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Journal articles on the topic 'Data management and data science'

Author: Grafiati
Published: 10 January 2023
Last updated: 28 January 2023

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1

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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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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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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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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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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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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Timinsky, Alexander, Anna Kolomiiets, and Olga Mezentseva. "Project management models to create IT company in the field of Data Science." Advanced Information Technology, no. 1 (1) (2021): 86–94. http://dx.doi.org/10.17721/ait.2021.1.11.

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The basis for the emergence of projects to create IT companies in the field of Data Science is considered. The relevance of such projects is substantiated. The feasibility of their study using models and methods of project management is proven. Literary sources in three directions are analyzed. Such areas include classical project management, flexible approaches to project management, value-oriented management. Insufficient research of the described subject is proved. Groups of models and methods are described that describe the relevant areas of project management knowledge that will be necessary and minimally sufficient to develop the scientific basis of the IT company creation project in the field of Data Science. Namely: project team management, project value management, project concept development, project content management, project communications management, flexible project management tools. In the analysis of each area, the relevant models and methods are highlighted. Their applicability to the researched project is analyzed. A model for selecting a set of models and methods within the identified key knowledge areas for the research project in the form of a convolution of criteria is proposed. SWOT-analysis of the proposed approach was performed. The strengths, weaknesses, opportunities and threats associated with the proposed approach are highlighted. Conclusions to the study are formulated. Prospects for further research in the chosen direction are outlined.
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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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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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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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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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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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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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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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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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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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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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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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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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Tonidandel, Scott, Eden B. King, and Jose M. Cortina. "Big Data Methods." Organizational Research Methods 21, no. 3 (November 16, 2016): 525–47. http://dx.doi.org/10.1177/1094428116677299.

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Advances in data science, such as data mining, data visualization, and machine learning, are extremely well-suited to address numerous questions in the organizational sciences given the explosion of available data. Despite these opportunities, few scholars in our field have discussed the specific ways in which the lens of our science should be brought to bear on the topic of big data and big data's reciprocal impact on our science. The purpose of this paper is to provide an overview of the big data phenomenon and its potential for impacting organizational science in both positive and negative ways. We identifying the biggest opportunities afforded by big data along with the biggest obstacles, and we discuss specifically how we think our methods will be most impacted by the data analytics movement. We also provide a list of resources to help interested readers incorporate big data methods into their existing research. Our hope is that we stimulate interest in big data, motivate future research using big data sources, and encourage the application of associated data science techniques more broadly in the organizational sciences.
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Semeler, Alexandre Ribas, Adilson Luiz Pinto, and Helen Beatriz Frota Rozados. "Data science in data librarianship: Core competencies of a data librarian." Journal of Librarianship and Information Science 51, no. 3 (November 26, 2017): 771–80. http://dx.doi.org/10.1177/0961000617742465.

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Currently, data are stored in an always-on condition, and can be globally accessed at any point, by any user. Data librarianship has its origins in the social sciences. In particular, the creation of data services and data archives, in the United Kingdom (Data Archives Services) and in the United States and Canada (Data Library Services), is a key factor for the emergence of data librarianship. The focus of data librarianship nowadays is on the creation of new library services. Data librarians are concerned with the proposition of services for data management and curation in academic libraries and other research organizations. The purpose of this paper is to understand how the complexity of the data can serve as the basis for identifying the technical skills required by data librarians. This essay is systematically divided, first introducing the concepts of data and research data in data librarianship, followed by an overview of data science as a theory, method, and technology to assess data. Next, the identification of the competencies and skills required by data scientists and data librarians are discussed. Our final remarks highlight that data librarians should understand that the complexity and novelty associated with data science praxis. Data science provides new methods and practices for data librarianship. A data librarian need not become a programmer, statistician, or database manager, but should be interested in learning about the languages and programming logic of computers, databases, and information retrieval tools. We believe that numerous kinds of scientific data research provide opportunities for a data librarian to engage with data science.
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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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Doonan, Ashley, Dharma Akmon, and Evan Cosby. "An Exploratory Analysis of Social Science Graduate Education in Data Management and Data Sharing." International Journal of Digital Curation 15, no. 1 (July 22, 2020): 18. http://dx.doi.org/10.2218/ijdc.v15i1.671.

