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Автор: Grafiati
Опубліковано: 10 січня 2023
Оновлено: 29 січня 2023

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1

"Paul" Zhang, Xihui, Ming Wang, M. Shane Banks, Qiunan Zhang, and Colin G. Onita. "Design and Delivery of an Online Information Systems Management Course for MBA Programs." Journal of Information Technology Education: Innovations in Practice 19 (2020): 047–74. http://dx.doi.org/10.28945/4600.

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Анотація:
Aim/Purpose: In this paper, we present our experience in design and delivery of a graduate Information Systems Management (ISM) course in an online MBA program. Also presented are a detailed examination of the design and delivery of the online course, survey results of students’ perceptions and backgrounds, course evaluation results, best practices and lessons learned, and potential changes and future actions. Background: This graduate ISM course needs to not only cover a broad range of dynamic technology and business topics, but also strike a balance between the width and depth of the content. Effective course design and delivery are critical to improved teaching and learning, especially when the course is delivered online. Methodology: We provided a comprehensive review of the related literature to develop guidelines for the design and delivery of our ISM course; we collected survey data to evaluate the students’ backgrounds and their perceptions of the course; we used data analysis and content analysis methods to assess the course evaluation results. Contribution: A review of the related literature indicates that IS researchers and educators have not adequately studied online graduate education. Given the importance of the graduate ISM course in most MBA programs, and the lack of attention from the IS community, it is critical to address this gap in the research. We believe we have done so with this paper. Findings: The paper’s major findings are embedded in a detailed examination of the design and delivery of the online course, survey results of students’ perceptions and backgrounds, course evaluation results, best practices and lessons learned, and potential changes and future actions. Recommendations for Practitioners: Even though our experience may not be fully applicable to other institutions, we hope our IS colleagues can learn from the design and delivery of this online course, as well as our best practices and lessons learned to improve the teaching and learning effectiveness in IS online graduate education, in general. Furthermore, we provide instructors with an actionable framework onto which they can map their current course offering, and compare their current pedagogical offering to literature driven best practices for ISM courses, in particular. Recommendation for Researchers: It is our hope that the design and delivery of this online course, and our best practices and lessons learned can inspire our IS colleagues to search for innovative ways to improve the teaching and learning effectiveness in IS online graduate education. In addition, we distill a literature driven framework for ISM courses design and delivery that can help researchers frame their pedagogical research questions. Impact on Society: The online course in this study prepares students for more efficiently and effectively delivering IT systems in organizations. Many MBA students work for non-profits and other socially-focused organizations and are able to use the skills learned in the course for the betterment of society. Future Research: We will continue to monitor the impact of the changes on student learning effectiveness and attempt to identify additional innovative ways to improve the design and delivery of this online ISM course.
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2

Utli, Hediye, and Birgül Vural Doğru. "The relationship between patient activation level and self-care management in elderly patients with chronic illness in the southeastern anatolian region of Turkey." Progress in Health Sciences 12, no. 1 (April 12, 2022): 14–21. http://dx.doi.org/10.5604/01.3001.0015.8874.

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Анотація:
Purpose: High level of patient activation is important for better patient outcomes in chronic illnesses. The purpose of the study was to determine the relationship between patient activation level and self-care management among elderly patients with chronic illness. Materials and methods: This descriptive and relational study was conducted with 503 patients aged 65 and older. "Personal Information Form", "Self-care Management Scale in Chronic Illness" and " Patient Activation Measure" was used to collect data. The Chi-squared test, Mann Whitney U,Kruskal Wallis tests and Spearman correlation test were used to evaluate the data. Results: The mean age of the elderly patients with chronic illness was 75.8±7.6. The mean Patient Activation Measure score was 51.3±14.8, and the mean Self-care Management Scale in Chronic Illness score was 99.1 ±10.7. 46.5% of the participants had a low level of activation. A positive and statistically significant correlation was found between the Patient Activation Measure and Self-care Management scale scores. Conclusion: There was a poor association between patient activation and self-care management in these elderly patients with chronic illness. As the activity of the elderly patients participating in this study increased, their self-care management levels also increased. It is thought that the evaluation of activity and self-care levels in order to improve the health outcomes of elderly patients with chronic diseases is important in terms of determining the interventions that should be applied individually. Keywords: Chronic illness; elderly; patient activation; self-care; self-management
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3

Ranjbarfard, Mina, and Zeynab Hatami. "Critical Success Factors for Implementing Business Intelligence Projects (A BI Implementation Methodology Perspective)." Interdisciplinary Journal of Information, Knowledge, and Management 15 (2020): 175–202. http://dx.doi.org/10.28945/4607.

