Journal articles: 'Google general'

1

Chartier, Timothy P. "A Googol of Information about Google." Computing in Science & Engineering 10, no. 6 (November 2008): 11–12. http://dx.doi.org/10.1109/mcse.2008.137.

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Cusumano, Michael A. "Google." Communications of the ACM 48, no. 2 (February 2005): 15–17. http://dx.doi.org/10.1145/1042091.1042107.

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Jones, Edgar. "Google Books as a General Research Collection." Library Resources & Technical Services 54, no. 2 (April 1, 2010): 77–89. http://dx.doi.org/10.5860/lrts.54n2.77.

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Barcia-Zambrano, Alberto Stalin, and Patricio Alfredo Vallejo-Valdivieso. "Google drive y Google classroom en el proceso de enseñanza - aprendizaje en bachillerato general unificado." EPISTEME KOINONIA 3, no. 6 (December 7, 2020): 370. http://dx.doi.org/10.35381/e.k.v3i6.1030.

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La investigación tiene por objetivo analizar el uso didáctico del Google drive y Google classroom en el proceso de enseñanza - aprendizaje en bachillerato general unificado como medio de innovación educativa desde las tecnologías de la información y comunicación (TIC). Se abordó desde el enfoque cuantitativo y tipo descriptivo en una muestra poblacional de 65 estudiantes y 13 docentes. En los resultados, se destaca la aceptación de los estudiantes en relación al trabajo cooperativo, situación que contribuye a generar habilidades sociales basadas en el respeto, la equidad y la sinergia, para construir el aprendizaje. El aprendizaje basado en TIC es el enfoque pedagógico del presente y futuro educativo; por lo tanto, su eficaz implementación es más que necesaria. De esta forma, en función de los resultados obtenidos, se confirma su aporte a un aprendizaje cooperativo – colaborativo, ya que promueve competencias creativas para la generación de una educación innovadora.

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Peterson, Michael, Konal J. Dobson, Kevin Fandry, and William Shrader. "Techniques in Google Earth and Google Maps." Cartographic Perspectives, no. 72 (June 1, 2012): 75–90. http://dx.doi.org/10.14714/cp72.423.

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Aasland, Olaf G. "Google-leger." Tidsskrift for Den norske legeforening 136, no. 12/13 (2016): 1132. http://dx.doi.org/10.4045/tidsskr.16.0495.

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7

Conn, Vicki S. "Google Consultation." Western Journal of Nursing Research 40, no. 5 (January 3, 2018): 615–16. http://dx.doi.org/10.1177/0193945917752095.

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Horn, Roger A., and Stefano Serra-Capizzano. "A General Setting for the Parametric Google Matrix." Internet Mathematics 3, no. 4 (January 2006): 385–411. http://dx.doi.org/10.1080/15427951.2006.10129131.

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9

Niamtu, Joe. "Google Glass." Dermatologic Surgery 40, no. 10 (October 2014): 1150–52. http://dx.doi.org/10.1097/01.dss.0000452655.37260.c5.

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10

Marx, Werner, and Hermann Schier. "CAS kontra Google." Nachrichten aus der Chemie 53, no. 12 (December 2005): 1228–32. http://dx.doi.org/10.1002/nadc.20050531210.

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BAUM, RUDY M. "The Google Model." Chemical & Engineering News 85, no. 45 (November 5, 2007): 3. http://dx.doi.org/10.1021/cen-v085n045.p003.

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12

Sprigman, Christopher Jon. "Oracle v. Google." Communications of the ACM 58, no. 5 (April 23, 2015): 27–29. http://dx.doi.org/10.1145/2742691.

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Menon, Tulsi. "GOOGLE SCHOLAR WEBSITE." ANZ Journal of Surgery 77, no. 7 (July 2007): 595. http://dx.doi.org/10.1111/j.1445-2197.2007.04161.x.

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Samuelson, Pamela. "Oracle v. Google." Communications of the ACM 55, no. 11 (November 2012): 25–27. http://dx.doi.org/10.1145/2366316.2366325.

