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Journal articles on the topic 'Data box'

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Published: 4 June 2021
Last updated: 18 February 2022

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1

Waldrop, M. Mitchell. "Data Center in a Box." Scientific American 297, no. 2 (August 2007): 90–93. http://dx.doi.org/10.1038/scientificamerican0807-90.

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2

Chaudhry, Amir, Jon Crowcroft, Heidi Howard, Anil Madhavapeddy, Richard Mortier, Hamed Haddadi, and Derek McAuley. "Personal Data: Thinking Inside the Box." Aarhus Series on Human Centered Computing 1, no. 1 (October 5, 2015): 4. http://dx.doi.org/10.7146/aahcc.v1i1.21312.

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<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>We are in a ‘personal data gold rush’ driven by advertising being the primary revenue source for most online companies. These companies accumulate extensive personal data about individuals with minimal concern for us, the subjects of this process. This can cause many harms: privacy infringement, personal and professional embarrassment, restricted access to labour markets, restricted access to highest value pricing, and many others. There is a critical need to provide technologies that enable alternative practices, so that individuals can par- ticipate in the collection, management and consumption of their personal data. In this paper we discuss the Databox, a personal networked device (and associated services) that col- lates and mediates access to personal data, allowing us to re- cover control of our online lives. We hope the Databox is a first step to re-balancing power between us, the data subjects, and the corporations that collect and use our data. </span></p></div></div></div>
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3

Bryan, Elizabeth H. "Activities: Exploring Data with Box Plots." Mathematics Teacher 81, no. 8 (November 1988): 658–63. http://dx.doi.org/10.5951/mt.81.8.0658.

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4

Zhang, Tonglin, and Baijian Yang. "Box–Cox Transformation in Big Data." Technometrics 59, no. 2 (April 3, 2017): 189–201. http://dx.doi.org/10.1080/00401706.2016.1156025.

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5

Zhang, Tianwen, Xiaoling Zhang, Jianwei Li, Xiaowo Xu, Baoyou Wang, Xu Zhan, Yanqin Xu, et al. "SAR Ship Detection Dataset (SSDD): Official Release and Comprehensive Data Analysis." Remote Sensing 13, no. 18 (September 15, 2021): 3690. http://dx.doi.org/10.3390/rs13183690.

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SAR Ship Detection Dataset (SSDD) is the first open dataset that is widely used to research state-of-the-art technology of ship detection from synthetic aperture radar (SAR) imagery based on deep learning (DL). According to our investigation, up to 46.59% of the total 161 public reports confidently select SSDD to study DL-based SAR ship detection. Undoubtedly, this situation reveals the popularity and great influence of SSDD in the SAR remote sensing community. Nevertheless, the coarse annotations and ambiguous standards of use of its initial version both hinder fair methodological comparisons and effective academic exchanges. Additionally, its single-function horizontal-vertical rectangle bounding box (BBox) labels can no longer satisfy the current research needs of the rotatable bounding box (RBox) task and the pixel-level polygon segmentation task. Therefore, to address the above two dilemmas, in this review, advocated by the publisher of SSDD, we will make an official release of SSDD based on its initial version. SSDD’s official release version will cover three types: (1) a bounding box SSDD (BBox-SSDD), (2) a rotatable bounding box SSDD (RBox-SSDD), and (3) a polygon segmentation SSDD (PSeg-SSDD). We relabel ships in SSDD more carefully and finely, and then explicitly formulate some strict using standards, e.g., (1) the training-test division determination, (2) the inshore-offshore protocol, (3) the ship-size reasonable definition, (4) the determination of the densely distributed small ship samples, and (5) the determination of the densely parallel berthing at ports ship samples. These using standards are all formulated objectively based on the using differences of existing 75 (161 × 46.59%) public reports. They will be beneficial for fair method comparison and effective academic exchanges in the future. Most notably, we conduct a comprehensive data analysis on BBox-SSDD, RBox-SSDD, and PSeg-SSDD. Our analysis results can provide some valuable suggestions for possible future scholars to further elaborately design DL-based SAR ship detectors with higher accuracy and stronger robustness when using SSDD.
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Jere, Stanley, and Edwin Moyo. "Modelling Epidemiological Data Using Box-Jenkins Procedure." Open Journal of Statistics 06, no. 02 (2016): 295–302. http://dx.doi.org/10.4236/ojs.2016.62025.

