Identifying optimal cluster numbers in hierarchical cluster analysis. by Dolores Bernenko (16528426)

Clustering results. by Yuxuan Cai (19226202)

Estimated changes in follow-up depressive symptoms associated with 60-minute reallocations from the mean baseline movement behaviour composition^b'*'. by Hugues Sampasa-Kanyinga (606145)

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Significant spatial clusters of open defecation practice among households in Ethiopia, EDHS 2016. by Daniel Gashaneh Belay (12334351)

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Clustering-based metaheuristic. by Almiqdad Elzein (13141038)

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Estimation of individual genetic ancestry. by J. Tyson McDonald (3687694)

Fitted estimates and significance levels of linear mixed models for seedling germination, survival and growth. by Laura García de Jalón (8939456)

Estimates of the differencing parameter, d, in Eq (1). by Luis Alberiko Gil-Alana (14659817)

Estimates of the differencing parameter, d, in Eq (1). by Luis Alberiko Gil-Alana (14659817)

Significant distribution of BL genes among annotated bacterial sources from patients comparing the pre- and post-eradication states. by Dita Gudra (4439767)

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DART-ID decreases missing datapoints across runs. by Albert Tian Chen (6900215)

Map with the correlation between gene expression and ALE-derived gray matter decrease of each cluster. by Alessia Camasio (14185864)

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Result of AgriGo V2 clustering of significantly expressed genes in barley root and shoot. by Michael Schneider (1806265)

Result of AgriGo V2 clustering of significantly expressed genes in tomato root and shoot. by Michael Schneider (1806265)

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.…”

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

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)

Clustered mutations and cluster summary. by Bettina Meier (274833)

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)