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15

Beaton, James, and Cheri Smith. "Google versus PubMed." Annals of The Royal College of Surgeons of England 87, no. 6 (November 1, 2005): 491–92. http://dx.doi.org/10.1308/003588405x71207.

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Peterson, Michael. "Choropleth Google Maps." Cartographic Perspectives, no. 60 (June 1, 2008): 80–83. http://dx.doi.org/10.14714/cp60.237.

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17

Macknight, Vicki, and Fabien Medvecky. "(Google-)Knowing Economics." Social Epistemology 34, no. 3 (January 13, 2020): 213–26. http://dx.doi.org/10.1080/02691728.2019.1702735.

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18

Williams, Nigel. "Google on down." Current Biology 19, no. 4 (February 2009): R150. http://dx.doi.org/10.1016/j.cub.2009.02.008.

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Valtysson, Bjarki, Rikke Frank Jørgensen, and Johan Lau Munkholm. "Co-constitutive complexity." Nordicom Review 42, no. 1 (January 1, 2021): 124–40. http://dx.doi.org/10.2478/nor-2021-0033.

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Abstract Google is the gateway to the Internet for billions of people. However, to use Google’s multiple platforms and services, users must accept Google’s terms. With the advent of the EU’s GDPR (General Data Protection Regulation), Google made significant changes to these terms. In this article, we scrutinise the intertextual relations between Google’s privacy policies and terms of service (ToS) and the GDPR – and the discursive co-constitutive complexity within and between these frameworks. We argue that the material and communicative articulation of Google’s privacy policies and ToS should be understood as deliberative data politics delimiting users’ agency, consent, and privacy. Furthermore, we emphasise complexity and the demands of reducing complexity as two opposing dynamics. While the GDPR required Google to make its terms and policies clearer and more understandable, ironically, in the process of accommodating GDPR’s demand of increased transparency, the discursive complexity of Google’s policies has in fact increased.

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20

Baird, William H., Clifford W. Padgett, and Jeffery A. Secrest. "Google Earth science." Physics Education 50, no. 2 (February 20, 2015): 224–31. http://dx.doi.org/10.1088/0031-9120/50/2/224.

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21

Oberdan, Thomas. "Google and Gödel." Bulletin of Science, Technology & Society 29, no. 6 (November 13, 2009): 464–69. http://dx.doi.org/10.1177/0270467609349046.

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Aguiar, C. E., and A. R. Souza. "Google Earth physics." Physics Education 44, no. 6 (October 27, 2009): 624–26. http://dx.doi.org/10.1088/0031-9120/44/6/009.

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23

Andrews, John S. "Do You Google?" Ambulatory Pediatrics 8, no. 3 (May 2008): 152–53. http://dx.doi.org/10.1016/j.ambp.2008.03.031.

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24

Shibarshina, Svetlana. "About trust in Google, and more." Digital Scholar Philosopher s Lab 4, no. 1 (2021): 37–44. http://dx.doi.org/10.32326/2618-9267-2021-4-1-37-44.

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This text is a reply in the discussion about the challenges of digitalization for the technogenic civilization. In the context of the general problematic field of reflections initiated by Evgeniy Maslanov, the author focuses on the power of algorithms starting from M. Foucault’s concept of power and correlating it with the actor-network theory and John Urri’s thought on path dependence. The author suggests a few examples of how algorithms collect, compare and analyze user behavior in the Internet space (also with reference to the research of M. Kosinski), demonstrating how all of these allow to reconstruct an amazingly accurate digital portrait of a particular individual.

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25

Maxymuk, John. "Searching beyond Google." Bottom Line 21, no. 3 (October 31, 2008): 97–100. http://dx.doi.org/10.1108/08880450810912862.

