Dataset Information

Data Note: The Digital Ludeme Project Database.

ABSTRACT: This document outlines the types of data collected for the Digital Ludeme Project, an ERC-funded research project that aims to improve our understanding of the development of games throughout human history through computational analysis of the available (partial) historical data of games. This document outlines how this data is collected, formatted and stored, and how it can be accessed. It is the aim of the Digital Ludeme Project to provide a data resource of unprecedented depth and scope for the benefit of historical games researchers worldwide. Special attention is paid to the FAIR Guiding Principles for scientific data management and stewardship.

SUBMITTER: Browne C

PROVIDER: S-EPMC10973761 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Data Note: The Digital Ludeme Project Database.

Browne Cameron C Stephenson Matthew M Crist Walter W

Open research Europe 20230928

This document outlines the types of data collected for the Digital Ludeme Project, an ERC-funded research project that aims to improve our understanding of the development of games throughout human history through computational analysis of the available (partial) historical data of games. This document outlines how this data is collected, formatted and stored, and how it can be accessed. It is the aim of the Digital Ludeme Project to provide a data resource of unprecedented depth and scope for t ...[more]

PMID: 38550771

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Project description:The chicken microbiota is often analyzed to address questions about the effects of diet or disease on poultry health. To analyze the microbiota, bioinformatic platforms such as QIIME 2 and mothur are used, which incorporate public taxonomic databases such as Greengenes, the ribosomal database project (RDP), and SILVA to assign taxonomies to bacterial sequences. Many chicken microbiota studies continue to incorporate the Greengenes database, which has not been updated since 2013. To determine whether a choice of database could affect results, this study compared the results of bioinformatic analyses obtained using the Greengenes, RDP, and SILVA databases on a cecal luminal microbiome dataset. The QIIME 2 platform was used to process 16S bacterial sequences and assign taxonomies with Greengenes, RDP, and SILVA. Linear discriminant analysis effect size (LEfSe) was performed, allowing for the comparison of taxonomies considered significantly differentially abundant between the three databases. Some notable differences between databases were observed in results, in particular the ability of SILVA database to classify members of the family Lachnospiraceae into separate genera, while these members remained in one group of unclassified Lachnospiraceae through Greengenes and RDP. LEfSe analyses showed that the SILVA database produced more differentially abundant genera, in large part due to the classification of these separate Lachnospiraceae genera. Additionally, the relative abundance of unclassified Lachnospiraceae in SILVA results was significantly lower than in RDP results. Our results show the choice of taxonomic database can influence the results of a microbiota study at the genus level, potentially affecting the interpretation of the results. The use of the SILVA database is recommended over Greengenes in chicken microbiota studies, as more specific classifications at the genus level may provide more accurate interpretations of changes in the microbiota.

Dataset Information

Data Note: The Digital Ludeme Project Database.

Publications

Data Note: The Digital Ludeme Project Database.

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