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Published: 10 December 2022
Last updated: 28 January 2023

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1

Euclid, ed. Dedomena: Euclid's Data, or, The importance of being given. Copenhagen: Museum Tusculanum Press, 2003.

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2

Comin, Diego. Using investment data to assess the importance of price mismeasurement. Cambridge, MA: National Bureau of Economic Research, 2004.

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3

Goldstein, David K. Computer-based data and organizational learning: The importance of managers' stories. Cambridge, Mass: Center for Information Systems Research, Sloan School of Management, Massachusetts Institute of Technology, 1992.

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4

Patterson, K. D. Modelling the Livingston price expectations data: The importance of monthly information. Reading: University of Reading Department of Economics, 1985.

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5

Maclean, Alex. Analysing the importance of wireless technology andinvestigating data transmission over wireless networks. Oxford: Oxford Brookes University, 2001.

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6

Moshirpour, Mohammad, Behrouz Far, and Reda Alhajj, eds. Highlighting the Importance of Big Data Management and Analysis for Various Applications. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-60255-4.

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7

Mairesse, Jacques. The importance of R & D for innovation: A reassesment using French survey data. Cambridge, MA: National Bureau of Economic Research, 2004.

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8

McCardell, Orla. The importance of building maintenance and the availablity of building maintenance cost data. [s.l: The Author], 1997.

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9

Mairesse, Jacques. The importance of R & D for innovation: A reassessment using French survey data. Cambridge, Mass: National Bureau of Economic Research, 2004.

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10

Slawson, Nadine. The value and importance of differentiation when teaching handling data to junior age children. Cardiff: CIHE, 1995.

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11

Thornton, Michael. Computer systems in the public dental service: The importance of managing information. Dublin: University College Dublin, 1997.

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12

The role and importance of managing information for competitive positioning in economic development. Norwood, NJ: Ablex Pub. Corp., 1990.

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13

Payne, Judith E. A change of course: The importance to DOD of international standards for electronic commerce. Santa Monica, CA: RAND, 1991.

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14

Ben-Dov, Yair. A systematic catalogue of the soft scale insects of the world (Homoptera:Coccoidea:Coccidae): With data on geographical distribution, host plants, biology, and economics importance. Gainesville, Fla: Sandhill Crane Press, 1993.

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15

Simon, Christophe, Philippe Weber, and Mohamed Sallak. Data Uncertainty and Important Measures. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119489375.

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16

Henry's important date. New York: Parents Magazine Press, 1993.

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17

London, Sotheby Parke-Bernet. Importanti gioiellí: Data dell'asta mercoledí 27 Novembre 1996 .... Milano: Sotheby's, 1996.

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18

Data driven: Profiting from your most important business asset. Boston, Mass: Harvard Business School Press, 2008.

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19

Patrick, Charlotte, and Dawn Bushaus. Growing Importance of Data Governance. TM Forum, 2021.

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20

M, Solow Robert, ed. Making the humanities count: The importance of data. Cambridge, MA: American Academy of Arts & Sciences, 2002.

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21

Sridevi, Ms. T. V. G., 1st, Rajani K. V. 2nd, and 3rd Mohini. Data Science Importance in Engineering and Social Sciences. Selfypage Developers Pvt Ltd, 2021.

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22

Jugulum, Rajesh, and Donald H. Gray. Competing with Data Quality: Relevance and Importance in Industry. Wiley & Sons, Incorporated, John, 2014.

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23

Quality charting a new course: The importance of patient data. 1991.

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24

Alhajj, Reda, Mohammad Moshirpour, and Behrouz Far. Highlighting the Importance of Big Data Management and Analysis for Various Applications. Springer, 2017.

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25

Alhajj, Reda, Mohammad Moshirpour, and Behrouz Far. Highlighting the Importance of Big Data Management and Analysis for Various Applications. Springer, 2018.

