深圳大学图书馆

开放科学专题

彭朝霞 — Tue, 06 May 2025 03:13:21 +0000

Open Science

　This section is dedicated to popularizing the concept of open science, providing an introduction to related concepts, access to open resources, information on related policies, and the latest examples of practices in various fields, and providing comprehensive support for teachers, students, and researchers. The content of this section is continuously updated, so please check this page regularly for the latest news.

Related concepts

Open Access (OA)

This concept was first introduced in the Budapest Open Access Initiative in 2002, and refers to the open publication of scientific results by authors directly on the Internet, allowing the public to freely access, reproduce, disseminate, or make any other legal use of them without infringing on the authors' reserved rights.

Basic forms of open access

Gold Open Access (Gold OA): open access in the narrow sense, also known as open publication. It refers to the provision of free open access to a journal paper through the journal's website immediately after peer-reviewed publication.

Green OA: Also known as Open Storage or Author Self-Archiving, refers to the process whereby authors deposit the final, peer-reviewed, finalized manuscript of a journal paper into the institutional knowledge base after publication, and make it available for open access after a certain period of time or immediately.

Open Access Transformation Agreements (TA)

This is a transitional open access transformation strategy that changes the business model of scholarly journal publishing from a subscription fee model to one in which publishers are paid for their open access publishing services.

Main Open Access Routes

1.Preprint

Preprint refers to scientific research papers, scientific reports, conference papers, and other academic results that researchers voluntarily release at academic conferences or through the Internet for the purpose of communication with peers before they have been peer-reviewed and published in official publications. It is free and open for all to search, share, cite and comment. The following are some commonly used preprint platforms at home and abroad:

Disciplinary synthesis:
　　arXiv：The largest repository of preprints, created in 1991 and currently managed and maintained by Cornell University.
　　preprints.org：A preprint platform created by leading open access publisher MDPI.
　　ChinaXiv：The full name is Chinese Academy of Sciences (CAS) Scientific and Technical Papers Pre-Release Platform, which is a preprint exchange platform constructed by the Documentation and Intelligence Center of the Chinese Academy of Sciences (CAS) with the support of the Bureau of Science Communication of the Chinese Academy of Sciences (CAS) in 2016.
　　National preprint platform：Jointly constructed by the China Institute of Science and Technology Information and the National Science and Technology Library and Documentation Center.

Medical biology：
　　bioRxiv：Founded in 2013 by Cold Spring Harbor Laboratory, USA, in the field of life sciences.
　　PsyArXiv：2016 Created by the Association for the Advancement of Psychological Science in collaboration with OSFPreprints, field of psychology.
　　medRxiv：2019 by Cold Spring Harbor Laboratory, BMJ Publishing Group and Yale University in the fields of medicine, clinical and health sciences.
　Preprint System for Biomedical Science and Technology Papers：Founded by the Institute of Medical Information of the Chinese Academy of Medical Sciences, it focuses on the field of biomedicine.

Chemical：
　　ChemRxiv：Preprint platform specializing in chemistry and related fields.

　Technology and Engineering：
　　China Science Paper Online：Approved by the ministry of education, by the ministry of education science and technology development center hosts the science and technology thesis website.

Social sciences：
　　Social Science Research Network（SSRN）：Founded in 1994 to provide social science researchers with a platform for rapid access to social science research information.
　　SocArXiv：A preprint platform in the social sciences hosted by the University of Maryland.
　　Preprint Citation Index：The preprint citation indexing platform introduced by Corevion in 2023 in the Web of Science platform provides one-stop access to nearly 2.5 million preprinted papers including preprint platforms such as arXiv, bioRxiv, medRxiv, chemRxiv and preprints.org.

2. Open Access Resource Integration Platform

Various types of open access resource integration platforms are also one of the main channels for open access resources, and the following are commonly used open access resource integration platforms at home and abroad:

Periodicals：
　　Directory of Open Access Journals（DOAJ）：Organized by Lund University Library, Sweden, one of the most influential open access journal databases in the world.
　　Open Access Library：An open access database created by Open Access Library, Inc.
　　PubMed Central：A large-scale search platform for biomedicine sponsored by the National Library of Medicine of the National Institutes of Health.
　　BioMed Central：A series of open access journals hosted by the UK Biomedical Center, covering all areas of biomedicine and science.
　　Open Research Europe（ORE）：An open access publishing platform provided by the European Commission.
　　SpringerOpen：An open access journal platform owned by the renowned publisher Springer Springer.
　　HighWire Press：The world's largest publisher of scholarly literature offering free full text, founded in 1995 by Stanford University Libraries.
　　China Open Access Platform for Science and Technology Journals COAJ：By Chinese academy of sciences supervises, Chinese science and technology publication media company limited company sponsors open access, academic, non-profit science and technology literature resources portal.
　　National Social Science Fund Academic Journals Database NSSD：By the national philosophy and social science planning leading group approval construction, the Chinese academy of social sciences construction national, open, public welfare philosophy and social science information platform.
　　GoOA：Funded by the Chinese Academy of Sciences, the Chinese Academy of Sciences Documentation and Intelligence Center construction launched open resources.
　　Open Access Journals Recommended List OARL：China Book Import and Export (Group) Co., Ltd. in conjunction with Tsinghua University Library and China University Science and Technology Journal Research Association jointly initiated.
　　SciOpen：Open access resources for specialized science and technology content published by Tsinghua University Press and partners.
　　Hans Publishers Chinese Open Access Academic Journals Communication Platform：Hans Publishing is an international comprehensive open source academic journal publisher, specializing in the publication and distribution of international open source Chinese journals.
　　China Optics Journals Network Database：Supported by the Chinese Academy of Sciences and the Chinese Association for Science and Technology, it covers all the journal resources of the five top optical institutes in China.

Books：
　　Directory of Open Access Books：Global OA book search platform.
　　Taylor & Francis Open Access Books：Taylor&Francis publishes an open access system for books, monographs or co-authored books, anthologies, and stand-alone chapters.
　　Open Textbook Library：Open Textbook Library, created by the College of Education and Human Development, University of Minnesota, USA.
　　Gutenberg：Project Gutenberg, with a focus on Western classics.
　　Shuge：Open Online Antiquarian Library.

Courses：
　　Lifelong Education Platform of the Open University of China：It brings together the self-built learning resources of the National Open University, the course resources of 338 famous universities, and the special courses of 10 head platforms, totaling 500,000 courses, and is committed to meeting the diversified and personalized learning needs of the general public.
　　China University MOOC：Launched by NetEase and Higher Education Society, the online education platform provides high-quality course resources from many colleges and universities, with a wide range of course types covering a variety of fields such as science and technology, humanities, and social sciences.
　　National Higher Education Smart Platform：A treasure trove of higher education knowledge specifically for adults and current college students. The platform covers almost all university courses and allows users to study online by searching for majors or courses.
　　XuetangX：Developed by Tsinghua University, the Chinese MOOC platform is the research exchange and results application platform of the Online Education Research Center of the Ministry of Education, providing online courses for the world.
　　MIT OpenCourseWare：MIT's Open Courses program provides free online learning resources for a large number of undergraduate and graduate courses in a wide range of fields, including science, engineering, and the humanities.
　　Coursera：Massive open online course program, founded by two computer science professors at Stanford University, USA. It aims to provide open online courses in collaboration with the world's leading universities.

Comprehensive：
　　Socolar-OA：A multidisciplinary open access academic resource platform developed by China Education Book Import and Export Corporation.
　　National Digital Library of China：The Digital Library Project launched by the National Library of China aims to digitize the library's collection resources and provide more convenient and efficient digital reading services.
　　China Engineering Science and Technology Knowledge Center：The Chinese Academy of Engineering alliance of more than 30 domestic research institutes, colleges and universities to build the field of engineering and science and technology information convergence center, data mining center and knowledge service center.
　　National Platform for High-End Exchange of Scientific Research Papers and Technological Information：A general term for a series of high-level information service platforms for China's scientific research practice and management activities.

Thematic Resources：
　　Data Platform of the Anti-Japanese War and Modern Sino-Japanese Relations：It is the fundamental and landmark core project and main project of the National Social Science Foundation's special project for the study of the War of Resistance against Japan, led by the Chinese Academy of Social Sciences, the National Library, the State Archives and the Chinese Academy of History, and undertaken by the Institute of Modern History of the Chinese Academy of Social Sciences.
　　Chinese Classics Resource Library：By Beijing Normal University, the Chinese Calligraphers Association and other ten universities and departments, organized including ancient literature, philosophy and calligraphy, more than 40 domestic and foreign well-known experts to participate in the recording of the great treasure of classic literature.
　　Allhistory：With AI knowledge mapping as the core driving force, we are committed to constructing and presenting rich and diverse digital humanities content in the form of highly temporalized and correlated data, especially for historical knowledge.

3. Open Institutional Knowledge Repository

Also known as OA warehousing, the main body is generally for university libraries, research institutes or academic organizations, the storage object for the internal members of the organization or institution in the process of academic research produced a variety of valuable resources.
　　GSE Open Archive：Stanford University Institutional Knowledge Repository
　　DSpace@MIT：MIT Knowledge Repository
　　Digital Access to Scholarship at Harvard（DASH）：Harvard University Institutional Knowledge Repository
　　Apollo - University of Cambridge Repository：University of Cambridge Institutional Knowledge Repository
　　Oxford University Research Archive：University of Oxford Institutional Knowledge Repository
　　escholarship：University of California Institutional Knowledge Repository
　　DukeSpace：Duke University Institutional Knowledge Repository
　　National Natural Science Foundation of China Basic Research Knowledge Repository
　　HKUST Institutional Repository ：Hong Kong University of Science and Technology Institutional Knowledge Repository
　　HKU Scholars Hub ：University of Hong Kong Academic Repository

Global Open Access Developments

Recent News：

　　　　New Guidelines Help German Universities Advance Open Science Strategies（2025-01-03）
　　　　A Review of the Major Events in Global Open Science in 2024（2024-12-27）
　　　　The International Directory of Recommended Open Access Journals (OARL) has been released（2024-12-26）
　　　　NASA Advances Disaster Research and Recovery with Artificial Intelligence and Open Science（2024-11-26）　

Related Policies and Regulations

International

International Organization

　　UNESCO Recommendation on Open Science（UNESCO 2021）
　　UNESCO's open science recommendations: A publisher action plan（UNESCO 2022）
　　Open educational resources: policy, costs, transformation（UNESCO 2016）
　
Government Organization

　　EU：Policy RECommendations for Open Access to Research Data in Europe (RECODE)（2013）、Horizon 2020（2014）、FOSTER:The future of science is OPEN（2014）、European Open Science Cloud, EOSC（2018）、Open Research Europe，ORE（2021）、Open Science and Intellectual Property Rights （2022）

G8：Open Data Charter（2013）

　　United States of America：The Openness Promotes Effectiveness in Our National Government Act of 2007（2007）、U.S. Open Data Action Plan（2014）、Open Science at NASA、Year of Open Science（2023）

　　Germany：Open Government Data Germany（2012）

　　the Netherlands：Amsterdam Call for Action on Open Science（2016）、Nationaal plan open science（2017）、Open Science 2030 in the Netherlands（2022）

　　England：Open standards for government data and technology（2017）

　　French：Second French Plan for Open Science （2021）

　　Japan：Japan Open Data Charter Action Plan（2013）、JST Policy on Open Access to Research Publications and Research Data Management（2017）

　　Finland：Declaration for Open Science and Research 2020–2025（2020）
1. Research Organization
  
  　　Max Planck Digital Library, Germany, etc.：Berlin Declaration on Open Access to Knowledge in the Sciences and Humanities（2003）、OA2020 Progress Report（2016）、OA2020 Plan S（2018）
  
  　　Research Councils UK（RCUK）：RCUK Policy on Open Access and Supporting Guidance（2005）、Concordat on Open Research
  Data（2016）
  
  　　Centre National de la Recherche Scientifique (CNRS)：CNRS Roadmap for Open Science（2019）
  
  　　European University Association (EUA)：2025 The EUA Open Science Agenda 2025、European University Association CoARA Action Plan (2023-2027)
  
  　　German Research Foundation (DFG), etc.：Efficiency and Standards for Article Charges (ESAC) （2014）
University Open Access Policy

　　Stanford University Open Access Protocol：https://openarchive.stanford.edu/policy

　　Harvard University Open Access Policy：https://osc.hul.harvard.edu/policies/

　　University of California Open Access Policy：https://escholarship.org/ucoapolicies

　　MIT Open Access Policy：Open access policies at MIT | Scholarly Communications - MIT Libraries

　　Duke University Open Access Column：Duke ScholarWorks » Open Access

　　University of Minnesota Open Science Policy：https://www.lib.umn.edu/services/open-access

　　Oxford University Open Access Policy：https://openaccess.ox.ac.uk/home

　　University of Cambridge Open Access Policy：https://www.openaccess.cam.ac.uk/university-policies-guidance

　　University College London Open Access Policy：Open Access | Library Services - UCL – University College London

　　University of Birmingham Open Science Policy：https://www.birmingham.ac.uk/research/environment-culture/open-research-statement

　　University of Bristol Open Science Policy：Open Research | Staff | University of Bristol

　　National University Library of Japan: examples of reference work related to open science and research data management（オープンサイエンス及び研究データ管理に係る参考となる取組事例）

Domestic

　　　　Policy Statement on Open Access to Research Papers from Funded Projects by the National Natural Science Foundation of China（2014）

　　　　Opinions of the State Council on the Opening of National Major Scientific Research Infrastructure and Large-scale Scientific Instruments to the Society（2014）

　　　　Implementation Rules of the Open Access Policy for the Basic Research Knowledge Repository of the National Natural Science Foundation of China（2017）

　　　　Science and Technology Progress Law of the People's Republic of China (Revised in 2021)：The requirement to promote the development of open science marks China's formal establishment of open science, including open access, as one of the development directions of national science and technology.

　　　　For more practices related to the field of open science, see：
　　　　China's Implementation of Action Areas in Line with UNESCO's Recommendation on Open Science

Open access policy search site

　　　　Open Policy Finder，An open access policy lookup tool provided by Jisc (UK Joint Information Systems Committee) summarizes global open access policies of research funding agencies, academic publishers, and journals that can help users:

Check whether publishing in a specific journal complies with the funder's open access policy;
Get an overview of open access archiving conditions for each journal and book publisher;
Check the funder's conditions for open access publishing.

Other relevant resources

　　　　Global Open Science Cloud

　　　　European Open Science Cloud

　　　　Center for Open Science（COS）

　 Committee on Data for Science and Technology （CODATA）

　　　　Research Center for Open Science at the Library and Information Center, Chinese Academy of Sciences

　　　　Tsinghua University Library-Open Science Section

　　　　the Library of Southern University of Science and Technology-Open Access Resources

　　　　the Library of The Hong Kong University of Science and Technology-Open Science Topic

Challenges and responses to open science

Challenges: data privacy and security, intellectual property and copyright, cultural and attitudinal shifts; cost-bearing issues in open scientific publishing, uneven quality of publications, lack of equity in publishing opportunities, publication bias; lack of a guiding policy agenda, conflicts of interest among participating actors, etc.
How should we, as users, respond to the challenges of open science?

Reference suggestions:

(1) Raise awareness of data privacy and security: Familiarize yourself with relevant data protection laws and regulations, use tools with guaranteed security, desensitize sensitive information before sharing data, and ensure that data is anonymous and unidentifiable.

(2) Respect for intellectual property and copyright: Familiarize yourself with copyright laws and regulations, understand the legal framework for open access and open data, and ensure compliance with relevant regulations when using and sharing resources. When using open access resources, ensure that reasonable scope of use is followed and avoid unauthorized copying and modification. When using other people's research results, ensure proper citation and attribution, and respect the intellectual property rights of the original authors.

(3) Actively participate in cultural and conceptual change: Enhance one's scientific literacy, learn to recognize the authenticity of scientific information, and avoid being misled by erroneous information. Participate in relevant training and educational activities to improve the understanding and awareness of open science. In scientific research and academic activities, actively advocate the concept of open science, encourage peers to participate in data sharing and cooperation, and promote the development of an open science culture.

(4) Support and participate in quality control mechanisms: if given the opportunity, actively participate in open data and open peer review to improve the reproducibility and transparency of research. When using open access resources, provide feedback and suggestions to help improve the quality and reliability of the resources. Prioritize the use and recommendation of high-quality open access resources, and support journals and platforms with good reputations and rigorous quality control.

(5) Promote equitable access and use: Use and support open source tools and platforms to lower the technological threshold and ensure that more people can access and utilize research results equally. Join relevant communities and forums to participate in discussions and exchanges, help solve technical problems, and promote knowledge sharing. Make recommendations to policymakers and institutional managers to promote the formulation and implementation of policies and measures in favor of open science.

彭朝霞 Fri, 05/09/2025 - 16:51

数据库使用指南

admin — Fri, 30 Aug 2024 10:15:25 +0000

E-Resources User Guide 彭朝霞 Tue, 09/24/2024 - 14:31

The institution has acquired a diverse range of database resources, including e-books, journal articles, dissertations, conference papers, patents, standards, video tutorials, research reports, and more. These resources cover various academic disciplines such as natural sciences, social sciences, humanities and arts, and engineering technology, providing comprehensive, authoritative, and timely information support for faculty and students.

It is recommended that teachers and students carefully read the database usage guide before using it to understand the access methods, precautions, and troubleshooting methods. Additionally, participating in database usage training sessions organized by the library or consulting library staff can provide more professional help and guidance.

Access Methods

Step 1: Open the database navigation page

Library Home Page → Main Menu "Resources" Dropdown List → Databases Navigation/Common Databases

Step 2: Filter the Database

Access the database navigation page to filter databases by initial letter, subject, resource type, language, or authorization method. Alternatively, directly visit the popular databases in the top right corner. The top right corner of the page also displays whether the IP address is within the campus network.

Step 3: Read the Database Introduction

After locating the required database, click on the database name to enter the database introduction page. Carefully read the access methods, introduction, user guide, contact information, copyright statement, and other related content.

Step 4: Select "Access Channel"

If the IP address is within the campus network, the database can be accessed through the campus channel.

If the IP address is outside the campus network, access to the database can be achieved through the off-campus channel.

If there is a "recommended dedicated channel," please prioritize using the "recommended dedicated channel."

Notes

It is important to note that some databases are restricted to on-campus access only, as indicated in the list on the database navigation page.

Off-campus access can directly open the library homepage and follow the above process for access without using EasyConnect, WebVPN, or other VPN tools.

Self-troubleshooting

In the event of a database access failure, follow the troubleshooting steps outlined below:

Access the database through the library website (https://www.chengren-av.cc) without logging into EasyConnect or WebVPN, whether on or off campus.
Unless explicitly stated that a mobile app can be used to access certain electronic resources, please use a computer to access other electronic resources.
Use Google Chrome or Firefox browser.
To check the DNS server, use the shortcut Win+R to open the Run dialog, type "cmd" and press Enter to open the Command Prompt. Then, run the command "ipconfig /all" and verify if the DNS server is set to 210.39.0.33. If it is not, manually change the DNS to 210.39.0.33.
Clear the browser cache (Google: Press the CTRL+SHIFT+DEL key combination, select "All time" for the time range, check "Browsing history," and click "Clear data" to clear the cache).
Restart the computer.
Need to download data in bulk using software? If so, call 26537002 (internal extension: 87002).
If accessing the "Shenzhen University Unified Identity Authentication" system from off-campus fails, first log in to the university's service hall (http://ehall.szu.edu.cn) to ensure the account is functioning properly. If unable to log in to the service hall, contact the Information Center at 26536188 (internal extension: 86188).

After completing the above steps, if the issue persists, please call 26537002 (campus extension: 87002).

Consultation

If any issues arise while using the database, assistance can be sought from the librarians through the following methods:

Tel: 26537002 (Campus extension: 87002)

Email: szulib@szu.edu.cn

QQ Inquiry Groups: 755084585, 303194633

学术搜索指南

admin — Mon, 26 Aug 2024 14:25:45 +0000

Academic Search Guide

Liyuan Scholar Search is the unified resource discovery system of Shenzhen University Library, offering a one-stop academic literature search service. Through basic or advanced searches, it allows for quick and efficient retrieval of needed information from a vast array of academic resources, including over one billion types of literature such as journal articles, conference papers, dissertations, books, and newspaper articles.

Billions of metadata records, integrated and consolidated heterogeneous database clusters, efficient, precise, and unified academic resource search in both Chinese and English. The search results can be clustered, cited, and visually analyzed in multiple dimensions, showcasing knowledge associations and revealing academic trends. The academic literature is comprehensively included and updated promptly. Various search methods are provided in terms of functionality (simple search, advanced search, search within results, result filtering, etc.), and direct access to the full text in the source database is available (readable or downloadable if subscribed by the library). If not subscribed, a document delivery service is offered.

Here, a variety of resources ordered by the library can be searched, along with discovering more external resources. This aims to reduce the difficulty of retrieving documents from different databases separately. For searching literature on specific topics, using the database is recommended. Valuable feedback is welcome. For better utilization of the Lychee Garden Academic Search, please refer to the usage help.

Academic papers

The data includes nearly 700 million academic articles (including e-books and dissertations) from major Chinese and foreign databases such as Elsevier, Gale, Springer, Wiley, Ebsco, CNKI, Wanfang,VIP and so on.

Books：

The Shenzhen University Library houses a collection of print books and journals, as well as electronic books and journals.

Usage steps:

1.Navigate to the homepage of "Shenzhen University Library." In the search box of "Liyuan Scholar" enter the relevant search term. For example, input "Artificial Intelligence" and then click on the search button.

2.On-campus users can directly access the search results page, while off-campus users can access the search page after completing unified authentication login.

3.After entering the search results page, to refine the search results, click on the conditions in the filter bar on the left and apply the filters.

4.After completing the search, it is possible to directly read or download the academic resources by clicking on the blue links. If the library has not yet subscribed to the corresponding resources, they can be obtained by clicking on "Document Delivery."

彭朝霞 Tue, 09/24/2024 - 11:28

深圳大学图书馆馆员素养赋能平台

钟庆虹 — Fri, 23 Aug 2024 06:11:10 +0000

Library Librarian Competency Empowerment Platform

Platform Introduction

The Shenzhen University Library Librarian Competency Empowerment Platform aims to enhance librarians' research literacy, professional literacy, and knowledge skills by providing various training and resources tailored to their development needs. It serves as a knowledge repository for the cultivation of library talent.

Latest News

Liyuan Tu Zhi

Curating cutting-edge information related to library science and information science from abroad, covering topics such as library development strategies and environmental trends, scientific data management, library automation, electronic resource management, innovative library services, library publishing, open access, and other popular themes.

Click here to view all at Liyuan Tu Zhi.

Librarian Competency Training

Questionnaire on librarian needs

Training Courses

Practical Courses：

Chart beautification with Excel

Editing long documents with Word

Decoding AI

Forums and lectures

Librarian Forum (Regularly held librarian forums, incorporating each session's theme and content, providing meeting recordings and explanatory PPTs)
- Application and Reflections of Artificial Intelligence in Libraries, presented by Li Haiyan
- Cataloging and Collection Work Efficiency at Shenzhen University Library, Presenter: Zhou Danyan
- Innovation in Intelligent Reference Services in University Libraries under the Context of "New Quality" and "Digital Intelligence," presented by Peng Zhaoxia
- Information Literacy Education in the Era of Artificial Intelligence: Integration Practices and Exploration, presented by Gao Tianyue
- Digital Humanities Services in Libraries and Prospects for Our Library's Digital Humanities Services, Presenter: Chen Juntian
- "Empowering with Digital Intelligence, Open Connectivity - Sharing Outcomes from the Global University Libraries Practice Frontier Conference" Presenter: Chen Zheyan
Academic Exchange and Sharing
- Shenzhen University - CNKI Exchange Conference: CNKI AI and Future Learning Center
- Sichuan University Library Hu Lin sharing of experience

Resource center

Generative Artificial Intelligence
Introduction to common tools for research：https://www.chengren-av.cc/en/research/tools

彭朝霞 Mon, 09/23/2024 - 09:04

馆藏撷珍

admin — Sun, 25 Aug 2024 09:45:09 +0000

Treasuries of Library

Treasures of Library

Calligraphy and Painting Artworks

A database of images and information on calligraphy and painting artworks housed in the library. The charm of brush and ink endures, capturing moments in time and preserving cultural heritage with masterful craftsmanship.

彭朝霞 Fri, 09/20/2024 - 17:04

情报分析服务

admin — Mon, 26 Aug 2024 13:35:49 +0000

Intelligence Analysis Services

Introduction:

The library, centered on the university's "Double First-Class" initiative, utilizes metrology theory as a guide. By leveraging advanced professional information analysis and mining tools, along with various intelligence analysis methods, it conducts an in-depth analysis of literature data. This analysis spans dimensions such as research papers, high-end talent, research projects, research awards, discipline reputation, and institutional profiles. It provides highly customized intelligence analysis services, including decision-making information support and evaluation consultation based on factual data, scientific and technological information, and literature resources, to functional departments, faculties, and disciplines. These services assist in grasping discipline development trends, determining institutional development levels, and offering decision-making support.

ESI Subject Rankings and Situational Analysis

Case 1: ESI Discipline Monitoring Series Report

Since September 2019, the library has been providing four ESI subject analysis reports every two months, based on databases such as Clarivate Analytics' InCites, ESI, JCR, and SCIE. These reports include "Shenzhen University ESI Subject Data Brief," "Shenzhen University ESI Subject Potential Value Monitoring Bulletin," "Shenzhen University ESI Performance in Guangdong Province and Comparative Analysis with Benchmark Universities," and "Shenzhen University ESI Highly Cited Papers Academic Performance Bulletin." The reports primarily analyze the overall construction status of Shenzhen University's ESI subjects, the performance of various ESI subjects at international, national, and provincial levels, predictions of potential entry into the top 1% or 0.1%, and the performance of highly cited papers in terms of subjects, journals, and collaborating institutions.

Case 2: Analysis of ESI Discipline Trends

An overview of the ten-year development of ESI disciplines at Shenzhen University, along with a comprehensive analysis of the involvement of various colleges and related authors in the development of ESI disciplines. This analysis includes aspects such as the publication of highly cited papers, the publication and citation status of colleges, the publication and citation status of authors, and journal publications. To date, analyses have been completed for ESI disciplines including Social Sciences, General; Engineering; Chemistry; Computer Science; Materials Science; Agricultural Sciences; Environment/Ecology; and Molecular Biology & Genetics.

Dynamic analysis of university competitiveness

Case 1: "Annual Report on the Development of Academic Disciplines at Shenzhen University"

In collaboration with the Development Planning Department, data from various sources related to academic development have been systematically compiled. This includes data on degree authorization points, ESI discipline data, high-quality paper data, Clarivate Highly Cited Researchers list, Stanford University's list of the top 2% of scientists globally, data from the four major authoritative world university rankings, and the Nature Index. These data have been visualized and analyzed in a multi-faceted and comprehensive manner to systematically reflect the annual development status of the school.

Case 2: Analysis of Shenzhen University's Academic Performance in the Nature Index Annual Rankings

Based on the "Nature Index 2021 Annual List" published by the Nature Index website, an overall analysis of the research performance of Shenzhen University, the data of domestic institutions entering the global TOP 100 universities, and the top 25 fastest-rising institutions in China is conducted. Additionally, the overall research output of the university in the four disciplines tracked by the Nature Index is revealed.

Case 3: Analysis Report on the Competitiveness of Research Achievements and Social Service Impact of Shenzhen University

The overall academic output of Shenzhen University from 2018 to 2023 has been evaluated, including dimensions such as outstanding research output, thematic research at the micro level, level of collaboration, conversion rate of research results into patents, policy citations, and media exposure.

Analysis of Scholars' Academic Influence

Case 1: Analysis of the Top 2% of Global Scientists from Stanford

The analysis of the "Lifetime Scientific Influence Rankings" and the "2019 Annual Scientific Influence Rankings" for domestic universities included in the list of the top 2% of scientists globally will be conducted from the perspectives of the number of individuals listed and institutional rankings. Additionally, an in-depth analysis will be carried out on the distribution of colleges and academic fields for Shenzhen University scientists included in the "Lifetime Scientific Influence Rankings" and the "2019 Annual Scientific Influence Rankings" of the top 2% of scientists globally.

Case 2: "Evaluation Report on Prospective Talent"

Analyze the number of publications, top journal papers, citation frequency, research collaborations, patents, talent evaluations, and benchmarking scholar analysis of the scholars to be introduced, providing relevant references for the human resources department to implement talent introduction.

Frontier and hotspot analyses

Case 1: "Analysis Report on the Research Trends and Hotspots of MOOCs in UOOC Alliance Member Universities"

Based on the CNKI database, using BICOMB and SPASS tools, an analysis was conducted on the overall situation of MOOC-related theme papers published by UOOC alliance member universities from 2014 to 2020. This analysis covers the volume of publications, distribution of research institutions/regions, distribution of authors, distribution of journals, as well as trend studies and hotspot analysis based on high-frequency keywords.

Faculty Competitiveness Analysis

Using databases such as InCites, ESI, JCR, Scival, and the Shenzhen University Academic Portal, along with tools like Citespace or VOSviewer, conduct a comprehensive analysis and objective evaluation of academic competitiveness and research performance from dimensions such as research papers, high-end talent, and benchmark schools. This approach aids in understanding the dynamics of discipline development, determining the level of institutional development, and providing decision support services.

Contact Information:

Location: Shenzhen University Library Development Research Department, North Building 614

Tel: 0755-26557397

Email: Peid@szu.edu.cn

彭朝霞 Fri, 09/20/2024 - 17:02

生成式人工智能专题

高天玥 — Thu, 08 Aug 2024 03:59:17 +0000

Generative AI Tools and Resources

The generative artificial intelligence section will be continuously updated to reflect developments in the field. Please check this page regularly for the latest information.

Latest Artificial Intelligence Policies

Introduction to Generative AI

With the rapid advancement of artificial intelligence technology, Generative Artificial Intelligence (GenAI), as one of its important branches, is gradually changing the way life and work are conducted. Generative AI, by learning from vast amounts of data, can autonomously generate new data, text, images, and more, showcasing unprecedented creativity and application potential. The application fields of Generative AI are extensive, and here are some common examples:

Text Generation: Automatically drafting press releases, articles, reports, social media content, and more.
Image Creation: Generate artworks, design patterns, advertising images, etc.
Music Composition: Create musical melodies, accompaniments, and even entire songs.
Video Generation: Create video content based on text descriptions or edit and enhance existing videos.
Game Development: Automatically generate game levels, characters, storylines, and more.
Data Augmentation: In the field of data science, generating additional data samples to expand the training set and enhance the model's generalization ability.
Personalized Recommendations: Generate personalized content or product recommendations based on user behavior and preferences.
Language Translation: Automatically translate one language into another, improving the quality and efficiency of translation.
Code Generation: Assist programmers in automatically generating or completing code, thereby improving development efficiency.
Virtual Assistants: Provide natural language interaction to help users obtain information or complete specific tasks.
Educational Assistance: Generate personalized learning materials and practice questions to cater to the diverse learning needs of students.
Medical Assistance: Aid in diagnosing, generating medical reports, or suggesting medication formulations.

These applications demonstrate the potential of generative artificial intelligence in creative tasks and automated processes. As technology continues to advance, the scope and effectiveness of its applications will continue to improve.

Currently, most questions about calculations and formulas cannot be answered by generative AI.

Gen AI Tools Navigation

The selected text translates to: "On this page, some recently developed generative AI tools for learning and research are listed.

When selecting AI tools, it is also important to consult with course instructors and publishers to confirm whether they allow the use of AI tools in the preparation of research or study materials, and to be aware of potential copyright, privacy, and data retention issues.

Shenzhen University Library currently does not provide access support for the tools listed on this page."

Generic GenAI tools：

GenAI Research Assistance

Literature reading and paper writing

AI translation tools

GenAI office, learning assistant

PPT

Forms

Mind map

AI office

AI image/video/audio processing

AI image

AI video

AI audio

AI programming tools

AI search engine

Intelligent body

Common Issues

What is artificial intelligence?

With the rapid advancement of artificial intelligence technology, Generative Artificial Intelligence (GenAI), as one of its important branches, is gradually transforming the way life and work are conducted. By learning from vast amounts of data, Generative Artificial Intelligence can autonomously generate new data, text, images, and more, showcasing unprecedented creativity and application potential.

Generative artificial intelligence is a form of AI that generates new content through algorithms and models. It can be applied in various fields, including the creation of text, images, music, and videos. This type of AI learns from large amounts of data, mastering specific forms of output, and then generates content that is similar to the original data but entirely new. Common methods behind these technologies include deep learning, neural networks, particularly Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN). Generative AI not only creates new content but also helps optimize and automate many creative and analytical tasks.

Generative Artificial Intelligence (Generative AI) is a type of artificial intelligence technology capable of generating new data instances based on existing data. These instances are similar in structure and distribution to the original data but contain entirely new content. This technology typically involves generative models in machine learning, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).

Before using Gen AI, certain key points need to be understood.

Generative AI is neither conscious nor intelligent. It is a text prediction machine. This means that the output from artificial intelligence may be inaccurate, off-topic, superficial, or illogical. When using Gen AI, it is necessary to:

-Learn subject knowledge to evaluate AI-generated content.

- Understand that for tasks requiring human judgment, the output will be unsatisfactory.

- Recognize that the output may not include accurate sources or citations, which may need to be manually inserted or checked. Generative artificial intelligence has broad application potential in many fields, but it also comes with challenges such as issues with the quality and authenticity of generated content, copyright concerns, and ethical considerations.

Where to learn more about AI tools?

Navigate to the library homepage and select the "Generative AI Specification" .

Access the "Academic Research" page, click on the "Generative AI Specification" link to enter and explore the relevant content.

Regarding the use of artificial intelligence tools, the library will launch a series of AI exploration training sessions this semester. Please stay tuned to the official WeChat account and homepage for notifications.

Is it wrong to use AI tools?

Overall, there is nothing inherently wrong with using AI tools. As the AI era approaches, it is essential to continually explore the potential of Gen AI and acquire skills in this significant technological field. Remember, many AI tools are based on technologies like machine learning that have been developed over many years and adhere to certain ethical standards. The current challenges mainly focus on the sources of AIGC and instances where individuals attempt to pass off AI-generated content as their original work. Additionally, if AI is being used to complete research, it is crucial to understand the regulations regarding AI usage set by the submission entity.

Which AI tools can be used for free?

Multiple AI toolsets are available for free use. Access them through the following links:

Generative Artificial Intelligence Tools Navigation

Many AI tools offer premium versions at higher prices, but many are free. It is important to remember that user data in such applications is not kept confidential.

Can artificial intelligence be detected in assignments and publications?

Some platforms claim to detect the use of AI in submitted assignments or papers, but many institutions cannot access AI-generated content with their traditional plagiarism detection tools. It is important to note that content written by AIGC often significantly deviates from one's own writing style. Additionally, AIGC-generated content carries the risk of containing false information or having a limited knowledge base, necessitating the selection of appropriate detection tools.

Does Gen AI provide a source of information?

Articles, news media, and websites that provide sources can be indicators of reliability, but it is necessary to independently verify the authenticity and reliability of the information when using them. Since AI tools may "lie," further evaluation of the sources can be done by clicking on links and retrieving references.

Does using AI-generated content require permission, and would it be considered cheating?

There is no unified standard answer to whether AI-generated text is considered cheating. Most educators expect students to submit written work completed independently, but students might believe that by using AI-generated text and then proofreading and modifying it, they have contributed to the final product, making it acceptable.

Before using any tools or assistive technologies, including Gen AI, to complete coursework or write papers and theses, authorization from the school, teachers, etc., must be obtained. Instructors can specify in the syllabus or requirements whether AI-generated text is accepted and how generative AI can be used in the classroom. If students choose to use generative AI technology for writing, they need to ensure that they inform their teachers of its use to comply with academic integrity, respect intellectual property, and meet the expectations of relevant individuals or organizations.

If artificial intelligence is used, should it be credited and cited in the work?

Yes, the use of generative AI tools should be cited. It is important to promptly cite or mark the use of Gen AI tools and application plugins. This kind of marking and citation ensures that others are aware when Gen AI is used to generate content and reduces misunderstandings about the source of information. When writing or publishing using Gen AI, be sure to follow the guidance provided by course instructors, journals, or manuscript publishers/editors. For instance, some may require listing Gen AI as an author, while others may only require an acknowledgment.

For generating emails and grammar modifications, there is no need to cite AI unless simply copying and pasting the AI's response to the query. Remember, even if it is generated by AI, responsibility for all content still lies with the user.
For presentations, coursework, and similar tasks, any use of generative AI tools must be approved by the instructor. When used for publications or papers, publishers typically require the source of the AI to be cited. For more information, refer to the guidelines on citing generative artificial intelligence.

When using Gen AI, how can personal data and information be effectively protected?

To protect personal data and information, do not input sensitive, confidential, or restricted information into Gen AI tools, including but not limited to national secrets, any intellectual property, unpublished research data, export-controlled data, and other sensitive human resources, business, or administrative data.

Can the content generated by Gen AI be completely trusted?

Remember, Gen AI systems can make mistakes. Responses may be inaccurate, misleading, or even completely fabricated. Therefore, always check and evaluate the accuracy of all outputs generated by Gen AI tools before relying on or publicly sharing them.

Academic Ethics and Citation Standards

Generative AI Citation Specification Recommendations：

If choosing to use ChatGPT or other AI technologies for writing, ensure compliance with the relevant policies and permissions outlined by these tools. Each citation style offers different guidelines for referencing generative AI tools like ChatGPT.

APA 7:
- Citation format:Open AI. (Year). ChatGPT (month day version) [Large Language Model].
- When using ChatGPT or other AI tools in research, describe the usage of the tool in the "Methods" section or a similar part of the paper. For literature reviews or other types of papers or response papers, describe the usage of the tool in the introduction. In the text, provide the prompts used and then offer any relevant portions of the text generated as a response.
MLA 9 Citation format:"Prompt text" prompt. ChatGPT, day month. version, OpenAI, day month year, chat.openai.com/chat
Chicago Citation format:ChatGPT, response to "Prompt text," OpenAI, month, day, year,
Elsevier: Authors are permitted to use generative AI and AI-assisted technologies during the writing process before submission, but only to enhance the language and readability of the paper, and must disclose this appropriately according to Elsevier's guidelines. The author guidelines open in a new tab/window. Editors can find such disclosures in a separate section before the reference list at the bottom of the paper. If editors suspect that authors or reviewers have violated the AI policy, they should inform the publisher.
IEEE：In any paper submitted to IEEE publications, the use of content generated by artificial intelligence (AI) (including but not limited to text, graphics, images, and code) should be disclosed in the acknowledgments section. The specific AI system used should be identified, and the particular parts of the paper where AI-generated content was utilized should be specified, along with a brief description of the extent to which the AI system was used to generate the content. The use of AI systems for editing and grammar enhancement is a common practice and is generally not intended to fall under the aforementioned policy. In such cases, the aforementioned disclosure is recommended.
Nature： AI Author Attribution: Large language models (LLMs), such as ChatGPT, do not meet our authorship criteria. It is important to note that authorship attribution carries responsibility for the work, which cannot be effectively applied to LLMs. The use of LLMs should be properly documented in the methods section of the manuscript (or in an appropriate alternative section if there is no methods section).
Taylor & Francis：Generative AI tools should not be listed as authors because these tools cannot take responsibility for the submitted content, nor can they manage copyright and licensing agreements. Copyright requires accountability for the content, agreement to publish through a publishing contract, and contractual guarantees regarding the integrity of the work, among other principles. These are uniquely human responsibilities that generative AI tools cannot assume. Authors must explicitly acknowledge any use of generative AI tools in their articles or books through a statement that includes the full name of the tool (with version number), how it was used, and the reason for its use. For article submissions, this statement must be included in the "Methods or Acknowledgments" section. Book authors must disclose their intention to use generative AI tools to their editorial contact as early as possible to obtain approval—whether at the proposal stage (if known) or, if necessary, during the manuscript writing stage. If approved, book authors must include this statement in the book's preface or introduction. This level of transparency ensures that editors can assess whether generative AI tools have been used and whether they have been used responsibly. Taylor & Francis reserves the discretion to publish works to ensure that integrity and guidelines are maintained.
PLOS：PLOS expects articles to report the authors' own work and ideas. Contributions from other sources should be listed as authors, as such tools cannot take responsibility for the submitted content nor manage copyright and licensing agreements. Authors are required to take responsibility for the content, agree to publication through a publishing agreement, provide contractual guarantees regarding the integrity of the work, and adhere to other principles. These are unique human responsibilities, and any contributions from generative AI must be clearly and accurately attributed. Contributions of AI tools and technologies to the research or article content must be explicitly reported in a dedicated section of the "Methods" or, for article types lacking a "Methods" section, in the "Acknowledgments." This section should include the names of any tools used, a description of how the authors used the tool and assessed the validity of its output, and a clear statement of which aspects of the research, article content, data, or supporting files were affected/generated by the use of AI tools.
Sage：When submitting any form of content to Sage or Corwin (including journal articles, manuscripts, and book proposals), it is necessary to disclose any AI-generated content (including text, images, or translations) present in the work. This will enable the editorial team to make informed publishing decisions regarding the submission.

Additional AI Resources

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xiaoan — Mon, 26 Aug 2024 06:20:30 +0000

SZU Academic Achievement Library

By establishing the Shenzhen University Academic Achievement Library, the library collects data on journal and conference papers, patents, and books from both domestic and international sources since the university's founding. This data is then organized, linked, cleaned, attributed, and statistically analyzed. The academic portal system preserves, manages, and showcases the research achievements of Shenzhen University, aiming to promote academic exchange and enhance the academic influence of Shenzhen University and its scholars.

The Academic Achievement Library offers three fundamental functional platforms: "Departments," "Scholars," and "Achievements."

Departmental Platform: Showcases research achievements by department and provides statistical data on the output of each department.
Scholar Platform: Displays researchers' scientific achievements, dynamically tracks the inclusion and citation of academic results, visualizes researchers' academic and research networks, and provides metric evaluation analysis reports.
Research Achievements Platform: This platform showcases the overall research achievements of Southeast University, currently including journal papers, conference papers, dissertations, patents, standards, and monographs.

The Academic Achievement Library is accessible only to faculty members. Everyone is welcome to log in and make use of it.

For any related inquiries, please contact:

For inquiries regarding thesis data issues, please contact us at 0755-26557397 or peid@szu.edu.cn.

Visit the website: https://www.chengren-av.cc/ir

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外文资源

1. Web of Science - SCIE
2. Web of Science - SSCI
3. 中国科学院文献情报中心期刊分区表
4. IEEE Electronic Library（IEL）
5. JCR
6. ENDNOTE文献管理机构版
7. Nature电子期刊数据库
8. Engineering Village平台
9. Essential Science Indicators
10. PQDT学位论文全文库
11. Cell Press
12. Web of Science - Derwent Innovations Index

中文资源

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