Big Scholarly Data: A Survey

Xia, Feng; Wang, Wei; Bekele, Teshome Megersa; Liu, Huan

doi:10.1109/tbdata.2016.2641460

Cited by 242 publications

(145 citation statements)

References 121 publications

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“…Scientific data are typically attached to scientific papers or manually extracted from them successively. Therefore, scholarly data repositories, which typically contain information about authors, citations, figures, tables, etc., are often also a source of experimental results [33,60]. Moreover, being able to analyze the sources of experimental datasets, as well as data themselves, may lead to new analysis opportunities.…”

Section: G Scalia Et Al / Towards a Scientific Data Framework To Sumentioning

confidence: 99%

“…Moreover, being able to analyze the sources of experimental datasets, as well as data themselves, may lead to new analysis opportunities. The management of big scholarly data has been surveyed in [60] and currently there are initiatives to publish open bibliographic citation information as linked data, such as Open Citations [42]. The feasibility of tools to explore such data has been demonstrated [22,38], facing challenges related to statistical analysis, semantic exploration and visual analytics.…”

Section: G Scalia Et Al / Towards a Scientific Data Framework To Sumentioning

confidence: 99%

“…Detailed and structured scholarly data support exploration and enable scholarly-related data aggregation, for example to aggregate analysis results for experiments published by the same author or journal. Currently, many open scholarly data repositories and analysis tools are available [22,60], as initiatives to publish open bibliographic citation information as linked data, such as Open Citations [42].…”

Section: Data Modelmentioning

confidence: 99%

See 2 more Smart Citations

Towards a scientific data framework to support scientific model development

Scalia

Pelucchi

Stagni

et al. 2019

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Editor: Silvio Peroni (https://orcid.org/0000-0003-0530-4305) Solicited reviews: HannaĆwiek-Kupczyńska (https://orcid.org/0000-0001-9113-567X); Aliaksandr Birukou (https://orcid.org/0000-0002-4925-9131); Stian Soiland-Reyes (https://orcid.org/0000-0001-9842-9718)Abstract. The sharing of scientific and scholarly data has been increasingly promoted over the last decade, leading to open repositories in many different scientific domains. However, data sharing and open data are not final goals in themselves, the real benefit is in data reuse, which allows leveraging investments in research and enables large-scale data-driven research progress.This article is published online with Open Access and distributed under the terms of the Creative Commons Attribution License (CC BY 4.0). 2451-8484 © 2019 -IOS Press and the authors. G. Scalia et al. / Towards a scientific data framework to support scientific model developmentFocusing on reuse, this paper discusses the design of an integrated framework to automatically take advantage of large amounts of scientific data extracted from the literature to support research, and in particular scientific model development. Scientific models reproduce and predict complex phenomena and their development is a rather challenging task, within which scientific experiments have a key role in their continuous validation. Starting from the combustion kinetics domain, this paper discusses a set of use cases and a first prototype for such a framework which leads to a set of new requirements and an architecture that can be generalized to other domains. The paper analyzes the needs, the challenges and the research directions for such a framework, in particular those related to data management, automatic scientific model validation, data aggregation and data analysis, to leverage large amounts of published scientific data for new knowledge extraction.

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Section: G Scalia Et Al / Towards a Scientific Data Framework To Sumentioning

confidence: 99%

Section: G Scalia Et Al / Towards a Scientific Data Framework To Sumentioning

confidence: 99%

Section: Data Modelmentioning

confidence: 99%

See 1 more Smart Citation

Towards a scientific data framework to support scientific model development

Scalia

Pelucchi

Stagni

et al. 2019

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“…However, scholars are lost in the overloaded academic information, and it becomes arduous for them to hunt for valuable collaborators. Xia et al [1] summarized the management, analysis methods and applications of big scholarly data in their survey. They also suggested that the technologies designed for the recommendations of academic entities help scholars accessing information more easily.…”

Section: Introductionmentioning

confidence: 99%

To Your Surprise: Identifying Serendipitous Collaborators

Wan

Yuan

Xia

et al. 2021

IEEE Trans. Big Data

Self Cite

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Scientific collaboration has become a universal phenomenon in recent years. Meanwhile, scholars tend to hunt for surprising collaborators for broadening their horizons. Serendipity initially denotes the fortunate discovery. Although a lot of literature is available on the topic of serendipity, little research has investigated serendipity in scientific collaborations. The objective of this paper is to identify serendipitous scientific collaborators of target scholars based on their collaboration data. First, we induce the definition of serendipitous scientific collaborators by three components, which are relevance, unexpectedness, and value, respectively. They are quantified as three intuitive indices corresponding to the network proximity, topic diversity, and collaborator influence, respectively. Second, we propose a classification model, called RUVMod, to classify all collaborators based on the analysis of three indices in definition. The serendipitous collaborator has lower network proximity, higher topic diversity and higher influence than his/her target scholar relatively. Finally, we cluster all collaborators via Self Organizing Maps and identify the serendipitous collaborator class according to the classes divided in our RUVMod. We apply our definition to the scientific collaborators extracted from DBLP dataset. The evaluation from the serendipity-based metrics suggests that RUVMod is effective in identifying serendipitous scientific collaborators.

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“…As knowledge is being amassed rapidly, domain knowledge entities accumulate as data sets in scholarly documents. These scholarly documents have become huge reservoir of data that contain diverse information such as authors, references, and keywords (Khan, Liu, Shakil, & Alam, ; Xia, Wang, Bekele, & Liu, ). The data are encapsulated as strings in the literature (Ding et al, ).…”

Section: Introductionmentioning

confidence: 99%