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Effective data management and data sharing are crucial components of the research lifecycle, yet evidence suggests that many social science graduate programs are not providing training in these areas. The current exploratory study assesses how U.S. masters and doctoral programs in the social sciences include formal, non-formal, and informal training in data management and sharing. We conducted a survey of 150 graduate programs across six social science disciplines, and used a mix of closed and open-ended questions focused on the extent to which programs provide such training and exposure. Results from our survey suggested a deficit of formal training in both data management and data sharing, limited non-formal training, and cursory informal exposure to these topics. Utilizing the results of our survey, we conducted a syllabus analysis to further explore the formal and non-formal content of graduate programs beyond self-report. Our syllabus analysis drew from an expanded seven social science disciplines for a total of 140 programs. The syllabus analysis supported our prior findings that formal and non-formal inclusion of data management and data sharing training is not common practice. Overall, in both the survey and syllabi study we found a lack of both formal and non-formal training on data management and data sharing. Our findings have implications for data repository staff and data service professionals as they consider their methods for encouraging data sharing and prepare for the needs of data depositors. These results can also inform the development and structuring of graduate education in the social sciences, so that researchers are trained early in data management and sharing skills and are able to benefit from making their data available as early in their careers as possible.
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Roskladka, Andrii, Nataliia Roskladka, Anatolii Karpuk, Andriy Stavytskyy, and Ganna Kharlamova. "The data science tools for research of emigration processes in Ukraine." Problems and Perspectives in Management 18, no. 1 (February 11, 2020): 70–81. http://dx.doi.org/10.21511/ppm.18(1).2020.07.

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The process of world globalization, labor, and academic mobility, the visa-free regime with the EU countries have caused a significant revival of migration processes in Ukraine. However, there is still the research gap in the most informative, and, at the same time, accurate method of the assessment and forecasting of the migration flows. Thus, the object of research is migration processes (mostly emphasizing the emigration flows). The motives, causes of emigration processes, and their relationship with the economic state were analyzed. The impact factors of external labor migration on the economy of the host countries were revealed, particularly the negative and positive impacts of emigration on the socio-economic situation in Ukraine and the migration attitude of Ukrainians were assessed.The main result of study is further development of the econometric model for forecasting the number of emigrants from Ukraine to other countries in the nearest future. The model considers the factors of minimum wage lavel in Ukraine, the number of open vacancies in the countries of Eastern Europe, and the level of competition for jobs. According to the results of forecasting based on Maple computer algebra system and Microsoft Power BI analytical platform, by the end of 2019, the number of emigrants from Ukraine supposed to be the largest in the last four years and to reach the estimates in the range from 2,444 to 2,550 million people, which may indicate a new third wave of emigration processes.
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Bishop, Bradley Wade, Ashley Marie Orehek, and Hannah R. Collier. "Job Analyses of Earth Science Data Librarians and Data Managers." Bulletin of the American Meteorological Society 102, no. 7 (July 2021): E1384—E1393. http://dx.doi.org/10.1175/bams-d-20-0163.1.

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AbstractThis study’s purpose is to capture the skills of Earth science data managers and librarians through interviews with current job holders. Job analysis interviews were conducted of 14 participants—six librarians and eight data managers—to assess the types and frequencies of job tasks. Participants identified tasks related to communication, including collaboration, teaching, and project management activities. Data-specific tasks included data discovery, processing, and curation, which require an understanding of the data, technology, and information infrastructures to support data use, reuse, and preservation. Most respondents had formal science education and six had a master’s degree in Library and Information Sciences. Most of the knowledge, skills, and abilities for these workers were acquired through on-the-job experience, but future professionals in these careers may benefit from tailored education informed through job analyses.
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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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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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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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Vamathevan, Jessica, Rolf Apweiler, and Ewan Birney. "Biomolecular Data Resources: Bioinformatics Infrastructure for Biomedical Data Science." Annual Review of Biomedical Data Science 2, no. 1 (July 20, 2019): 199–222. http://dx.doi.org/10.1146/annurev-biodatasci-072018-021321.

