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Data Analysis and Knowledge Discovery  2023, Vol. 7 Issue (6): 38-49    DOI: 10.11925/infotech.2096-3467.2022.0662
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Review of Detection Methods for Scientific Data Citations
Zhou Jiayin,Qian Qing,Tang Mingkun,Wu Sizhu()
Institute of Medical Information, Chinese Academy of Medical Sciences/Beijing Union Medical College, Beijing 100020, China
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Abstract  

[Objective] This paper analyzes the characteristics of the existing data citation practices and summarizes their recognition methods. It also explores current research and future development trends. [Methods] The existing data citation detection methods could be divided into three categories: rule-based recognition, supervised machine learning algorithm, and semi-supervised machine learning algorithm. We also reviewed each method’s principles, characteristics, existing problems, performance, and applications of each method. [Results] The existing technologies are concentrated on supervised machine learning algorithms. Detecting data citation with the help of citing behaviors and extracting data citation elements are the future direction. [Limitations] This paper summarizes the characteristics of data citations and existing recognition algorithms. It did not elaborate on the technical details of these algorithms. [Conclusions] There are still some problems in detecting data citation, such as research field limitations, lack of diversity in methods, and insufficient consideration of data citation characteristics, which need further optimization.
Key words： Scientific Data      Data Citation      Data Sharing      Citation Identification     
Received: 27 June 2022      Published: 09 August 2023
ZTFLH:  G203  
  G301  
Fund:Medical and Health Science and Technology Innovation Project of Chinese Academy of Medical Sciences(2021-I2M-1-057)
Corresponding Authors: Wu Sizhu， ORCID： 0000-0003-4540-9910， E-mail： wu.sizhu@imicams.ac.cn。   
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Cite this article:

Zhou Jiayin, Qian Qing, Tang Mingkun, Wu Sizhu. Review of Detection Methods for Scientific Data Citations. Data Analysis and Knowledge Discovery, 2023, 7(6): 38-49.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.0662     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2023/V7/I6/38