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26

Alley, William M., and Charles J. Taylor. Ground-Water-Level Monitoring and the Importance of Long-Term Water-Level Data. Diane Pub Co, 2003.

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27

Taisbak, Christian Marinus. Euclid's Data: The Importance of Being Given (Acta Historica Scientiarum Naturalum Et Medicinalium). Museum Tusculanum, 2003.

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28

Fife-Schaw, Chris. Analysing categorical data. Oxford University Press, 2015. http://dx.doi.org/10.1093/med:psych/9780198527565.003.0016.

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This chapter explores the analysis of categorical data and the chi-squared test. This includes logistic regression (how it works, and goodness of fit), the importance of predictors, and cautionary notes about the use of regression analysis. The chapter also discusses the prediction of multi-category outcome variables (multinomial logistic regression, and configural frequency analysis (CFA)).
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29

Lee, Kenneth K. C., Wai Yee Choon, Chee Jen Chang, Jeff Guo, and Paul Scuffham, eds. Increasing Importance of Patients-generated Real World Data for Healthcare Policy Decisions About Medicinal Products. Frontiers Media SA, 2022. http://dx.doi.org/10.3389/978-2-88976-068-8.

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30

Huang, Xu, Hossein Hassani, and Emmanuel Sirimal Silva. Fusing Big Data, Blockchain and Cryptocurrency: Their Individual and Combined Importance in the Digital Economy. Palgrave Pivot, 2019.

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31

Martens, David. Data Science Ethics. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192847263.001.0001.

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Data science ethics is all about what is right and wrong when conducting data science. Data science has so far mainly been used for positive outcomes for businesses and society. However, just as with any technology, data science has also come with some negative consequences: an increase of privacy invasion, data-driven discrimination against sensitive groups, and decision making by complex models without explanations. This book looks at the different concepts and techniques related to data science ethics. Data scientists and business managers are not inherently unethical, but at the same time not trained to think this through either. This book aims to address this important gap. The techniques discussed range from k-anonymity and differential privacy to homomorphic encryption and zero-knowledge proofs to address privacy concerns, measurements to assess, and techniques to remove discrimination against sensitive groups, and various explainable AI techniques. The real-life cautionary tales further illustrate the importance and potential impact of data science ethics, including tales of racist bots, search censoring, government backdoors, discrimination in recruitment, predicting pregnancy, redlining, re-identification of persons based on movie viewing and location data, cheating academics, and face recognition. Additionally, 10 discussions are provided with hypothetical scenarios, for example:‘you are the founder of a startup …’, which then present an ethical dilemma related to data science. These can serve as structured exercises to be completed with your fellow colleagues, and will teach you how to balance the ethical concerns and the utility of your data.
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32

Haig, Brian D. Exploratory Data Analysis. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190222055.003.0002.

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Chapter 2 is concerned with modern data analysis. It focuses primarily on the nature, role, and importance of exploratory data analysis, although it gives some attention to computer-intensive resampling methods. Exploratory data analysis is a process in which data are examined to reveal potential patterns of interest. However, the use of traditional confirmatory methods in data analysis remains the dominant practice. Different perspectives on data analysis, as they are shaped by four different accounts of scientific method, are provided. A brief discussion of John Tukey’s philosophy of teaching data analysis is presented. The chapter does not consider the more recent exploratory data analytic developments, such as the practice of statistical modeling, the employment of data-mining techniques, and more flexible resampling methods.
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33

Duitscher, Teddy. Impact Your Business Help Businesses : the Importance of Having a Data-Driven Company: Four Strategies to Capture and Create Value from Big Data. Independently Published, 2021.

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34

Schmittem, Jacqueline. Data-Driven Travel Marketing: The Importance of Business Intelligence for Affiliate Marketing in the Travel Industry. Lit Verlag, 2021.

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35

Laycock, Helen. Nocebo and its importance in clinical practice. Edited by Paul Farquhar-Smith, Pierre Beaulieu, and Sian Jagger. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198834359.003.0045.

