Scholarly data mining: A systematic review of its applications

Dridi, Amna; Gaber, Mohamed Medhat; Azad, R. Muhammad Atif; Bhogal, Jagdev

doi:10.1002/widm.1395

Cited by 13 publications

(9 citation statements)

References 90 publications

(213 reference statements)

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“…The navigation may take a lot of time and probably miss some important references, especially for literature who cite hundreds of references. A similar finding is drawn in recent studies [6,21,25,30]. Bibliographic search engines, e.g., Google Scholar, Microsoft Academic Graph, CiteSeerX, etc., provide options to find related work of the literature.…”

Section: Introductionsupporting

confidence: 89%

Discovering the Evolution Structure of Literature

2023

Preprint

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A common task in research work is to survey research work in bioliographic databases. Scientists are finding the survey task notably difficult as the volume of the databases has been increased considerably over the past few decades. Making a good use of a survey paper can vastly lower the difficulty but there may be no such paper existed in some emerging research topics. In this work, we propose a novel Literature Evolution Discovery (LED) process that aims to provide an explainable evolution structure of literature. The explain- ability is based on co-citation analysis and latent relationship extraction, which are done by Steiner tree and context-consistent factor graph model, respectively. The experiments show the superiority of our context-consistent factor graph model, compared with the state-of-the-art baselines. Our case studies and visualization results demonstrate the effectiveness and interpretability of our proposed algorithms in practice.

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Section: Introductionsupporting

confidence: 89%

Discovering the Evolution Structure of Literature

2023

Preprint

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“…To expand materials data, especially for experimental data, mining data resources from open-source literature has gained extensive interest. [62][63][64] However, the complexity and diversity of literature make automatic data mining technology face huge challenges. For example, intelligently mining the numerical values of polarization and electric field from different diagrams of the ferroelectric hysteresis loop is extremely hard due to the diversity of image format, location, style, size, and coordinatometer in different kinds of literature.…”

Section: Datamentioning

confidence: 99%

Machine learning in energy storage materials

Shen

Liu

Shen

et al. 2022

Interdisciplinary Materials

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With its extremely strong capability of data analysis, machine learning has shown versatile potential in the revolution of the materials research paradigm. Here, taking dielectric capacitors and lithium-ion batteries as two representative examples, we review substantial advances of machine learning in the research and development of energy storage materials. First, a thorough discussion of the machine learning framework in materials science is presented. Then, we summarize the applications of machine learning from three aspects, including discovering and designing novel materials, enriching theoretical simulations, and assisting experimentation and characterization. Finally, a brief outlook is highlighted to spark more insights on the innovative implementation of machine learning in materials science.

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“…Such datasets across the materials landscape would result in a step-change in the speed of materials research. Projects are underway to aggregate historical, published experimental data and make it widely accessible (Tetko et al, 2016;Dridi et al, 2021). This is challenging work.…”

Section: Datamentioning

confidence: 99%

Materials informatics and sustainability—The case for urgency

Melia

Muckley

Saal

2021

DCE

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The development of transformative technologies for mitigating our global environmental and technological challenges will require significant innovation in the design, development, and manufacturing of advanced materials and chemicals. To achieve this innovation faster than what is possible by traditional human intuition-guided scientific methods, we must transition to a materials informatics-centered paradigm, in which synergies between data science, materials science, and artificial intelligence are leveraged to enable transformative, data-driven discoveries faster than ever before through the use of predictive models and digital twins. While materials informatics is experiencing rapidly increasing use across the materials and chemicals industries, broad adoption is hindered by barriers such as skill gaps, cultural resistance, and data sparsity. We discuss the importance of materials informatics for accelerating technological innovation, describe current barriers and examples of good practices, and offer suggestions for how researchers, funding agencies, and educational institutions can help accelerate the adoption of urgently needed informatics-based toolsets for science in the 21st century.

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Scholarly data mining: A systematic review of its applications

Cited by 13 publications

References 90 publications

Discovering the Evolution Structure of Literature

Discovering the Evolution Structure of Literature

Machine learning in energy storage materials

Materials informatics and sustainability—The case for urgency

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