Dataset Information

Environmentally Driven Migration in a Social Network Game.

ABSTRACT: Cooperative behaviors are common in humans, and they are the fundamental basis of our society. Theoretical and experimental studies have modeled environments where the behaviors of humans, or agents, have been restricted to analyze their social behavior. However, it is important that such studies can be generalized to a less restrictive environment in order to understand human society. Social network games (SNGs) provide a powerful tool for the quantitative study of human behavior using a less restrictive environment than in previous studies. We focused on multilevel selection, one of the theoretical frameworks used to study the evolution of cooperation. The evolution of cooperation by multilevel selection requires that the continual assortment between cooperators and noncooperators is generated; thus, humans may have acquired mechanisms that ensure assortment (e.g., migration between groups). This study aims to investigate this mechanism in a less restrictive environment. For this purpose, we researched the effect of migration based on data analysis in an SNG where the players could behave more freely than was possible in the environments used in the previous studies. We showed that players maintained assortment between cooperators and defectors in this SNG, where it appears that environmentally driven migration generated the assortment.

SUBMITTER: Takano M

PROVIDER: S-EPMC4517046 | biostudies-other | 2015

REPOSITORIES: biostudies-other

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Project description:High-throughput sequencing has become ubiquitous in biomedical sciences. As new technologies emerge and sequencing costs decline, the diversity and volume of available data increases exponentially, and successfully navigating the data becomes more challenging. Though datasets are often hosted by public repositories, scientists must rely on inconsistent annotation to identify and interpret meaningful data. Moreover, the experimental heterogeneity and wide-ranging quality of high-throughput biological data means that even data with desired cell lines, tissue types, or molecular targets may not be readily interpretable or integrated. We have developed ORSO (Online Resource for Social Omics) as an easy-to-use web application to connect life scientists with genomics data. In ORSO, users interact within a data-driven social network, where they can favorite datasets and follow other users. In addition to more than 30,000 datasets hosted from major biomedical consortia, users may contribute their own data to ORSO, facilitating its discovery by other users. Leveraging user interactions, ORSO provides a novel recommendation system to automatically connect users with hosted data. In addition to social interactions, the recommendation system considers primary read coverage information and annotated metadata. Similarities used by the recommendation system are presented by ORSO in a graph display, allowing exploration of dataset associations. The topology of the network graph reflects established biology, with samples from related systems grouped together. We tested the recommendation system using an RNA-seq time course dataset from differentiation of embryonic stem cells to cardiomyocytes. The ORSO recommendation system correctly predicted early data point sources as embryonic stem cells and late data point sources as heart and muscle samples, resulting in recommendation of related datasets. By connecting scientists with relevant data, ORSO provides a critical new service that facilitates wide-ranging research interests.

Dataset Information

Environmentally Driven Migration in a Social Network Game.

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