Bibliografías temáticas / Data management

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Literatura académica sobre el tema "Data management"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 25 de julio de 2025

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Artículos de revistas sobre el tema "Data management"

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Gade, Kishore. "Data Analytics: Data Mesh Architecture and Its Implications for Data Management." International Journal of Science and Research (IJSR) 8, no. 11 (2019): 2061–67. http://dx.doi.org/10.21275/sr19113110630.

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Sri Vardhan Pentapati, Harsha. "Management Data and Business Analytics." International Journal of Science and Research (IJSR) 10, no. 9 (2021): 1603–7. https://doi.org/10.21275/sr21927211738.

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Moreno, P., M. Ruiz, and F. J. Gorines. "TBM Process Data Management System." International Journal of Engineering and Technology 7, no. 5 (2015): 431–34. http://dx.doi.org/10.7763/ijet.2015.v7.832.

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Reddy Desani, Nithin. "Enhancing Data Governance through AI - Driven Data Quality Management and Automated Data Contracts." International Journal of Science and Research (IJSR) 12, no. 8 (2023): 2519–25. http://dx.doi.org/10.21275/es23812104904.

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ЛИТОВЧЕНКО, В. Б., Д. В. ЗЕНКОВА, and К. В. ВОВК. "DATA-DRIVEN IN MANAGEMENT." Экономика и предпринимательство, no. 7(168) (August 6, 2024): 1060–64. http://dx.doi.org/10.34925/eip.2024.168.7.211.

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В данной статье рассмотрены работа и применение Data-Driven в менеджменте. Data-driven (англ. «управляемый данными») - это новый подход к управлению, основывающийся на собираемых данных. Только они имеют значение, при принятии того или иного решения для бизнеса. Dataориентированную компанию отличает умение её сотрудников анализировать данные, учитывая рыночный контекст. В статье подробно описано как начать использовать данный подход, какими инструментами следует пользоваться, на что делать акцент. Также приведены конкретные примеры применения в пяти успешных компаниях с подробным описанием и в
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Sashi, K., and Antony Selvadoss Thanamani. "Dynamic Replica Management for Data Grid." International Journal of Engineering and Technology 2, no. 4 (2010): 329–33. http://dx.doi.org/10.7763/ijet.2010.v2.142.

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I. Pachankis, Yang. "Three-Body Logging for Data Management." Journal of Theoretical Physics & Mathematics Research 1, no. 1 (2023): 01–06. https://doi.org/10.64030/3065-8802.01.01.01.

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The research develops a logging method in observational astronomy from a nuclear science perspective. It takes the existing optics based observational parameters to a three-body parameter in the observational vantage points. By taking the earth’s atmosphere as a positive meniscus lens, the celestial plane and the sun-earth-moon three-body plane are differentiated in dimensions. The complex planes are differentiated between observational preconditions and data depth in light quanta, and the light quanta in data structure can be further corrected to motion biases and gravitational lensing. The a
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Iqbal, Ms Snober, K. SreeNiketh Raj, K. Vineeth Reddy, and Mohd Mudaseer Mazharuddin. "Decentralized Education Data Management Using Blockchain." International Journal of Research Publication and Reviews 6, no. 6 (2025): 3173–81. https://doi.org/10.55248/gengpi.6.0625.2122.

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Lee, Jonghyun Harry, Tyler Hesser, Matthew Farthing, Spicer Bak, and Katherine DeVore. "SCALABLE REAL-TIME DATA ASSIMILATION WITH VARIOUS DATA TYPES FOR ACCURATE SPATIOTEMPORAL NEARSHORE BATHYMETRY ESTIMATION." Coastal Engineering Proceedings, no. 37 (October 2, 2023): 156. http://dx.doi.org/10.9753/icce.v37.management.156.

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Immediate estimation of nearshore bathymetry is crucial for accurate prediction of nearshore wave conditions and coastal flooding events. However, direct bathymetry data collection is expensive and time-consuming, while accurate airborne lidar-based survey is limited by breaking waves and decreased light penetration affected by water turbidity. Several recent efforts have been made to apply interpolation and inverse modeling approaches to indirect remote sensed observations along with sparse direct survey data points. Example indirect observations include video-based observations such as time-
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Briney, Kristin, Heather Coates, and Abigail Goben. "Foundational Practices of Research Data Management." Research Ideas and Outcomes 6 (July 27, 2020): e56508. https://doi.org/10.3897/rio.6.e56508.