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Landuyt, Cathryn, and Jerry Chin. "AN INTRODUCTION OF COMBINATIONS AND PERMUTATIONS WITH GOOGLE SPREADSHEET’S GOOGLE SCRIPT." International Journal of Business Research 20, no. 3 (October 1, 2020): 29–36. http://dx.doi.org/10.18374/ijbr-20-3.3.

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27

Omar, Muhammad, Arif Mehmood, Gyu Sang Choi, and Han Woo Park. "Global mapping of artificial intelligence in Google and Google Scholar." Scientometrics 113, no. 3 (October 4, 2017): 1269–305. http://dx.doi.org/10.1007/s11192-017-2534-4.

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28

Smith, Graham. "Advocate General advises CJEU on copyright liability of online sharing platforms." Journal of Intellectual Property Law & Practice 15, no. 11 (October 2, 2020): 857–58. http://dx.doi.org/10.1093/jiplp/jpaa158.

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Abstract Opinion of Advocate General Saugmandsgaard Øe in Joined Cases C-682/18 Peterson v Google LLC, YouTube LLC, YouTube Inc and Google Germany GmbH and C-683/18 Elsevier Inc v Cyando AG, EU:C:2020:586, 16 July 2020

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29

The Lancet. "Good deeds and Google." Lancet 371, no. 9621 (April 2008): 1310. http://dx.doi.org/10.1016/s0140-6736(08)60570-x.

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30

Das, S. "Disruptiveness of Google Health." BMJ 337, no. 19 1 (November 19, 2008): a2596. http://dx.doi.org/10.1136/bmj.a2596.

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31

Watts, G. "Google watches over flu." BMJ 337, dec31 1 (December 31, 2008): a3076. http://dx.doi.org/10.1136/bmj.a3076.

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32

cas. "Google fischt Patienteninfos ab." Der Freie Zahnarzt 59, no. 10 (October 2015): 8. http://dx.doi.org/10.1007/s12614-015-5870-x.

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33

Renier, Romain. "Faut-il casser Google ?" Alternatives Économiques N° 342, no. 1 (January 1, 2015): 70. http://dx.doi.org/10.3917/ae.342.0070.

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34

Savoia, Alberto, and Patrick Copeland. "Entrepreneurial Innovation at Google." Computer 44, no. 4 (April 2011): 56–61. http://dx.doi.org/10.1109/mc.2011.62.

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35

Mutanga, Onisimo, and Lalit Kumar. "Google Earth Engine Applications." Remote Sensing 11, no. 5 (March 12, 2019): 591. http://dx.doi.org/10.3390/rs11050591.

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36

Elnashar, Abdelrazek, Hongwei Zeng, Bingfang Wu, Ning Zhang, Fuyou Tian, Miao Zhang, Weiwei Zhu, et al. "Downscaling TRMM Monthly Precipitation Using Google Earth Engine and Google Cloud Computing." Remote Sensing 12, no. 23 (November 25, 2020): 3860. http://dx.doi.org/10.3390/rs12233860.

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Accurate precipitation data at high spatiotemporal resolution are critical for land and water management at the basin scale. We proposed a downscaling framework for Tropical Rainfall Measuring Mission (TRMM) precipitation products through integrating Google Earth Engine (GEE) and Google Colaboratory (Colab). Three machine learning methods, including Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), and Artificial Neural Network (ANN) were compared in the framework. Three vegetation indices (Normalized Difference Vegetation Index, NDVI; Enhanced Vegetation Index, EVI; Leaf Area Index, LAI), topography, and geolocation are selected as geospatial predictors to perform the downscaling. This framework can automatically optimize the models’ parameters, estimate features’ importance, and downscale the TRMM product to 1 km. The spatial downscaling of TRMM from 25 km to 1 km was achieved by using the relationships between annual precipitations and annually-averaged vegetation index. The monthly precipitation maps derived from the annual downscaled precipitation by disaggregation. According to validation in the Great Mekong upstream region, the ANN yielded the best performance when simulating the annual TRMM precipitation. The most sensitive vegetation index for downscaling TRMM was LAI, followed by EVI. Compared with existing downscaling methods, the proposed framework for downscaling TRMM can be performed online for any given region using a wide range of machine learning tools and environmental variables to generate a precipitation product with high spatiotemporal resolution.

