Mapping platforms into a new open science model for machine learning

Abstract Data-centric disciplines like machine learning and data science have become major research areas within computer science and beyond. However, the development of research processes and tools did not keep pace with the rapid advancement of the disciplines, resulting in several insufficiently tackled challenges to attain reproducibility, replicability, and comparability of achieved results. In this discussion paper, we review existing tools, platforms and standardization efforts for addressing these challenges. As a common ground for our analysis, we develop an open science centred process model for machine learning research, which combines openness and transparency with the core processes of machine learning and data science. Based on the features of over 40 tools, platforms and standards, we list the, in our opinion, 11 most central platforms for the research process in this paper. We conclude that most platforms cover only parts of the requirements for overcoming the identified challenges.

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Eliciting probabilistic expectations: Collaborations between psychologists and economists

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1615461114 ◽

2017 ◽

Vol 114 (13) ◽

pp. 3297-3304 ◽

Cited By ~ 8

Author(s):

Wändi Bruine de Bruin ◽

Baruch Fischhoff

Keyword(s):

Common Ground ◽

Research Process ◽

Economic Modeling ◽

Common Language ◽

Mutual Benefit ◽

Purchasing Decisions ◽

Major Research ◽

Sense Of Ownership ◽

Research Goal ◽

Essential Support

We describe two collaborations in which psychologists and economists provided essential support on foundational projects in major research programs. One project involved eliciting adolescents’ expectations regarding significant future life events affecting their psychological and economic development. The second project involved eliciting consumers’ expectations regarding inflation, a potentially vital input to their investment, saving, and purchasing decisions. In each project, we sought questions with the precision needed for economic modeling and the simplicity needed for lay respondents. We identify four conditions that, we believe, promoted our ability to sustain these transdisciplinary collaborations and coproduce the research: (i) having a shared research goal, which neither discipline could achieve on its own; (ii) finding common ground in shared methodology, which met each discipline’s essential evidentiary conditions, but without insisting on its culturally acquired tastes; (iii) sharing the effort throughout, with common language and sense of ownership; and (iv) gaining mutual benefit from both the research process and its products.

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How Should Data Science Education Be?

International Journal of Energy Optimization and Engineering ◽

10.4018/ijeoe.2020040103 ◽

2020 ◽

Vol 9 (2) ◽

pp. 25-36

Author(s):

Necmi Gürsakal ◽

Ecem Ozkan ◽

Fırat Melih Yılmaz ◽

Deniz Oktay

Keyword(s):

Machine Learning ◽

Big Data ◽

Science Education ◽

Data Science ◽

Doctoral Programs ◽

Time Data ◽

High Demand ◽

The Core ◽

The World ◽

The Subject

The interest in data science is increasing in recent years. Data science, including mathematics, statistics, big data, machine learning, and deep learning, can be considered as the intersection of statistics, mathematics and computer science. Although the debate continues about the core area of data science, the subject is a huge hit. Universities have a high demand for data science. They are trying to live up to this demand by opening postgraduate and doctoral programs. Since the subject is a new field, there are significant differences between the programs given by universities in data science. Besides, since the subject is close to statistics, most of the time, data science programs are opened in the statistics departments, and this also causes differences between the programs. In this article, we will summarize the data science education developments in the world and in Turkey specifically and how data science education should be at the graduate level.

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Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence

Information ◽

10.3390/info11040193 ◽

2020 ◽

Vol 11 (4) ◽

pp. 193 ◽

Cited By ~ 7

Author(s):

Sebastian Raschka ◽

Joshua Patterson ◽

Corey Nolet

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Data Science ◽

Gpu Computing ◽

Graphics Processing Unit ◽

General Purpose ◽

Processing Unit ◽

The Core ◽

Critical Components ◽

High Level

Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical machine learning and scalable general-purpose graphics processing unit (GPU) computing, have become critical components of artificial intelligence, enabling many of these astounding breakthroughs and lowering the barrier to adoption. Python continues to be the most preferred language for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and clean high-level APIs. This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it. We cover widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward.

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Fusion Skills and Industry 5.0: Conceptions and Challenges

10.5772/intechopen.100096 ◽

2021 ◽

Author(s):

John Mitchell ◽

David Guile

Keyword(s):

Machine Learning ◽

Engineering Education ◽

Data Science ◽

Core Curriculum ◽

Digital Technologies ◽

Education Curriculum ◽

Engineering Knowledge ◽

The Core ◽

The Future

The nature of work is changing rapidly, driven by the digital technologies that underpin industry 5.0. It has been argued worldwide that engineering education must adapt to these changes which have the potential to rewrite the core curriculum across engineering as a broader range of skills compete with traditional engineering knowledge. Although it is clear that skills such as data science, machine learning and AI will become fundamental skills of the future it is less clear how these should be integrated into existing engineering education curricula to ensure relevance of graduates. This chapter looks at the nature of future fusion skills and the range of strategies that might be adopted to integrated these into the existing engineering education curriculum.