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Анотація:
Aim/Purpose: The purpose of this paper is to identify Critical Success Factors (CSFs) for Business Intelligence (BI) implementation projects by studying the existing BI project implementation methodologies and to compare these methodologies based on the identified CSFs. Background: The implementation of BI project has become one of the most important technological and organizational innovations in modern organizations. The BI project implementation methodology provides a framework for demonstrating knowledge, ideas and structural techniques. It is defined as a set of instructions and rules for implementing BI projects. Identifying CSFs of BI implementation project can help the project team to concentrate on solving prior issues and needed resources. Methodology: Firstly, the literature review was conducted to find the existing BI project implementation methodologies. Secondly, the content of the 13 BI project implementation methodologies was analyzed by using thematic analysis method. Thirdly, for examining the validation of the 20 identified CSFs, two questionnaires were distributed among BI experts. The gathered data of the first questionnaire was analyzed by content validity ratio (CVR) and 11 of 20 CSFs were accepted as a result. The gathered data of the second questionnaire was analyzed by fuzzy Delphi method and the results were the same as CVR. Finally, 13 raised BI project implementation methodologies were compared based on the 11 validated CSFs. Contribution: This paper contributes to the current theory and practice by identifying a complete list of CSFs for BI projects implementation; comparison of existing BI project implementation methodologies; determining the completeness degree of existing BI project implementation methodologies and introducing more complete ones; and finding the new CSF “Expert assessment of business readiness for successful implementation of BI project” that was not expressed in previous studies. Findings: The CSFs that should be considered in a BI project implementation include: “Obvious BI strategy and vision”, “Business requirements definition”, “Business readiness assessment”, “BI performance assessment”, “Establishing BI alignment with business goals”, “Management support”, “IT support for BI”, “Creating data resources and source data quality”, “Installation and integration BI programs”, “BI system testing”, and “BI system support and maintenance”. Also, all the 13 BI project implementation methodologies can be divided into four groups based on their completeness degree. Recommendations for Practitioners: The results can be used to plan BI project implementation and help improve the way of BI project implementation in the organizations. It can be used to reduce the failure rate of BI implementation projects. Furthermore, the 11 identified CSFs can give a better understanding of the BI project implementation methodologies. Recommendation for Researchers: The results of this research helped researchers and practitioners in the field of business intelligence to better understand the methodology and approaches available for the implementation and deployment of BI systems and thus use them. Some methodologies are more complete than other studied methodologies. Therefore, organizations that intend to implement BI in their organization can select these methodologies according to their goals. Thus, Findings of the study can lead to reduce the failure rate of implementation projects. Future Research: Future researchers may add other BI project implementation methodologies and repeat this research. Also, they can divide CSFs into three categories including required before BI project implementation, required during BI project implementation and required after BI project implementation. Moreover, researchers can rank the BI project implementation CSFs. As well, Critical Failure Factors (CFFs) need to be explored by studying the failed implementations of BI projects. The identified CSFs probably affect each other. So, studying the relationship between them can be a topic for future research.
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4

BAKHSH, A., I. BASHIR, H. U. FARID, and S. A. WAJID. "USING CERES-WHEAT MODEL TO SIMULATE GRAIN YIELD PRODUCTION FUNCTION FOR FAISALABAD, PAKISTAN, CONDITIONS." Experimental Agriculture 49, no. 3 (February 26, 2013): 461–75. http://dx.doi.org/10.1017/s0014479713000185.

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SUMMARYUsing computer simulation model as a management tool requires model calibration and validation against field data. A three-year (2008–2009 to 2010–2011) field study was conducted at the Postgraduate Agricultural Research Station of the University of Agriculture, Faisalabad, Pakistan, to simulate wheat grain yield production as a function of urea fertilizer applications using Crop Environment REsource Synthesis (CERES)-Wheat model. The model was calibrated using yield data for treatment of urea fertilizer application at the rate of 247 kg-urea ha−1 during growing season 2009–2010 and was validated against independent data sets of yield of two years (2008–2009 and 2010–2011) for a wide variety of treatments ranging from no urea application to 247 kg-urea ha−1 application. The model simulations were found to be acceptable for calibration as well as validation period, as the model evaluation indicators showed a mean difference of 8.9%, ranging from 0.05 to 15.38%, root mean square error of 356 having its range from 242 to 471 kg ha−1, against all observed grain yield data. The scenario simulations showed maximum grain yield of 4100 kg ha−1 for 350 kg-urea ha−1 in 2008–2009; 4600 kg ha−1 for 300 kg-urea ha−1 in 2009–2010 and 5200 kg ha−1 for 340 kg-urea ha−1 in 2010–2011. Any further increase in urea application resulted in decline of grain yield function. These results show that model has the ability to simulate effects of urea fertilizer applications on wheat yield; however, the simulated maximum grain yield data need field-based verification.
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5

Ramirez, Jorge C. G., Diane J. Cook, Lynn L. Peterson, and Dolores M. Peterson. "An event set approach to sequence discovery in medical data." Intelligent Data Analysis 4, no. 6 (December 22, 2000): 513–30. http://dx.doi.org/10.3233/ida-2000-4605.

