurement for political science research." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258161.

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Calo, Seraphin, Emil Lupu, Elisa Bertino, et al. "Research challenges in dynamic policy-based autonomous security." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258266.

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Zhang, Yin, and Jiming Hu. "Discovering the interdisciplinary nature of big data research." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258573.

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Underwood, William, and Richard Marciano. "Computational Thinking in Archival Science Research and Education." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9005682.

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Reports on the topic "Big Data Research"

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Author, Not Given. Accelerating High-Level Waste Glass Corrosion Research with Big Data. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1469277.

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Hsieh, Chung-Chu (George). Building a Cloud Computing and Big Data Infrastructure for Cybersecurity Research and Education. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ad1001077.

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Metzler, Katie, David A. Kim, Nick Allum, and Angella Denman. Who Is Doing Computational Social Science? Trends in Big Data Research A SAGE White Paper. SAGE Publishing, 2016. http://dx.doi.org/10.4135/wp160926.

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Chahal, Husanjot, Helen Toner, and Ilya Rahkovsky. Small Data's Big AI Potential. Center for Security and Emerging Technology, 2021. http://dx.doi.org/10.51593/20200075.

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Conventional wisdom suggests that cutting-edge artificial intelligence is dependent on large volumes of data. An overemphasis on “big data” ignores the existence—and underestimates the potential—of several AI approaches that do not require massive labeled datasets. This issue brief is a primer on “small data” approaches to AI. It presents exploratory findings on the current and projected progress in scientific research across these approaches, which country leads, and the major sources of funding for this research.
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Kim, Jinwon, and Jucheol Moon. Congestion Costs and Scheduling Preferences of Car Commuters in California: Estimates Using Big Data. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2031.

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On average, California car commuters waste 4–5 minutes per morning commute due to congestion. Multiplied across all California car commuters, those few minutes entail a yearly total of approximately 2.3 billion hours of time wasted, costing 6 billion dollars. The objective of this study is to quantify congestion costs and determine how commuters adapt to the level of congestion they face (i.e., commuters’ scheduling utility functions). To that end, this research developed a model of trip scheduling under congestion to construct California commuters’ travel-time profiles, i.e., the menu of trav
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Salter, R., Quyen Dong, Cody Coleman, et al. Data Lake Ecosystem Workflow. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40203.

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The Engineer Research and Development Center, Information Technology Laboratory’s (ERDC-ITL’s) Big Data Analytics team specializes in the analysis of large-scale datasets with capabilities across four research areas that require vast amounts of data to inform and drive analysis: large-scale data governance, deep learning and machine learning, natural language processing, and automated data labeling. Unfortunately, data transfer between government organizations is a complex and time-consuming process requiring coordination of multiple parties across multiple offices and organizations. Past succ
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Lehberger, Mira, and Sven Grüner. (Why) Do farmers’ Big Five personality traits matter? – A systematic literature review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.3.0138.

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Review question / Objective: We aim at answering the following two research questions: 1. How and when do researchers use the Big 5 personality traits when focusing on farmers (research questions, measurement of personality traits)? 2. Do the Big 5 personality traits contribute to explaining (economic) behaviors of farmers? Eligibility criteria: We consider all studies published in the English language, which include primary empirical data from farmers and explicitly focus on the construct of the big five-personality traits and include a measurement of at least one big five-personality traits.
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Sharp, Matthew. Revisiting digital inclusion: A survey of theory, measurement and recent research. Digital Pathways at Oxford, 2022. http://dx.doi.org/10.35489/bsg-dp-wp_2022/04.

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Focusing on the internet as a foundational technology, this paper begins by summarising recent developments in digital inclusion theory, particularly as this relates to developing countries. It sets out a framework of core components of digital inclusion - including ac-cess/use, quality of access/use, affordability, and digital skills - and briefly considers policy implications. The paper then surveys the ways these components are currently measured in household and firm surveys and by international organisations, highlighting some of the often-overlooked weaknesses of current measures, and su
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Saville, Alan, and Caroline Wickham-Jones, eds. Palaeolithic and Mesolithic Scotland : Scottish Archaeological Research Framework Panel Report. Society for Antiquaries of Scotland, 2012. http://dx.doi.org/10.9750/scarf.06.2012.163.

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Why research Palaeolithic and Mesolithic Scotland? Palaeolithic and Mesolithic archaeology sheds light on the first colonisation and subsequent early inhabitation of Scotland. It is a growing and exciting field where increasing Scottish evidence has been given wider significance in the context of European prehistory. It extends over a long period, which saw great changes, including substantial environmental transformations, and the impact of, and societal response to, climate change. The period as a whole provides the foundation for the human occupation of Scotland and is crucial for understan
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Martín, Yago, and Pilar Paneque. Análisis de contenido de Twitter: sequía en España (2017-2020). Universidad Pablo de Olavide, 2021. http://dx.doi.org/10.46661/rio.11698.

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Este informe es resultado de un análisis de la red social Twitter sobre contenidos relacionados con la sequía en España en el período 2017-2020 en el marco de los trabajos del Observatorio Ciudadano de la Sequía. El informe está basado en el análisis de tweets geolocalizados cedidos por el Geoinformation and Big Data Research Laboratory (GIBD) de la University of South Carolina (Estados Unidos), y permite comprender mejor la percepción social y la opinión pública sobre la sequía en España.
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