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7

Tominaga, Yukio. "Data Structure Comparison Using Box Counting Analysis." Journal of Chemical Information and Computer Sciences 38, no. 5 (September 1998): 867–75. http://dx.doi.org/10.1021/ci9802070.

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8

Price, W. Nicholson. "Big data and black-box medical algorithms." Science Translational Medicine 10, no. 471 (December 12, 2018): eaao5333. http://dx.doi.org/10.1126/scitranslmed.aao5333.

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9

Kim, Myung-Geun. "Outliers in Multivariate Box-Cox Transformed Data." Communications for Statistical Applications and Methods 11, no. 1 (April 1, 2004): 189–96. http://dx.doi.org/10.5351/ckss.2004.11.1.189.

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10

Luo, Tianren, Xueyong Li, Kun Ma, and Xiaoying Luo. "Toward mobile smart data file protection box." International Journal of Autonomic Computing 2, no. 3 (2017): 282. http://dx.doi.org/10.1504/ijac.2017.086992.

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Luo, Xiaoying, Kun Ma, Tianren Luo, and Xueyong Li. "Toward mobile smart data file protection box." International Journal of Autonomic Computing 2, no. 3 (2017): 282. http://dx.doi.org/10.1504/ijac.2017.10008073.

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12

Tomlinson, Roger. "Finding Out More from Box Office Data." Market Research Society. Journal. 34, no. 4 (July 1992): 1–11. http://dx.doi.org/10.1177/147078539203400408.

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13

Ljung, Lennart, Qinghua Zhang, Peter Lindskog, and Anatoli Juditski. "Estimation of grey box and black box models for non-linear circuit data." IFAC Proceedings Volumes 37, no. 13 (September 2004): 399–404. http://dx.doi.org/10.1016/s1474-6670(17)31256-9.

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14

Chen, Runyu, Wei Xu, and Xinghan Zhang. "Dynamic box office forecasting based on microblog data." Filomat 30, no. 15 (2016): 4111–24. http://dx.doi.org/10.2298/fil1615111c.

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Movies, as one of the most rapidly developing industries? outcomes, have gained much attention these years. Especially in China, the world? s second largest film market with a rapid growing speed, many film companies intend to foresee the future box office in advance to better arrange their income and expenditure. Unlike some traditional forecasting model based on several movie-related features, this paper comprehensively utilizes the real-time social media, microblog, to realize a more accurate weekly box office forecasting model. The features weekly extracted from microblogs can be divided into count based features and context based features, along with the existing box office and the screen arrangements, to predict the box office in next week. For count based features, not only the total volume of related microblogs and the diffusion effect considers the number of followers, several unnoticed features like authentication users, gender ratio and mobile-users ratio are also introduced into the original predicting model. For content based features, a duplicate semantic analysis method is proposed. The number of tweets which can indeed influence others? purchase decision, along with the number of tweets with positive and negative influence is the results of the analysis system. On this basis, guided effect for each influential tweets are identified by the praise, comment and retweet times. Some machine learning models are then adopted after using genetic algorithm (GA) for feature selection. The empirical study shows that our research can dynamic forecast box office with a sustainable good performance.
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Lee, O.-Joun, Seung-Bo Park, Daul Chung, and Eun-Soon You. "Movie Box-office Analysis using Social Big Data." Journal of the Korea Contents Association 14, no. 10 (October 28, 2014): 527–38. http://dx.doi.org/10.5392/jkca.2014.14.10.527.

