標(biāo)題: Titlebook: Big Data Factories; Collaborative Approa Sorin Adam Matei,Nicolas Jullien,Sean P. Goggins Book 2017 Springer International Publishing AG 20 [打印本頁(yè)] 作者: 猛烈抨擊 時(shí)間: 2025-3-21 16:35
書(shū)目名稱Big Data Factories影響因子(影響力)
書(shū)目名稱Big Data Factories影響因子(影響力)學(xué)科排名
書(shū)目名稱Big Data Factories網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Big Data Factories網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Big Data Factories被引頻次
書(shū)目名稱Big Data Factories被引頻次學(xué)科排名
書(shū)目名稱Big Data Factories年度引用
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書(shū)目名稱Big Data Factories讀者反饋
書(shū)目名稱Big Data Factories讀者反饋學(xué)科排名
作者: JEER 時(shí)間: 2025-3-21 22:17
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作者: Pantry 時(shí)間: 2025-3-22 02:07 作者: ostensible 時(shí)間: 2025-3-22 07:40 作者: 提名 時(shí)間: 2025-3-22 11:04
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作者: 透明 時(shí)間: 2025-3-22 13:48
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作者: Tortuous 時(shí)間: 2025-3-22 17:41
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.作者: 旋轉(zhuǎn)一周 時(shí)間: 2025-3-22 21:53 作者: 群居男女 時(shí)間: 2025-3-23 04:53 作者: 地名詞典 時(shí)間: 2025-3-23 08:20
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.作者: AXIOM 時(shí)間: 2025-3-23 11:50 作者: NIP 時(shí)間: 2025-3-23 17:55 作者: 保守 時(shí)間: 2025-3-23 18:33
The Ten Adoption Drivers of Open Source Software That Enables e-Research in Data Factories for Open ta. 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.作者: Calculus 時(shí)間: 2025-3-23 23:09
Democratizing Data Science: The Community Data Science Workshops and Classeshey used data to understand themselves and communicate with each other? What if data science was treated not as a highly specialized set of skills but as a basic literacy in an increasingly data-driven world?作者: Budget 時(shí)間: 2025-3-24 05:01
Stephen A. Krawetz,David D. Womblecritical feminist discussion of big data collaboration. Of particular interest are also the manner in which specific characteristics of big data projects, especially volume and velocity, may affect multidisciplinary collaborations.作者: 營(yíng)養(yǎng) 時(shí)間: 2025-3-24 08:32 作者: 上漲 時(shí)間: 2025-3-24 10:43
Book 2017rge 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. Furtherm作者: Evocative 時(shí)間: 2025-3-24 17:34
2509-9574 resents methods for teaching data factoring.Proposes a set oThe 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 e作者: 使更活躍 時(shí)間: 2025-3-24 20:56
Henrik Christensen,John Elmerdahl Olsenifferent kind than more conventional social and behavioural science data, posing challenges to use. This paper adopts a data framework from Earth observation science and applies it to trace data to identify possible issues in analysing trace data. Application of the framework also reveals issues for sharing and reusing data.作者: STAT 時(shí)間: 2025-3-24 23:18
Synthesis Lectures on Biomedical EngineeringTo avoid these pitfalls, data analysts should focus and embrace specific principles and practices that aim to represent complete, contextualized, comparable, and scalable information in a way that reveals rather than isolates the viewer and the problem at hand from the problem space it reflects.作者: 現(xiàn)存 時(shí)間: 2025-3-25 04:56 作者: Consensus 時(shí)間: 2025-3-25 09:05
Teaching Students How (Not) to Lie, Manipulate, and Mislead with Information VisualizationTo avoid these pitfalls, data analysts should focus and embrace specific principles and practices that aim to represent complete, contextualized, comparable, and scalable information in a way that reveals rather than isolates the viewer and the problem at hand from the problem space it reflects.作者: Disk199 時(shí)間: 2025-3-25 11:50
Introduction, every day. These traces of human behavior online are a unique source for understanding contemporary life behaviors, beliefs, interactions, and knowledge flows. The social connections we make online, which reveal multiple types of human connection, are also recorded on a scale and to a level of gran作者: Aura231 時(shí)間: 2025-3-25 17:38 作者: reserve 時(shí)間: 2025-3-25 23:38 作者: 用不完 時(shí)間: 2025-3-26 03:49 作者: 打火石 時(shí)間: 2025-3-26 06:57