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6

Fitzgerald, Nurgul, and Shailja Mathur. "Assessment of Dietary Intake Among South Asian Adults in the United States." Current Developments in Nutrition 5, Supplement_2 (June 2021): 124. http://dx.doi.org/10.1093/cdn/nzab035_032.

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Анотація:
Abstract Objectives To examine the dietary intake patterns of South Asian adults by using three different assessment methods. Methods The participants were a convenience sample of 62 adults from South Asian descent, who lived in the United States and participated in a community-based diabetes self-management program. Dietary intake data were collected through Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24), self-administered Diet History Questionnaire III (self-DHQ), and researcher-administered DHQ III (res-DHQ) (National Cancer Institute). Thirty-seven participants completed ASA24 and self-DHQ back-to-back during in-person sessions, and 25 participants completed res-DHQ through video conferencing sessions with the researcher. Group level data were examined using IBM SPSS Statistics software. Results On average, participants’ daily energy intake levels were estimated to be 805.8 ± 551.3, 1686.4 ± 985.9 and 1469.7 ± 887.5 kcal/d by self-DHQ, ASA24, and res-DHQ, respectively. Self-DHQ produced the lowest of the estimates (mean ± SD) for daily protein (28.9 ± 18.8 vs 63.1 ± 35.2, and 53.1 ± 27.9 g/d), carbohydrate (106.4 ± 68.0 vs 224.9 ± 128.4 and 199.9 ± 119.7 g/d), and total fat (31.7 ± 29.2 vs. 63.5 ± 46.5 and 56.2 ± 40.9 g/d) intakes in comparison to ASA24 and res-DHQ, respectively. Conclusions In this study, self-administered DHQ produced substantially lower estimates of daily macronutrient and energy intake levels. The ASA24 or researcher-administered DHQ were relatively more reliable methods of dietary assessment in this sample of South Asian adults. Funding Sources NJ Department of Health, Office of Minority and Multicultural Health.
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7

Schäffer, Utz, and Jürgen Weber. "Management von Data Science." Controlling & Management Review 65, no. 8 (November 2021): 3. http://dx.doi.org/10.1007/s12176-021-0423-4.

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8

Kayumba, Ephrem, and Claude Rusibana. "Employee Turnover and Operational Performance of Commercial Banks in Rwanda." Journal of Advance Research in Business Management and Accounting (ISSN: 2456-3544) 7, no. 5 (May 31, 2021): 01–09. http://dx.doi.org/10.53555/nnbma.v7i5.990.

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Анотація:
Employee turnover was the movement through which an organization hired and missed its employees. This might be voluntary, involuntary, internal transfer, and retirement turnover. The objective of this study was to analyse the employee turnover and operational performance of commercial banks in Rwanda, a case of KCB Bank Rwanda located in Nyarugenge District, Rwanda. The specific objectives were to analyse the effect of employee compensation on operational performance, to determine the impact of employee overscheduling on operational performance, and to find out the impact of employee favouritism on the operational performance of KCB Bank Rwanda. This quantitative research used the descriptive research survey design with questionnaire as research instrument where 80 questionnaires were distributed to 80 employees by using both physical and digital approaches forms due to situations of COVID-19. The data collection took six months and consisted of 15 Microsoft forms, 40 physical forms, and 25 emails responses. The data analysis was done by using Statistical Package for Social Science (SPSS) version 20 through which the census method was applied, and the descriptive method was used to make the conclusion and has been applied to determine the reliability and validity at 0.8%. This research contributed to the management of employee turnover to improve operational performance of commercial banks in Rwanda. It indicated that KCB Bank Rwanda recognized a considerable rate of employee turnover at a percentage of 46.5% since its creation in 2008 year to December 2020 where the low number of recruited employees compared to the number of employees who exited. This was caused by factors including poor employee compensation, employee overscheduling and employee favouritism. The study discovered that the research objectives were major causes of employee turnover that affected the operational performance of KCB Bank Rwanda at a percentage of 13.8%. Data analysis showed that compensation affected the bank’s operations at a percentage of 73.8% (see table 4.9.), overscheduling at 50.1% (see table 4.7), and favouritism at 56.3% (see table 4.8). The study discovered that the most concern of KCB Bank Rwanda was not the relevance of number of employees who left but the quality of those employee and the targets they had set during the set and submission of the annual balanced scorecard, which affects the operational performance review. The research recommended that the management should review the compensation policy to match the operational performance, reduce favouritism by approaching marginal employees, and reduce overscheduling by re-examining the job descriptions and visiting employee’s office to discover added and non-corresponding duties that attracted the employee turnover in the KCB Bank Rwanda.
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9

Chen, Jinchuan, Yueguo Chen, Xiaoyong Du, Cuiping Li, Jiaheng Lu, Suyun Zhao, and Xuan Zhou. "Big data challenge: a data management perspective." Frontiers of Computer Science 7, no. 2 (April 2013): 157–64. http://dx.doi.org/10.1007/s11704-013-3903-7.

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10

Wygant, Robert M. "Data file management." Computers & Industrial Engineering 11, no. 1-4 (January 1986): 367–71. http://dx.doi.org/10.1016/0360-8352(86)90113-0.

