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Готові списки джерел за темами / Data work

Добірка наукової літератури з теми "Data work"

Автор: Grafiati

Опубліковано: 7 липня 2024

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Статті в журналах з теми "Data work":

1

Birnholtz, Jeremy P., and Matthew J. Bietz. "Data at work." ACM SIGGROUP Bulletin 24, no. 1 (April 2003): 20. http://dx.doi.org/10.1145/1027232.1027288.

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2

Menke, William, and Roger Creel. "Why Differential Data Work." Bulletin of the Seismological Society of America 112, no. 2 (December 14, 2021): 597–607. http://dx.doi.org/10.1785/0120210014.

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ABSTRACT This article explains the features of differential data that make them attractive, their shortcomings, and the situations for which they are best suited. The use of differential data is ubiquitous in the seismological community, in which they are used to determine earthquake locations via the double-difference method and the Earth’s velocity structure via geotomography; furthermore, they have important applications in other areas of geophysics, as well. A common assumption is that differential data are uncorrelated and have uniform variance. We show that this assumption is well justified when the original, undifferenced data covary with each other according to a two-sided exponential function. It is not well justified when they covary according to a Gaussian function. Differences of exponentially correlated data are approximately uncorrelated with uniform variance when they are regularly spaced in distance. However, when they are irregularly spaced, they are uncorrelated with a nonuniform variance that scales with the spacing of the data. When differential data are computed by taking differences of the original, undifferenced data, model parameters estimated using ordinary least squares applied to the differential data are almost exactly equal to those estimated using weighed least squares applied to the original, undifferenced data (with the weights given by the inverse covariance matrix). A better solution only results when the differential data are directly estimated and their variance is smaller than is implied by differencing the original data. Differential data may be appropriate for global seismic travel-time data because the covariance of errors in predicted travel times may have a covariance close to a two-sided exponential, on account of the upper mantle being close to a Von Karman medium with exponent κ≪12.

3

M, Brauer. "Putting data to work." Environmental Epidemiology 3 (October 2019): 41. http://dx.doi.org/10.1097/01.ee9.0000606076.59456.22.

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4

Williams, Diane B. "Putting Data to Work." Lippincott's Case Management 7, no. 5 (September 2002): 169. http://dx.doi.org/10.1097/00129234-200209000-00001.

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5

Bossen, Claus. "Data work and digitization." XRDS: Crossroads, The ACM Magazine for Students 26, no. 3 (April 2, 2020): 22–25. http://dx.doi.org/10.1145/3383370.

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6

Richardson, W. Scott, Lawrence G. Smith, Carol M. Ashton, and Nelda P. Wray. "Work rounds data collection." Journal of General Internal Medicine 10, no. 2 (February 1995): 115–16. http://dx.doi.org/10.1007/bf02600242.

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7

Chandhiramowuli, Srravya, Alex S. Taylor, Sara Heitlinger, and Ding Wang. "Making Data Work Count." Proceedings of the ACM on Human-Computer Interaction 8, CSCW1 (April 17, 2024): 1–26. http://dx.doi.org/10.1145/3637367.

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In this paper, we examine the work of data annotation. Specifically, we focus on the role of counting or quantification in organising annotation work. Based on an ethnographic study of data annotation in two outsourcing centres in India, we observe that counting practices and its associated logics are an integral part of day-to-day annotation activities. In particular, we call attention to the presumption of total countability observed in annotation - the notion that everything, from tasks, datasets and deliverables, to workers, work time, quality and performance, can be managed by applying the logics of counting. To examine this, we draw on sociological and socio-technical scholarship on quantification and develop the lens of a 'regime of counting' that makes explicit the specific counts, practices, actors and structures that underpin the pervasive counting in annotation. We find that within the AI supply chain and data work, counting regimes aid the assertion of authority by the AI clients (also called requesters) over annotation processes, constituting them as reductive, standardised, and homogenous. We illustrate how this has implications for i) how annotation work and workers get valued, ii) the role human discretion plays in annotation, and iii) broader efforts to introduce accountable and more just practices in AI. Through these implications, we illustrate the limits of operating within the logic of total countability. Instead, we argue for a view of counting as partial - located in distinct geographies, shaped by specific interests and accountable in only limited ways. This, we propose, sets the stage for a fundamentally different orientation to counting and what counts in data annotation.