出版社 期刊 数据可用性声明 数据引用元素要求 数据引用信息
位置要求
Springer
Nature		 Humanities and Social Science
Communications[12]		 强制要求，并鼓励同行评审员检查稿件的数据可用性声明 至少包括作者、数据集名称、发布机构（数据仓储名称）、永久标识符等4项，数据唯一标识符应表示为完整URL 参考文献
Springer
Nature		 Scientific Data[13] 强制要求，同行评审员将检查稿件的数据可用性声明 至少包括作者、数据集名称、发布机构（数据仓储名称）、永久标识符等4项，数据唯一标识符应表示为完整URL 参考文献
SAGE Technology in Cancer
Research & Treatment[14]		 强制要求 作者、数据集名称、发布机构、永久标识符等4项 正文
Taylor&
Francis		 Annals of Medicine[15] 强制要求 至少包含作者、数据集名称、发布机构、永久标识符等4项 数据可用性
声明部分
Elsevier Med[16] 强制要求 作者、名称、发布机构、版本、永久标识符等5项 数据可用性
声明部分
Wiley Journal of Chemometrics[17] 强制要求 作者、年份、数据集名称、发布机构、版本、永久标识符等6项 参考文献列表
Periodical Data Citation Policies of Five Major Publisher
数据引用情况 引用元素完整性 识别对象位置 识别对象特征 举例 方法
规范引用 引用元素齐全，包含作者、数据集名称、数据唯一标识符、解析地址数据出版来源等关键元素 参考文献、数据可用性声明、附录 URL、数据唯一
标识符、数据集
名称等		 Elliott， Joshua （2013）： Simulated county- and state-level maize yields， 1979-2012. Version 1. Figshare. http：//dx.doi.org/10.6084/m9.figshare.501263”；西南大学（2006）：海岛棉，国家自然科技资源e平台，doi：10.3416/db.ninr.2151C0001A00014341 规则识别
不规范引用 引用元素缺失，位置分布分散 正文、表格、图片、参考文献、致谢、附录、脚注、注释 数据集名称、
数据仓储名称、
URL等		 Elliott’s Maize Yield Data （2013）. Data accessed from Figshare [June 15， 2015] ；感谢美国冰雪数据中心（NSIDC）提供ICESat数据 规则识别、
机器学习
算法
未引用 无引用或有引用行为但无关键引用元素 正文 特征词句例如
“感谢”、“来源”等		 文献中数据来源为线下问卷调研 机器学习
算法
Data Citation Features Analysis
数据库 登记号正则表达式 数据库 登记号正则表达式
ENA [A-Z][0-9]{5}；
[B- [A-Z]{2}[0-9]{6}；
[C-[A-Z] {3}[0-9]{5}；
[D-[A-Z]{4}[0-9]{8，10}；
[E-[A-Z] {5}[0-9]{7}		 ENCODE ENCSR000[A-Z]{3}
UniProt [A-N，R-Z][0-9][A-Z][A-Z， 0-9][A-Z， 0-9][0-9]；
[O，P，Q][0-9][A-Z， 0-9] [A-Z， 0-9][A-Z， 0-9][0-9]		 SRA ERX[0-9]{6}；
SR[A-Z][0-9]{6，7}
PDBe [0-9][A-Z， 0-9]{3} ENA ER（A|P|R）[0-9]{6}
InterPro IPR[0-9]{6} KEGG sce[0-9]{5}
Pfam PF（AM）?[0-9]{5} BioProject PRJ[A-Z]{2}[0-9]{4，6}
ArrayExpress E-[A-Z]{4}-[0-9]+ BioSample SAMN[0-9]{8}
OMIM [0-9]{6} GEO GSE\d +；
GSM\d +；
GPL[0-9]{4，5}；
Ensembl ENS[A-Z]*G[0-9]{11}+ Assembly GCA_\d{ 9} （ ?：[.]\d + ） ?；
GCF_\d{ 9} （ ?：[.]\d + ） ?
RefSeq （AC|AP|NC|NG|NM|NP|NR|NT|NW|
NZ|XM|XP|XR|YP|ZP|NS）_（[A-Z]{4}）*
[0-9]{6，9}（?：[.][0-9]+）?		 GenBank [A-Z]{3}[0-9]{5}（\.[0-9]{1}）?；
[A-Z]{2}[0-9]{6}（\.[0-9]{1，2}）?；
[A-Z]{4}[0-9]{8，9}（\.[0-9]{1}）?；
L[0-9]{5}（\.[0-9]{1}）?；
X[0-9]{5}（\.[0-9]{1}）?；
N[A-Z]{1}_[0-9]{6，9}（\.[0-9]{1}）?；
XP_[0-9]G[0-9]{5}
RefSNP RS[0-9]{5，9} ClinVar SCV\d{ 9} （ ?：[.]\d + ） ?；
RCV\d{ 9} （?：[.]\d + ） ?；
VCV\d{ 9} （?：[.]\d + ） ?
Regular Expression of Common Biological Database Registration Number
类型 特征词句
URL .edu ftp：//* .gov .com
机构名称 National Institutes of Health NIH
商业数据库 commercial Inc. laboratories Ltd.
高频动词 accession available [at，from]* obtained from purchased from
stored deposited donated download
added archived assigned posted
entered imported included inserted
loaded lodged placed
provided registered submitted uploaded_to
常见名词 dataset* repository [S，s]uppl [S，s]upplemental
survey sample sets publicly available database
Data availability statement Dataset on Github DATA_JOURNAL_DOIS DATA_AVAILABILITY
Common Characteristic Words and Sentences
方法 优点 缺点 适用范围 优化方向
基于规则识别 准确、对于特定
领域识别准确率
较高		 召回率不高，无法发现新的规则与规律；领域局限性、匹配特征需要专家制定，费时费力；对新领域语料移植性不高 某一领域、数据可用性声明较规范的期刊；使用数据唯一标识符进行数据引用的文献 根据期刊发表的数据引用政策及时更新特征词典；扩充数据唯一标识符特征库
基于统计的
机器学习方法		 准确率高，
泛化性好		 人工标注数据引用语句的起始、各个引用元素，受样本量大小影响 引文样本规模较小、维度较低的数据集；无法在全文中直接进行抽取 根据引文可能出现在文献中的位置，先进行定位，局部进行识别
深度学习 学习能力强、准确率
更高、可移植性强		 模型设计复杂、硬件需求高、计算量大，便携性差 引文样本规模较大、特征复杂的数据集；无法在全文中直接进行抽取
半监督机器
学习算法		 不需要人工标注、
自动筛选提取特征		 需要大量的样本学习，复杂，性能欠佳 引文样本规模较大、特征较为明显的数据集 通过调整种子特征词选择策略以优化算法
Comparison of Data Citation Identification Methods
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