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Technological advances have continuously driven the generation of bio-molecular data and the development of bioinformatics infrastructure, which enables data reuse for scientific discovery. Several types of data management resources have arisen, such as data deposition databases, added-value databases or knowledgebases, and biology-driven portals. In this review, we provide a unique overview of the gradual evolution of these resources and discuss the goals and features that must be considered in their development. With the increasing application of genomics in the health care context and with 60 to 500 million whole genomes estimated to be sequenced by 2022, biomedical research infrastructure is transforming, too. Systems for federated access, portable tools, provision of reference data, and interpretation tools will enable researchers to derive maximal benefits from these data. Collaboration, coordination, and sustainability of data resources are key to ensure that biomedical knowledge management can scale with technology shifts and growing data volumes.
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Di Maria, Riccardo, and Rizart Dona. "ESCAPE Data Lake." EPJ Web of Conferences 251 (2021): 02056. http://dx.doi.org/10.1051/epjconf/202125102056.

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The European-funded ESCAPE project (Horizon 2020) aims to address computing challenges in the context of the European Open Science Cloud. The project targets Particle Physics and Astronomy facilities and research infrastructures, focusing on the development of solutions to handle Exabyte-scale datasets. The science projects in ESCAPE are in different phases of evolution and count a variety of specific use cases and challenges to be addressed. This contribution describes the shared-ecosystem architecture of services, the Data Lake, fulfilling the needs in terms of data organisation, management, and access of the ESCAPE community. The Pilot Data Lake consists of several storage services operated by the partner institutes and connected through reliable networks, and it adopts Rucio to orchestrate data management and organisation. The results of a 24-hour Full Dress Rehearsal are also presented, highlighting the achievements of the Data Lake model and of the ESCAPE sciences.
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Grabis, Janis, Bohdan Haidabrus, Serhiy Protsenko, Iryna Protsenko, and Anna Rovna. "DATA SCIENCE APPROACH FOR IT PROJECT MANAGEMENT." ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference 2 (June 20, 2019): 51. http://dx.doi.org/10.17770/etr2019vol2.4163.

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Majority of the IT companies realized that ability to analyse and use data, could be one of the key factors for increasing of number of successful projects, portfolios, programs. Key performance indicators based on data analysis helps organizations be more prosperous in a long term perspective. Also, statistical data are very useful for monitoring and evaluation of project results which are very important for managers, delivery directors, CTO and others high level management of company. The Data Science methods could make more efficient project management in several of business problems. Analysis of historical data from the project life-cycle based on Data Science models could provide more efficient benefits for different stakeholders. Differential of the project data vector with target as an integral evaluation of the project success which allow for the complex correlations between separate features. Therefore, the influence of features importance and override creatures could be decreased on the target. This study propose new approach based on Data Science providing more efficient and accurately project management, taking into account best practices and project performance data.
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Zhao, Yuxiu, and Yanbao Liu. "A bibliometrics data analysis of management science." Journal of Data, Information and Management 2, no. 3 (March 16, 2020): 131–47. http://dx.doi.org/10.1007/s42488-020-00024-0.

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Bicalho, Tereza, Ildo Sauer, Alexandre Rambaud, and Yulia Altukhova. "LCA data quality: A management science perspective." Journal of Cleaner Production 156 (July 2017): 888–98. http://dx.doi.org/10.1016/j.jclepro.2017.03.229.

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Agarwal, Deborah A., Boris Faybishenko, Vicky L. Freedman, Harinarayan Krishnan, Gary Kushner, Carina Lansing, Ellen Porter, et al. "A science data gateway for environmental management." Concurrency and Computation: Practice and Experience 28, no. 7 (October 12, 2015): 1994–2004. http://dx.doi.org/10.1002/cpe.3697.