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11

Sévigny, Alex. "Data science and communications management." Journal of Professional Communication 5, no. 2 (October 12, 2018): 3–6. http://dx.doi.org/10.15173/jpc.v5i2.3745.

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Анотація:
In this editorial for issue two of volume five of the Journal of ProfessionalCommunication, the author discusses how data science is changingthe communications landscape. He suggests that advances intechnology are making it easier to learn about and communicate withpublics. The author challenges communciations professionals to makebetter use of this new technology in their own work.
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12

George, Gerard, Ernst C. Osinga, Dovev Lavie, and Brent A. Scott. "Big Data and Data Science Methods for Management Research." Academy of Management Journal 59, no. 5 (October 2016): 1493–507. http://dx.doi.org/10.5465/amj.2016.4005.

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13

Miksa, Tomasz, Simon Oblasser, and Andreas Rauber. "Automating Research Data Management Using Machine-Actionable Data Management Plans." ACM Transactions on Management Information Systems 13, no. 2 (June 30, 2022): 1–22. http://dx.doi.org/10.1145/3490396.

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Анотація:
Many research funders mandate researchers to create and maintain data management plans (DMPs) for research projects that describe how research data is managed to ensure its reusability. A DMP, being a static textual document, is difficult to act upon and can quickly become obsolete and impractical to maintain. A new generation of machine-actionable DMPs (maDMPs) was therefore proposed by the Research Data Alliance to enable automated integration of information and updates. maDMPs open up a variety of use cases enabling interoperability of research systems and automation of data management tasks. In this article, we describe a system for machine-actionable data management planning in an institutional context. We identify common use cases within research that can be automated to benefit from machine-actionability of DMPs. We propose a reference architecture of an maDMP support system that can be embedded into an institutional research data management infrastructure. The system semi-automates creation and maintenance of DMPs, and thus eases the burden for the stakeholders responsible for various DMP elements. We evaluate the proposed system in a case study conducted at the largest technical university in Austria and quantify to what extent the DMP templates provided by the European Commission and a national funding body can be pre-filled. The proof-of-concept implementation shows that maDMP workflows can be semi-automated, thus workload on involved parties can be reduced and quality of information increased. The results are especially relevant to decision makers and infrastructure operators who want to design information systems in a systematic way that can utilize the full potential of maDMPs.
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14

Lagoze, Carl, William C. Block, Jeremy Williams, John Abowd, and Lars Vilhuber. "Data Management of Confidential Data." International Journal of Digital Curation 8, no. 1 (June 14, 2013): 265–78. http://dx.doi.org/10.2218/ijdc.v8i1.259.

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Анотація:
Social science researchers increasingly make use of data that is confidential because it contains linkages to the identities of people, corporations, etc. The value of this data lies in the ability to join the identifiable entities with external data, such as genome data, geospatial information, and the like. However, the confidentiality of this data is a barrier to its utility and curation, making it difficult to fulfil US federal data management mandates and interfering with basic scholarly practices, such as validation and reuse of existing results. We describe the complexity of the relationships among data that span a public and private divide. We then describe our work on the CED2AR prototype, a first step in providing researchers with a tool that spans this divide and makes it possible for them to search, access and cite such data.
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15

Abedjan, Ziawasch. "Enabling data-centric AI through data quality management and data literacy." it - Information Technology 64, no. 1-2 (February 18, 2022): 67–70. http://dx.doi.org/10.1515/itit-2021-0048.

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Анотація:
Abstract Data is being produced at an intractable pace. At the same time, there is an insatiable interest in using such data for use cases that span all imaginable domains, including health, climate, business, and gaming. Beyond the novel socio-technical challenges that surround data-driven innovations, there are still open data processing challenges that impede the usability of data-driven techniques. It is commonly acknowledged that overcoming heterogeneity of data with regard to syntax and semantics to combine various sources for a common goal is a major bottleneck. Furthermore, the quality of such data is always under question as the data science pipelines today are highly ad-hoc and without the necessary care for provenance. Finally, quality criteria that go beyond the syntactical and semantic correctness of individual values but also incorporate population-level constraints, such as equal parity and opportunity with regard to protected groups, play a more and more important role in this process. Traditional research on data integration was focused on post-merger integration of companies, where customer or product databases had to be integrated. While this is often hard enough, today the challenges aggravate because of the fact that more stakeholders are using data analytics tools to derive domain-specific insights. I call this phenomenon the democratization of data science, a process, which is both challenging and necessary. Novel systems need to be user-friendly in a way that not only trained database admins can handle them but also less computer science savvy stakeholders. Thus, our research focuses on scalable example-driven techniques for data preparation and curation. Furthermore, we believe that it is important to educate the breadth of society on implications of a data-driven world and actively promote the concept of data literacy as a fundamental competence.
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16

Kaku, Kazuya, Takashi Yamazaki, Toshiaki Hashimoto, and Ryo Tanabe. "Data management/system." Geocarto International 12, no. 4 (December 1997): 79–85. http://dx.doi.org/10.1080/10106049709354620.