The Ten Adoption Drivers of Open Source Software That Enables e-Research in Data Factories for Open ically, the chapter discusses the emerging phenomena of big data and e-research, along with their various defining characteristics. Then the chapter makes a case for the importance of understanding the adoption of open source software for processing and harnessing big data. In other words, big data 作者: 全神貫注于 時(shí)間: 2025-3-26 10:45
Aligning Online Social Collaboration Data Around Social Order: Theoretical Considerations and Measurduce content that has immediate impact. Intellectual, analytic, or symbolic collaboration in academia, business, or even government is now almost inconceivable without online support. Work on text and narratives has been entirely transformed by Internet-based sites. Of these, wiki sites are the most作者: Libido 時(shí)間: 2025-3-26 13:02 作者: 撫育 時(shí)間: 2025-3-26 17:17 作者: antidote 時(shí)間: 2025-3-26 23:03
Democratizing Data Science: The Community Data Science Workshops and Classesists with the skills necessary to extract business value from burgeoning datasets created by online communities like Facebook, Twitter, and LinkedIn. This model of data science—professional data scientists mining online communities for the benefit of their employers—is only one possible vision for t作者: theta-waves 時(shí)間: 2025-3-27 04:07 作者: 公司 時(shí)間: 2025-3-27 05:20 作者: 變白 時(shí)間: 2025-3-27 09:31
Henrik Christensen,Lisbeth E. de Vriesucted piecemeal, one Internet address at a time, often without social or scholarly impact beyond the site’s own stakeholders. Scientists lack the tools, methods, and practices to combine, compare, contrast, and communicate about online behavior across Internet addresses or over time. In response, we作者: alabaster 時(shí)間: 2025-3-27 16:24
Henrik Christensen,John Elmerdahl Olsenan activity. The volume of data available offers great potential to advance social and behavioural science research. However, the data are of a very different kind than more conventional social and behavioural science data, posing challenges to use. This paper adopts a data framework from Earth obse作者: 情感脆弱 時(shí)間: 2025-3-27 20:50 作者: Insensate 時(shí)間: 2025-3-27 23:54
https://doi.org/10.1007/978-3-319-98428-5duce content that has immediate impact. Intellectual, analytic, or symbolic collaboration in academia, business, or even government is now almost inconceivable without online support. Work on text and narratives has been entirely transformed by Internet-based sites. Of these, wiki sites are the most作者: 印第安人 時(shí)間: 2025-3-28 03:08
Introduction to Biomedical Engineeringee/libre open source software (FLOSS) is made. Embodying some of the same FLOSS ethos, this team created a public-facing repository for their own data and analyses and encouraged other researchers to use it and contribute to it. This chapter tells the story of how the FLOSSmole project began, where 作者: Mucosa 時(shí)間: 2025-3-28 09:19 作者: 艱苦地移動(dòng) 時(shí)間: 2025-3-28 11:02
https://doi.org/10.1007/978-3-031-01638-7ists with the skills necessary to extract business value from burgeoning datasets created by online communities like Facebook, Twitter, and LinkedIn. This model of data science—professional data scientists mining online communities for the benefit of their employers—is only one possible vision for t作者: 努力趕上 時(shí)間: 2025-3-28 17:16 作者: Ferritin 時(shí)間: 2025-3-28 21:52 作者: Fsh238 時(shí)間: 2025-3-29 02:57
Big Data Factories978-3-319-59186-5Series ISSN 2509-9574 Series E-ISSN 2509-9582 作者: homeostasis 時(shí)間: 2025-3-29 04:51
https://doi.org/10.1007/978-3-319-59186-5trends in data collection; data recombination and reuse; creating collaborative spaces; fungible big da作者: 薄荷醇 時(shí)間: 2025-3-29 10:30
