Estimated coefficients based on the selected model in Table 4. by Luis Alberiko Gil-Alana (14659817)

Estimated coefficients based on the selected model in Table 3. by Luis Alberiko Gil-Alana (14659817)

Heat map and k-means clustering of proteins (384) for which any Vpu-mutant was significantly different from wild type. by Charlotte A. Stoneham (10819125)

Clustered heatmaps for GSE129254, GSE158969, GSE15783, and GSE99060 datasets. by Stephanie Cabarcas-Petroski (9631636)

Results of the M-K test in different seasons (significant level is 0.05). by Wang Runke (13256851)

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Spatial Dubin panel model estimation results. by Xiaoyu Wan (644814)

“…It is found that: (1) the scale and vitality of China’s digital technology innovation have increased significantly, and there are obvious spatial differentiation characteristics, and the innovation level of "eastern coastal—central and western interior" is decreasing, forming a cluster distribution pattern in the Yangtze River Delta region, Beijing, Guangdong, and other places, and the degree of agglomeration is decreasing. (2) The overall regional differences in China’s digital technology innovation are large, the differences between the East and the West dominate the interregional differences, and the net differences between regions are the main factors leading to regional differences. (3) There is a significant positive spatial correlation between the scale and vitality of digital technology innovation, which has a significant spatial spillover effect. (4) The results confirm that the level of economic development, digital access, financial scientific and technological support, technology market development level, and R&D intensity have a significant positive impact on the scale and vitality of digital technology innovation; The investment in scientific and technological talents has a significant positive impact on the scale of digital technology innovation, but has no significant impact on the vitality of digital technology innovation.…”

Clustered provinces of Pandemic COVID-19 in Thailand. by Thanin Methiyothin (14659815)

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Model estimates and significance levels from linear mixed-effect models of ECM root tip colonization. by Laura García de Jalón (8939456)

The results of SOM clustering. by Jeong-Soo Gim (18058588)

The identified significant high-rate spatial-temporal clusters of new detected leprosy cases in Yunnan, China, 2011–2020. by Xiaohua Chen (257762)

Points represent regions where the cluster coefficient was significantly larger in HC compared to MCI, at sparsity level of 0.15. by Therese M. Gilligan (7460906)

Sat scan analysis of significant clusters of anemia among reproductive age women in Ethiopia, EDHS 2016. by Daniel Gashaneh Belay (12334351)

Tables showing the peaks of all significant clusters found within our frontal masks that are reported in the main text. by Caroline I. Jahn (14522732)

Clustering using the unweighted pair group method with arithmetic mean (UPGMA). by Nguyen Thi Hai (12700147)

Estimated short- and long-term cumulative changes in CRC incidence and CRC mortality attributable to screening, diagnosis, and treatment decreases in 2020 and 2021. by Joachim Worthington (8394213)

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Size-exclusion chromatography analysis. by Trang Van Tran (16913946)

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List of the hub-genes whose degree decreased from IL and PL to LHB. by Souvik Seal (12437166)

Model estimates for overall abundance and richness. by Nicole B. Kucherov (11558817)

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Endogeneity estimation results. by Lingzhi Tan (19146572)

Coefficient estimates (log-odds scale). by Giulia Priolo (10146494)