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17

Moncoiffé, G., R. N. Cramer, R. K. Lowry, and J. Brown. "PROVESS Data Management and subsequent data access." Journal of Sea Research 48, no. 4 (December 2002): 333–34. http://dx.doi.org/10.1016/s1385-1101(02)00192-2.

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18

Feinberg, Stephen, Ron Williams, Gayle S. W. Hagler, Joshua Rickard, Ryan Brown, Daniel Garver, Greg Harshfield, et al. "Long-term evaluation of air sensor technology under ambient conditions in Denver, Colorado." Atmospheric Measurement Techniques 11, no. 8 (August 8, 2018): 4605–15. http://dx.doi.org/10.5194/amt-11-4605-2018.

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Анотація:
Abstract. Air pollution sensors are quickly proliferating for use in a wide variety of applications, with a low price point that supports use in high-density networks, citizen science, and individual consumer use. This emerging technology motivates the assessment under real-world conditions, including varying pollution levels and environmental conditions. A seven-month, systematic field evaluation of low-cost air pollution sensors was performed in Denver, Colorado, over 2015–2016; the location was chosen to evaluate the sensors in a high-altitude, cool, and dry climate. A suite of particulate matter (PM), ozone (O3), and nitrogen dioxide (NO2) sensors were deployed in triplicate and were collocated with federal equivalent method (FEM) monitors at an urban regulatory site. Sensors were evaluated for their data completeness, correlation with reference monitors, and ability to reproduce trends in pollution data, such as daily concentration values and wind-direction patterns. Most sensors showed high data completeness when data loggers were functioning properly. The sensors displayed a range of correlations with reference instruments, from poor to very high (e.g., hourly-average PM Pearson correlations with reference measurements varied from 0.01 to 0.86). Some sensors showed a change in response to laboratory audits/testing from before the sampling campaign to afterwards, such as Aeroqual, where the O3 response slope changed from about 1.2 to 0.6. Some PM sensors measured wind-direction and time-of-day trends similar to those measured by reference monitors, while others did not. This study showed different results for sensor performance than previous studies performed by the U.S. EPA and others, which could be due to different geographic location, meteorology, and aerosol properties. These results imply that continued field testing is necessary to understand emerging air sensing technology.
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19

Ausman, R. K., G. D. Baer, M. R. McGuire, R. A. Marks, R. Ewart, J. Carey, and R. L. West. "CLINICAL DATA MANAGEMENT*." Annals of the New York Academy of Sciences 128, no. 3 (December 16, 2006): 1100–1107. http://dx.doi.org/10.1111/j.1749-6632.1965.tb11719.x.

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20

Bailo, Daniele, Keith G. Jeffery, Kuvvet Atakan, Luca Trani, Rossana Paciello, Valerio Vinciarelli, Jan Michalek, and Alessandro Spinuso. "Data integration and FAIR data management in Solid Earth Science." Annals of Geophysics 65, no. 2 (April 29, 2022): DM210. http://dx.doi.org/10.4401/ag-8742.

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Анотація:
Integrated use of multidisciplinary data is nowadays a recognized trend in scientific research, in particular in the domain of solid Earth science where the understanding of a physical process is improved and made complete by different types of measurements – for instance, ground acceleration, SAR imaging, crustal deformation – describing a physical phenomenon. FAIR principles are recognized as a means to foster data integration by providing a common set of criteria for building data stewardship systems for Open Science. However, the implementation of FAIR principles raises issues along dimensions like governance and legal beyond, of course, the technical one. In the latter, in particular, the development of FAIR data provision systems is often delegated to Research Infrastructures or data providers, with support in terms of metrics and best practices offered by cluster projects or dedicated initiatives. In the current work, we describe the approach to FAIR data management in the European Plate Observing System (EPOS), a distributed research infrastructure in the solid Earth science domain that includes more than 250 individual research infrastructures across 25 countries in Europe. We focus in particular on the technical aspects, but including also governance, policies and organizational elements, by describing the architecture of the EPOS delivery framework both from the organizational and technical point of view and by outlining the key principles used in the technical design. We describe how a combination of approaches, namely rich metadata and service-based systems design, are required to achieve data integration. We show the system architecture and the basic features of the EPOS data portal, that integrates data from more than 220 services in a FAIR way. The construction of such a portal was driven by the EPOS FAIR data management approach, that by defining a clear roadmap for compliance with the FAIR principles, produced a number of best practices and technical approaches for complying with the FAIR principles. Such a work, that spans over a decade but concentrates the key efforts in the last 5 years with the EPOS Implementation Phase project and the establishment of EPOS-ERIC, was carried out in synergy with other EU initiatives dealing with FAIR data. On the basis of the EPOS experience, future directions are outlined, emphasizing the need to provide i) FAIR reference architectures that can ease data practitioners and engineers from the domain communities to adopt FAIR principles and build FAIR data systems; ii) a FAIR data management framework addressing FAIR through the entire data lifecycle, including reproducibility and provenance; and iii) the extension of the FAIR principles to policies and governance dimensions. .
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21

Maienschein, Jane, John N. Parker, Manfred Laubichler, and Edward J. Hackett. "Data Management and Data Sharing in Science and Technology Studies." Science, Technology, & Human Values 44, no. 1 (September 18, 2018): 143–60. http://dx.doi.org/10.1177/0162243918798906.