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Etuk, Ette Harrison, Bartholomew Uchendu, and Ephraim Okon Udo. "Box-Jenkins Modelling of Nigerian Stock Prices Data." Greener Journal of Science, Engineering and Technological Research 2, no. 2 (September 20, 2012): 032–38. http://dx.doi.org/10.15580/gjsetr.2012.2.1211.

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17

MCNAMARA, DAMIAN. "Atomoxetine Gets Black Box Despite Dearth of Data." Clinical Psychiatry News 33, no. 11 (November 2005): 1–8. http://dx.doi.org/10.1016/s0270-6644(05)70936-x.

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18

Nuzzo, Regina L. "The Box Plots Alternative for Visualizing Quantitative Data." PM&R 8, no. 3 (March 2016): 268–72. http://dx.doi.org/10.1016/j.pmrj.2016.02.001.

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HAMILTON-MILLER, J. M. T. "The box-plot method for illustrating MIC data." Journal of Antimicrobial Chemotherapy 31, no. 4 (1993): 607–8. http://dx.doi.org/10.1093/jac/31.4.607.

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DONNELLY, J. P. "Limitations of box-plots in summarizing MIC data." Journal of Antimicrobial Chemotherapy 33, no. 6 (1994): 1251. http://dx.doi.org/10.1093/jac/33.6.1251.

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21

Moran, Mark. "Suicide Data Prompt Call for Black-Box Review." Psychiatric News 42, no. 19 (October 5, 2007): 1–34. http://dx.doi.org/10.1176/pn.42.19.0001.

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22

Calciu, Irina, Siddhartha Sen, Mahesh Balakrishnan, and Marcos K. Aguilera. "Black-box Concurrent Data Structures for NUMA Architectures." ACM SIGOPS Operating Systems Review 51, no. 2 (April 4, 2017): 207–21. http://dx.doi.org/10.1145/3093315.3037721.

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Calciu, Irina, Siddhartha Sen, Mahesh Balakrishnan, and Marcos K. Aguilera. "Black-box Concurrent Data Structures for NUMA Architectures." ACM SIGPLAN Notices 52, no. 4 (May 12, 2017): 207–21. http://dx.doi.org/10.1145/3093336.3037721.

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Calciu, Irina, Siddhartha Sen, Mahesh Balakrishnan, and Marcos K. Aguilera. "Black-box Concurrent Data Structures for NUMA Architectures." ACM SIGARCH Computer Architecture News 45, no. 1 (May 11, 2017): 207–21. http://dx.doi.org/10.1145/3093337.3037721.

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25

McKenna, Mike P., Jill P. Mesirov, and Stavros A. Zenios. "Data Parallel Quadratic Programming on Box-Constrained Problems." SIAM Journal on Optimization 5, no. 3 (August 1995): 570–89. http://dx.doi.org/10.1137/0805029.

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26

Jun, Hee-Gook, Geun-Soo Hyun, Kyung-Bin Lim, Woo-Hyun Lee, and Hyoung-Joo Kim. "Big Data Preprocessing for Predicting Box Office Success." KIISE Transactions on Computing Practices 20, no. 12 (December 15, 2014): 615–22. http://dx.doi.org/10.5626/ktcp.2014.20.12.615.

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27

Angelov, Plamen. "Outside the box: an alternative data analytics framework." Journal of Automation, Mobile Robotics & Intelligent Systems 8, no. 2 (April 7, 2014): 29–35. http://dx.doi.org/10.14313/jamris_2-2014/16.

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28

Xingna, Hou, Ma Jun, Chen Shouhong, and Tao Daiyu. "Design of data collection box based on NRF24L01." MATEC Web of Conferences 173 (2018): 01006. http://dx.doi.org/10.1051/matecconf/201817301006.

