scholarly journals reciprocalspaceship: a Python library for crystallographic data analysis

2021 ◽  
Vol 54 (5) ◽  
pp. 1521-1529
Author(s):  
Jack B. Greisman ◽  
Kevin M. Dalton ◽  
Doeke R. Hekstra
Keyword(s):  
Data Analysis ◽  
X Rays ◽  
Space Groups ◽  
Reflection Data ◽  
Software Packages ◽  
Unit Cells ◽  
Automated Processing ◽  
Exploratory Data ◽  
Closed Source ◽  
Structure Of Matter

Crystallography uses the diffraction of X-rays, electrons or neutrons by crystals to provide invaluable data on the atomic structure of matter, from single atoms to ribosomes. Much of crystallography's success is due to the software packages developed to enable automated processing of diffraction data. However, the analysis of unconventional diffraction experiments can still pose significant challenges – many existing programs are closed source, sparsely documented, or challenging to integrate with modern libraries for scientific computing and machine learning. Described here is reciprocalspaceship, a Python library for exploring reciprocal space. It provides a tabular representation for reflection data from diffraction experiments that extends the widely used pandas library with built-in methods for handling space groups, unit cells and symmetry-based operations. As is illustrated, this library facilitates new modes of exploratory data analysis while supporting the prototyping, development and release of new methods.

scholarly journals Reciprocalspaceship: A Python Library for Crystallographic Data Analysis

2021 ◽  
Author(s):  
Jack B. Greisman ◽  
Kevin M. Dalton ◽  
Doeke R. Hekstra
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
X Ray ◽  
X Ray Crystallography ◽  
New Methods ◽  
Reflection Data ◽  
Software Packages ◽  
Automated Processing ◽  
Exploratory Data ◽  
Closed Source

AbstractX-ray crystallography is an invaluable technique for studying the atomic structure of macromolecules. Much of crystallography’s success is due to the software packages developed to enable the automated processing of diffraction data. However, the analysis of unconventional diffraction experiments can still pose significant challenges—many existing programs are closed-source, sparsely documented, or are challenging to integrate with modern libraries for scientific computing and machine learning. Here we describe reciprocalspaceship, a Python library for exploring reciprocal space. It provides a tabular representation for reflection data from diffraction experiments that extends the widely-used pandas library with built-in methods for handling space group, unit cell, and symmetry-based operations. As we illustrate, this library facilitates new modes of exploratory data analysis while supporting the prototyping, development, and release of new methods.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

UPAYA PENINGKATAN HASIL BELAJAR PASING BAWAH BOLA VOLI DENGAN PENDEKATAN BERMAIN PADA SISWA KELAS X MAN DEMAK

2020 ◽  
Vol 13 (2) ◽  
pp. 41-55
Author(s):  
Indrianto Arif Ramadhana ◽  
Jeff Agung Perdana
Keyword(s):  
Data Analysis ◽  
Learning Outcomes ◽  
Action Observation ◽  
Student Learning Outcomes ◽  
Affective Domain ◽  
Cognitive Domain ◽  
Reflection Data ◽  
Normalized Gain ◽  
Improve Student Learning ◽  
Planning Action

Forearm pass is one of the materials that must be mastered by students of class X Senior High School. In fact, many students do not yet master and know forearm pass techniques. This research is a classroom action research (CAR) with two cycles. Each cycle consists of 4 stages, namely: planning, action, observation and reflection. Data collection was carried out using observations and questionnaires. Data were analyzed using Hake's Normalized Gain formula. From the results of the study it is known that the psychomotor domain of students increased by 0.42 with average criteria from cycle 1 to cycle 2. The affective domain increased by 0.37 with average criteria. The cognitive domain increased by 0.39 with average criteria. Based on the results of the data analysis, it can be concluded that learning forearm pass techniques with games method can improve student learning outcomes.

Covid-19 Cases in India: A Visual Exploratory Data Analysis Model (Preprint)

2020 ◽  
Author(s):  
Jayesh S
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Case Fatality ◽  
Public Health Emergency ◽  
Virus Spread ◽  
Analysis Model ◽  
Case Fatality Ratio ◽  
First Case ◽  
Exploratory Data ◽  
The Government

UNSTRUCTURED Covid-19 outbreak was first reported in Wuhan, China. The deadly virus spread not just the disease, but fear around the globe. On January 2020, WHO declared COVID-19 as a Public Health Emergency of International Concern (PHEIC). First case of Covid-19 in India was reported on January 30, 2020. By the time, India was prepared in fighting against the virus. India has taken various measures to tackle the situation. In this paper, an exploratory data analysis of Covid-19 cases in India is carried out. Data namely number of cases, testing done, Case Fatality ratio, Number of deaths, change in visits stringency index and measures taken by the government is used for modelling and visual exploratory data analysis.

scholarly journals Multivariate Statistical Approach for Nephrines in Women with Obesity

Molecules ◽  
2021 ◽  
Vol 26 (5) ◽  
pp. 1393
Author(s):  
Ralitsa Robeva ◽  
Miroslava Nedyalkova ◽  
Georgi Kirilov ◽  
Atanaska Elenkova ◽  
Sabina Zacharieva ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Metabolic Syndrome ◽  
Data Analysis ◽  
Stress Response ◽  
Exploratory Data Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Obese Women ◽  
Exploratory Data

Catecholamines are physiological regulators of carbohydrate and lipid metabolism during stress, but their chronic influence on metabolic changes in obese patients is still not clarified. The present study aimed to establish the associations between the catecholamine metabolites and metabolic syndrome (MS) components in obese women as well as to reveal the possible hidden subgroups of patients through hierarchical cluster analysis and principal component analysis. The 24-h urine excretion of metanephrine and normetanephrine was investigated in 150 obese women (54 non diabetic without MS, 70 non-diabetic with MS and 26 with type 2 diabetes). The interrelations between carbohydrate disturbances, metabolic syndrome components and stress response hormones were studied. Exploratory data analysis was used to determine different patterns of similarities among the patients. Normetanephrine concentrations were significantly increased in postmenopausal patients and in women with morbid obesity, type 2 diabetes, and hypertension but not with prediabetes. Both metanephrine and normetanephrine levels were positively associated with glucose concentrations one hour after glucose load irrespectively of the insulin levels. The exploratory data analysis showed different risk subgroups among the investigated obese women. The development of predictive tools that include not only traditional metabolic risk factors, but also markers of stress response systems might help for specific risk estimation in obesity patients.

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