Dataset Information

The Experiment Factory: Standardizing Behavioral Experiments.

ABSTRACT: The administration of behavioral and experimental paradigms for psychology research is hindered by lack of a coordinated effort to develop and deploy standardized paradigms. While several frameworks (Mason and Suri, 2011; McDonnell et al., 2012; de Leeuw, 2015; Lange et al., 2015) have provided infrastructure and methods for individual research groups to develop paradigms, missing is a coordinated effort to develop paradigms linked with a system to easily deploy them. This disorganization leads to redundancy in development, divergent implementations of conceptually identical tasks, disorganized and error-prone code lacking documentation, and difficulty in replication. The ongoing reproducibility crisis in psychology and neuroscience research (Baker, 2015; Open Science Collaboration, 2015) highlights the urgency of this challenge: reproducible research in behavioral psychology is conditional on deployment of equivalent experiments. A large, accessible repository of experiments for researchers to develop collaboratively is most efficiently accomplished through an open source framework. Here we present the Experiment Factory, an open source framework for the development and deployment of web-based experiments. The modular infrastructure includes experiments, virtual machines for local or cloud deployment, and an application to drive these components and provide developers with functions and tools for further extension. We release this infrastructure with a deployment (http://www.expfactory.org) that researchers are currently using to run a set of over 80 standardized web-based experiments on Amazon Mechanical Turk. By providing open source tools for both deployment and development, this novel infrastructure holds promise to bring reproducibility to the administration of experiments, and accelerate scientific progress by providing a shared community resource of psychological paradigms.

SUBMITTER: Sochat VV

PROVIDER: S-EPMC4844768 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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The Experiment Factory: Standardizing Behavioral Experiments.

Sochat Vanessa V VV Eisenberg Ian W IW Enkavi A Zeynep AZ Li Jamie J Bissett Patrick G PG Poldrack Russell A RA

Frontiers in psychology 20160426

The administration of behavioral and experimental paradigms for psychology research is hindered by lack of a coordinated effort to develop and deploy standardized paradigms. While several frameworks (Mason and Suri, 2011; McDonnell et al., 2012; de Leeuw, 2015; Lange et al., 2015) have provided infrastructure and methods for individual research groups to develop paradigms, missing is a coordinated effort to develop paradigms linked with a system to easily deploy them. This disorganization leads ...[more]

PMID: 27199843

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Project description:BackgroundCOVID-19 vaccines will become available in China soon. Understanding communities' responses to the forthcoming COVID-19 vaccines is important. We applied the theory of planned behavior as the theoretical framework.ObjectiveThis study investigates the prevalence of and factors associated with behavioral intention to receive self-financed or free COVID-19 vaccinations among Chinese factory workers who resumed work during the pandemic. We examined the effects of factors including sociodemographics, perceptions related to COVID-19 vaccination, exposure to information about COVID-19 vaccination through social media, and COVID-19 preventive measures implemented by individuals and factories.MethodsParticipants were full-time employees 18 years or older who worked in factories in Shenzhen. Factory workers in Shenzhen are required to receive a physical examination annually. Eligible workers attending six physical examination sites were invited to complete a survey on September 1-7, 2020. Out of 2653 eligible factory workers, 2053 (77.4%) completed the online survey. Multivariate two-level logistic regression models and ordinal logistic regression models were fitted.ResultsThe prevalence of behavioral intention to receive a COVID-19 vaccination was 66.6% (n=1368, conditional on 80% vaccine efficacy and market rate) and 80.6% (n=1655, conditional on 80% vaccine efficacy and free vaccines). After adjusting for significant background characteristics, positive attitudes toward COVID-19 vaccination (adjusted odds ratio [AOR] 1.20, 95% CI 1.15-1.25 and AOR 1.24, 95% CI 1.19-1.30), perceived support from significant others for getting a COVID-19 vaccination (AOR 1.43, 95% CI 1.32-1.55 and AOR 1.37, 95% CI 1.25-1.50), and perceived behavioral control to get a COVID-19 vaccination (AOR 1.51, 95% CI 1.32-1.73 and AOR 1.28, 95% CI 1.09-1.51) were positively associated with both dependent variables (conditional on 80% vaccine efficacy and market rate or free vaccines, respectively). Regarding social media influence, higher frequency of exposure to positive information related to COVID-19 vaccination was associated with a higher intention to receive a COVID-19 vaccination at market rate (AOR 1.53, 95% CI 1.39-1.70) or a free vaccination (AOR 1.52, 95% CI 1.35-1.71). Higher self-reported compliance with wearing a face mask in the workplace (AOR 1.27, 95% CI 1.02-1.58 and AOR 1.67, 95% CI 1.24-2.27) and other public spaces (AOR 1.80, 95% CI 1.42-2.29 and AOR 1.34, 95% CI 1.01-1.77), hand hygiene (AOR 1.21, 95% CI 1.00-1.47 and AOR 1.52, 95% CI 1.19-1.93), and avoiding social gatherings (AOR 1.22, 95% CI 1.01-1.47 and AOR 1.55, 95% CI 1.23-1.95) and crowded places (AOR 1.24, 95% CI 1.02-1.51 and AOR 1.73, 95% CI 1.37-2.18) were also positively associated with both dependent variables. The number of COVID-19 preventive measures implemented by the factory was positively associated with the intention to receive a COVID-19 vaccination under both scenarios (AOR 1.08, 95% CI 1.04-1.12 and AOR 1.06, 95% CI 1.01-1.11).ConclusionsFactory workers in China reported a high behavioral intention to receive a COVID-19 vaccination. The theory of planned behavior is a useful framework to guide the development of future campaigns promoting COVID-19 vaccination.

Project description:BACKGROUND:The increasing use of computational simulation experiments to inform modern biological research creates new challenges to annotate, archive, share and reproduce such experiments. The recently published Minimum Information About a Simulation Experiment (MIASE) proposes a minimal set of information that should be provided to allow the reproduction of simulation experiments among users and software tools. RESULTS:In this article, we present the Simulation Experiment Description Markup Language (SED-ML). SED-ML encodes in a computer-readable exchange format the information required by MIASE to enable reproduction of simulation experiments. It has been developed as a community project and it is defined in a detailed technical specification and additionally provides an XML schema. The version of SED-ML described in this publication is Level 1 Version 1. It covers the description of the most frequent type of simulation experiments in the area, namely time course simulations. SED-ML documents specify which models to use in an experiment, modifications to apply on the models before using them, which simulation procedures to run on each model, what analysis results to output, and how the results should be presented. These descriptions are independent of the underlying model implementation. SED-ML is a software-independent format for encoding the description of simulation experiments; it is not specific to particular simulation tools. Here, we demonstrate that with the growing software support for SED-ML we can effectively exchange executable simulation descriptions. CONCLUSIONS:With SED-ML, software can exchange simulation experiment descriptions, enabling the validation and reuse of simulation experiments in different tools. Authors of papers reporting simulation experiments can make their simulation protocols available for other scientists to reproduce the results. Because SED-ML is agnostic about exact modeling language(s) used, experiments covering models from different fields of research can be accurately described and combined.

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The Experiment Factory: Standardizing Behavioral Experiments.

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The Experiment Factory: Standardizing Behavioral Experiments.

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