Subjects: Library Science，Information Science >> Information Science submitted time 2023-04-01 Cooperative journals: 《图书情报工作》
Abstract： [Purpose/significance] Timely tracking and analyzing the practice progress of open science at home and abroad can provide decision-making support for science and technology management departments to adjust open science policies and lay out the direction of open science.[Method/process] This paper summarized the status quo of research and practice of open science from four aspects:the conceptual connotation and practical significance of open science, practice progress of open science in abroad, practice progress of open science in domestic and the future development trend of open science.[Result/conclusion] The article believes that, open science is the inevitable choice for knowledge to move from occlusion to innovation. International countries have implemented good practices at multiple aspects, such as planning strategies of open science, open infrastructure, open scientific data, open access journals, etc. China has yet to improve its system planning, cultural construction, degree of openness and brand reputation of open science. Facing the future, open science will develop in the direction of globalization, FAIRization, ecologicalization, and cloudification.
Subjects: Library Science，Information Science >> Information Science submitted time 2023-04-01 Cooperative journals: 《图书情报工作》
Abstract： [Purpose/significance] This paper discusses the related research, implementation and influencing factors of the Scientific Data Management Rule since its promulgation, in order to provide reference for the further implementation of the Scientific Data Management Rule.[Method/process] Firstly, this paper systematically sorted out the relevant research on the Scientific Data Management Rule. Secondly, investigated the implementation of the Scientific Data Management Rule from the perspective of scientific data stakeholders. Thirdly, the positive and negative influencing factors for implementing the Scientific Data Management Rule were analyzed from various aspects. Finally, the paper put forward some strategies to promote the implementation of the Scientific Data Management Rule.[Result/conclusion] At present, the implementation of the Scientific Data Management Rule is mainly carried out by various government departments at all levels, but the overall implementation is not satisfactory. The positive factors of the implementation of the Scientific Data Management Rule are reflected in the following aspects:the value of data is highlighted, the data facilities are improved, the state attaches great importance to it, the government promotes obviously, and the related research is rich. The negative factors are reflected in the weak awareness of scientific data stakeholders, management of relevant institutions is out of order, implementation planning is ambiguous, lack of supervision and incentive mechanisms, and the practice research is insufficient. The implementation of the Scientific Data Management Rule can be promoted by improving existing research suggestions, strengthening the construction of mechanism and system, and improving the intensity of stakeholder cooperation.
Subjects: Psychology >> Applied Psychology Subjects: Computer Science >> Computer Application Technology submitted time 2020-03-08
Abstract： Personality affects the individual's work and life style, and has important guiding significance for the individual's psychological counseling and career development. Traditional methods use personality scales to evaluate personality scores, which include problems such as individual refusal to answer and blind answering. In recent years, with the development of machine learning, new ideas have been provided for personality recognition. This article uses participants' self-introduction videos and Big Five personality scale scores to obtain different prediction models for different personality dimensions through key point extraction, feature dimension reduction, modeling, and iterative tuning. This article uses participants' self-introduction videos and Big Five personality scale scores to obtain different prediction models for different personality dimensions through key point extraction, feature dimension reduction, modeling, and iterative tuning. The test results show that the personality prediction model based on the self-introduction video is close to or achieves medium correlation in all dimensions, and can provide non-intrusive automatic personality recognition,, which provides new ideas for personality measurement.
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