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The importance of research data has grown as researchers across disciplines seek to ensure reproducibility, facilitate data reuse, and acknowledge data as a valuable scholarly commodity. Researchers are under increasing pressure to share their data for validation and reuse. Adopting good data management practices allows researchers to efficiently locate their data, understand it, and use it throughout all of the stages of a project and in the future. Additionally, good data management can streamline data analysis, visualization, and reporting, thus making publication less stressful and time-co
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Tesis sobre el tema "Data management"

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Morshedzadeh, Iman. "Data Classification in Product Data Management." Thesis, Högskolan i Skövde, Institutionen för teknik och samhälle, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-14651.

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This report is about the product data classification methodology that is useable for the Volvo Cars Engine (VCE) factory's production data, and can be implemented in the Teamcenter software. There are many data generated during the life cycle of each product, and companies try to manage these data with some product data management software. Data classification is a part of data management for most effective and efficient use of data. With surveys that were done in this project, items affecting the data classification have been found. Data, attributes, classification method, Volvo Cars Engine f
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Yang, Ying. "Interactive Data Management and Data Analysis." Thesis, State University of New York at Buffalo, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10288109.

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<p> Everyone today has a big data problem. Data is everywhere and in different formats, they can be referred to as data lakes, data streams, or data swamps. To extract knowledge or insights from the data or to support decision-making, we need to go through a process of collecting, cleaning, managing and analyzing the data. In this process, data cleaning and data analysis are two of the most important and time-consuming components. </p><p> One common challenge in these two components is a lack of interaction. The data cleaning and data analysis are typically done as a batch process, operati
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Mathew, Avin D. "Asset management data warehouse data modelling." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/19310/1/Avin_Mathew_Thesis.pdf.

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Data are the lifeblood of an organisation, being employed by virtually all business functions within a firm. Data management, therefore, is a critical process in prolonging the life of a company and determining the success of each of an organisation’s business functions. The last decade and a half has seen data warehousing rising in priority within corporate data management as it provides an effective supporting platform for decision support tools. A cross-sectional survey conducted by this research showed that data warehousing is starting to be used within organisations for their engineering
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Mathew, Avin D. "Asset management data warehouse data modelling." Queensland University of Technology, 2008. http://eprints.qut.edu.au/19310/.

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Data are the lifeblood of an organisation, being employed by virtually all business functions within a firm. Data management, therefore, is a critical process in prolonging the life of a company and determining the success of each of an organisation’s business functions. The last decade and a half has seen data warehousing rising in priority within corporate data management as it provides an effective supporting platform for decision support tools. A cross-sectional survey conducted by this research showed that data warehousing is starting to be used within organisations for their engineering
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Sehat, Mahdis, and FLORES RENÉ PAVEZ. "Customer Data Management." Thesis, KTH, Industriell ekonomi och organisation (Avd.), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-109251.

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Abstract As the business complexity, number of customers continues to grow and customers evolve into multinational organisations that operate across borders, many companies are faced with great challenges in the way they manage their customer data. In today’s business, a single customer may have a relationship with several entities of an organisation, which means that the customer data is collected through different channels. One customer may be described in different ways by each entity, which makes it difficult to obtain a unified view of the customer. In companies where there are several so
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Scott, Mark. "Research data management." Thesis, University of Southampton, 2014. https://eprints.soton.ac.uk/374711/.

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Scientists within the materials engineering community produce a wide variety of data, ranging from large 3D volume densitometry files (voxel) generated by microfocus computer tomography (μCT) to simple text files containing results from tensile tests. Increasingly they need to share this data as part of international collaborations. The design of a suitable database schema and the architecture of a flexible system that can cope with the varying information is a continuing problem in the management of heterogeneous data. We discuss the issues with managing such varying data, and present a model
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Tran, Viet-Trung. "Scalable data-management systems for Big Data." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2013. http://tel.archives-ouvertes.fr/tel-00920432.

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Big Data can be characterized by 3 V's. * Big Volume refers to the unprecedented growth in the amount of data. * Big Velocity refers to the growth in the speed of moving data in and out management systems. * Big Variety refers to the growth in the number of different data formats. Managing Big Data requires fundamental changes in the architecture of data management systems. Data storage should continue being innovated in order to adapt to the growth of data. They need to be scalable while maintaining high performance regarding data accesses. This thesis focuses on building scalable data manage
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Schnyder, Martin. "Web 2.0 data management." Zürich : ETH, Eidgenössische Technische Hochschule Zürich, Department of Computer Science, Institute of Information Systems, Global Information Systems Group, 2008. http://e-collection.ethbib.ethz.ch/show?type=dipl&nr=403.