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For the increasingly demanding of real-time temperature monitoring in industrial and, agricultural production, a data collection box based on wireless communication module NRF24L01 is, designed, temperature is collected by a high-precision temperature sensor AD590.Design method of hardware and software of the system is described in detail, the configuration method of NRF24L01 is given., The application of this design in wireless temperature collection system is discussed.The experimental result shows that the design has realized two real-time monitoring on temperature of two points, it can display the, value in different environment, a sounder is equipped in the design to alarm for over-temperature.
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Zhou, Xiabing, Bin Li, Yanrong Qi, and Wanying Dong. "Mimic Encryption Box for Network Multimedia Data Security." Security and Communication Networks 2020 (October 28, 2020): 1–24. http://dx.doi.org/10.1155/2020/8868672.

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With the rapid development of the Internet, the security of network multimedia data has attracted increasingly more attention. The moving target defense (MTD) and cyber mimic defense (CMD) approaches provide a new way to solve this problem. To enhance the security of network multimedia data, this paper proposes a mimic encryption box for network multimedia data security. The mimic encryption box can directly access the network where the multimedia device is located, automatically complete the negotiation, provide safe and convenient encryption services, and effectively prevent network attacks. According to the principles of dynamization, diversification, and randomization, the mimic encryption box uses a reconfigurable encryption algorithm to encrypt network data and uses IP address hopping, port number hopping, protocol camouflage, and network channel change to increase the attack threshold. Second, the mimic encryption box has a built-in pseudorandom number generator and key management system, which can generate an initial random key and update the key with the hash value of the data packet to achieve “one packet, one key.” Finally, through the cooperation of the ARM and the FPGA, an access control list can be used to filter illegal data and monitor the working status of the system in real time. If an abnormality is found, the feedback reconstruction mechanism is used to “clean” the FPGA to make it work normally again. The experimental results and analysis show that the mimic encryption box designed in this paper has high network encryption performance and can effectively prevent data leakage. At the same time, it provides a mimic security defense mechanism at multiple levels, which can effectively resist a variety of network attacks and has high security.
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Balássy, Z. "Correction of data measured in a shear box." Soil and Tillage Research 19, no. 2-3 (February 1991): 165–73. http://dx.doi.org/10.1016/0167-1987(91)90084-b.

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31

Rayat, Arash, Seyed Hossein Amirshahi, and Farnaz Agahian. "Compression of spectral data using Box-Cox transformation." Color Research & Application 39, no. 2 (October 11, 2012): 136–42. http://dx.doi.org/10.1002/col.21771.

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32

Michell, Victoria. "National data opt-out: Please tick another box." BDJ In Practice 34, no. 8 (August 2021): 35. http://dx.doi.org/10.1038/s41404-021-0830-4.

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33

Zwitter, Andrej J., and Amelia Hadfield. "Governing Big Data." Politics and Governance 2, no. 1 (April 14, 2014): 1–3. http://dx.doi.org/10.17645/pag.v2i1.2.

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2.5 quintillion bytes of data are created every day through pictures, messages, gps-data, etc. "Big Data" is seen simultaneously as the new Philosophers Stone and Pandora's box: a source of great knowledge and power, but equally, the root of serious problems.
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Varouchakis, Emmanouil A. "Gaussian Transformation Methods for Spatial Data." Geosciences 11, no. 5 (May 1, 2021): 196. http://dx.doi.org/10.3390/geosciences11050196.

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Data gaussianity is an important tool in spatial statistical modeling as well as in experimental data analysis. Usually field and experimental observation data deviate significantly from the normal distribution. This work presents alternative methods for data transformation and revisits the applicability of a modified version of the well-known Box-Cox technique. The recently proposed method has the significant advantage of transforming negative sign (fluctuations) data in advance to positive sign ones. Fluctuations derived from data detrending cannot be transformed using common methods. Therefore, the Modified Box-Cox technique provides a reliable solution. The method was tested in average rainfall data and detrended rainfall data (fluctuations), in groundwater level data, in Total Organic Carbon wt% residuals and using random number generator simulating potential experimental results. It was found that the Modified Box-Cox technique competes successfully in data transformation. On the other hand, it improved significantly the normalization of negative sign data or fluctuations. The coding of the method is presented by means of a Graphical User Interface format in MATLAB environment for reproduction of the results and public access.
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Chen, Bin, Yiduo Wang, Rongxiao Wang, Zhengqiu Zhu, Liang Ma, Xiaogang Qiu, and Weihui Dai. "The Gray-Box Based Modeling Approach Integrating Both Mechanism-Model and Data-Model: The Case of Atmospheric Contaminant Dispersion." Symmetry 12, no. 2 (February 6, 2020): 254. http://dx.doi.org/10.3390/sym12020254.