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Анотація:
This paper presents reports on discussions among an international group of science and technology studies (STS) scholars who convened at the US National Science Foundation (January 2015) to think about data sharing and open STS. The first report, which reflects discussions among members of the Society for Social Studies of Science (4S), relates the potential benefits of data sharing and open science for STS. The second report, which reflects discussions among scholars from many professional STS societies (i.e., European Association for the Study of Science and Technology [ EASST], 4S, Society for the History of Technology [ SHOT], History of Science Society [ HSS], and Philosophy of Science Association [ PSA]), focuses on practical and conceptual issues related to managing, storing, and curating STS data. As is the case for all reports of such open discussions, a scholar’s presence at the meeting does not necessarily mean that they agree with all aspects of the text to follow.
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CAMPBELL, W., P. SMITH, R. PRICE, and L. ROELOFS. "Advancements in land science data management Pilot Land Data System." Science of The Total Environment 56 (November 15, 1986): 31–44. http://dx.doi.org/10.1016/0048-9697(86)90311-6.

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23

Vilar, Polona, and Vlasta Zabukovec. "Research data management and research data literacy in Slovenian science." Journal of Documentation 75, no. 1 (January 14, 2019): 24–43. http://dx.doi.org/10.1108/jd-03-2018-0042.

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Анотація:
PurposeThe purpose of this paper is to investigate the differences between scientific disciplines (SDs) in Slovenia in research data literacy (RDL) and research data management (RDM) to form recommendations regarding how to move things forward on the institutional and national level.Design/methodology/approachPurposive sample of active researchers was used from widest possible range of SD. Data were collected from April 21 to August 7, 2017, using 24-question online survey (5 demographic, 19 content questions (single/multiple choice and Likert scale type). Bivariate (ANOVA) and multivariate methods (clustering) were used.FindingsThe authors identified three perception-related and four behavior-related connections; this gave three clusters per area. First, perceptions – skeptical group, mainly social (SocS) and natural sciences (NatS): no clear RDM and ethical issues standpoints, do not agree that every university needs a data management plan (DMP). Careful group, again including mainly SocS and NatS: RDM is problematic and linked to ethical dilemmas, positive toward institutional DMPs. Convinced group, mainly from humanities (HUM), NatS, engineering (ENG) and medicine and health sciences (MedHeS): no problems regarding RDM, agrees this is an ethical question, is positive toward institutional DMP’s. Second, behaviors – sparse group, mainly from MedHeS, NatS and HUM, some agricultural scientists (AgS), and some SocS and ENG: do not tag data sets with metadata, do not use file-naming conventions/standards. Frequent group – many ENG, SocS, moderate numbers of NatS, very few AgS and only a few MedHeS and HUM: often use file-naming conventions/standards, version-control systems, have experience with public-domain data, are reluctant to use metadata with their RD. Slender group, mainly from AgS and NatS, moderate numbers of ENG, SocS and HUM, but no MedHeS: often use public-domain data, other three activities are rare.Research limitations/implicationsResearch could be expanded to a wider population, include other stakeholders and use qualitative methods.Practical implicationsResults are useful for international comparisons but also give foundations and recommendations on institutional and national RDM and RDL policies, implementations, and how to bring academic libraries into the picture. Identified differences suggest that different educational, awareness-raising and participatory approaches are needed for each group.Originality/valueThe findings offer valuable insight into RDM and RDL of Slovenian scientists, which have not yet been investigated in Slovenia.
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24

Brainerd, Elizabeth L., Richard W. Blob, Tyson L. Hedrick, Andrew T. Creamer, and Ulrike K. Müller. "Data Management Rubric for Video Data in Organismal Biology." Integrative and Comparative Biology 57, no. 1 (July 1, 2017): 33–47. http://dx.doi.org/10.1093/icb/icx060.

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25

Frew, James, and Jeff Dozier. "Data management for earth system science." ACM SIGMOD Record 26, no. 1 (March 1997): 27–31. http://dx.doi.org/10.1145/248603.248609.

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26

Chiu, Hung-Wen, and Yu-Chuan (Jack) Li. "Improving healthcare management with data science." Computer Methods and Programs in Biomedicine 154 (February 2018): A1. http://dx.doi.org/10.1016/s0169-2607(17)31508-0.

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27

Liebman, Michael N. "Data Management Systems: Science versus Technology?" OMICS: A Journal of Integrative Biology 7, no. 1 (January 2003): 67–69. http://dx.doi.org/10.1089/153623103322006634.