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The landmark paper discussed in this chapter is ‘When words are painful: unravelling the mechanism of the nocebo effect’, published in 2007 by Benedetti et al. This major review considered the placebo and nocebo effect in a more scientific framework compared to the previous nebulous concept of a placebo as an agent whose only role is was to act as a comparator for controlled trials. By expounding robust evidence, Benedetti added credence to the placebo effect with not just psychological but physiological data acknowledging it as an effective therapeutic action. Furthermore the importance of endogenous opioids and cholecystokinin in the mechanism underlying the relief of pain by placebos was put into sharp relief, giving an intuitive basis and scientific validation to this effect.
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36

Halperin, Sandra, and Oliver Heath. 7. What is Data? Oxford University Press, 2017. http://dx.doi.org/10.1093/hepl/9780198702740.003.0007.

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This chapter considers the main types of data used in Politics and International Relations (IR), as well as the main criteria by which to judge whether the data collected is good or not. It first describes the steps involved in the process of thinking about what data or evidence is relevant to answering a research question before discussing the importance of addressing issues of validity and reliability in research. Some of these issues are illustrated by referring to recent attempts to measure corruption, a major topic of interest in Politics and IR. The chapter also examines the issue of case selection as well as the collection of qualitative and quantitative data using methods such as interviewing and observation. Finally, it analyses the so-called ‘big data’ revolution in data collection and analysis and provides a data quality checklist.
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37

Cernat, Alexandru, and Joseph W. Sakshaug, eds. Measurement Error in Longitudinal Data. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198859987.001.0001.

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Understanding change is essential in most scientific fields. This is highlighted by the importance of issues such as shifts in public health and changes in public opinion regarding politicians and policies. Nevertheless, our measurements of the world around us are often imperfect. For example, measurements of attitudes might be biased by social desirability, while estimates of health may be marred by low sensitivity and specificity. In this book we tackle the important issue of how to understand and estimate change in the context of data that are imperfect and exhibit measurement error. The book brings together the latest advances in the area of estimating change in the presence of measurement error from a number of different fields, such as survey methodology, sociology, psychology, statistics, and health. Furthermore, it covers the entire process, from the best ways of collecting longitudinal data, to statistical models to estimate change under uncertainty, to examples of researchers applying these methods in the real world. The book introduces the reader to essential issues of longitudinal data collection such as memory effects, panel conditioning (or mere measurement effects), the use of administrative data, and the collection of multi-mode longitudinal data. It also introduces the reader to some of the most important models used in this area, including quasi-simplex models, latent growth models, latent Markov chains, and equivalence/DIF testing. Further, it discusses the use of vignettes in the context of longitudinal data and estimation methods for multilevel models of change in the presence of measurement error.
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38

Razo, Armando. Integration of Contextual Data. Edited by Lonna Rae Atkeson and R. Michael Alvarez. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190213299.013.20.

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This chapter discusses a conceptual framework that clarifies the nature and importance of context in social scientific research. It first explains how context fits into survey analysis, then addresses major problems that hamper use and collection of contextual data: vague or incomplete conceptual definitions of “context” and lack of methodological guidance to collect and analyze contextual data. It suggests that systematic research and cumulative knowledge on contextual effects are constrained by two factors: the lack of standardized contextual variables across surveys and sporadic empirical inquiries. Finally, it outlines directions for future research with an eye toward advancing contextual data collection and analysis as well as ascertaining the impact of context on public opinion and political behavior. It presents statistical approaches to provide a blueprint for explicit measurements and analysis of contextual data and considers the need to modify conventional sampling techniques to capture relevant contextual variability.
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39

Mechaem, E. Telehealth and Your Security: Digital Health Service, Reason for Its Importance, How to Protect Your Data from Hackers. Independently Published, 2020.

