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  • Time segmentation DBLP dataset is divided into eleven time windows (time span 01/01/2003 to 31/12/2013). Facebook Wall Posts dataset is divided into eight time windows (time span 01/01/2005 to 31/12/2008). Wiki-Talk dataset is segmented into six time windows (time span 24/11/2007 to 31/12/2007). Enron email dataset is segmented into twelve time windows (time span 01/01/2001 to 31/12/2001). Reddit-reply dataset is segmented into six time windows (time span 07/01/2014 to 13/01/2014). Stack Overflow dataset is segmented into six time windows (time span 24/01/2016 to 29/02/2016). Social group discovery Communities of each time window are discovered using Infomap, Label Propagation, and Leiden algorithms. For running the community detection algorithms, we assume that the datasets are undirected and unweighted graphs. The communities whose size was smaller than two members were ignored. Community evolution tracking and chain identification In order to track community evolution, we investigate each community to find its similar community or communities from previous time windows, which is called community matching. We employed ICEM (Identification of Community Evolution by Mapping) (Kadkhoda Mohammadmosaferi & Naderi, 2020) method in order to determine the evolution events because it is a highly efficient approach to track community evolution and considers partial evolution and non-consecutive time windows. ICEM has two parameters which are α and β, in this paper, the thresholds for being partially similar and very similar are set to α=10% and β=90%, respectively. Each uploaded dataset contains chains of evolution for a community detection algorithm. Reference: Kadkhoda Mohammadmosaferi, K., Naderi, H., 2020. Evolution of communities in dynamic social networks: An efficient map-based approach. Expert Syst. Appl. 147, 113221.
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  • 1) To calculate the average volume expansion coefficient reference data of liquid densities in the range of 20–50 °C were used, and the entropy was calculated for the middle of the specified interval (35 °C). 2) Calculations of the moments of inertia were made as follows. Initially, for a conformer (if a conformational isomerism was possible for the compound) with minimal energy, a nonempirical calculation of optimized atomic coordinates was performed using the GAMESS software package (ver. 2018-R1-pgi-mkl, the Hartree-Fock method, basis 6-31G*), which was then used to calculate moments of inertia in the program "Moments of inertia" written specially for this purpose. The correctness of the results obtained was verified (for those compounds for which it was possible) by comparison with the database of computational chemistry and comparative tests of the National Institute of Standards and Technology (NIST).
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  • This is the primary data used in our publication by Kori et al., 2020
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  • The aim of this research is to find how humen react to vibrational excitations in road field test environment.
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  • Deidentified data & code from Reliability of Reported Peri-Ictal Behavior to Identify Psychogenic Nonepileptic Seizures. Interactive calculator is available at https://wesleykerr.shinyapps.io/IctalBehavior/
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  • This study evaluates possible impacts of SPRR/A residues in viruses including SARS-CoV-2. An analysis on furin-cleavage proteins transcripts has been discussed in the light of available transcription data set from SARS-CoV-2 infected human cells. Based on sequence homology of virus interacting domain of ACE2 receptor, a sensitive host range has been predicted. Possible impacts of mutation in S gene has been analyzed keeping in view that antisense RNA mediated inhibition of cellular transcripts might be operated in the host cell.
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  • See description document.
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  • Deidentified data & code from An Objective Score to Identify Psychogenic Seizures Based on Age of Onset and History. Interactive calculator available at https://wesleykerr.shinyapps.io/History/
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  • Deidentified data and code from Diagnostic Implications of Review-Of-Systems Questionnaires to Differentiate Epileptic Seizures From Psychogenic Seizures.
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  • These are companion data to the paper "Unblending of transcriptional condensates in human repeat expansion disease" at CELL 2020, May 7, doi: https://doi.org/10.1016/j.cell.2020.04.018 Shaon Basu, Sebastian D. Mackowiak, Henri Niskanen, Dora Knezevic , Vahid Asimi, Stefanie Grosswendt, Hylkje Geertsema, Salaheddine Ali, Ivana Jerković, Helge Ewers, Stefan Mundlos, Alexander Meissner, Daniel M. Ibrahim, Denes Hnisz It includes raw data, microscopy images, computational datasets to generate the figures in the study. Programming code is available at https://github.com/hniszlab/hoxd13 GEO data is available GSE128818
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