LARGE-SCALE PREDICTOR VALIDATION IN PROJECT A: DATA COLLECTION PROCEDURES AND DATA BASE PREPARATION

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.

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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.

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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.

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Water-use data-collection programs and regional data base of the Great Lakes-St. Lawrence River Basin states and provinces; a comparison of withdrawal-data programs by water-use category and by state and province

Open-File Report ◽

10.3133/ofr86546 ◽

1986 ◽

Author(s):

D.S. Snavely

Keyword(s):

Data Collection ◽

Great Lakes ◽

Data Base ◽

River Basin ◽

Water Use ◽

Regional Data ◽

Collection Programs ◽

Use Category ◽

St Lawrence River

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Data-Driven Software Development at Large Scale : from Ad-Hoc Data Collection to Trustworthy Experimentation

10.24834/2043/24873 ◽

2018 ◽

Author(s):

Aleksander Fabijan

Keyword(s):

Data Collection ◽

Software Development ◽

Large Scale ◽

Ad Hoc ◽

Data Driven

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A Greedy Scanning Data Collection Strategy for Large-Scale Wireless Sensor Networks with a Mobile Sink

Sensors ◽

10.3390/s16091432 ◽

2016 ◽

Vol 16 (9) ◽

pp. 1432 ◽

Cited By ~ 7

Author(s):

Chuan Zhu ◽

Sai Zhang ◽

Guangjie Han ◽

Jinfang Jiang ◽

Joel Rodrigues

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Data Collection ◽

Large Scale ◽

Mobile Sink ◽

Wireless Sensor ◽

Data Collection Strategy

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Digital Biomarkers for supporting transitional care decisions: Protocol for a transnational feasibility study (Preprint)

10.2196/preprints.34573 ◽

2021 ◽

Author(s):

Despoina Petsani ◽

Sara Ahmed ◽

Vasileia Petronikolou ◽

Eva Kehayia ◽

Mika Alastalo ◽

...

Keyword(s):

Data Collection ◽

Large Scale ◽

Transitional Care ◽

Care Process ◽

Local Context ◽

Pilot Phase ◽

Living Lab ◽

Patient Reported ◽

Living Labs ◽

Digital Biomarkers

BACKGROUND VITALISE is a H2020 project that aims to harmonize Living Lab procedures and facilitate the access to European Health and Wellbeing research infrastructures. In this context, this study presents a joint research activity (JRA) that will be conducted within VITALISE, in transitional care domain, in order to test and validate the harmonized Living Lab procedures and infrastructures. The collection of data from various sources (ICT, clinical and patient reported outcome measures) demonstrated capacity to assess risk and support decision during care transitions but there is no harmonized way of combining this information. OBJECTIVE This study primarily aims to evaluate the feasibility and benefit of collecting multichannel data across Living Labs on the topic of transitional care and to harmonize the data processes and collection. Secondly, we aim to investigate the collection and use of digital biomarkers and explore initial patterns in the data that demonstrate the potential to predict transition outcomes such as readmissions and adverse events. METHODS The current research protocol presents a multi-center, prospective, observational cohort study that will consist of three phases, running consecutively in multiple sites: a co-creation phase, a testing and simulation phase and a transnational pilot phase. The co-creation phase aims to build a common understanding among different sites, investigate the differences of hospitalization discharge management among countries and the willingness of different stakeholders to use technological solutions in the transitional care process. The testing and simulation phase aims to explore ways of integrating observation of a patient’s clinical condition, patient involvement and discharge education in transitional care. The objective of the simulation phase is to evaluate the feasibility and the barriers that are faced by a healthcare professional in assessing transition readiness. The transnational pilot phase takes input from co-creation and testing and stimulation phase. The aim is to pilot the already designed activities from previous phases and collect data to conduct a first predictive analysis. RESULTS The co-creation phase will be completed by April 2022. The testing and simulation phase will begin in September 2022 and will partially overlap with the deployment of the transnational pilot phase that will start the same month. The data collection of the transnational pilots will be finalized by the end of June 2023. Data processing is expected to be completed by March 2024. The results will consist of guidelines and implementation pathway for large scale study and the analysis for identifying initial patterns in the acquired data. CONCLUSIONS The knowledge acquired though this research will lead to harmonized procedures and data collection for Living Labs that support transitions in care. In addition, this research contributes to the increase in capacity to perform Big Data analytics while accounting for each local context and across Living Labs.

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