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40

Ground-Water-Level Monitoring and the Importance of Long-Term Water-Level Data (U.S. Geological Survey Circular, 1217). 2nd ed. Geological Survey (USGS), 2001.

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41

Brown, Matthew. Recognition of the importance of neuropathic pain epidemiology. Edited by Paul Farquhar-Smith, Pierre Beaulieu, and Sian Jagger. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198834359.003.0071.

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The landmark paper discussed in this chapter is ‘Prevalence of chronic pain with neuropathic characteristics in the general population’, published by Bouhassira and Lantéri-Minet in 2008. Understanding the extent to which a specific condition affects a population is of great importance, for two main reasons. First, robust epidemiological data influences relevant legislators and policymaking, leading to improvements in the allocation of scarce healthcare resources. Second, epidemiology studies alert clinicians and academics to deficiencies and oversights in current treatment. For decades, chronic pain resided in a twilight world, under-recognized and under-resourced, while patients suffered. Papers such as this pioneering work by Bouhassira and Lantéri-Minet started to turn the tide with respect to chronic pain’s profile in the pathological pecking order. It is important because it demonstrated in unarguable fashion the degree to which chronic neuropathic pain blights the lives of a significant proportion of the population.
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42

Lattman, Eaton E., Thomas D. Grant, and Edward H. Snell. Examples of Data Collection and Processing. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199670871.003.0007.

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This chapter provides a detailed example of SAXS data analysis from a well behaving system. After collecting the SAXS data, several data analysis procedures are illustrated to ensure the data were of sufficient quality. Many of these steps are performed by comparing multiple concentrations from a dilution series, demonstrating the importance of this step in the data collection procedure for ensuring high quality data. Finally, real space modeling and shape reconstructions are shown to determine the oligomeric state as a tetramer, and identify the correct oligomeric assembly from crystal packing predictions.
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43

Geological Survey (U.S.), ed. Ground-Water-Level Monitoring and the Importance of Long-Term Water-Level Data, U.S. Geological Survey, Circular 1217, 2001. [S.l: s.n., 2001.

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44

Ray, Sumantra (Shumone), Sue Fitzpatrick, Rajna Golubic, Susan Fisher, and Sarah Gibbings, eds. Data capture tools: case report form (CRF). Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199608478.003.0016.

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This chapter outlines the overall design process, highlights the aspects of design that are significant for the success of the case report form (CRF) and considers the effects of electronic data capture (EDC) in the production of electronic CRFs (eCRFs). It defines the purpose and regulatory requirements in designing a CRF. The chapter describes the data to be recorded in the CRF to answer the hypothesis being posed and collect safety data. The layout and design features are reviewed ensuring an aesthetically pleasing, easy to complete format is achieved. Hint and tips for CRF completion are described and the chapter shows how important question construction is in achieving the objectives of a CRF. The process of CRF printing is described and the different methods for binding and their importance in the production of a robust CRF. The pros and cons of Paper vs. eCRFs is described and the use of tablets and mobile phones being used for data capture is explored, Whatever the method of data capture the design features of the CFR will largely remain the same.
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45

Welsman, Joanne R., and Neil Armstrong. Interpreting exercise performance data in relation to body size. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199232482.003.0002.

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The appropriate normalization of exercise performance data for differences in body size underpins the clarification of growth and maturational influences on physiological function. Therefore, scaling is an issue of fundamental importance for all paediatric exercise scientists. The selection and application of a scaling method appropriate for the data and research question being addressed is at least as important as ensuring that the methodology used to collect the data is valid, reliable, and appropriate for use with young people. Several scaling methods are available and some methods can be applied in different ways. Unfortunately, taken as a whole, the extant literature presents a confusing picture as to which of these techniques is preferable, how they should be applied, and the meaning of the results obtained. The aim of this chapter is to clarify these issues through a description of the techniques available for analysing both cross-sectional and longitudinal data sets, highlighting their statistical and theoretical derivations.
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46

Falcão, Cristina de Santiago Viana, Ana Maria Fontenelle Catrib, Christina César Praça Brasil, Patrícia Maria Santos Batista, Isabelle Cerqueira Sousa, and Juliana Carneiro Melo. Netnographic research on the importance of spirituality and religiosity after the diagnosis of breast cancer. Ludomedia, 2022. http://dx.doi.org/10.36367/ntqr.13.2022.e705.

