Datasets and results of the paper titled "Are citation networks relevant to explain academic promotions? An empirical analysis of the Italian national scientific qualification"

These are the <strong>input datasets</strong> and the <strong>results of the analyses</strong> reported on the paper titled <strong>"Are citation networks relevant to explain academic promotions? An empirical analysis of the Italian national scientific qualification"</strong>. <strong>Abstract:</strong> The aim of this paper is to study the role of citation network measures in the assessment of scientific maturity. Referring to the case of the Italian national scientific qualification (ASN), we investigate if there is a relationship between citation network indices and the results of the researchers’ evaluation procedures. In particular, we want to understand if network measures can enhance the prediction accuracy of the results of the evaluation procedures beyond basic performance indices. Moreover, we want to highlight which citation network indices prove to be more relevant in explaining the ASN results, and if quantitative indices used in the citation-based disciplines assessment can replace the citation network measures in non-citation-based disciplines. Data concerning Statistics and Computer Science disciplines are collected from different sources (ASN, Italian Ministry of University and Research, and Scopus) and processed in order to calculate the citation-based measures used in this study. Following, we apply classification models to estimate the effects of network variables. We find that network measures are strongly related to the results of the ASN and significantly improve the explanatory power of the models, especially for the research fields of Statistics. Additionally, citation networks in the specific sub-disciplines are far more relevant than those in the general disciplines. Finally, results show that the citation network measures are not a substitute of the citation-based bibliometric indices. <strong>Code</strong> The code to collect and process the data used in this paper is available on GitHub at https://github.com/DigitalDataLab/ASN16-18_CitationNetwork<strong>.</strong> <strong>Dataset description</strong> The files <strong>AdjacencyMatrix_01B1.csv</strong>, <strong>AdjacencyMatrix_09H1.csv</strong>, <strong>AdjacencyMatrix_13D1.csv</strong>, <strong>AdjacencyMatrix_13D2.csv</strong> and <strong>AdjacencyMatrix_13D3.csv</strong> are the citation matrices for Italian academics (i.e. ASN candidates and permanent positions in the Italian academic system) in the Recruitment Fields (RFs) 01/B1, 09/H1, 13/D1, 13/D2 and 13/D3, respectively. The files <strong>AdjacencyMatrix_CS.csv</strong> and <strong>AdjacencyMatrix_ST.csv</strong> are the citation matrices for the Italian academics in the Computer Science disciplines (i.e. RFs 01/B1 and 09/H1) and the Statistical disciplines (i.e. RFs 13/D1, 13/D2 and 13/D3), respectively. The files <strong>CS_01B1_1.csv, CS_09H1_1.csv, ST_13D1_1.csv, ST_13D2_1.csv</strong> and <strong>ST_13D3_1.csv</strong> contain the data used to build the logistic regression models presented in the paper for the Italian academics at the Full Professor (FP) level. The files <strong>CS_01B1_2.csv, CS_09H1_2.csv, ST_13D1_2.csv, ST_13D2_2.csv</strong> and <strong>ST_13D3_2.csv</strong> contain the data used to build the logistic regression models presented in the paper for the Italian academics at the Associate Professor (AP) level. The file <strong>Codebook.pdf</strong> is the codebook of the previous ten files. The file <strong>Appendix.pdf</strong> contains the final results of the stepwise logistic regressions computed for each level (i.e. Full Professor and Associate Professor) and Recruitment Field in the Computer Science and Statistics disciplines. The file <strong>NormalityAssessment.pdf</strong> contains the normality assessment of citation network indices.本数据集为发表于论文《"引文网络能否用于解释学术晋升？意大利国家科学资格认证的实证分析"》的**输入数据集**与**分析结果**。 **摘要**：本文旨在探究引文网络（citation network）指标在学术成熟度评估中的作用。以意大利国家科学资格认证（ASN）为研究场景，本文分析引文网络指数与科研人员评估流程结果之间的关联。具体而言，本文旨在厘清相较于基础绩效指标，网络指标能否提升评估流程结果的预测精度。此外，本文希望明确哪些引文网络指数在解释ASN评估结果时更具相关性，以及基于引文的学科评估量化指标能否在非引文计量学科中替代引文网络指标。 本文从ASN、意大利大学与研究部、Scopus等多个来源收集统计学与计算机科学学科的相关数据，并进行处理以计算本文所用的引文类指标。随后，本文应用分类模型评估网络变量的影响。研究发现，网络指标与ASN评估结果存在显著关联，且可显著提升模型的解释力，尤其在统计学研究领域。此外，特定子学科的引文网络相较于通用学科的相关网络更具研究价值。最后，结果表明引文网络指标无法替代基于引文的文献计量指标。 **代码**：用于收集与处理本文所用数据的代码已上传至GitHub，地址为https://github.com/DigitalDataLab/ASN16-18_CitationNetwork。 **数据集说明**： 文件`AdjacencyMatrix_01B1.csv`、`AdjacencyMatrix_09H1.csv`、`AdjacencyMatrix_13D1.csv`、`AdjacencyMatrix_13D2.csv`与`AdjacencyMatrix_13D3.csv`分别对应招聘领域（Recruitment Fields, RFs）01/B1、09/H1、13/D1、13/D2及13/D3内意大利学术人员（即ASN候选人与意大利学术体系内的固定教职人员）的邻接矩阵（Adjacency Matrix，引文矩阵）。 文件`AdjacencyMatrix_CS.csv`与`AdjacencyMatrix_ST.csv`分别对应意大利计算机科学学科（即招聘领域01/B1与09/H1）与统计学学科（即招聘领域13/D1、13/D2及13/D3）内学术人员的邻接矩阵。 文件`CS_01B1_1.csv`、`CS_09H1_1.csv`、`ST_13D1_1.csv`、`ST_13D2_1.csv`与`ST_13D3_1.csv`包含了用于构建本文中针对正教授（Full Professor, FP）级别意大利学术人员的逻辑回归模型（logistic regression models）的数据。 文件`CS_01B1_2.csv`、`CS_09H1_2.csv`、`ST_13D1_2.csv`、`ST_13D2_2.csv`与`ST_13D3_2.csv`包含了用于构建本文中针对副教授（Associate Professor, AP）级别意大利学术人员的逻辑回归模型的数据。 文件`Codebook.pdf`为上述十个文件的代码手册。 文件`Appendix.pdf`包含了针对计算机科学与统计学学科各招聘领域及职称层级（正教授与副教授）的逐步逻辑回归（stepwise logistic regression）分析最终结果。 文件`NormalityAssessment.pdf`包含了引文网络指数的正态性评估结果。