cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.3146692.v11
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [Data Record 4: DHPTMs]

description: <b>DESCRIPTION</b><br>This data record contains tables of all differential histone post-translational modifications (DHPTMs), where each table contains different compared conditions for time point 1h, tissue CA1, and each cell type (Neu or Non) and histone mark (H3K27AC, H3K27ME3, H3K4ME1, H3K4ME3, H3K79ME3 or H3K9AC). Each mark is analysed for different DHPTM region set, where H3K27ac and H3K4me1 are analysed for peak regions, H3K27me3 and H3K79me3 are analysed for gene bodies and H3K4me3 and H3K9ac are analysed for TSS regions [-500,+1000] bases around the TSS. The compared conditions concern the different sets of mouse, N-C compares naïve (N) to context (C) mice, N-CS compares naive (N) to context shock (CS) mice, and C-CS compares context (C) to context shock (CS) mice.<br><br><b>TABLES</b><br>The table contains columns with HPTM coordinates and differential occupation statistics. Columns “chromosome”, “DHPTM start”, “DHPTM end” and “Strand” contain the position in the genome where the DHPTM was detected (mm10 assembly), where peak-based tests (marks H3K27ac and H3K4me1) were run as strand-independent, so for such tests column “Strand” contains “.” for all entries. Column “baseMean” shows the average normalised read counts in each DHPTM. Column “logFC” shows the fold change of the comparison between the conditions in log 2. Columns “pvalue” and “p-adjust” contain the pvalue and the adjusted p-value according to the FDR test.<br><br><b>DATA GENERATION</b><br>The code used to identify DHPTMs can be found in Figshare (https://dx.doi.org/10.6084/m9.figshare.3153400).<br><br>
DOI: 10.6084/m9.figshare.3146650
title: The Bayesian Decoding of Force Stimuli from Slowly Adapting Type I Fibers in Humans

description: The data contains an Excel spreadsheet of 1.5mb containing the spike times from Slowly Adapting Type I (SAI) mechanoreceptors. The data was collected from human subjects via microneurography of the median nerve, in response to force applied to the subject’s finger by an industrial robot. The recorded data has been spike sorted and the spike times in response to the force presentation have been identified and are listed, along side the force profile recorded by a force transducer. There is one force presentation, lasting 1000ms per trial. There is one trial per worksheet, with 27 trials contained in the Excel spreadsheet.
DOI: 10.6084/m9.figshare.3146656.v1
title: PhiSpy prophage predictions

description: Fasta format files of prophage predictions from 11,278 Bacterial and Archaeal genomes. Each fasta file is labeled with the NCBI taxonomy ID of the organism and the name of the organism, and each entry in the fasta file is one prophage prediction.<br>There are 9,086 genome files and each file has the prophages predicted for a different genome
DOI: 10.6084/m9.figshare.3146689.v11
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [Data Record 5: CRMs]

description: <b>DESCRIPTION</b><br>Neuronal and non-neuronal cis-regulatory modules in tissues ACC and CA1 were predicted, together with their activity, using a discriminative random forest classifier RFECS that was trained on a published set of positive and negative transcription factor binding sites and histone post-translational modifications.<br><br><b>DATA REPOSITORIES<br></b>EPIGENOME BROWSER (https://memory-epigenome-browser.dzne.de/)<br>Contains the visualisation tracks for the CRMs (i.e. positions of CRMs) under category "ENHANCER-DATA". Sample names include cell-type (NonNeuron or Neuron) and tissue (ACC or CA1) separated by dashes. GFF files can also be downloaded for local visualisation.<br>Figshare<br>The tables here contain all predicted cis-regulatory modules for each cell-type and tissue combination, where each table includes the CRM coordinates and the RFECS probabilities for each CRM.<br><br><b>DATA GENERATION</b><br>The binary file containing the RFECS trained forest used for the CRM predictions can be found in Figshare (https://dx.doi.org/10.6084/m9.figshare.3153406), and a clarification on how to run the analysis can be found in the reference mentioned below.<br><br>
DOI: 10.6084/m9.figshare.3146593
title: Woody arctic shrubs: stem and leaf data

description: This dataset contains biomass and length data of stems of arctic shrubs harvested over a two year time period. Age boundaries were determined for each sample, and stem segments of the same age class were weighed and measured together. In total,five woody species are included in this dataset, harvested from three locations in the Arctic tundra of northern Alaska.
DOI: 10.6084/m9.figshare.3146692.v12
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [Data Record 6: DHPTMs]