8

Boston, Carol. "Data Systems, Data Sets, and Work Transformation." JONA: The Journal of Nursing Administration 24, no. 6 (June 1994): 11–12. http://dx.doi.org/10.1097/00005110-199406000-00005.

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9

Darian, Shiva, Aarjav Chauhan, Ricky Marton, Janet Ruppert, Kathleen Anderson, Ryan Clune, Madeline Cupchak, et al. "Enacting Data Feminism in Advocacy Data Work." Proceedings of the ACM on Human-Computer Interaction 7, CSCW1 (April 14, 2023): 1–28. http://dx.doi.org/10.1145/3579480.

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In this paper, we present the results of a study that examines the role of data in nonprofit advocacy work. We conducted semi-structured interviews with 25 individuals who play critical roles in the data work of 18 different advocacy organizations. Our analysis reveals five key stakeholders in advocacy data work-beneficiaries, policymakers, funding and partner organizations, gatekeepers, and local publics. It also contributes a framework of four functions of data work in nonprofit organizations-data as amplifier, activator, legitimizer, and incubator. We characterize the challenges in data work that exist, particularly in widespread attempts to reappropriate data work across functions. These challenges in reappropriation are often rooted in participants' effects to enact data feminist principles from the margins of the data economy. Finally, we discuss how nonprofit institutions operate outside of the dominant data work goals known as the three Ss (surveillance, selling, and science) and propose a fourth S, social good, that is working to challenge the norms of the data economy and should be considered in research regarding the data economy moving forward.

10

Martin, Elaine, and Judith Healy. "Social work as women's work: Census data 1976–1986." Australian Social Work 46, no. 4 (December 1993): 13–18. http://dx.doi.org/10.1080/03124079308411100.

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Дисертації з теми "Data work":

1

Carrick, Grady Thomas. "Data collection needs for work zone incidents." [Gainesville, Fla.] : University of Florida, 2006. http://purl.fcla.edu/fcla/etd/UFE0015618.

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2

Yarbrough, Walthea V. "Postural data incorporated into traditional work measurement." Diss., This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-06062008-164306/.

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3

Emenike, Stanley Ugochukwu. "Data loss prevention in a remote work environment." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20203.

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Data is one of the most valuable organizational assets that are susceptible to compromise when appropriate security measures are not in place. Preventing data loss is a dynamic and ongoing process that requires frequent effort and investment from organizations. This study provides a comprehensive overview of the risk to organizational data (i.e., data loss) due to the increase in remote work taking over the business landscape. In seeking answers to the research question, the study applies thematic analysis in analyzing qualitative data from the interview of 6 respondents with over 5 years of information security experience. The analysis identified four themes (threats, risk, security incident and security posture) that are relevant in answering the research questions. The findings show that there was an increase in phishing, malware and DOS attack against the organisation information assets since the inception of the global pandemic which has led to data loss and affected the organization’s competitive advantage and reputation. Also, the security posture before the pandemic was not effective in dealing with the increase in cyber attacks during the pandemic. The pandemic has led organizations to reassess their security posture to identify areas that need to be strengthened. The challenge in achieving an effective security posture is the attack surface is expanding and changing rapidly as well as the insufficient resources available (both human and financial). The organisation reassessed their security posture to identify gaps that need to be addressed. Employee training and awareness need to be done more frequently as well as implementing different technical security measures. Also, policies and procedures are implemented that outlines the acceptable use and management of the organization information assets.

4

Amado, Vanessa. "Knowledge discovery and data mining from freeway section traffic data." Diss., Columbia, Mo. : University of Missouri-Columbia, 2008. http://hdl.handle.net/10355/5591.

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Thesis (Ph. D.)--University of Missouri-Columbia, 2008.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on June 8, 2009) Vita. Includes bibliographical references.

