These are the input datasets and the results of the analyses reported on the paper titled "Are citation networks relevant to explain academic promotions? An empirical analysis of the Italian national scientific qualification". Abstract: 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. Code The code to collect and process the data used in this paper is available on GitHub at https://github.com/DigitalDataLab/ASN16-18_CitationNetwork. Dataset description The files AdjacencyMatrix_01B1.csv, AdjacencyMatrix_09H1.csv, AdjacencyMatrix_13D1.csv, AdjacencyMatrix_13D2.csv and AdjacencyMatrix_13D3.csv 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 AdjacencyMatrix_CS.csv and AdjacencyMatrix_ST.csv 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 CS_01B1_1.csv, CS_09H1_1.csv, ST_13D1_1.csv, ST_13D2_1.csv and ST_13D3_1.csv 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 CS_01B1_2.csv, CS_09H1_2.csv, ST_13D1_2.csv, ST_13D2_2.csv and ST_13D3_2.csv 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 Codebook.pdf is the codebook of the previous ten files. The file Appendix.pdf 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 NormalityAssessment.pdf contains the normality assessment of citation network indices.