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Chukanova, Svitlana. "The Notion of "Research Data": Types and Kinds of Research Data in the Context of Data Management Practice." Ukrainian Journal on Library and Information Science, no. 8 (December 20, 2021): 128–38. http://dx.doi.org/10.31866/2616-7654.8.2021.247590.

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With the rapid development of the concept of Open Science, the quantitative growth of data obtained during the research, scientific attention to the practice of research data management (research data management) increases, which actualizes the definition of “research data” and identifying types of research data within the practice of their management, justification and coverage of the specifics of such data. The methodological tools of the study are based on the terminological method, the use of which was due to the need to identify relevant interpretations of the concept of “research data”, as well as analysis of repositories for data from various fields of science, indexed by re3data.org., in the general areas presented in the register, namely: descriptions of repositories, including information on the types of data deposited by scientists and data curators. The analysis made it possible to define research data as materials obtained and collected to substantiate the scientific results of research in any field and in any form: numerical, textual, computer code, etc., as well as to identify types of data specific to different branches of science, which, in turn, allowed us to conclude the existing data formats, the most common among both natural and human sciences: text, numerical and graphic formats. As a result of the analysis, it was found that research data can be considered textual, numerical, software, archival, graphic and other objects (files) that serve as the basis of the study and the factual basis for scientific conclusions in a particular field of science. It was found that the type of data directly depends on the nature of the study and the characteristics of the discipline or field of research.
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Müller, Wolfgang, and Can Türker. "Data Management in Life Sciences." it - Information Technology 53, no. 5 (September 2011): 215–16. http://dx.doi.org/10.1524/itit.2011.9071.

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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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Fox, Peter, and James Hendler. "The Science of Data Science." Big Data 2, no. 2 (June 2014): 68–70. http://dx.doi.org/10.1089/big.2014.0011.

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Charles, Vincent, Juan Aparicio, and Joe Zhu. "Data science for better productivity." Journal of the Operational Research Society 72, no. 5 (May 4, 2021): 971–74. http://dx.doi.org/10.1080/01605682.2021.1892466.

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Nolte, Viktoria, Tanja Sindram, Jürgen Mazarov, and Jochen Deuse. "Industrial Data Science erfolgreich implementieren." Zeitschrift für wirtschaftlichen Fabrikbetrieb 115, no. 10 (October 1, 2020): 734–37. http://dx.doi.org/10.1515/zwf-2020-1151020.

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Abstract Die Potenziale von Industrial Data Science haben Unternehmen unlängst erkannt, scheitern jedoch an deren Umsetzung. In diesem Beitrag werden die Ergebnisse einer branchenübergreifenden Interviewstudie mit über 50 Führungskräften und Fachexperten vorgestellt, wobei Durchführungshemmnisse und Erfolgsfaktoren identifiziert werden. Zudem werden Anforderungen an das Change Management diskutiert sowie konkrete Handlungsempfehlungen für Unternehmen gegeben.
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Jeng, Wei, and Liz Lyon. "A Report of Data-Intensive Capability, Institutional Support, and Data Management Practices in Social Sciences." International Journal of Digital Curation 11, no. 1 (October 28, 2016): 156. http://dx.doi.org/10.2218/ijdc.v11i1.398.

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We report on a case study which examines the social science community’s capability and institutional support for data management. Fourteen researchers were invited for an in-depth qualitative survey between June 2014 and October 2015. We modify and adopt the Community Capability Model Framework (CCMF) profile tool to ask these scholars to self-assess their current data practices and whether their academic environment provides enough supportive infrastructure for data related activities. The exemplar disciplines in this report include anthropology, political sciences, and library and information science. Our findings deepen our understanding of social disciplines and identify capabilities that are well developed and those that are poorly developed. The participants reported that their institutions have made relatively slow progress on economic supports and data science training courses, but acknowledged that they are well informed and trained for participants’ privacy protection. The result confirms a prior observation from previous literature that social scientists are concerned with ethical perspectives but lack technical training and support. The results also demonstrate intra- and inter-disciplinary commonalities and differences in researcher perceptions of data-intensive capability, and highlight potential opportunities for the development and delivery of new and impactful research data management support services to social sciences researchers and faculty.Â
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Federer, Lisa M., and Jian Qin. "Beyond the data management plan: Expanding roles for librarians in data science and open science." Proceedings of the Association for Information Science and Technology 56, no. 1 (January 2019): 529–31. http://dx.doi.org/10.1002/pra2.82.