5

Hakky, Rafee. "A computer program for calculating and teaching earth work." Virtual Press, 1985. http://liblink.bsu.edu/uhtbin/catkey/508052.

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This creative project was a research effort in order to develop a computer program which could be of assistance to students learning the design process and the calculation of earth work cut and fill volumes. Two programs which calculate cut and fill volumes were analyzed. The first by Mark Lindult: 1980, and the second by E. Bruce MacDougal: 1981. Points of weakness were carefully studied and taken into consideration while developing the CATE program.CATE program (Calculating And Teaching Earth Work) has two major purposes. The first is to teach the grid method for calculating earth work. The second is to calculate cut and fill volumes using this method. It presents accurate results especially in basement and walls studies. The program is designed to be used by students who have no background in computers. The program has been tested twice to prove its abilities in teaching and calculating earth work.
Department of Landscape Architecture

6

Wong, C. W., and 王振威. "An adaptive information retrieval environment for collaborative architectural design work." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B45015089.

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7

Frear, Susan W. "A Construct Validity Analysis of the Work Perceptions Profile Data." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc799499/.

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Анотація:

As work environments become more complex and demanding, organizations are becoming more interested in measuring the impact of their human resource development programs and initiatives. With this increased attention on data and measurement, human resource professionals have been encouraged to utilize data collection and data analysis techniques to make more objective and rationale human capital decisions and to verify business impact. As a result, the human resource profession has seen a significant increase in the use of surveys to measure anything from training effectiveness to the efficacy of recruitment procedures. The increase in the use of survey instruments requires that more focused attention is placed on the reliability and validity of data from any instrument used to make important human resource and business decisions. One instrument that is currently being used to measure career plateaus and job fit is the Work Perceptions Profile. The purpose of this research study was to conduct a construct validity analysis of the Work Perceptions Profile data and to determine the factor structure of data from its items. The data in this analysis supported a two-factor model structure with the first factor measuring Work Characteristics and a second factor measuring Performance. The results of this analysis will be helpful in exploring further how employees perceive their work place, their careers and their relationships with others within the organization.

8

Ortiz, Logan A. "Highway work zone capacity estimation using field data from Kansas." Thesis, Kansas State University, 2014. http://hdl.handle.net/2097/18224.

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Master of Science
Department of Civil Engineering
Sunanda Dissanayake
Although extensive research has been conducted on urban freeway capacity estimation methods, minimal research has been carried out for rural highway sections, especially sections within work zones. This study filled that void for rural highways in Kansas. This study estimated capacity of rural highway work zones in Kansas. Six work zone locations were selected. An average of six days’ worth of field data was collected, from mid-October 2013 to late November 2013, at each of these work zone sites. Two capacity estimation methods were utilized, including the Maximum Observed 15-minute Flow Rate Method and the Platooning Method divided into 15-minute intervals. The Maximum Observed 15-minute Flow Rate Method provided an average capacity of 1469 passenger cars per hour per lane (pcphpl) with a standard deviation of 141 pcphpl, while the Platooning Method provided a maximum average capacity of 1195 pcphpl and a standard deviation of 28 pcphpl. Based on observed data and analysis carried out in this study, the recommended capacity to be used is 1500 pcphpl when designing work zones for rural highways in Kansas. This research provides the proposed standard value of rural highway work zone capacities so engineers and city planners can effectively mitigate congestion that would have otherwise occurred due to impeding construction/maintenance.

9

Snowdon, Jane Louise. "Workflow control for surges from a batch work station." Diss., Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/25100.

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10

Aman, Carolyn J. "The devaluation of women's work: Analysis of national and experimental data." Diss., The University of Arizona, 1999. http://hdl.handle.net/10150/289050.