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With the profound understanding of the world, modeling and simulation has been used to solve the problems of complex systems. Generally, mechanism-models are often used to model the engineering systems following the Newton laws, and this kind of modeling approach is called white-box modeling; however, when the internal structure and characteristics of some systems are hard to understand, the black-box modeling based on statistic and data-modeling is often used. For most complex real systems, a single modeling approach can hardly describe the target system accurately. In this paper, we firstly discuss and compare the white-box and black-box modeling approaches. Then, to mitigate the limitations of these two modeling methods in mechanism-partially-observed systems, the gray-box based modeling approach integrating both a mechanism model and data model is proposed. In order to explain the idea of gray-box based modeling, the atmosphere dispersion modeling is studied in practical cases from two symmetric aspects. Specifically, the framework of data assimilation is used to illustrate the modeling from white-box to gray-box, while the Gauss features based Support Vector Regression (SVR) models are used to illustrate the modeling from black-box to gray-box. To verify the feasibility of the gray-box modeling method, we conducted both simulation experiments and real dataset symmetry experiments. The experiment results show the enhanced performance of the gray-box based modeling approach. In the end, we expect that this gray-box based modeling approach will be an alternative modeling approach for different existing systems.
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EBACH, MALTE C., MARCELO R. DE CARVALHO, and DAVID M. WILLIAMS. "Opening Pandora’s Molecular Box." Zootaxa 2946, no. 1 (July 8, 2011): 60. http://dx.doi.org/10.11646/zootaxa.2946.1.13.

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Mooi & Gill (2010) have prised open the cap of the molecular systematics vial and caused a debate to take-off in the ichthyological community. Molecular trees and their supporting evidence are the first two items to leave this Pandora’s box, closely followed by DNA barcoding and DNA taxonomy. In short, the debate is fuelled by the nature of molecular data: can nucleotide sequences provide the necessary evidence for relationship? The majority (Wiley et al., 2011) believe that DNA contains informative data; however, in our view, they have failed to ascertain the truth of their claim. Not all data are informative. Data may provide supporting evidence, conflicting evidence, or no evidence at all. Assuming that all data are informative apriori to analysis is a theoretical position, not an empirical one. We claim that systematics is, quite the contrary, empirical, and relies on evidence rather than on implicit measurements of data. Consequently, this assertion leads back to the original question of evidence in molecular systematics, namely molecular homology.
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Lu, Hailin, Jing Hao, Jiwei Zhong, Yafei Wang, and Hongyin Yang. "Analysis of Sunshine Temperature Field of Steel Box Girder Based on Monitoring Data." Advances in Civil Engineering 2020 (January 30, 2020): 1–10. http://dx.doi.org/10.1155/2020/9572602.

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In this study, based on the recorded meteorological data of the bridge site, a spatial-temporal temperature model of a 3-span steel box girder is developed through applying the thermal analysis software TAITHERM. Firstly, the rationality and dependability of the proposed spatial-temporal temperature model are adequately verified by means of implementing the comparison with the measurement data. Then the temperature distribution of the steel box girder is analyzed and discussed in detail. The analytical results show that the time of the bottom of pavement reaching the daily maximum temperature lags behind the top of pavement by 2 or 3 hours due to the thermal insulation effect of pavement, and the maximum vertical temperature gradient of the structure exceeds the existing standards. Moreover, with the help of the analytical model, a parametric study of comprehensively meteorological factors is also performed. The results of the sensitivity analysis indicate that solar radiation is the most significant factor affecting the maximum vertical temperature gradient of the steel box girder, followed by air temperature and wind speed. After that, with the representative values of the extreme meteorological parameters during 100-year return period in Wuhan City in China being considered as the thermal boundary conditions, the temperature distribution of the steel box girder is further studied for investigation purpose. The results demonstrate that the heat conduction process of the steel box girder has distinct “box-room effect,” and it is of great necessity to consider both the actual weather conditions at the bridge site and the “box-room effect” of steel box girder when calculating thermal behaviors of bridge structures. Finally, it is related that the particular method proposed in this paper possesses a satisfactory application prospect for temperature field analysis upon various types of bridges in different regions.
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38

