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Dataset for file fragment classification of textual file formats.

ABSTRACT: OBJECTIVES:Classification of textual file formats is a topic of interest in network forensics. There are a few publicly available datasets of files with textual formats. Therewith, there is no public dataset for file fragments of textual file formats. So, a big research challenge in file fragment classification of textual 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 five textual file formats: Binary file format for Word 97-Word 2003, Microsoft Word open XML format, portable document format, rich text file, and standard text document. This dataset contains the file fragments in three different languages: English, Persian, and Chinese. For each pair of file format and language, 1500 file fragments are provided. So, the dataset of file fragments contains 22,500 file fragments.

SUBMITTER: Mansouri Hanis F

PROVIDER: S-EPMC6907108 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Dataset for file fragment classification of textual file formats.

Mansouri Hanis Fatemeh F Teimouri Mehdi M

BMC research notes 20191211 1

<h4>Objectives</h4>Classification of textual file formats is a topic of interest in network forensics. There are a few publicly available datasets of files with textual formats. Therewith, there is no public dataset for file fragments of textual file formats. So, a big research challenge in file fragment classification of textual 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 contains fi ...[more]

PMID: 31829258

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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.

Project description:BackgroundAlthough subjective expressions and linguistic fluency have been shown as important factors in processing and interpreting textual facts, analyses of these traits in textual health information for different audiences are lacking. We analyzed the readability and linguistic psychological and emotional characteristics of different textual summary formats of Cochrane systematic reviews.MethodsWe performed a multitrait-multimethod cross-sectional study of Press releases available at Cochrane web site (n = 162) and corresponding Scientific abstracts (n = 158), Cochrane Clinical Answers (n = 35) and Plain language summaries in English (n = 156), French (n = 101), German (n = 41) and Croatian (n = 156). We used SMOG index to assess text readability of all text formats, and natural language processing tools (IBM Watson Tone Analyzer, Stanford NLP Sentiment Analysis and Linguistic Inquiry and Word Count) to examine the affective states and subjective information in texts of Scientific abstracts, Plain language summaries and Press releases.ResultsAll text formats had low readability, with SMOG index ranging from a median of 15.6 (95% confidence interval (CI) 15.3-15.9) for Scientific abstracts to 14.7 (95% CI 14.4-15.0) for Plain language summaries. In all text formats, "Sadness" was the most dominantly perceived emotional tone and the style of writing was perceived as "Analytical" and "Tentative". At the psychological level, all text formats exhibited the predominant "Openness" tone, and Press releases scored higher on the scales of "Conscientiousness", "Agreeableness" and "Emotional range". Press releases had significantly higher scores than Scientific abstracts and Plain language summaries on the dimensions of "Clout", and "Emotional tone".ConclusionsAlthough the readability of Plain language summaries was higher than that of text formats targeting more expert audiences, the required literacy was much higher than the recommended US 6th grade level. The language of Press releases was generally more engaging than that of Scientific abstracts and Plain language summaries, which are written by the authors of systematic reviews. Preparation of textual summaries about health evidence for different audiences should take into account readers' subjective experiences to encourage cognitive processing and reaction to the provided information.

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Dataset for file fragment classification of textual file formats.

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Dataset for file fragment classification of textual file formats.

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