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Knowledge Representation and Management: Interest in New Solutions for Ontology Curation

Yearbook of Medical Informatics ◽

10.1055/s-0041-1726508 ◽

2021 ◽

Vol 30 (01) ◽

pp. 185-190

Author(s):

Ferdinand Dhombres ◽

Jean Charlet ◽

Keyword(s):

Machine Learning ◽

Electronic Health Records ◽

Knowledge Representation ◽

Medical Informatics ◽

Open Science ◽

Research Interest ◽

Health Records ◽

Knowledge Representations ◽

Research Areas ◽

Significant Research

Summary Objective: To select, present and summarize some of the best papers in the field of Knowledge Representation and Management (KRM) published in 2020. Methods: A comprehensive and standardized review of the medical informatics literature was performed to select the most interesting papers of KRM published in 2020, based on PubMed queries. This review was conducted according to the IMIA Yearbook guidelines. Results: Four best papers were selected among 1,175 publications. In contrast with the papers selected last year, the four best papers of 2020 demonstrated a significant focus on methods and tools for ontology curation and design. The usual KRM application domains (bioinformatics, machine learning, and electronic health records) were also represented. Conclusion: In 2020, ontology curation emerges as a significant topic of research interest. Bioinformatics, machine learning, and electronics health records remain significant research areas in the KRM community with various applications. Knowledge representations are key to advance machine learning by providing context and to develop novel bioinformatics metrics. As in 2019, representations serve a great variety of applications across many medical domains, with actionable results and now with growing adhesion to the open science initiative.

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Trials, Skills, and Future Standpoints of AI Based Research in Bioinformatics

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.a1920.059120 ◽

2020 ◽

Vol 9 (1) ◽

pp. 968-972

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Future Research ◽

Multidisciplinary Research ◽

Major Research ◽

Research Areas ◽

Genomics And Proteomics ◽

Drug Designing ◽

Level Analysis

In recent times, computer field has entered in all types of business and industries. Recent advancements in the information technology field, has open up many possibilities in multidisciplinary research. Machine learning, deep learning, convolution neural network, etc. are recent development in computer fields which has change the way of development of algorithms. Such algorithms can learn over a period of time while in execution and improves its performance and continue learning. Bioinformatics is the recent example of the science which strives to use such recent technologies of computer science for betterment in its own field. This article reviews Artificial Intelligence subset such as Machine learning and Deep learning in the genomics and proteomics domain. This article provides profound insights of various AI techniques which can be incorporated in the field of bioinformatics. The paper also highlighted the future research potential of this field. Computational biology, genomics, proteomics, Drug designing, gene expression level analysis are the major research areas in bioinformatics. These areas are also discussed in the paper.

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Open Science and Data Science

Data Intelligence ◽

10.1162/dint_a_00082 ◽

2021 ◽

Vol 3 (1) ◽

pp. 95-105

Author(s):

Peter Wittenburg

Keyword(s):

Data Science ◽

Open Science ◽

Distributed Data ◽

Research Areas ◽

Global Companies ◽

Data Collections

Data Science (DS) as defined by Jim Gray is an emerging paradigm in all research areas to help finding non-obvious patterns of relevance in large distributed data collections. “Open Science by Design” (OSD), i.e., making artefacts such as data, metadata, models, and algorithms available and re-usable to peers and beyond as early as possible, is a pre-requisite for a flourishing DS landscape. However, a few major aspects can be identified hampering a fast transition: (1) The classical “Open Science by Publication” (OSP) is not sufficient any longer since it serves different functions, leads to non-acceptable delays and is associated with high curation costs. Changing data lab practices towards OSD requires more fundamental changes than OSP. 2) The classical publication-oriented models for metrics, mainly informed by citations, will not work anymore since the roles of contributors are more difficult to assess and will often change, i.e., other ways for assigning incentives and recognition need to be found. (3) The huge investments in developing DS skills and capacities by some global companies and strong countries is leading to imbalances and fears by different stakeholders hampering the acceptance of Open Science (OS). (4) Finally, OSD will depend on the availability of a global infrastructure fostering an integrated and interoperable data domain—“one data-domain” as George Strawn calls it—which is still not visible due to differences about the technological key pillars. OS therefore is a need for DS, but it will take much more time to implement it than we may have expected.

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Literature Review on the Applications of Machine Learning and Blockchain Technology in Smart Healthcare Industry: A Bibliometric Analysis

Journal of Healthcare Engineering ◽

10.1155/2021/9739219 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Yang Li ◽

Biaoan Shan ◽

Beiwei Li ◽

Xiaoju Liu ◽

Yi Pu

Keyword(s):

Machine Learning ◽

Web Of Science ◽

Future Research ◽

Healthcare Industry ◽

Research Subjects ◽

Major Research ◽

Research Areas ◽

Blockchain Technology ◽

Smart Healthcare ◽

Applications Of Machine Learning

The emergence of machine learning (ML) and blockchain (BC) technology has greatly enriched the functions and services of healthcare, giving birth to the new field of “smart healthcare.” This study aims to review the application of ML and BC technology in the smart medical industry by Web of Science (WOS) using bibliometric visualization. Through our research, we identify the countries with the greatest output, the major research subjects, funding funds, and the research hotspots in this field. We also find out the key themes and future research areas in application of ML and BC technology in healthcare area. We reveal the different aspects of research under the two technologies and how they relate to each other around five themes.

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Data science in economics: comprehensive review of advanced machine learning and deep learning methods

10.31232/osf.io/4pxq2 ◽

2020 ◽

Author(s):

Saeed Nosratabadi ◽

Amir Mosavi ◽

Puhong Duan ◽

Pedram Ghamisi ◽

Ferdinand Filip ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Data Science ◽

State Of The Art ◽

Science Methods ◽

Learning Models ◽

Diverse Range ◽

Hybrid Machine ◽

Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

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