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Introduction: Breast cancer affects women of different age groups, generating feelings of fear, anguish and insecurity in the face of the diagnosis. In this context, spirituality and religiosity help in coping with the disease. Objective: To analyze the perception of women about the importance of spirituality and religiosity after the diagnosis of breast cancer. Methodology: This is a netnographic research carried out on the website “Oncoguia”, by reading the testimonies of patients diagnosed with breast cancer, who, in the report of their experience, had some word or brief phrases that referred to spirituality and/or or religiosity. Data collection took place through the reading of several testimonies, excluding those that did not contemplate the object of study of this research. Content analysis in the thematic modality was used to analyze the selected statements, allowing the identification of two themes. The data were interpreted, from a netnographic perspective, from theories that deal with spirituality/religiosity in coping with diseases, emerging the themes: feelings at the time of diagnosis and spirituality/religiosity and adherence to treatment. Results: Twenty (20) reports were selected. The analysis themes are about "feelings at the time of diagnosis" and "spirituality/religiosity and adherence to treatment". It was observed that faith and spirituality help in coping with the disease, increasing strength and hope in healing, both for women and for their families. Conclusions: Spirituality/religiosity should be better explored and strengthened in the process of coping with breast cancer. Knowledge of this perspective helps in oncology treatment strategies.
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47

Baur-Ahrens, Andreas. The power of cyberspace centralisation: analysing the example of data territorialisation. Manchester University Press, 2017. http://dx.doi.org/10.7228/manchester/9781526107459.003.0003.

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This chapter engages the (re)organisation of cyberspace by examining the ongoing debates on data territorialisation. Building on cybersecurity discourses after the Snowden revelations, the chapter analyses how the movement of data is supposed to be constrained such that it literally would not leave the territory of a nation state on its way from sender to receiver. The chapter thereby highlights–against the placeless notion of cyberspace–the importance of the physical infrastructure of servers and data exchange points that exist in concrete buildings on national territories. The argument behind the rerouting initiatives is that data, once it would not physically leave the country on its travels, would be easier to protect. However, as the chapter argues, such a political intervention into the open architecture of the Internet entails deep-seated transformations of power in cyberspace.
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48

Solymosi, Reka, and Kate J. Bowers. The Role of Innovative Data Collection Methods in Advancing Criminological Understanding. Edited by Gerben J. N. Bruinsma and Shane D. Johnson. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190279707.013.35.

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Environmental criminology emphasizes the importance of situational factors associated with increased risk in terms of crime opportunities. One branch of research in this field is oriented toward strengthening the scientific approach to understanding the link between exposure to risk and crime. To achieve this, we need data about how potential victims and potential offenders spend their time, and what places they visit as part of their daily activities. This chapter lays out the potential of novel data sets and then considers in detail two of these new approaches. The first approach involves utilizing advances in technology and sensing to develop bespoke surveys created with specific research studies in mind. The second makes use of existing “big data” or “open-access data” sources on people’s everyday interactions with the environment, and combines multiple data sources to make inferences about routine activities and their link to perception of crime and place.
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49

Simon, Christopher, Mohamed Sallak, and Philippe Weber. Data Uncertainty and Important Measures. Wiley & Sons, Incorporated, John, 2018.

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50

Simon, Christophe, Mohamed Sallak, and Philippe Weber. Data Uncertainty and Important Measures. Wiley & Sons, Incorporated, John, 2018.

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