Dataset Information

Dataset for file fragment classification of image file formats.

ABSTRACT: OBJECTIVES:File fragment classification of image file formats is a topic of interest in network forensics. There are a few publicly available datasets of files with image formats. Therewith, there is no public dataset for file fragments of image file formats. So, a big research challenge in file fragment classification of image file formats is to compare the performance of the developed methods over the same datasets. DATA DESCRIPTION:In this study, we present a dataset that contains file fragments of ten image file formats: Bitmap, Better Portable Graphics, Free Lossless Image Format, Graphics Interchange Format, Joint Photographic Experts Group, Joint Photographic Experts Group 2000, Joint Photographic Experts Group Extended Range, Portable Network Graphic, Tagged Image File Format, and Web Picture. Corresponding to each format, the dataset contains the file fragments of image files with different compression settings. For each pair of file format and compression setting, 800 file fragments are provided. Totally, the dataset contains 25,600 file fragments.

SUBMITTER: Fakouri R

PROVIDER: S-EPMC6881973 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Dataset for file fragment classification of image file formats.

Fakouri Reyhane R Teimouri Mehdi M

BMC research notes 20191127 1

<h4>Objectives</h4>File fragment classification of image file formats is a topic of interest in network forensics. There are a few publicly available datasets of files with image formats. Therewith, there is no public dataset for file fragments of image file formats. So, a big research challenge in file fragment classification of image file formats is to compare the performance of the developed methods over the same datasets.<h4>Data description</h4>In this study, we present a dataset that conta ...[more]

PMID: 31775855

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Project description:BACKGROUND:Bioinformatics software often requires human-generated tabular text files as input and has specific requirements for how those data are formatted. Users frequently manage these data in spreadsheet programs, which is convenient for researchers who are compiling the requisite information because the spreadsheet programs can easily be used on different platforms including laptops and tablets, and because they provide a familiar interface. It is increasingly common for many different researchers to be involved in compiling these data, including study coordinators, clinicians, lab technicians and bioinformaticians. As a result, many research groups are shifting toward using cloud-based spreadsheet programs, such as Google Sheets, which support the concurrent editing of a single spreadsheet by different users working on different platforms. Most of the researchers who enter data are not familiar with the formatting requirements of the bioinformatics programs that will be used, so validating and correcting file formats is often a bottleneck prior to beginning bioinformatics analysis. MAIN TEXT:We present Keemei, a Google Sheets Add-on, for validating tabular files used in bioinformatics analyses. Keemei is available free of charge from Google's Chrome Web Store. Keemei can be installed and run on any web browser supported by Google Sheets. Keemei currently supports the validation of two widely used tabular bioinformatics formats, the Quantitative Insights into Microbial Ecology (QIIME) sample metadata mapping file format and the Spatially Referenced Genetic Data (SRGD) format, but is designed to easily support the addition of others. CONCLUSIONS:Keemei will save researchers time and frustration by providing a convenient interface for tabular bioinformatics file format validation. By allowing everyone involved with data entry for a project to easily validate their data, it will reduce the validation and formatting bottlenecks that are commonly encountered when human-generated data files are first used with a bioinformatics system. Simplifying the validation of essential tabular data files, such as sample metadata, will reduce common errors and thereby improve the quality and reliability of research outcomes.

Dataset Information

Dataset for file fragment classification of image file formats.

Publications

Dataset for file fragment classification of image file formats.

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