Challenges in Conducting Large-Scale Studies of Curricular Effectiveness: Data Collection and Analyses in the COSMIC Project

In today’s competitive environment, its survival of the businesses has been quite difficult. Together with rapidly increasing competition, there are various disputes between groups and personals and conflicts occur. The conflict is disagreements, discrepancies between two or more people. Businesses should manage these conflicts a good way to make advantageous emerging conflicts. The conflicts sometimes support the creativity, provide that’s emergence, sometimes hamper it. In the same way, as a result of creativity the conflicts can be occurred. To determine the relationship between creativity and conflicts which have an important role in terms of performance of organizations is very important. The aim of this study is to reveal the relationship between conflict and creativity in organizations that arise. This study is basically prepared in two different sizes. The theoretical dimension focuses on interaction, creativity, conflict and creativity. In the research part, the method was first explained, then the results obtained were analyzed. The survey method was used as data collection tools. Our research was carried out in a large-scale industrial enterprise that operating in the province of Konya. The obtained data were analyzed with SPSS. With this study, it has been revealed that emerging conflicts in businesses positively affect the ability of creativity.

Download Full-text

Collecting large datasets of rotational electron diffraction with ParallEM and SerialEM

10.1101/2020.06.01.128793 ◽

2020 ◽

Author(s):

Kiyofumi Takaba ◽

Saori Maki-Yonekura ◽

Koji Yonekura

Keyword(s):

Electron Diffraction ◽

Data Collection ◽

Large Scale ◽

General Purpose ◽

Task Specialization ◽

Camera Control ◽

Combined Use ◽

Electron Microscopes ◽

Diffraction Patterns ◽

Automated Protocol

AbstractA semi-automated protocol has been developed for rotational data collection of electron diffraction patterns by combined use of SerialEM and ParallEM, where SerialEM is used for positioning of sample crystals and ParallEM for rotational data collection. ParallEM calls standard camera control software through an AutoIt script, which adapts to software operational changes and to new GUI programs guiding other cameras. Development included periodic flashing and pausing of data collection during overnight or day-long recording with a cold field-emission beam. The protocol proved to be efficient and accurate in data collection of large-scale rotational series from two JEOL electron microscopes, a general-purpose JEM-2100 and a high-end CRYO ARM 300. Efficiency resulted from simpler steps and task specialization. It is possible to collect 12–20 rotational series from ∼ −68º to ∼ 68º at a rotation speed of 1º /s in one hour without human supervision.

Download Full-text

Opportunities for increased reproducibility and replicability of developmental neuroimaging

10.31234/osf.io/fxjzt ◽

2019 ◽

Cited By ~ 1

Author(s):

Eduard Klapwijk ◽

Wouter van den Bos ◽

Christian K. Tamnes ◽

Nora Maria Raschle ◽

Kathryn L. Mills

Keyword(s):

Data Collection ◽

Cognitive Neuroscience ◽

Statistical Power ◽

Large Scale ◽

Small Samples ◽

Developmental Studies ◽

Data Analyses ◽

Methodological Rigor ◽

Research Findings ◽

Tools And Techniques

Many workflows and tools that aim to increase the reproducibility and replicability of research findings have been suggested. In this review, we discuss the opportunities that these efforts offer for the field of developmental cognitive neuroscience, in particular developmental neuroimaging. We focus on issues broadly related to statistical power and to flexibility and transparency in data analyses. Critical considerations relating to statistical power include challenges in recruitment and testing of young populations, how to increase the value of studies with small samples, and the opportunities and challenges related to working with large-scale datasets. Developmental studies involve challenges such as choices about age groupings, lifespan modelling, analyses of longitudinal changes, and data that can be processed and analyzed in a multitude of ways. Flexibility in data acquisition, analyses and description may thereby greatly impact results. We discuss methods for improving transparency in developmental neuroimaging, and how preregistration can improve methodological rigor. While outlining challenges and issues that may arise before, during, and after data collection, solutions and resources are highlighted aiding to overcome some of these. Since the number of useful tools and techniques is ever-growing, we highlight the fact that many practices can be implemented stepwise.

Download Full-text