2Ch–2N square and hexagon interactions: a combined crystallographic data analysis and computational study

2019 ◽  
Vol 21 (38) ◽  
pp. 21568-21576 ◽  
Author(s):  
Yunxiang Lu ◽  
Wenxia Li ◽  
Weiwei Yang ◽  
Zhengdan Zhu ◽  
Zhijian Xu ◽  
...  
Keyword(s):  
Data Analysis ◽  
Computational Study ◽  
Crystallographic Data ◽  
Computational Approaches

2Ch–2N square and hexagon interactions were extensively investigated by the combination of crystallographic data analysis and computational approaches.

scholarly journals Computational approaches for high‐throughput single‐cell data analysis

FEBS Journal ◽  
2018 ◽  
Vol 286 (8) ◽  
pp. 1451-1467 ◽  
Author(s):  
Helena Todorov ◽  
Yvan Saeys
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
High Throughput ◽  
Computational Approaches ◽  
Cell Data

scholarly journals A Computational Study of Metallacycles Formed by Pyrazolate Ligands and the Coinage Metals M = Cu(I), Ag(I) and Au(I): (pzM)n for n = 2, 3, 4, 5 and 6. Comparison with Structures Reported in the Cambridge Crystallographic Data Center (CCDC)

Molecules ◽  
2020 ◽  
Vol 25 (21) ◽  
pp. 5108
Author(s):  
José Elguero ◽  
Ibon Alkorta
Keyword(s):  
Data Center ◽  
Computational Study ◽  
Crystallographic Data ◽  
Cambridge Structural Database ◽  
X Ray ◽  
Cambridge Crystallographic Data Center ◽  
Coinage Metals ◽  
Structural Database

The structures reported in the Cambridge Structural Database (CSD) for neutral metallacycles formed by coinage metals in their valence (I) (cations) and pyrazolate anions were examined. Depending on the metal, dimers and trimers are the most common but some larger rings have also been reported, although some of the larger structures are not devoid of ambiguity. M06-2x calculations were carried out on simplified structures (without C-substituents on the pyrazolate rings) in order to facilitate a comparison with the reported X-ray structures (geometries and energies). The problems of stability of the different ring sizes were also analyzed.

Evolution of Data Cube Computational Approaches

2011 ◽  
pp. 469-476
Author(s):  
Rebecca Boon-Noi Tan
Keyword(s):  
Decision Support ◽  
Data Analysis ◽  
Support System ◽  
Database Systems ◽  
Data Cube ◽  
Multidimensional Data ◽  
Multidimensional Data Analysis ◽  
Computational Approaches ◽  
Simultaneous Group ◽  
Analytical Processing

Aggregation is a commonly used operation in decision support database systems. Users of decision support queries are interested in identifying trends rather than looking at individual records in isolation. Decision support system (DSS) queries consequently make heavy use of aggregations, and the ability to simultaneously aggregate across many sets of dimensions (in SQL terms, this translates to many simultaneous group-bys) is crucial for Online Analytical Processing (OLAP) or multidimensional data analysis applications (Datta, VanderMeer, & Ramamritham, 2002; Dehne, Eavis, Hambrusch, & Rau-Chaplin, 2002; Elmasri & Navathe, 2004; Silberschatz, Korth & Sudarshan, 2002).

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