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28

Thomason, L. W., and J. P. Vernier. "Improved SAGE II cloud/aerosol categorization and observations of the Asian tropopause aerosol layer: 1989–2005." Atmospheric Chemistry and Physics 13, no. 9 (May 3, 2013): 4605–16. http://dx.doi.org/10.5194/acp-13-4605-2013.

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Abstract. We describe the challenges associated with the interpretation of extinction coefficient measurements by the Stratospheric Aerosol and Gas Experiment (SAGE II) in the presence of clouds. In particular, we have found that tropospheric aerosol analyses are highly dependent on a robust method for identifying when clouds affect the measured extinction coefficient. Herein, we describe an improved cloud identification method that appears to capture cloud/aerosol events more effectively than early methods. In addition, we summarize additional challenges to observing the Asian Tropopause Aerosol Layer (ATAL) using SAGE II observations. Using this new approach, we perform analyses of the upper troposphere, focusing on periods in which the UTLS (upper troposphere/lower stratosphere) is relatively free of volcanic material (1989–1990 and after 1996). Of particular interest is the Asian monsoon anticyclone where CALIPSO (Cloud-Aerosol Lidar Pathfinder Satellite Observations) has observed an aerosol enhancement. This enhancement, called the ATAL, has a similar morphology to observed enhancements in long-lived trace gas species like CO. Since the CALIPSO record begins in 2006, the question of how long this aerosol feature has been present requires a new look at the long-lived SAGE II data sets despite significant hurdles to its use in the subtropical upper troposphere. We find that there is no evidence of ATAL in the SAGE II data prior to 1998. After 1998, it is clear that aerosol in the upper troposphere in the ATAL region is substantially enhanced relative to the period before that time. In addition, the data generally supports the presence of the ATAL beginning in 1999 and continuing through the end of the mission, though some years (e.g., 2003) are complicated by the presence of episodic enhancements most likely of volcanic origin.
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29

Schmidtke, J., and K. Schmidt. "Data management and data base implementation for GMO monitoring." Journal für Verbraucherschutz und Lebensmittelsicherheit 1, S1 (November 2006): 92–94. http://dx.doi.org/10.1007/s00003-006-0096-0.

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30

Adams, Nico, and Ulrich S. Schubert. "From Data to Knowledge: Chemical Data Management, Data Mining, and Modeling in Polymer Science." Journal of Combinatorial Chemistry 6, no. 1 (January 2004): 12–23. http://dx.doi.org/10.1021/cc034021b.

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31

Paton, N. W. "Autonomics and data management." Concurrency and Computation: Practice and Experience 20, no. 17 (December 10, 2008): 2075–88. http://dx.doi.org/10.1002/cpe.1306.

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32

Parry, Michael J. "Data Management for Libraries." Electronic Library 34, no. 6 (November 7, 2016): 1051. http://dx.doi.org/10.1108/el-02-2016-0028.

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33

Lee, Fook‐Hou, Thiam‐Soon Tan, G. P. Karunaratne, and Seng‐Lip Lee. "Geotechnical Data Management System." Journal of Computing in Civil Engineering 4, no. 3 (July 1990): 239–54. http://dx.doi.org/10.1061/(asce)0887-3801(1990)4:3(239).

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34

Gelly, Bernard. "The IRIS data management." Solar Physics 133, no. 1 (May 1991): 31–42. http://dx.doi.org/10.1007/bf00149820.

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35

Brown, Kevin J. "IEPs and Data Management:." Computers in the Schools 3, no. 3-4 (February 2, 1987): 85–90. http://dx.doi.org/10.1300/j025v03n03_10.

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36

Riebau, A. R. "Wilderness monitoring and data management." Environmental Software 9, no. 4 (January 1994): 227–32. http://dx.doi.org/10.1016/0266-9838(94)90021-3.

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37

Pascuzzi, Pete, and Megan Sapp Nelson. "Integrating Data Science Tools into a Graduate Level Data Management Course." Journal of eScience Librarianship 7, no. 3 (December 20, 2018): e1152. http://dx.doi.org/10.7191/jeslib.2018.1152.

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38

Klensin, John C. "When the metadata exceed the data: data management with uncertain data." Statistics and Computing 5, no. 1 (March 1995): 73–84. http://dx.doi.org/10.1007/bf00140667.

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39

Carretero, Jesus, and Jose Daniel Garcia. "Scalability in data management." Journal of Supercomputing 47, no. 3 (May 20, 2008): 253–54. http://dx.doi.org/10.1007/s11227-008-0213-1.

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40

De Tré, Guy, Janusz Kacprzyk, Gabriella Pasi, Sławomir Zadrożny, and Antoon Bronselaer. "Human Centric Data Management." International Journal of Intelligent Systems 33, no. 10 (August 3, 2018): 1989–91. http://dx.doi.org/10.1002/int.21918.

