Management of scientific and technological research

Arellano, Chadwick Carreto; Ruiz, Gabrielade Jesus Lopez; Segundo, Laura Méndez

doi:10.1109/iccsat.2015.7362958

Cited by 3 publications

(3 citation statements)

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“…According to previous research [ 22 ] and the aim of our paper, we propose a project data structure as follows:

P = {P i d, T i t l e, A b s t r a c t, D i r e c t i o n, F i e l d}

P i d

is the major key of the project.…”

Section: Scientific Research Project Datamentioning

confidence: 99%

“…At home and abroad, scientific research project management systems are widely used. Most Latin American organizations producing scientific research adopt software system to manage research projects [ 18 ] . In recent years, Japan has also established some efficient scientific research project management systems [ 19 ] .…”

Section: Related Workmentioning

confidence: 99%

“…Project: According to the directions, scientific researchers create projects and prepare related information. A project contains various data, which is divided into structured data (such as project title, abstract, direction, and field) and unstructured text data in the format of .doc or .pdf [ 22 ] . We are interested in the structured data, which contribute to the keyword extraction.…”

Section: Scientific Research Project Datamentioning

confidence: 99%

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Keyword Extraction from Scientific Research Projects Based on SRP‐TF‐IDF

Wang

2021

Chinese J of Electronics

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Keyword extraction by Term frequency-Inverse document frequency (TF-IDF) is used for text information retrieval and mining in many domains, such as news text, social contact text, and medical text. However, keyword extraction in special domains still needs to be improved and optimized, particularly in the scientific research field. The traditional TF-IDF algorithm considers only the word frequency in documents, but not the domain characteristics. Therefore, we propose the Scientific research project TF-IDF (SRP-TF-IDF) model, which combines TF-IDF with a weight balance algorithm designed to recalculate candidate keywords. We have implemented the SRP-TF-IDF model and verified that our method has better precision, recall, and F1 score than the traditional TF-IDF and TextRank methods. In addition, we investigated the parameter of our weight balance algorithm to find an optimal value for keyword extraction from scientific research projects.

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“…According to previous research [ 22 ] and the aim of our paper, we propose a project data structure as follows:

P = {P i d, T i t l e, A b s t r a c t, D i r e c t i o n, F i e l d}

P i d

is the major key of the project.…”

Section: Scientific Research Project Datamentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Scientific Research Project Datamentioning

confidence: 99%

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Keyword Extraction from Scientific Research Projects Based on SRP‐TF‐IDF

Wang

2021

Chinese J of Electronics

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Data Visualization on the Life Cycle of Science and Technology Projects

Wang

2021

2021 IEEE 6th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA)

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Quality Research Management Improves Design Research Effectiveness

Holm

Merwe

2019

SAJIE

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Design research projects are often more complex than initially perceived due to a requirement for novelty, the creativity inherent in the design process, and the limited experience of novice researchers. Also, expectations from institutions and external funding stakeholders must be met. Therefore, a framework was needed to support the research effort in the form of quality research management. The focus of this framework is primarily to align stakeholders. To this end, engineering management methods were consulted to define a quality research management framework that provides review baselines, requirements traceability, and a general process model. The framework is based on the well-known 'house of quality' principle that provides requirements traceability. A series of matrices was developed, with a research validation matrix resulting from the combination of traceability matrices. This matrix allows the allocation of research challenges to a need, concept solutions to research challenges, and specific research solutions to concept solutions. Also, information sources are allocated to challenges and solutions to verify their authenticity. It is thus possible to visualise clearly a summary of the research project. Additional benefits include improved communication, visualisastion of progress, traceability of requirements, and verification and validation, which illustrates the value of quality research management.

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Management of scientific and technological research

Cited by 3 publications

References 0 publications

Keyword Extraction from Scientific Research Projects Based on SRP‐TF‐IDF

Keyword Extraction from Scientific Research Projects Based on SRP‐TF‐IDF

Data Visualization on the Life Cycle of Science and Technology Projects

Quality Research Management Improves Design Research Effectiveness

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