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He, Ying Surveying &amp Spatial Information Systems Faculty of Engineering UNSW. "Spatial data quality management." Publisher:University of New South Wales. Surveying & Spatial Information Systems, 2008. http://handle.unsw.edu.au/1959.4/43323.

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The applications of geographic information systems (GIS) in various areas have highlighted the importance of data quality. Data quality research has been given a priority by GIS academics for three decades. However, the outcomes of data quality research have not been sufficiently translated into practical applications. Users still need a GIS capable of storing, managing and manipulating data quality information. To fill this gap, this research aims to investigate how we can develop a tool that effectively and efficiently manages data quality information to aid data users to better understand a
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Voigt, Hannes. "Flexibility in Data Management." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-136681.

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With the ongoing expansion of information technology, new fields of application requiring data management emerge virtually every day. In our knowledge culture increasing amounts of data and work force organized in more creativity-oriented ways also radically change traditional fields of application and question established assumptions about data management. For instance, investigative analytics and agile software development move towards a very agile and flexible handling of data. As the primary facilitators of data management, database systems have to reflect and support these developments. H
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Libros sobre el tema "Data management"

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Cooper, Richard, and Jessie Kennedy, eds. Data Management. Data, Data Everywhere. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73390-4.

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Purba, Sanjiv. Data Management. 3rd ed. Auerbach Publications, 2021. http://dx.doi.org/10.1201/9780429114878.

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Gronwald, Klaus-Dieter. Data Management. Springer Berlin Heidelberg, 2024. http://dx.doi.org/10.1007/978-3-662-68668-3.

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Mahanti, Rupa. Data Governance and Data Management. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3583-0.

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Mamoulis, Nikos. Spatial Data Management. Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-031-01884-8.

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Golab, Lukasz, and M. Tamer Özsu. Data Stream Management. Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-031-01837-4.

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Jonker, Willem, and Milan Petković, eds. Secure Data Management. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15546-8.

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Fletcher, George, Jan Hidders, and Josep Lluís Larriba-Pey, eds. Graph Data Management. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96193-4.

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Zhao, Liang, Sherif Sakr, Anna Liu, and Athman Bouguettaya. Cloud Data Management. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04765-2.

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Li, Guoliang, Jiannan Wang, Yudian Zheng, Ju Fan, and Michael J. Franklin. Crowdsourced Data Management. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7847-7.

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Capítulos de libros sobre el tema "Data management"

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Bingham, John. "Data Management." In Data Processing. Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-19938-9_12.

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Swart, William. "Restaurant Management." In Data Analytics. Auerbach Publications, 2019. http://dx.doi.org/10.1201/9781315267555-5.

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Hallstrom, Elyse. "Inventory Management." In Data Analytics. Auerbach Publications, 2019. http://dx.doi.org/10.1201/9781315267555-7.

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Lee, T. Y., Michael Minor, and Lionel D. Edwards. "Data Management." In Principles and Practice of Pharmaceutical Medicine. Wiley-Blackwell, 2010. http://dx.doi.org/10.1002/9781444325263.ch29.

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Andrews, Joshua Kelly, Karen M. Cheek, Matthew P. Jennings, David W. Rogers, and Vincent M. Walden. "Data Management." In Litigation Services Handbook. John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119204794.ch14.

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Alagić, Suad. "Data Management." In Software Engineering: Specification, Implementation, Verification. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61518-9_5.

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Muenchen, Robert A. "Data Management." In Statistics and Computing. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0685-3_10.

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Bingham, John, and Garth Davies. "Data Management." In Systems Analysis. Macmillan Education UK, 1992. http://dx.doi.org/10.1007/978-1-349-12833-4_18.

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Müllner, Marcus. "Data Management." In Erfolgreich wissenschaftlich Arbeiten in der Klinik. Springer Vienna, 2002. http://dx.doi.org/10.1007/978-3-7091-3755-0_21.

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Muenchen, Robert A. "Data Management." In Statistics and Computing. Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-09418-2_14.

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Actas de conferencias sobre el tema "Data management"

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Anto, Tony Rosset, Borja Albert Gramaje, Lukasz Wisniewski, and Stylianos Karatzas. "Data Management Platform for Indoor Air Quality Management." In 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE, 2024. http://dx.doi.org/10.1109/etfa61755.2024.10710780.

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"Data Management." In CLADE 2005. Proceedings Challenges of Large Applications in Distributed Environments, 2005. IEEE, 2005. http://dx.doi.org/10.1109/clade.2005.1520894.

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Meseck, Reed M. "Data management." In the 2001 ACM SIGMOD international conference. ACM Press, 2001. http://dx.doi.org/10.1145/375663.375745.