Zhang, Ai Guo, and Qun Yong Wu. "Mobile Cache of CGML Spatial Data." Applied Mechanics and Materials 263-266 (December 2012): 1664–68. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.1664.

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As the development of wireless network and 3rd generation communication, LBS will have broad application prospects. At the same time, CGML spatial data is evolving as the transmission and exchange standard of mobile spatial data because of its compression and rich information. Therefore, a mobile cache strategy of CGML spatial data is proposed. In the strategy, firstly, the basic and expand query boxes are generated in the center of terminal’s location. And then, the cartographic data received in the basic box is formed into CGML file. Finally, the received data in the expand box is added to the built CGML file, which is used to follow-up operation of cartography.
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Olivier, Jake, and Melissa M. Norberg. "Positively skewed data: revisiting the box-cox power transformation." International Journal of Psychological Research 3, no. 1 (June 30, 2010): 68–77. http://dx.doi.org/10.21500/20112084.846.

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Although the normal probability distribution is the cornerstone of applying statistical methodology; data do not always meet the necessary normal distribution assumptions. In these cases, researchers often transform non-normal data to a distribution that is approximately normal. Power transformations constitute a family of transformations, which include logarithmic and fractional exponent transforms. The Box-Cox method offers a simple method for choosing the most appropriate power transformation. Another option for data that is positively skewed, often used when measuring reaction times, is the Ex-Gaussian distribution which is a combination of the exponential and normal distributions. In this paper, the Box-Cox power transformation and Ex-Gaussian distribution will be discussed and compared in the context of positively skewed data. This discussion will demonstrate that the Box-Cox power transformation is simpler to apply and easier to interpret than the Ex-Gaussian distribution.
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40

Birchall, Clare. "‘Data.gov-in-a-box’." European Journal of Social Theory 18, no. 2 (March 1, 2015): 185–202. http://dx.doi.org/10.1177/1368431014555259.

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Given that the Obama administration still relies on many strategies we would think of as sitting on the side of secrecy, it seems that the only lasting transparency legacy of the Obama administration will be data-driven or e-transparency as exemplified by the web interface ‘ data.gov ’. As the data-driven transparency model is exported and assumes an ascendant position around the globe, it is imperative that we ask what kind of publics, subjects, and indeed, politics it will produce. Open government data is not just a matter concerning accountability but is seen as a necessary component of the new ‘data economy’. To participate and benefit from this info-capitalist-democracy, the data subject is called upon to be both auditor and entrepreneur. This article explores the implications of responsibilization, outsourcing, and commodification on the contract of representational democracy and asks if there are other forms of transparency that might better resist neoliberal formations and re-politicize the public sphere.
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YAMAGUCHI, Yumi, Yuko IKEHATA, Takayuki ITOH, and Yasumasa KAJINAGA. "Visualization of Web access logs using Data Jewelry Box." Journal of the Visualization Society of Japan 22, no. 1Supplement (2002): 111–14. http://dx.doi.org/10.3154/jvs.22.1supplement_111.

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42

Gibbons, Phillip B., John L. Bruno, and Steven Phillips. "Black-Box Correctness Tests for Basic Parallel Data Structures." Theory of Computing Systems 35, no. 4 (July 3, 2002): 391–432. http://dx.doi.org/10.1007/s00224-002-1046-6.