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37

O’Shea, Donald C. "Tower of Google II." Optical Engineering 45, no. 3 (March 1, 2006): 030101. http://dx.doi.org/10.1117/1.2190700.

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38

Roberston, L. "Anecdotes [Google search engine]." IEEE Annals of the History of Computing 27, no. 3 (July 2005): 96–97. http://dx.doi.org/10.1109/mahc.2005.33.

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39

The Lancet. "A UK Google for guidelines." Lancet 373, no. 9675 (May 2009): 1578. http://dx.doi.org/10.1016/s0140-6736(09)60830-8.

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40

Stein, A. "Dr Google or Dr Lazy?" QJM 104, no. 4 (January 19, 2011): 373–77. http://dx.doi.org/10.1093/qjmed/hcr004.

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41

Giustini, Dean. "How Google is changing medicine." BMJ 331, no. 7531 (December 22, 2005): 1487–88. http://dx.doi.org/10.1136/bmj.331.7531.1487.

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42

Füeßl, H. S. "Mit Google geht’s nach Babel." MMW - Fortschritte der Medizin 157, no. 3 (February 2015): 35. http://dx.doi.org/10.1007/s15006-015-2694-1.

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43

Rishi, Rajesh, and Pinki Doloi. "Home Automation Using Google Assistant." IJIREEICE 9, no. 6 (June 30, 2021): 116–27. http://dx.doi.org/10.17148/ijireeice.2021.9626.

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44

Duong, Diana. "Time to embrace Dr. Google?" Canadian Medical Association Journal 193, no. 16 (April 18, 2021): E571—E572. http://dx.doi.org/10.1503/cmaj.1095935.

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45

Delépine, Justin. "Google Maps présente la facture." Alternatives Économiques N° 380, no. 6 (June 1, 2018): 46. http://dx.doi.org/10.3917/ae.380.0046.

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46

Cazenave, Franck, Marc Chevallier, and Astrid Gruyelle. "Google veut révolutionner la mobilité." Alternatives Économiques N° 343, no. 2 (February 1, 2015): 74. http://dx.doi.org/10.3917/ae.343.0074.

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47

Delépine, Justin. "Bruxelles met Google à l'amende." Alternatives Économiques N° 370, no. 7 (July 1, 2017): 52. http://dx.doi.org/10.3917/ae.370.0052.

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48

Nathan, Hervé. "Pourquoi il faut démanteler Google." Alternatives Économiques N° 377, no. 3 (March 1, 2018): 40. http://dx.doi.org/10.3917/ae.377.0040.

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49

Zhao, Qiang, Le Yu, Xuecao Li, Dailiang Peng, Yongguang Zhang, and Peng Gong. "Progress and Trends in the Application of Google Earth and Google Earth Engine." Remote Sensing 13, no. 18 (September 21, 2021): 3778. http://dx.doi.org/10.3390/rs13183778.

