Journal articles: '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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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.

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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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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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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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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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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.

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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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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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Greif, Irene, and Sunil Sarin. "Data sharing in group work." ACM Transactions on Information Systems 5, no. 2 (April 1987): 187–211. http://dx.doi.org/10.1145/27636.27640.

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Hockenhull, Michael, and Marisa Leavitt Cohn. "Speculative Data Work & Dashboards." Proceedings of the ACM on Human-Computer Interaction 4, CSCW3 (January 5, 2021): 1–31. http://dx.doi.org/10.1145/3434173.

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13

Passi, Samir, and Phoebe Sengers. "Making data science systems work." Big Data & Society 7, no. 2 (July 2020): 205395172093960. http://dx.doi.org/10.1177/2053951720939605.

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How are data science systems made to work? It may seem that whether a system works is a function of its technical design, but it is also accomplished through ongoing forms of discretionary work by many actors. Based on six months of ethnographic fieldwork with a corporate data science team, we describe how actors involved in a corporate project negotiated what work the system should do, how it should work, and how to assess whether it works. These negotiations laid the foundation for how, why, and to what extent the system ultimately worked. We describe three main findings. First, how already-existing technologies are essential reference points to determine how and whether systems work. Second, how the situated resolution of development challenges continually reshapes the understanding of how and whether systems work. Third, how business goals, and especially their negotiated balance with data science imperatives, affect a system’s working. We conclude with takeaways for critical data studies, orienting researchers to focus on the organizational and cultural aspects of data science, the third-party platforms underlying data science systems, and ways to engage with practitioners’ imagination of how systems can and should work.

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Brewer, T. D. "Putting Census Data to Work." Science 329, no. 5994 (August 19, 2010): 901–2. http://dx.doi.org/10.1126/science.329.5994.901-a.

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15

Hodson, Hal. "Put your data to work." New Scientist 231, no. 3090 (September 2016): 22. http://dx.doi.org/10.1016/s0262-4079(16)31650-5.

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Palfreyman, Simon. "Using Health Data – Applying Technology to Work SmarterUsing Health Data – Applying Technology to Work Smarter." Nursing Standard 24, no. 38 (May 26, 2010): 30. http://dx.doi.org/10.7748/ns2010.05.24.38.30.b1061.

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17

Mitchell, Michelle K. "Putting Library Assessment Data to Work." Technical Services Quarterly 38, no. 1 (January 2, 2021): 105–6. http://dx.doi.org/10.1080/07317131.2020.1854590.

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Anderson, William L. "CODATA work in archiving scientific data." Information Services & Use 22, no. 2-3 (April 1, 2002): 63–67. http://dx.doi.org/10.3233/isu-2002-222-304.

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19

Livshitz, Ilya. "Data privacy assurance for remote work." Energy Safety and Energy Economy 1 (February 2022): 57–62. http://dx.doi.org/10.18635/2071-2219-2022-1-57-62.

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Today, more employees are working remotely than ever before. Remote work poses unique security challenges for companies including data privacy assurance challenges. In this paper, current statistics of national and international expert communities demonstrates resent trends and practices in personal data privacy assurance. The author’s experiences of information security audits show certain violations of current regulatory limits. The results presented in this paper can be used for planning, conducting, and evaluating information during security audits, especially when it comes to personal data.

20

Bossen, Claus, Kathleen H. Pine, Federico Cabitza, Gunnar Ellingsen, and Enrico Maria Piras. "Data work in healthcare: An Introduction." Health Informatics Journal 25, no. 3 (August 12, 2019): 465–74. http://dx.doi.org/10.1177/1460458219864730.

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21

Gilbert, Catherine. "Putting Library Assessment Data to Work." Journal of the Australian Library and Information Association 69, no. 2 (April 2, 2020): 280–81. http://dx.doi.org/10.1080/24750158.2020.1757581.

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22

Møller, Naja Holten, Claus Bossen, Kathleen H. Pine, Trine Rask Nielsen, and Gina Neff. "Who does the work of data?" Interactions 27, no. 3 (April 17, 2020): 52–55. http://dx.doi.org/10.1145/3386389.

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23

Schuessler, Joseph H., Del Nagy, H. Kevin Fulk, and Art Dearing. "Data Breach Laws: Do They Work?" Journal of Applied Security Research 12, no. 4 (October 2, 2017): 512–24. http://dx.doi.org/10.1080/19361610.2017.1354275.

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24

Perez, Jorge E. "Putting Library Assessment Data to Work." Journal of Electronic Resources in Medical Libraries 17, no. 1-2 (April 2, 2020): 56–57. http://dx.doi.org/10.1080/15424065.2020.1757549.

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25

Cariceo, Oscar, Murali Nair, and Jay Lytton. "Data science for social work practice." Methodological Innovations 11, no. 3 (September 2018): 205979911881439. http://dx.doi.org/10.1177/2059799118814392.