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43

Eastwood, David B., Morgan D. Gray, and John R. Brooker. "Forecasting Item Movement with Scan Data: Box-Jenkins Results." Northeastern Journal of Agricultural and Resource Economics 20, no. 1 (April 1991): 42–51. http://dx.doi.org/10.1017/s0899367x00002841.

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Preliminary forecasts using the Box-Jenkins methodology for supermarket scan data for ground beef and roast item movement are described. The functional form and the accuracy of the forecasts vary by product. Results suggest that further analyses incorporating price and advertising may increase the accuracy of the forecasts.
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Yang, Lingjian, Chrysanthi Ainali, Aristotelis Kittas, Frank O. Nestle, Lazaros G. Papageorgiou, and Sophia Tsoka. "Pathway-level disease data mining through hyper-box principles." Mathematical Biosciences 260 (February 2015): 25–34. http://dx.doi.org/10.1016/j.mbs.2014.09.005.

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45

Gouvier, Drew, Warren T. Jackson, Donald T. Stuss, and L. L. Stethem. "Rapid visual data analysis in neuropsychological research: Box graphs." Clinical Neuropsychologist 6, no. 1 (January 1992): 92–97. http://dx.doi.org/10.1080/13854049208404121.

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46

Rayens, William S., and Cidambi Srinivasan. "Box-Cox transformations in the analysis of compositional data." Journal of Chemometrics 5, no. 3 (May 1991): 227–39. http://dx.doi.org/10.1002/cem.1180050310.

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47

Iyengar, Malini, and Dipak K. Dey. "Box–Cox transformations in Bayesian analysis of compositional data." Environmetrics 9, no. 6 (November 1998): 657–71. http://dx.doi.org/10.1002/(sici)1099-095x(199811/12)9:6<657::aid-env329>3.0.co;2-1.

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48

Jegierski, Hubert, and Stanislaw Saganowski. "An “Outside the Box” Solution for Imbalanced Data Classification." IEEE Access 8 (2020): 125191–209. http://dx.doi.org/10.1109/access.2020.3007801.

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Hubers, Mireille D., Cindy L. Poortman, Kim Schildkamp, Jules M. Pieters, and Adam Handelzalts. "Opening the black box: knowledge creation in data teams." Journal of Professional Capital and Community 1, no. 1 (January 11, 2016): 41–68. http://dx.doi.org/10.1108/jpcc-07-2015-0003.

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Abstract:
Purpose – In this study, Nonaka and Takeuchi’s socialization, externalization, combination and internalization (SECI) model of knowledge creation is used to gain insight into the process of knowledge creation in data teams. These teams are composed of school leaders and teachers, who work together to improve the quality of education. They collaboratively create knowledge related to data use and to an educational problem they are studying. The paper aims to discuss these issues. Design/methodology/approach – A qualitative micro-process case study was conducted for two data teams. The modes, transitions and content of the knowledge creation process were analyzed for all data team meetings over a two-year period. In addition, all team members were interviewed twice to triangulate the findings. Findings – Results show that the knowledge creation process was cyclical across meetings, but more iterative within meetings. Furthermore, engagement in the socialization and internalization mode provided added value in this process. Finally, the SECI model clearly differentiated between team members’ processes. Team members who engaged more often in the socialization and internalization modes and displayed more personal engagement in those modes gained greater and deeper knowledge. Research limitations/implications – The SECI model is valuable for understanding how teams gain new knowledge and why they differ in those gains. Practical implications – Stimulation of active personal engagement in the socialization and internalization mode is needed. Originality/value – This is one of the first attempts to concretely observe the process of knowledge creation. It provides essential insights into what educators do in professional development contexts, and how support can best be provided.
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50

England, J. "Pacemaker Battery Longevity Projections/Box Change Data for Audits." Heart, Lung and Circulation 21 (January 2012): S132. http://dx.doi.org/10.1016/j.hlc.2012.05.333.

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