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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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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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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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Srivastava, Divesh, Monica Scannapieco, and Thomas C. Redman. "Ensuring High-Quality Private Data for Responsible Data Science." Journal of Data and Information Quality 11, no. 1 (January 18, 2019): 1–9. http://dx.doi.org/10.1145/3287168.

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Warner, Guy C., Jesse M. Blum, Simon B. Jones, Paul S. Lambert, Kenneth J. Turner, Larry Tan, Alison S. F. Dawson, and David N. F. Bell. "A social science data-fusion tool and the Data Management through e-Social Science (DAMES) infrastructure." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368, no. 1925 (August 28, 2010): 3859–73. http://dx.doi.org/10.1098/rsta.2010.0159.

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The last two decades have seen substantially increased potential for quantitative social science research. This has been made possible by the significant expansion of publicly available social science datasets, the development of new analytical methodologies, such as microsimulation, and increases in computing power. These rich resources do, however, bring with them substantial challenges associated with organizing and using data. These processes are often referred to as ‘data management’. The Data Management through e-Social Science (DAMES) project is working to support activities of data management for social science research. This paper describes the DAMES infrastructure, focusing on the data-fusion process that is central to the project approach. It covers: the background and requirements for provision of resources by DAMES; the use of grid technologies to provide easy-to-use tools and user front-ends for several common social science data-management tasks such as data fusion; the approach taken to solve problems related to data resources and metadata relevant to social science applications; and the implementation of the architecture that has been designed to achieve this infrastructure.
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Reed, David. "Data smart: Using data science to transform information into insight." Journal of Direct, Data and Digital Marketing Practice 15, no. 4 (April 2014): 354–55. http://dx.doi.org/10.1057/dddmp.2014.33.

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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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43

Mazarov, Jürgen, Patrick Wolf, Julian Schallow, Fabian Nöhring, Jochen Deuse, and Ralph Richter. "Industrial Data Science in Wertschöpfungsnetzwerken." ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb 114, no. 12 (December 17, 2019): 874–77. http://dx.doi.org/10.3139/104.112205.

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Nolte, Viktoria, Tanja Sindram, Jürgen Mazarov, and Jochen Deuse. "Industrial Data Science erfolgreich implementieren." ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb 115, no. 10 (October 28, 2020): 734–37. http://dx.doi.org/10.3139/104.112420.

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Woltmann, Lucas, Peter Volk, Michael Dinzinger, Lukas Gräf, Sebastian Strasser, Johannes Schildgen, Claudio Hartmann, and Wolfgang Lehner. "Data Science Meets High-Tech Manufacturing – The BTW 2021 Data Science Challenge." Datenbank-Spektrum 22, no. 1 (December 21, 2021): 5–10. http://dx.doi.org/10.1007/s13222-021-00398-4.

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AbstractFor its third installment, the Data Science Challenge of the 19th symposium “Database Systems for Business, Technology and Web” (BTW) of the Gesellschaft für Informatik (GI) tackled the problem of predictive energy management in large production facilities. For the first time, this year’s challenge was organized as a cooperation between Technische Universität Dresden, GlobalFoundries, and ScaDS.AI Dresden/Leipzig. The Challenge’s participants were given real-world production and energy data from the semiconductor manufacturer GlobalFoundries and had to solve the problem of predicting the energy consumption for production equipment. The usage of real-world data gave the participants a hands-on experience of challenges in Big Data integration and analysis. After a leaderboard-based preselection round, the accepted participants presented their approach to an expert jury and audience in a hybrid format. In this article, we give an overview of the main points of the Data Science Challenge, like organization and problem description. Additionally, the winning team presents its solution.
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Vieira, Ricardo, Filipe Ferreira, José Barateiro, and José Borbinha. "Data Management with Risk Management in Engineering and Science Projects." New Review of Information Networking 19, no. 2 (July 3, 2014): 49–66. http://dx.doi.org/10.1080/13614576.2014.918519.