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41

Clarke, Drew. "Antarctic data management." Antarctic Science 5, no. 3 (September 1993): 237. http://dx.doi.org/10.1017/s0954102093000318.

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Antarctic science is inter-disciplinary in character, multi-national in execution, and globally relevant. Data management in this environment must be examined from political, scientific and economic perspectives. The Antarctic Treaty calls on parties to exchange and make freely available scientific observations and results from Antarctica, so establishing the political context for addressing data management. The scientific context arises from the increasingly large and complex issues being addressed, including environmental monitoring and global change programmes, while the economic context considers data and information as the primary assets derived from Antarctic expenditure.
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42

Reichwald, Ralf, and Juan-Ignacıo Conrat. "Engineering Change Data Management." Zeitschrift für wirtschaftlichen Fabrikbetrieb 91, no. 9 (September 1, 1996): 398–401. http://dx.doi.org/10.1515/zwf-1996-910911.

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43

KiHo Hong, Sang Boem Lim, Junghee Kim, Mu Wook Pyeon, and In Chae Jeong. "Ubiquitous Sensor Data Management System." Journal of Next Generation Information Technology 4, no. 1 (February 28, 2013): 1–8. http://dx.doi.org/10.4156/jnit.vol4.issue1.1.

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44

Hensley, Zachary, Jibonananda Sanyal, and Joshua New. "Provenance in Sensor Data Management." Queue 11, no. 12 (December 2013): 50–63. http://dx.doi.org/10.1145/2559899.2574836.

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45

Hensley, Zachary, Jibonananda Sanyal, and Joshua New. "Provenance in sensor data management." Communications of the ACM 57, no. 2 (February 2014): 55–62. http://dx.doi.org/10.1145/2556647.2556657.

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46

Liu, Y., L. Meng, J. Guan, H. Yi, and S. Yin. "0713 Association Between Obesity Indices And Obstructive Sleep Apnea Is Modified By Age In A Sex-specific Manner." Sleep 43, Supplement_1 (April 2020): A271—A272. http://dx.doi.org/10.1093/sleep/zsaa056.709.

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Abstract Introduction The beneficial effects of weight loss on obstructive sleep apnea (OSA) are highly variable. Whether the variability is associated with the effects of age and sex remains unclear. This study examined this issue with large cross-sectional data. Methods A total of 4600 adult males and 1156 females with suspected OSA were included in the study. Anthropometric measurements, polysomnographic variables, biochemical indicators, and medical history were collected for each subject. Multivariable linear regression with interaction terms was used to estimate the modification effect of age on the associations between OSA severity (assessed by apnea-hypopnea index, AHI) with obesity indices (body mass index, BMI; neck circumference, NC; waist circumference, WC) in a sex-specific manner, and vice versa. Results BMI, NC, and WC were all positively correlated with AHI after adjusting for potential confounders in all populations. In males, these associations were much stronger and more significant in younger than older individuals (P for interaction < 0.001). For example, a 10% increase in BMI was independently associated with a 31.6% increase in AHI for males < 40 years old, whereas the corresponding increases were 20.8% and 16.7% for males 40-60 and >60 years old, respectively. By contrast, no modification effect of age was observed in females (P for interaction > 0.05). A 10% increase in BMI was associated with 25.6%, 26.8%, and 23.8% increases in AHI for females < 40, 40-60, and >60 years old, respectively. Conclusion Age modifies the associations between obesity indices and OSA severity in a sex-specific manner, and vice versa. These findings may broaden the understanding of age- and sex-related heterogeneities in the pathogenic role of obesity in OSA, and may be beneficial for individualized risk evaluation and treatment management for patients with OSA. Support This study was funded by Shanghai Municipal Commission of Science and Technology (grant number.18DZ2260200); the National Key R&D Program of China (grant number: 2017YFC0112500); Multi-Center Clinical Research Project from the School of Medicine, Shanghai Jiao Tong University (grant number: DLY201502); and the Shanghai Shen-Kang Hospital Management Center Project (grant number: SHDC12015101 and 16CR3103B).
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Pardi, Silvio, and Guido Russo. "A Big Data approach for multi-experiment data management." International Journal of Grid and Utility Computing 10, no. 2 (2019): 159. http://dx.doi.org/10.1504/ijguc.2019.098219.

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48

Pardi, Silvio, and Guido Russo. "A Big Data approach for multi-experiment data management." International Journal of Grid and Utility Computing 10, no. 2 (2019): 159. http://dx.doi.org/10.1504/ijguc.2019.10018614.

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49

Joshi, Margi, and Sharon S. Krag. "Issues in Data Management." Science and Engineering Ethics 16, no. 4 (July 6, 2010): 743–48. http://dx.doi.org/10.1007/s11948-010-9223-5.

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50

Hoven, John van den. "DATA BASE MANAGEMENT IRM: An Enterprisewide View of Data." Information Systems Management 12, no. 3 (January 1995): 69–72. http://dx.doi.org/10.1080/07399019508962989.

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