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Titlebook: Big Data Factories; Collaborative Approa Sorin Adam Matei,Nicolas Jullien,Sean P. Goggins Book 2017 Springer International Publishing AG 20

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發(fā)表于 2025-3-21 16:35:12 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱(chēng)Big Data Factories
期刊簡(jiǎn)稱(chēng)Collaborative Approa
影響因子2023Sorin Adam Matei,Nicolas Jullien,Sean P. Goggins
視頻videohttp://file.papertrans.cn/186/185642/185642.mp4
發(fā)行地址Provides basic researchers and practitioners direct guidelines and best case scenarios for developing activities related to data factoring.Presents methods for teaching data factoring.Proposes a set o
學(xué)科分類(lèi)Computational Social Sciences
圖書(shū)封面Titlebook: Big Data Factories; Collaborative Approa Sorin Adam Matei,Nicolas Jullien,Sean P. Goggins Book 2017 Springer International Publishing AG 20
影響因子The book proposes a systematic approach to big data collection, documentation and development of analytic procedures that foster collaboration on a large scale. This approach, designated as “data factoring” emphasizes the need to think of each individual dataset developed by an individual project as part of a broader data ecosystem, easily accessible and exploitable by parties not directly involved with data collection and documentation. Furthermore, data factoring uses and encourages pre-analytic operations that add value to big data sets, especially recombining and repurposing..The book proposes a research-development agenda that can undergird an ideal data factory approach. Several programmatic chapters discuss specialized issues involved in data factoring (documentation, meta-data specification, building flexible, yet comprehensive data ontologies, usability issues involved in collaborative tools, etc.). The book also presents case studies for data factoring and processing that can lead to building better scientific collaboration and data sharing strategies and tools..Finally, the book presents the teaching utility of data factoring and the ethical and privacy concerns related
Pindex Book 2017
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https://doi.org/10.1007/978-1-59259-335-4 behaviors, are now available to the academic, governmental, or industry research and teaching communities. They promise faster access to real-time social behavior and better understanding of how people behave and interact. Such “social” data include complete records of Wikipedia edits, interactions
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Introduction, behaviors, are now available to the academic, governmental, or industry research and teaching communities. They promise faster access to real-time social behavior and better understanding of how people behave and interact. Such “social” data include complete records of Wikipedia edits, interactions
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Aligning Online Social Collaboration Data Around Social Order: Theoretical Considerations and Measur order that informs them. Yet, researchers struggle with a fragmentary approach to aligning and comparing the social processes that generate social order. Furthermore, social order is itself under dispute, with some claiming that online collaborative communities represent a completely new form of or
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Lessons Learned from a Decade of FLOSS Data Collectiono face many challenges in the future, including the continual need to provide broader access and more sophisticated and relevant data and analyses and to do all this in a way that is sustainable and community driven.
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Learning Materials in Biosciencesta. The chapter also includes critical questions community stakeholders should keep in mind when promoting the diffusion and dissemination of good software applications that will support data factories for open innovations.
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