Supplemental file for peer review_version2
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens (human)
SUBMITTER:
PROVIDER: S-BSST1696 | biostudies-other |
REPOSITORIES: biostudies-other
Similar Datasets
Project description:ObjectiveThere has been a consistent concern about the inadvertent disclosure of personal information through peer-to-peer file sharing applications, such as Limewire and Morpheus. Examples of personal health and financial information being exposed have been published. We wanted to estimate the extent to which personal health information (PHI) is being disclosed in this way, and compare that to the extent of disclosure of personal financial information (PFI).DesignAfter careful review and approval of our protocol by our institutional research ethics board, files were downloaded from peer-to-peer file sharing networks and manually analyzed for the presence of PHI and PFI. The geographic region of the IP addresses was determined, and classified as either USA or Canada.MeasurementWe estimated the proportion of files that contain personal health and financial information for each region. We also estimated the proportion of search terms that return files with personal health and financial information. We ascertained and discuss the ethical issues related to this study.ResultsApproximately 0.4% of Canadian IP addresses had PHI, as did 0.5% of US IP addresses. There was more disclosure of financial information, at 1.7% of Canadian IP addresses and 4.7% of US IP addresses. An analysis of search terms used in these file sharing networks showed that a small percentage of the terms would return PHI and PFI files (ie, there are people successfully searching for PFI and PHI on the peer-to-peer file sharing networks).ConclusionThere is a real risk of inadvertent disclosure of PHI through peer-to-peer file sharing networks, although the risk is not as large as for PFI. Anyone keeping PHI on their computers should avoid installing file sharing applications on their computers, or if they have to use such tools, actively manage the risks of inadvertent disclosure of their, their family's, their clients', or patients' PHI.
| S-EPMC3000774 | biostudies-literature
Project description: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.
| S-EPMC6881973 | biostudies-literature
Project description:OBJECTIVES:File fragment classification of video file formats is a topic of interest in network forensics. There are some publicly available datasets for file fragments of various file types such as textual, audio, and image file formats. However, there is no public dataset for file fragments of video file formats. So, in order to evaluate and compare the performance of the classification methods, a challenge is the need to have such datasets. DATA DESCRIPTION:In this study, we present a dataset that contains file fragments of 10 video file formats: 3GP, AVI, ASF, FLV, MKV, MOV, MP4, WebM, OGV, and RMVB. Corresponding to each format, the dataset contains the file fragments of video files with different video codec types: H.263, MPEG-4, WMV, H.264, FLV1, H.265, VP8, VP9, Theora, and RealVideo. Totally, 20 different pairs of video format and codec are employed. For each pair of video format and codec, 30,000 file fragments are provided. Totally, the dataset contains 600,000 file fragments.
| S-EPMC7160908 | biostudies-literature
Project description: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.
| S-EPMC6907108 | biostudies-literature
Project description:OBJECTIVES:File fragment classification of audio file formats is a topic of interest in network forensics. There are a few publicly available datasets of files with audio formats. Therewith, there is no public dataset for file fragments of audio file formats. So, a big research challenge in file fragment classification of audio 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 20 audio file formats: AMR, AMR-WB, AAC, AIFF, CVSD, FLAC, GSM-FR, iLBC, Microsoft ADPCM, MP3, PCM, WMA, A-Law, µ-Law, G.726, G.729, Microsoft GSM, OGG Vorbis, OPUS, and SPEEX. Corresponding to each format, the dataset contains the file fragments of audio files with different compression settings. For each pair of file format and compression setting, 210 file fragments are provided. Totally, the dataset contains 20,160 file fragments.
| S-EPMC6925457 | biostudies-literature
Project description:Ulcerative colitis related gene expressions analysis in colonic biopsy samples from control and ulcerative colitis patients
2019-07-10 | GSE134025 | GEO
Project description:A catalog of transcription factor (TF) binding sites in the genome is critical for deciphering regulatory relationships. Here we present the culmination of the efforts of the modENCODE (Model Organism ENCyclopedia Of DNA Elements) and modERN (model organism Encyclopedia of Regulatory Networks) consortia to systematically assay TF binding events in vivo in two major model organisms, Drosophila melanogaster (fly) and Caenorhabditis elegans (worm). These datasets comprise 605 TFs identifying 3.6M sites in the fly and 356 TFs identifying 0.9 M sites in the worm, and represent the majority of the regulatory space in each genome. We demonstrate that TFs associate with chromatin in clusters termed “metapeaks”, that larger metapeaks have characteristics of high occupancy target (HOT) regions, and that the importance of consensus sequence motifs bound by TFs depends on metapeak size and complexity. Combining ChIP-seq data with single cell RNA-seq data in a machine learning model identifies particular TFs with a prominent role in promoting target gene expression in specific cell types, even differentiating between parent-daughter cells during embryogenesis. These data are a rich resource for the community that should fuel and guide future investigations into TF function. To facilitate data accessibility and utility, all strains expressing GFP-tagged TFs are available at the stock centers for each organism. The chromatin immunoprecipitation sequencing data are available through the ENCODE Data Coordinating Center, GEO, and through a direct interface (http://epic.gs.washington.edu/modERN/) that provides rapid access to processed data sets and summary analyses, as well as widgets to probe the cell type-specific TF-target relationships.
2024-11-16 | GSE280213 | GEO
Project description:Differentiating embryonic stem cells into cardiomyocytes is inefficient, and we discover that FGF-10 can induce embryonic stem cells differentiation into cardiomyocytes. We use microarray to gain insight into the global gene expression and elucidate the machenism that FGF-10 induces embryonic stem cells differentiation into cardiomyocytes.
2010-07-01 | GSE22637 | GEO
Project description:A catalog of transcription factor (TF) binding sites in the genome is critical for deciphering regulatory relationships. Here we present the culmination of the efforts of the modENCODE (Model Organism ENCyclopedia Of DNA Elements) and modERN (model organism Encyclopedia of Regulatory Networks) consortia to systematically assay TF binding events in vivo in two major model organisms, Drosophila melanogaster (fly) and Caenorhabditis elegans (worm). These datasets comprise 605 TFs identifying 3.6M sites in the fly and 356 TFs identifying 0.9 M sites in the worm, and represent the majority of the regulatory space in each genome. We demonstrate that TFs associate with chromatin in clusters termed “metapeaks”, that larger metapeaks have characteristics of high occupancy target (HOT) regions, and that the importance of consensus sequence motifs bound by TFs depends on metapeak size and complexity. Combining ChIP-seq data with single cell RNA-seq data in a machine learning model identifies particular TFs with a prominent role in promoting target gene expression in specific cell types, even differentiating between parent-daughter cells during embryogenesis. These data are a rich resource for the community that should fuel and guide future investigations into TF function. To facilitate data accessibility and utility, all strains expressing GFP-tagged TFs are available at the stock centers for each organism. The chromatin immunoprecipitation sequencing data are available through the ENCODE Data Coordinating Center, GEO, and through a direct interface (http://epic.gs.washington.edu/modERN/) that provides rapid access to processed data sets and summary analyses, as well as widgets to probe the cell type-specific TF-target relationships.
2024-11-16 | GSE280215 | GEO