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Data science is merging of several techniques that include statistics, computer programming, hacking skills, and a solid expertise in specific fields, among others. This approach represents opportunities for social work research and intervention. Thus, practitioners can take advantage of data science methods and reach new standards for quality performances at different practice levels. This article addresses key terms of data science as a new set of methodologies, tools, and technologies, and discusses machine learning techniques in order to identify new skills and methodologies to support social work interventions and evidence-based practice. The challenge related to data sciences application on social work practice is the shift on the focus of interventions. Data science supports data-driven decisions to predict social issues, rather than providing an understanding of reasons for social problems. This can be both a limitation and an opportunity depending on context and needs of users and professionals.

26

Mallinckrodt, A. John, and Harvey S. Leff. "Work, Energy, and Kinematic Car Data." Physics Teacher 40, no. 9 (December 2002): 516–17. http://dx.doi.org/10.1119/1.1534811.

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27

Dobson, A. D. M., E. J. Milner-Gulland, Nicholas J. Aebischer, Colin M. Beale, Robert Brozovic, Peter Coals, Rob Critchlow, et al. "Making Messy Data Work for Conservation." One Earth 2, no. 5 (May 2020): 455–65. http://dx.doi.org/10.1016/j.oneear.2020.04.012.

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28

Boyles, Jan Lauren, and Eric Meyer. "Newsrooms accommodate data-based news work." Newspaper Research Journal 38, no. 4 (November 14, 2017): 428–38. http://dx.doi.org/10.1177/0739532917739870.

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Similar to prior cycles of newsroom specialization, news organizations must integrate the expertise of data journalists. Based upon 18 in-depth interviews with data journalism leaders within American newspapers, this study examines how newsrooms are restructuring to accommodate data news work. More specifically, the research identifies four “critical junctures” by which newspapers expand data journalism operations. The interviews establish that expanding a paper’s commitment to data journalism requires reorganizing the newsroom with new layers of structural complexity.

29

Van Eechoud, Mireille. "Making Access to Government Data Work." Masaryk University Journal of Law and Technology 9, no. 2 (September 30, 2015): 61–83. http://dx.doi.org/10.5817/mujlt2015-2-4.

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The EU Directive on Re-use of Public Sector Information of 2013 (the PSI Directive) is a key instrument for open data policies at all levels of government in Member States. It sets out a general framework for the conditions governing the right to re-use information resources held by public sector bodies. It includes provisions on non-discrimination, transparent licensing and the like. However, what the PSI Directive does not do is give businesses, civil society or citizens an actual claim to access. Access is of course a prerequisite to (re)use. It is largely a matter for individual Member States to regulate what information is in the public record. This article explores what the options for the EC are to promote alignment of rights to information and re-use policy. It also flags a number of important data protection problems that have not been given serious enough consideration, but have the potential to paralyze open data policies.

30

Acar, Umut A., Guy E. Blelloch, and Robert D. Blumofe. "The Data Locality of Work Stealing." Theory of Computing Systems 35, no. 3 (May 1, 2002): 321–47. http://dx.doi.org/10.1007/s00224-002-1057-3.

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31

Cruz, Luis C., Heider Sanchez, Víctor M. González, and Romain Robbes. "Work fragmentation in developer interaction data." Journal of Software: Evolution and Process 29, no. 3 (January 30, 2017): e1839. http://dx.doi.org/10.1002/smr.1839.

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32

Langstrup, Henriette. "Patient-reported data and the politics of meaningful data work." Health Informatics Journal 25, no. 3 (December 31, 2018): 567–76. http://dx.doi.org/10.1177/1460458218820188.

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Patient-reported outcome data have moved from the realm of research to center stage in efforts to provide patient-centered care. In a Danish context, health authorities are seeking to promote and standardize the use of patient-reported outcome data. This involves normative articulations of what counts as meaningful data work in a healthcare system characterized by intensified data-sourcing. Based on ethnographic material, I suggest that an assemblage of actors, both human and technological, has accomplished the articulation of meaningful data work, with patient-reported outcome as being dependent on the active application of data in clinical trajectories—in contrast to supplying data “passively” for secondary use for research or governance. This normative articulation of “Active patient-reported outcome” legitimizes the Danish patient-reported outcome assemblage by showing alignment of the concerns of patients, clinicians and health authorities. At the same time, “Active patient-reported outcome” foreshadows challenges in making data work meaningful in local practice.

33

Burr, Hermann. "Presentation of a Work Process Classification and Comparison of Work Process Data with Job and Industry Data." Applied Occupational and Environmental Hygiene 10, no. 4 (April 1995): 341–44. http://dx.doi.org/10.1080/1047322x.1995.10389044.

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34

HASEGAWA, Tetsuya, and Masaharu KUMASHIRO. "A study on the work hour for data entry work with VDT." Japanese journal of ergonomics 30, no. 6 (1994): 405–13. http://dx.doi.org/10.5100/jje.30.405.

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35

Duomarco, José Luis. "Solar-to-work conversion from meteorological data." Journal of Cleaner Production 313 (September 2021): 127666. http://dx.doi.org/10.1016/j.jclepro.2021.127666.