description: <b>DESCRIPTION</b><br>This data record contains tables of all differential histone post-translational modifications (DHPTMs), where each table contains different compared conditions for time point 1h, tissue CA1, and each cell type (Neu or Non) and histone mark (H3K27AC, H3K27ME3, H3K4ME1, H3K4ME3, H3K79ME3 or H3K9AC). Each mark is analysed for different DHPTM region set, where H3K27ac and H3K4me1 are analysed for peak regions, H3K27me3 and H3K79me3 are analysed for gene bodies and H3K4me3 and H3K9ac are analysed for TSS regions [-500,+1000] bases around the TSS. The compared conditions concern the different sets of mouse, N-C compares naïve (N) to context (C) mice, N-CS compares naive (N) to context shock (CS) mice, and C-CS compares context (C) to context shock (CS) mice.<br><br><b>TABLES</b><br>The table contains columns with HPTM coordinates and differential occupation statistics. Columns “chromosome”, “DHPTM start”, “DHPTM end” and “Strand” contain the position in the genome where the DHPTM was detected (mm10 assembly), where peak-based tests (marks H3K27ac and H3K4me1) were run as strand-independent, so for such tests column “Strand” contains “.” for all entries. Column “baseMean” shows the average normalised read counts in each DHPTM. Column “logFC” shows the fold change of the comparison between the conditions in log 2. Columns “pvalue” and “p-adjust” contain the pvalue and the adjusted p-value according to the FDR test.<br><br><b>DATA GENERATION</b><br>The code used to identify DHPTMs can be found in Figshare (https://dx.doi.org/10.6084/m9.figshare.3153400).<br><br>
DOI: 10.6084/m9.figshare.3146587
title: MPG Ranch soil metadata from metagenomics samples

description: Samples chosen for metagenomic sequencing from the MPG Ranch invasive species study: http://dx.doi.org/10.6084/m9.figshare.1504117
DOI: 10.6084/m9.figshare.3146590
title: Arctic graminoid leaf and tiller data

description: Graminoid data include number of tillers, tiller weight, leaf area and specific leaf measurements of four species of graminoids harvested from arctic tundra over two years.
DOI: 10.6084/m9.figshare.3146689.v12
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [Data Record 5: Predicted CRM regions]

description: <b>DESCRIPTION</b><br>Neuronal and non-neuronal cis-regulatory modules in tissues ACC and CA1 were predicted, together with their activity, using a discriminative random forest classifier RFECS that was trained on a published set of positive and negative transcription factor binding sites and histone post-translational modifications.<br><br><b>DATA REPOSITORIES<br></b>EPIGENOME BROWSER (https://memory-epigenome-browser.dzne.de/)<br>Contains the visualisation tracks for the CRMs (i.e. positions of CRMs) under category "ENHANCER-DATA". Sample names include cell-type (NonNeuron or Neuron) and tissue (ACC or CA1) separated by dashes. GFF files can also be downloaded for local visualisation.<br>Figshare<br>The tables here contain all predicted cis-regulatory modules for each cell-type and tissue combination, where each table includes the CRM coordinates and the RFECS probabilities for each CRM.<br><br><b>DATA GENERATION</b><br>The binary file containing the RFECS trained forest used for the CRM predictions can be found in Figshare (https://dx.doi.org/10.6084/m9.figshare.3153406), and a clarification on how to run the analysis can be found in the reference mentioned below.<br><br>
DOI: 10.6084/m9.figshare.3146692.v13
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [Data Record 6: DHPTMs]

description: <b>DESCRIPTION</b><br>This data record contains tables of all differential histone post-translational modifications (DHPTMs), where each table contains different compared conditions for time point 1h, tissue CA1, and each cell type (Neu or Non) and histone mark (H3K27AC, H3K27ME3, H3K4ME1, H3K4ME3, H3K79ME3 or H3K9AC). Each mark is analysed for different DHPTM region set, where H3K27ac and H3K4me1 are analysed for peak regions, H3K27me3 and H3K79me3 are analysed for gene bodies and H3K4me3 and H3K9ac are analysed for TSS regions [-500,+1000] bases around the TSS. The compared conditions concern the different sets of mouse, N-C compares naïve (N) to context (C) mice, N-CS compares naive (N) to context shock (CS) mice, and C-CS compares context (C) to context shock (CS) mice.<br><br><b>TABLES</b><br>The table contains columns with HPTM coordinates and differential occupation statistics. Columns “chromosome”, “DHPTM start”, “DHPTM end” and “Strand” contain the position in the genome where the DHPTM was detected (mm10 assembly), where peak-based tests (marks H3K27ac and H3K4me1) were run as strand-independent, so for such tests column “Strand” contains “.” for all entries. Column “baseMean” shows the average normalised read counts in each DHPTM. Column “logFC” shows the fold change of the comparison between the conditions in log 2. Columns “pvalue” and “p-adjust” contain the pvalue and the adjusted p-value according to the FDR test.<br><br><b>DATA GENERATION</b><br>The code used to identify DHPTMs can be found in Figshare (https://dx.doi.org/10.6084/m9.figshare.3153400).<br><br>
DOI: 10.6084/m9.figshare.3146590.v2
title: Arctic graminoid leaf and tiller data

description: Graminoid data include number of tillers, tiller weight, leaf area and specific leaf measurements of four species of graminoids harvested from arctic tundra over two years.
DOI: 10.6084/m9.figshare.3146689.v14
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [Data Record 5: Predicted CRM regions]

description: <b>DESCRIPTION</b><br>Neuronal and non-neuronal cis-regulatory modules in tissues ACC and CA1 were predicted, together with their activity, using a discriminative random forest classifier RFECS that was trained on a published set of positive and negative transcription factor binding sites and histone post-translational modifications.<br><br><b>DATA REPOSITORIES<br></b>EPIGENOME BROWSER (https://memory-epigenome-browser.dzne.de/)<br>Contains the visualisation tracks for the CRMs (i.e. positions of CRMs) under category "ENHANCER-DATA". