期刊论文支撑数据FAIR原则的应用评估与案例分析

[目的/意义]FAIR原则在科技期刊的应用有助于增强期刊论文支撑数据的可发现、交互、共享与重用。评估FAIR实施情况有利于其推广实施,以期为我国期刊论文的支撑数据共享与重用提供有益参考。[方法/过程]本文在国外FAIR原则评估模型的基础上,综合考虑各模型的优势和指标设计特点,结合《数据分析与知识发现》期刊论文相关的科学数据特征,构建FAIR原则指标评价体系,基于该体系从4个维度分析调研结果,最后对中文期刊论文的支撑数据FAIR应用提出合理化建议与优化策略。[结果/结论]FAIR原则在期刊论文支撑数据的应用仍需进一步完善,科研人员的数据共享意识及对于FAIR原则的认知度远远不够,建议从宏观和微观两个层面推广FAIR原则及其实施,推动数据更加开放和可重用。

政府开放数据平台FAIR原则评估指标体系及实证研究

[目的/意义]评估政府开放数据平台FAIR原则的实施效果有助于平台规范标识符、统一元数据标准等,增强政府数据的发现、共享和重用。[方法/过程]采用网络调研法,借鉴国际代表性评估框架,结合调研构建面向政府开放数据平台的FAIR原则评估指标体系,并选取国内外8个样本平台作为案例,从可发现、可访问等4个维度展开评估分析,比较FAIR原则实施状况和差异。[结果/结论]从永久性标识符方案、元数据标准规范、数据使用许可等方面提出相应改进策略,完善国内政府开放数据平台数据管理规范,为后续相关平台的建设与利用提供理论指导和实践借鉴。

国际数据管理计划工具比较研究

采纳一定的标准,特别是以主流、用户量大的数据管理计划(DMP)工具作为代表,遴选出6种研究数据管理计划工具作为研究对象,结合实际试用、前人研究和专家访谈,从科研人员使用和体验视角采用综合性研究方法,厘清工具比较评估的核心要素,构建工具比较评估的三级指标体系,就基本功能、DMP模板内容、用户体验开展比较分析,研究结果呈现通用DMP工具的典型特性和未来发展趋势,供我国出台相关政策和研发工具借鉴参考。

基于FAIR原则的数据期刊开放共享与关联模型研究

[目的/意义]为揭示数据论文与期刊论文关联出版的新形态,对目前数据期刊的开放共享、数据论文与期刊论文之间的关联进行研究,有助于推动科学数据的开放共享发展,促进科学数据的高效流通,使科学数据在多层维度释放数据价值。[方法/过程]基于FAIR原则,从元数据元素、文献服务等角度出发,构建数据流向视角下数据论文与期刊论文之间的互关联模型,分析数据论文与期刊论文之间的关联过程,并选取代表性数据期刊Data in Brief的数据论文为实例展开模型验证与实践参照。[结果/结论]本文基于“可访问”“可发现”对“开放共享”展开研究;基于“可互操作”和“可重用”对“关联”展开研究。通过构建理论模型、实例验证,厘清数据论文与期刊论文之间的关联模式以及验证理论模型的可行性与合理性。

政策文本视角下我国国家科学数据中心FAIR化特征初探

[目的/意义]对我国国家科学数据中心数据政策的FAIR化特征进行探索,为我国数据中心的数据管理政策制定和工作优化提供初步参考。[方法/过程]综合运用网络调研和文本挖掘的方法,使用KH Coder内容挖掘软件对20家数据中心的79部数据政策进行量化文本分析。通过对FAIR原则在政策文本中的出现频次和高相似词汇的分析,揭示FAIR原则在各个数据中心、不同类型政策文本中表现出的关注度差异与语义特征。[结果/结论]数据中心的数据政策已体现了一定的FAIR原则理念,但对每项FAIR原则的关注度不均衡;不同类型的数据政策关注FAIR原则的不同方面,共性在于对可发现原则和可互操作原则比较关注;对元数据给予了重点关注。[创新/价值]建议数据中心在数据政策制定中突出“元数据”在数据全生命周期管理中的作用,推动“数据增值驱动”的数据政策体系构建,并立足我国科学数据管理实际,适度引入FAIR原则。

免疫学数据库和分析门户ImmPort建设经验与启示

目的/意义总结免疫学数据库和分析门户ImmPort的建设经验,为我国建设大型免疫学数据库提供经验和参考。方法/过程全面分析ImmPort的整体架构,讨论各模块功能,同时梳理其数据收集、组织、共享和分析复用的标准化数据流,总结ImmPort平台实践成果。结果/结论我国在大型免疫学数据库建设过程中应重视数据标准化组织和建模;积极引用标准化术语提供语义支持;在分类分级管理中共享数据,同时开发配套工具体系,保障免疫学数据安全有效地共享和利用。

A Formal Model for Analyzing Fair Exchange Protocols Based on Event Logic

Fair exchange protocols play a critical role in enabling two distrustful entities to conduct electronic data exchanges in a fair and secure manner.These protocols are widely used in electronic payment systems and electronic contract signing,ensuring the reliability and security of network transactions.In order to address the limitations of current research methods and enhance the analytical capabilities for fair exchange protocols,this paper proposes a formal model for analyzing such protocols.The proposed model begins with a thorough analysis of fair exchange protocols,followed by the formal definition of fairness.This definition accurately captures the inherent requirements of fair exchange protocols.Building upon event logic,the model incorporates the time factor into predicates and introduces knowledge set axioms.This enhancement empowers the improved logic to effectively describe the state and knowledge of protocol participants at different time points,facilitating reasoning about their acquired knowledge.To maximize the intruder’s capabilities,channel errors are translated into the behaviors of the intruder.The participants are further categorized into honest participants and malicious participants,enabling a comprehensive evaluation of the intruder’s potential impact.By employing a typical fair exchange protocol as an illustrative example,this paper demonstrates the detailed steps of utilizing the proposed model for protocol analysis.The entire process of protocol execution under attack scenarios is presented,shedding light on the underlying reasons for the attacks and proposing corresponding countermeasures.The developedmodel enhances the ability to reason about and evaluate the security properties of fair exchange protocols,thereby contributing to the advancement of secure network transactions.

Ada-FFL:Adaptive computing fairness federated learning

As the scale of federated learning expands,solving the Non-IID data problem of federated learning has become a key challenge of interest.Most existing solutions generally aim to solve the overall performance improvement of all clients;however,the overall performance improvement often sacrifices the performance of certain clients,such as clients with less data.Ignoring fairness may greatly reduce the willingness of some clients to participate in federated learning.In order to solve the above problem,the authors propose Ada-FFL,an adaptive fairness federated aggregation learning algorithm,which can dynamically adjust the fairness coefficient according to the update of the local models,ensuring the convergence performance of the global model and the fairness between federated learning clients.By integrating coarse-grained and fine-grained equity solutions,the authors evaluate the deviation of local models by considering both global equity and individual equity,then the weight ratio will be dynamically allocated for each client based on the evaluated deviation value,which can ensure that the update differences of local models are fully considered in each round of training.Finally,by combining a regularisation term to limit the local model update to be closer to the global model,the sensitivity of the model to input perturbations can be reduced,and the generalisation ability of the global model can be improved.Through numerous experiments on several federal data sets,the authors show that our method has more advantages in convergence effect and fairness than the existing baselines.

Aerodynamic Analysis and Optimization of Pantograph Streamline Fairing for High-Speed Trains

A pantograph serves as a vital device for the collection of electricity in trains.However,its aerodynamic resistance can limit the train’s running speed.As installing fairings around the pantograph is known to effectively reduce the resistance,in this study,different fairing lengths are considered and the related aerodynamic performances of pantograph are assessed.In particular,this is accomplished through numerical simulations based on the k-ωShear Stress Transport(SST)two-equation turbulence model.The results indicate that the fairing diminishes the direct impact of high-speed airflow on the pantograph,thereby reducing its aerodynamic resistance.However,it also induces interferences in the flow field around the train,leading to variations in the aerodynamic resistance and lift of train components.It is shown that a maximum reduction of 56.52%in pantograph aerodynamic resistance and a peak decrease of 3.38%in total train aerodynamic resistance can be achieved.

Resource Allocation in Multi-User Cellular Networks:A Transformer-Based Deep Reinforcement Learning Approach

To meet the communication services with diverse requirements,dynamic resource allocation has shown increasing importance.In this paper,we consider the multi-slot and multi-user resource allocation(MSMU-RA)in a downlink cellular scenario with the aim of maximizing system spectral efficiency while guaranteeing user fairness.We first model the MSMURA problem as a dual-sequence decision-making process,and then solve it by a novel Transformerbased deep reinforcement learning(TDRL)approach.Specifically,the proposed TDRL approach can be achieved based on two aspects:1)To adapt to the dynamic wireless environment,the proximal policy optimization(PPO)algorithm is used to optimize the multi-slot RA strategy.2)To avoid co-channel interference,the Transformer-based PPO algorithm is presented to obtain the optimal multi-user RA scheme by exploring the mapping between user sequence and resource sequence.Experimental results show that:i)the proposed approach outperforms both the traditional and DRL methods in spectral efficiency and user fairness,ii)the proposed algorithm is superior to DRL approaches in terms of convergence speed and generalization performance.

Convergence over AI Governance Amid Differences

As the EU passes the first AI law,the U.S.grapples with bipartisanship,and China proactively advances the issue-based approach and advocates the Global AI Governance Initiative,will AI become“a force for good,ensuring safety,and promoting fairness”?

Dilemmas and Optimization of Third-Party Assessment System in Chinese Fair Competition Review

The current predominant self-review mechanism by policy-making bodies suffers from deficiencies such as insufficient motivations, limited review capabilities, and weak external supervision. Third-party assessment, characterized by independence and specialization, is designed to mitigate these shortcomings. However, the implementation of third-party assessment faces challenges too. This paper intends to improve the third-party assessment system and to realize the legislative purpose of the system. Based on social research, discussions and exchanges with relevant parties, and the existing research results, this paper analyzes the challenges and possible optimization measures for the third-party assessment. The challenges include repulsion from policy-making bodies, insufficient independence of assessment bodies, disparity of assessment quality, and limited application of assessment outcomes. Possible optimization measures include promoting fair competition culture, increasing the acceptance of third-party assessment from policy-making bodies, enhancing the quality of third-party assessment, clarifying the relationship between policy-making bodies and assessment bodies, ensuring the independence of third-party assessments, and promoting the application of assessment results.

Media Watch

UPGRADING GAOKAO Sanlian Lifeweek 29 April With April coming to a close,high school seniors across China have o"cially entered the homestretch of their race towards the gaokao,a standardised national college entrance exam upon which college admission largely depends.This year is the 10th year of ongoing gaokao reforms,in which targeted policies are continuously launched to increase fairness.The most prominent change to the exam under the reforms is that,instead of choosing to study arts or sciences alongside the mandatory Chinese.

The Impact of Corporate Governance and Fair Value Accounting on Debt Contracts

This paper investigates the theoretical relationship between corporate governance,fair value accounting,and debt contracts.It primarily examines the individual impacts of corporate governance and fair value accounting on debt contracts,while also exploring the influence of corporate governance on fair value accounting.The study emphasizes the importance of considering the interests and legal status of creditors in the context of debt contracts.The findings indicate that strong corporate governance can reduce the likelihood of debt default and that the company’s restructuring costs in the event of a default determine whether improved corporate governance will increase or decrease debt costs.Additionally,the study reveals that the strength of corporate governance affects the value relevance of fair value accounting.However,the impact of fair value accounting on debt contracts is not inherently positive or negative;for instance,companies may use fair value adjustments with manipulative intent to enhance performance.Ultimately,the research highlights that discussions about corporate governance should not prioritize shareholder interests exclusively but also consider the legitimate position of creditors.

PARIS原则:开放协作环境下科学数据的可用性

科学数据利用的需求日益迫切,且在“第四范式”“融合科学”等新型科研范式带来的开放协作环境下,呈现出跨边界、端到端、动态性和协作化的特征。作为“数据仓储时代”的产物,FAIR、TRUST原则已无法为开放协作环境下科学数据的高效利用提供深入的指导。详细分析了科学数据利用的典型场景,提出开放协作环境下促进科学数据利用的PARIS原则:可处理(processable)、可问答(askable)、可信赖(reliable)、可联合(incorporable)与可供给(suppliable),并重点分析了PARIS原则对科学数据可用性的促进作用。最后,探讨了实现PARIS原则可参考的技术路径。作为FAIR、TRUST原则的有益扩展,期望PARIS原则能有效提升科学数据的可用性。

基于Fair函数神经网络的厚度传感器输出特性分析

针对最小二乘法的BP学习算法对训练数据准确性要求较高的问题,提出将Fair估计函数应用于神经网络,优化神经网络的计算,当拟合数据中含有过失误差和随机误差时,基于Fair估计函数学习算法拟合效果要明显优于传统基于最小二乘法BP学习算法,具有较高的鲁棒性。将该算法应用于厚度传感器输出特性拟合,实验结果表明,厚度传感器能准确测量纸张厚度,测量相对误差小于1.5%。

两种评分方法在 G.V. Black Ⅱ类洞预备中的对比研究

目的:探讨Fair Grader 100三维评价系统和教师评分两种评分方法对G.V.Black Ⅱ类洞的评价有无差异,并评价教改前后学生预备Ⅱ类洞的质量。方法:选取广州医科大学口腔医学专业2017级本科生为研究对象,分别收集试验组和对照组预备的Ⅱ类洞样本,用上述两种评价方法对两组样本评分,并进行统计学分析。结果:试验组成绩优于对照组成绩,有统计学差异(P<0.01);两种评分方法的结果一致,无统计学差异(P>0.05)。结论:Fair Grader 100数字化评估系统可以在实验课教学中推广使用。

基于FAIR原则的中国科学数据资源现状分析及启示

为推动开放科学实践,促进科学数据共享与重用,选取re3data、FAIRsharing和Data Citation Index这3个国际主要科学数据仓储(SDR)注册和发现平台作为主要数据源,开展中国科学数据资源现状与国外的对比分析,发现我国有国际影响力的SDR数量与科学数据集的体量不匹配,包括我国在内的各国科学数据集的传播共享主要依靠欧美国家的SDR;基于FAIR原则分析我国典型SDR面临的挑战,结果表明,我国的国家科学数据中心在数据和元数据的可发现性、可访问性方面受到数据标识符分配不足和无法正常解析的影响。在数据和元数据的可交互性和可重用性方面,存在引用信息和使用说明不完整、不清晰等问题,阻碍数据的传播和共享。国际组织和机构的典型经验与举措为我国的科学数据管理提供了启示。基于此,未来我国可以通过开展对FAIR原则的政策支持和规范实施,提高我国科学数据资源的开放共享水平。

领域数据库的FAIR原则实践——以生物信息学为例

文章深化国内外领域数据库的FAIR原则实践研究,奠定进一步开展科学数据的管理工作布局与实践基础,为不同学科领域的数据开放路径提供支持。采用文本分析法对国内外11个生物信息学领域数据库的介绍文献内容进行编码,探索其FAIR原则实践进展。通过分析生物信息学领域数据库的可发现、可访问、可互操作和可重用的FAIR原则实践,发现FAIR原则在生物信息学领域的数据实践研究中存在管理与应用两方面的不足。建议在后续FAIR原则实践中,生物信息学领域数据库应创新设计和规范管理标准,丰富领域数据相关描述,遵循通用且符合FAIR原则的数据库规定,实现领域中各数据库平台的互联互通,提高数据开放共享水平。

我国国家科学数据中心FAIR原则的实践现状调查与分析

FAIR原则是规范科学数据管理和促进数据共享的指导原则,调查分析我国国家科学数据中心FAIR原则的实践现状,对推动科学数据的发现、访问、获取和重用具有重要意义。文章通过分析FAIR原则的主要内容,把握国际科学数据开放共享原则;调研20个国家科学数据中心对FAIR原则的实践现状,从元数据标准和用户授权两个层面分析结果并提出改进建议。研究发现:我国国家科学数据中心对FAIR原则的应用还有待提升,需要在持久性标识符的采用、元数据标准的公开访问、数据许可声明的明确等方面加强规范化、标准化管理。