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36

Puussaar, Aare, Ian G. Johnson, Kyle Montague, Philip James, and Peter Wright. "Making Open Data Work for Civic Advocacy." Proceedings of the ACM on Human-Computer Interaction 2, CSCW (November 2018): 1–20. http://dx.doi.org/10.1145/3274412.

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37

Leonelli, Sabina, Nicholas Smirnoff, Jonathan Moore, Charis Cook, and Ruth Bastow. "Making open data work for plant scientists." Journal of Experimental Botany 64, no. 14 (September 16, 2013): 4109–17. http://dx.doi.org/10.1093/jxb/ert273.

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38

Du, Jun, Charles X. Ling, and Zhi-Hua Zhou. "When Does Cotraining Work in Real Data?" IEEE Transactions on Knowledge and Data Engineering 23, no. 5 (May 2011): 788–99. http://dx.doi.org/10.1109/tkde.2010.158.

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39

Becker, Ami B., Traci L. Galinsky, Naomi G. Swanson, and Steven L. Sauter. "Stress Control Interventions in Data Entry Work." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 40, no. 24 (October 1996): 1279. http://dx.doi.org/10.1177/154193129604002456.

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40

Saunders, Margaret K. "In Denmark, Big Data Goes To Work." Health Affairs 33, no. 7 (July 2014): 1245. http://dx.doi.org/10.1377/hlthaff.2014.0513.

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41

Morgan, Ruth. "Make It Work: Dominate Your IEP Data." ASHA Leader 18, no. 4 (April 2013): 26–27. http://dx.doi.org/10.1044/leader.miw.18042013.26.

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42

Evans-Pughe, C. "Analogue makes light work of big data." Engineering & Technology 9, no. 12 (December 1, 2014): 36–40. http://dx.doi.org/10.1049/et.2014.1202.

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43

Schnell, Tatjana, Thomas Höge, and Edith Pollet. "Predicting meaning in work: Theory, data, implications." Journal of Positive Psychology 8, no. 6 (November 2013): 543–54. http://dx.doi.org/10.1080/17439760.2013.830763.

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44

Dunifon, Rachel, Ariel Kalil, Danielle A. Crosby, Jessica Houston Su, and Thomas DeLeire. "Measuring Maternal Nonstandard Work in Survey Data." Journal of Marriage and Family 75, no. 3 (May 20, 2013): 523–32. http://dx.doi.org/10.1111/jomf.12017.

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45

Meng, Amanda, Carl DiSalvo, and Ellen Zegura. "Collaborative Data Work Towards a Caring Democracy." Proceedings of the ACM on Human-Computer Interaction 3, CSCW (November 7, 2019): 1–23. http://dx.doi.org/10.1145/3359144.

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46

Shankar, Kalpana, and Kristin Eschenfelder. "Organizational and institutional work in data infrastructures." Proceedings of the Association for Information Science and Technology 54, no. 1 (January 2017): 595–98. http://dx.doi.org/10.1002/pra2.2017.14505401082.

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47

韩, 磊. "The Data Realization of Security Management Work." Modern Management 13, no. 12 (2023): 1792–96. http://dx.doi.org/10.12677/mm.2023.1312225.

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48

Vassilakopoulou, Polyxeni, and Margunn Aanestad. "Communal data work: Data sharing and re-use in clinical genetics." Health Informatics Journal 25, no. 3 (March 19, 2019): 511–25. http://dx.doi.org/10.1177/1460458219833117.

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In this article, we examine work with communal data in the context of clinical genetic testing. Drawing from prior research on digital research infrastructures and from the analysis of our empirical data on genetic testing, we describe how data generated in laboratories distributed all over the world are shared and re-used. Our research findings point to six different human-driven activities related to expanding, disambiguating, sanitizing and assessing the relevance, validity and combinability of data. We contribute to research within Health Informatics with a framework that foregrounds human-driven activities for data interoperability.

49

Bonde, Morten, Claus Bossen, and Peter Danholt. "Data-work and friction: Investigating the practices of repurposing healthcare data." Health Informatics Journal 25, no. 3 (June 24, 2019): 558–66. http://dx.doi.org/10.1177/1460458219856462.

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The focus on digital data for improved management and quality of healthcare is paramount. In particular, the vast volumes of accumulated data in clinical systems have created high hopes for repurposing data to serve secondary purposes beyond the practices of direct clinical care, such as research, improvement and efficiency. This article contributes with an understanding of the pivotal, but often unnoticed “data-work” involved in such efforts. The article is based on a regional project in Danish healthcare, in which nine hospital departments were given the task of developing new indicators for quality to substitute the previous accountability regime based on Diagnosis-Related Groups. Using the concept of “friction,” we analyze the challenges of turning clinical ideas into data-supported indicators and of collecting data from existing repositories. Especially, we turn attention to the interaction between clinicians and it-personnel to focus on the interdisciplinary and collaborative aspects of this work.

50

Fiske, Amelia, Barbara Prainsack, and Alena Buyx. "Data Work: Meaning-Making in the Era of Data-Rich Medicine." Journal of Medical Internet Research 21, no. 7 (July 9, 2019): e11672. http://dx.doi.org/10.2196/11672.

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Journal articles on the topic 'Data work'

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