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Bhatt, Ritikesh, Toufiq Shaikh, Dr Sandeep Patil, Mohnish Harwani, and Bibhu Kumar. "Automating Medical Data and Using Data Science For Heart Disease Prediction." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 3933–36. http://dx.doi.org/10.22214/ijraset.2022.43278.

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Abstract: Technology has aided the improvement of individual health, healthcare, biomedical research as well as public health. Therefore, healthcare institutions are seeking to develop integrated information-management environments to consolidate the inevitable application of big data to health care. There exist various entry points into the medical world where computational tools assist patient care matters; reporting results of tests, allowing direct entry of orders or patient information by clinicians, facilitating access to transcribed reports, and in some cases supporting telemedicine applications, because of disorganized and incomplete patient records pose an obstacle to patient care. The most common medium by which records of medical history are kept is paper making data management a severe impediment to productivity. However, the promise of a more efficient healthcare service is obvious through the use of automated health records management systems. Heart disease is a common disease that is overlooked by most. In this study, we discuss how a person can figure out if they need to go to a doctor for a health check-up for any heart-related issues using machine learning algorithms. Keywords: Data Science, Statistics, Python, Data mining, Machine learning, Analytics, Big Data, Disease Prediction, Firebase, Supervised Learning, Unsupervised Learning, ElectrocardioGram(ECG).
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Antell, Karen, Jody Bales Foote, Jaymie Turner, and Brian Shults. "Dealing with Data: Science Librarians’ Participation in Data Management at Association of Research Libraries Institutions." College & Research Libraries 75, no. 4 (July 1, 2014): 557–74. http://dx.doi.org/10.5860/crl.75.4.557.

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As long as empirical research has existed, researchers have been doing “data management” in one form or another. However, funding agency mandates for doing formal data management are relatively recent, and academic libraries’ involvement has been concentrated mainly in the last few years. The National Science Foundation implemented a new mandate in January 2011, requiring researchers to include a data management plan with their proposals for funding. This has prompted many academic libraries to work more actively than before in data management, and science librarians in particular are uniquely poised to step into new roles to meet researchers’ data management needs. This study, a survey of science librarians at institutions affiliated with the Association of Research Libraries, investigates science librarians’ awareness of and involvement in institutional repositories, data repositories, and data management support services at their institutions. The study also explores the roles and responsibilities, both new and traditional, that science librarians have assumed related to data management, and the skills that science librarians believe are necessary to meet the demands of data management work. The results reveal themes of both uncertainty and optimism—uncertainty about the roles of librarians, libraries, and other campus entities; uncertainty about the skills that will be required; but also optimism about applying “traditional” librarian skills to this emerging field of academic librarianship.
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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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Lewis Priestley, Jennifer, and Robert J. McGrath. "The Evolution of Data Science." International Journal of Knowledge Management 15, no. 2 (April 2019): 97–109. http://dx.doi.org/10.4018/ijkm.2019040106.

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Is data science a new field of study or simply an extension or specialization of a discipline that already exists, such as statistics, computer science, or mathematics? This article explores the evolution of data science as a potentially new academic discipline, which has evolved as a function of new problem sets that established disciplines have been ill-prepared to address. The authors find that this newly-evolved discipline can be viewed through the lens of a new mode of knowledge production and is characterized by transdisciplinarity collaboration with the private sector and increased accountability. Lessons from this evolution can inform knowledge production in other traditional academic disciplines as well as inform established knowledge management practices grappling with the emerging challenges of Big Data.
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