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Grillenberger, Andreas. "Big data and data management." In the tenth annual conference. ACM Press, 2014. http://dx.doi.org/10.1145/2632320.2632325.

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Dewilde, P., and J. Annevelink. "VLSI Data-Management." In Twelfth European Solid-State Circuits Conference. IEEE, 1986. http://dx.doi.org/10.1109/esscirc.1986.5468402.

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"Data Management II." In HPDC-14. Proceedings. 14th IEEE International Symposium on High Performance Distributed Computing, 2005. IEEE, 2005. http://dx.doi.org/10.1109/hpdc.2005.1520951.

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"Data Management II." In CLADE 2005. Proceedings Challenges of Large Applications in Distributed Environments, 2005. IEEE, 2005. http://dx.doi.org/10.1109/clade.2005.1520910.

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Indrawan, Maria. "Grid data management." In the 12th International Conference. ACM Press, 2010. http://dx.doi.org/10.1145/1967486.1967491.

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Drakeley, Brian, Svein Omdal, and Sigurd Moe. "Subsea Data Management." In Offshore Technology Conference. Offshore Technology Conference, 2007. http://dx.doi.org/10.4043/18744-ms.

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Lu, Hua, and Muhammad Aamir Cheema. "Indoor data management." In 2016 IEEE 32nd International Conference on Data Engineering (ICDE). IEEE, 2016. http://dx.doi.org/10.1109/icde.2016.7498358.

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Informes sobre el tema "Data management"

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Augustine, Victor. Data management. ResearchHub Technologies, Inc., 2022. http://dx.doi.org/10.55277/researchhub.nm21xcav.

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Rambo, Neil. Research Data Management. Ithaka S+R, 2015. http://dx.doi.org/10.18665/sr.274643.

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García-Espinosa, J., and C. Soriano. Data management plan. Scipedia, 2021. http://dx.doi.org/10.23967/prodphd.2021.9.003.

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This document presents the deliverable D8.1 – the Data Management Plan (DMP) of work package 8 of the prodPhD project. It aims to present the plan for the management, generation, collection, security, preservation and sharing of data generated through the prodPhD project. The DMP is a key element for organizing the project’s data. It provides an analysis of the data, which will be collected, processed and published by the prodPhD consortium. The project embraces the initiatives of the European Commission to promote the open access to research data, aiming to improve and maximize access to and
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Paglialonga, Lisa, and Carsten Schirnick. Data management plan. OceanNETs, 2022. http://dx.doi.org/10.3289/oceannets_d8.1.

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This is the data management plan for the research project OceanNETs. It compiles OceanNETs research data output and describes the data handling during and after the projects duration with the aim to make OceanNETs research data FAIR – sustainably available for the scientific community. This data management plan is a living document; it will be continuously developed in close cooperation with the consortium members throughout the project duration
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Bishop, Bradley Wade. Data from Data Management Plan Compliance. University of Tennessee, Knoxville Libraries, 2020. http://dx.doi.org/10.7290/pebuwhcq7l.

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Fermi Research Alliance, Fermi Alliance. Simons Foundation Data Management. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1568825.

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Novellino, Antonio, George Petihakis, and Joaquin Tintore. DMP: Data Management Plan. EuroSea, 2020. http://dx.doi.org/10.3289/eurosea_d3.1.

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McCullouch, Bob. Construction Data Management 2000. Purdue University, 2000. http://dx.doi.org/10.5703/1288284313164.

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Paglialonga, Lisa, and Carsten Schirnick. OceanNETs Data Management Plan. OceanNETs, 2020. http://dx.doi.org/10.3289/oceannets_dmp_v1.

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This is the data management plan for the research project OceanNETs. It compiles OceanNETs research data output and describes the data handling during and after the projects duration with the aim to make OceanNETs research data FAIR – sustainably available for the scientific community. This data management plan is a living document; it will be continously developed in close cooperation with the consortium members throughout the project duration.
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Soriano, C., R. Rossi, and Q. Ayoul-Guilmard. D8.1 Data Management Plan. Scipedia, 2021. http://dx.doi.org/10.23967/exaqute.2021.2.019.

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The ExaQUte project participates in the Pilot on Open Research Data launched by the European Commission (EC) along with the H2020 program. This pilot is part of the Open Access to Scientific Publications and Research Data program in H2020. The goal of the program is to foster access to research data generated in H2020 projects. The use of a Data anagement Plan (DMP) is required for all projects participating in the Open Research Data Pilot, in which they will specify what data will be kept for the longer term. The underpinning idea is that Horizon 2020 beneficiaries have to make their research
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