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Different explanations have been given for the sex gap in pay between male and female occupations. Comparable worth proponents argue predominantly female occupations pay less than comparable male occupations because of their sex composition, that occupations' sex composition affects their wages. In contrast, Reskin and Roos (1990) and Strober (1984) argue the correspondence between occupational sex composition and wages is due to employers' preferences for male workers. Given first choice of occupations, males choose the better compensated occupations, which results in a causal effect of occupational wages on sex composition. Despite these opposing causal claims, few studies have attempted to ascertain the causal order between occupational sex composition and wages. This research focuses on the relationship between occupational sex composition and wages during the 1980s. Consistent with causal assumption of comparable worth proponents, analyses of Current Population Survey data (Study 1) support a causal effect of occupational sex composition on wages. Study 1 demonstrates that sex composition has a linear effect on wages for females and a nonlinear effect on wages for males. For both males and females, sex composition has a negative effect on wages over the entire range of sex composition. Study 2 revisits the causality question using 1980 and 1990 Census data, supplemented by additional controls from other data sets and finds a nonlinear effect of occupational sex composition on wages for females, but not for males. A negative effect of wages on sex composition was not found in any of the models. These results suggest that males may be less susceptible to the negative effects of sex composition than females. Study III uses an experimental study to determine if a "devaluation by association process" accounts for the lower wages of female occupations. The study found males but not females engage in a devaluation by association process, but neither males nor females devalue occupations based on their association with women. This may be indicative of a decline in the importance of sex as a diffuse status characteristic. The combined results of these studies suggest cautious optimism as far as reducing the sex gap in pay is concerned.

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Книги з теми "Data work":

1

Kaffenberger, Carol. Making data work. 3rd ed. Alexandria, VA: American School Counselor Association, 2013.

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2

Young, Anita. Making DATA work. 2nd ed. [Alexandria, Va.]: American School Counselor Association, 2009.

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3

Mahadevan, Malathi. Data Professionals at Work. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3967-4.

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4

Gutierrez, Sebastian. Data Scientists at Work. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-6599-3.

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5

Rankin, Jenny Grant. Designing Data Reports that Work. New York, NY: Routledge, 2016.: Routledge, 2016. http://dx.doi.org/10.4324/9781315665849.

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6

Taylor, Graham. Work out computer studies. 3rd ed. Basingstoke: Macmillan Education, 1991.

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7

Davies, Dick. Information technology at work. London: Heinemann Educational Books, 1986.

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8

Zimmerman, Kathryn A. Pavement Management Systems: Putting Data to Work. Washington, D.C.: Transportation Research Board, 2017. http://dx.doi.org/10.17226/24681.

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9

Zimmerman, Kathryn A. Pavement Management Systems: Putting Data to Work. Washington, D.C.: Transportation Research Board, 2017. http://dx.doi.org/10.17226/24682.

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10

Central Council for Education and Training in Social Work., ed. Forensic social work: Competence and workforce data. London: Central Council for Education and Training in Social Work, 1995.

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Частини книг з теми "Data work":

1

Rose, Doug. "Starting the Work." In Data Science, 67–75. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2253-9_8.

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2

Weik, Martin H. "work data set." In Computer Science and Communications Dictionary, 1930. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_21195.

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3

Livraga, Giovanni. "Related Work." In Protecting Privacy in Data Release, 11–33. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16109-9_2.

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4

Hua, Ming, and Jian Pei. "Related Work." In Ranking Queries on Uncertain Data, 33–50. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9380-9_3.

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5

DeRose, Steven J., and David G. Durand. "Locating Data Objects." In Making Hypermedia Work, 121–58. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2754-1_8.

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6

Odaro, Edosa. "The Wars of the Roses." In Making Data Work, 83–94. Boca Raton: Auerbach Publications, 2022. http://dx.doi.org/10.1201/9781003278276-10.

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7

Odaro, Edosa. "Too Small to Save?" In Making Data Work, 17–29. Boca Raton: Auerbach Publications, 2022. http://dx.doi.org/10.1201/9781003278276-3.

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8

Odaro, Edosa. "The Awakening." In Making Data Work, 65–79. Boca Raton: Auerbach Publications, 2022. http://dx.doi.org/10.1201/9781003278276-8.

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9

Odaro, Edosa. "Harnessing the Power of Frustration." In Making Data Work, 47–54. Boca Raton: Auerbach Publications, 2022. http://dx.doi.org/10.1201/9781003278276-6.