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Earth system science has changed rapidly due to global environmental changes and the advent of Earth observation technology. Therefore, new tools are required to monitor, measure, analyze, evaluate, and model Earth observation data. Google Earth (GE) was officially launched by Google in 2005 as a ”geobrowser”, and Google Earth Engine (GEE) was released in 2010 as a cloud computing platform with substantial computational capabilities. The use of these two tools or platforms in various applications, particularly as used by the remote sensing community, has developed rapidly. In this paper, we reviewed the applications and trends in the use of GE and GEE by analyzing peer-reviewed articles, dating up to January 2021, in the Web of Science (WoS) core collection using scientometric analysis (i.e., by using CiteSpace) and meta-analysis. We found the following: (1) the number of articles describing the use of GE or GEE increased substantially from two in 2006 to 530 in 2020. The number of GEE articles increased much faster than those concerned with the use of GE. (2) Both GE and GEE were extensively used by the remote sensing community as multidisciplinary tools. GE articles covered a broader range of research areas (e.g., biology, education, disease and health, economic, and information science) and appeared in a broader range of journals than those concerned with the use of GEE. (3) GE and GEE shared similar keywords (e.g., “land cover”, “water”, “model”, “vegetation”, and “forest”), which indicates that their application is of great importance in certain research areas. The main difference was that articles describing the use of GE emphasized its use as a visual display platform, while those concerned with GEE placed more emphasis on big data and time-series analysis. (4) Most applications of GE and GEE were undertaken in countries, such as the United States, China, and the United Kingdom. (5) GEE is an important tool for analysis, whereas GE is used as an auxiliary tool for visualization. Finally, in this paper, the merits and limitations of GE and GEE, and recommendations for further improvements, are summarized from an Earth system science perspective.

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50

Newton Miller, Laura. "Physicists and Astronomers Use Google as a Starting Point for Specific Queries, but Do Not Intentionally Use It to Search for Articles." Evidence Based Library and Information Practice 6, no. 1 (March 16, 2011): 81. http://dx.doi.org/10.18438/b8zs6n.

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A Review of: Jamali, H. R., & Asadi, S. (2010). Google and the scholar: The role of Google in scientists' information seeking behaviour. Online Information Review, 34(2), 282-294. Objective – To determine how Google’s general search engine impacts the information-seeking behaviour of physicists and astronomers. Design – Using purposive stratified non-random sampling, a mixed-methods study was conducted which included one-on-one interviews, information-event cards, and an online questionnaire survey. Setting – Department of Physics and Astronomy at University College London. Subjects – The researchers interviewed 26 PhD students and 30 faculty members (23% of the department’s 242 faculty and students), and 24 of those participants completed information-event cards. A total of 114 respondents (47.1% of the department members) participated in the online survey. Methods – The researchers conducted 56 interviews which lasted an average of 44 minutes each. These were digitally recorded, fully transcribed, and coded. The researchers asked questions related to information-seeking behaviour and scholarly communication. Four information-event cards were given to volunteer interviewees to gather critical incident information on their first four information-seeking actions after the interview. These were to be completed preferably within the first week of receiving the cards, with 82 cards completed by 24 participants. Once initial analysis of the interviews was completed, the researchers sent an online survey to the members of the same department. Main Results – This particular paper examined only the results related to the scholars’ information-seeking behaviour in terms of search engines and web searching. Details of further results are examined in Jamali (2008) and Jamali and Nicholas (2008). The authors reported that 18% of the respondents used Google on a daily basis to identify articles. They also found that 11% searched subject databases, and 9% searched e-journal websites on a daily basis. When responses on daily searching were combined with those from participants who searched two to three times per week, the most popular method for finding research was by tracking references at the end of an article (61%). This was followed by Google (58%) and ToC email alerts (35%). Responses showed that 46% never used Google Scholar to discover research articles. When asked if they intentionally searched Google to find articles, all except two participants answered that they do not, instead using specific databases to find research. The researchers noted that finding articles in Google was not the original intention of participants’ searches, but more of a by-product of Google searching. In the information-event card study, two categories emerged based on the kinds of information required. This included participants looking for general information on a specific topic (64%, with 22 cases finding this information successfully), and participants knowing exactly what piece of information they were seeking (36%, with 28 cases finding information successfully). There was no occurrence of using Google specifically to conduct a literature search or to search for a paper during this information-event card study, although the researchers say that Google is progressively showing more scholarly information within its search results. (This cannot be ascertained from these specific results except for one response from an interviewee.) The researchers found that 29.4% of respondents used Google to find specific pieces of information, although it was not necessarily scholarly. Conclusion – Physics and astronomy researchers do not intentionally use Google’s general search engine to search for articles, but, Google seems to be a good starting point for problem-specific information queries.

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Journal articles on the topic 'Google general'

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