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10

Odaro, Edosa. "Reward – Empowering Flow." In Making Data Work, 117–28. Boca Raton: Auerbach Publications, 2022. http://dx.doi.org/10.1201/9781003278276-13.

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Тези доповідей конференцій з теми "Data work":

1

Birnholtz, Jeremy P., and Matthew J. Bietz. "Data at work." In the 2003 international ACM SIGGROUP conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/958160.958215.

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2

Zhang, Yunming, Vladimir Kiriansky, Charith Mendis, Saman Amarasinghe, and Matei Zaharia. "Making caches work for graph analytics." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8257937.

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3

Pine, Kathleen H., Claus Bossen, Yunan Chen, Gunnar Ellingsen, Miria Grisot, Melissa Mazmanian, and Naja Holten Møller. "Data Work in Healthcare." In CSCW '18: Computer Supported Cooperative Work and Social Computing. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3272973.3273017.

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4

Jeon, Kyungho, Sharath Chandrashekhara, Feng Shen, Shikhar Mehra, Oliver Kennedy, and Steven Y. Ko. "PigOut: Making multiple Hadoop clusters work together." In 2014 IEEE International Conference on Big Data (Big Data). IEEE, 2014. http://dx.doi.org/10.1109/bigdata.2014.7004218.

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5

Ho, Kin-Hon, Tse-Tin Chan, Haoyuan Pan, and Chin Li. "Do Candlestick Patterns Work in Cryptocurrency Trading?" In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671826.

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6

Muller, Michael, Ingrid Lange, Dakuo Wang, David Piorkowski, Jason Tsay, Q. Vera Liao, Casey Dugan, and Thomas Erickson. "How Data Science Workers Work with Data." In CHI '19: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3290605.3300356.

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7

Greif, Irene, and Sunil Sarin. "Data sharing in group work." In the 1986 ACM conference. New York, New York, USA: ACM Press, 1986. http://dx.doi.org/10.1145/637069.637092.

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8

"Data Ownership Does Not Work?" In Summer 2023 Int’l Conference Proceedings. Higher Education and Innovation Group, 2023. http://dx.doi.org/10.17758/heaig14.h0923504.

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9

Pine, Kathleen, Claus Bossen, Naja Holten Møller, Milagros Miceli, Alex Jiahong Lu, Yunan Chen, Leah Horgan, Zhaoyuan Su, Gina Neff, and Melissa Mazmanian. "Investigating Data Work Across Domains." In CHI '22: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3491101.3503724.

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10

Surkov, Maxim, Vladislav Mosin, and Ivan Yamshchikov. "Do Data-based Curricula Work?" In Proceedings of the Third Workshop on Insights from Negative Results in NLP. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.insights-1.16.

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Звіти організацій з теми "Data work":

1

Kramer, Mitchell. Data Mining at Work. Boston, MA: Patricia Seybold Group, June 2004. http://dx.doi.org/10.1571/psgp6-10-04cc.

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2

Beddingfield, Erin. Putting Data to Work Final Technical Report. Office of Scientific and Technical Information (OSTI), April 2020. http://dx.doi.org/10.2172/1617191.

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3

R. Green. Technical Work Plan For: Meteorological Monitoring Data Analysis. Office of Scientific and Technical Information (OSTI), February 2006. http://dx.doi.org/10.2172/899930.

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Dillon, Amanda Dillon, Jen Bokoff Bokoff, and Erin Nylen-Wysocki Nylen-Wysocki. Using a Data Lens to Strengthen Your Work. New York, NY United States: Foundation Center, November 2017. http://dx.doi.org/10.15868/socialsector.29181.

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Zinn, Zach. Data Work and its Layers of (In)visibility. Just Tech, Social Science Research Council, September 2023. http://dx.doi.org/10.35650/jt.3060.d.2023.

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Harville, Donald L. Analyzing Work Sample Task Performance Using Three Data Sets. Fort Belvoir, VA: Defense Technical Information Center, January 1999. http://dx.doi.org/10.21236/ada363981.

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Swauger, Shea. Never Neutral: Data, Equity, and How They Can Work Together. University of Colorado Denver, 2019. http://dx.doi.org/10.25261/ir00000103.

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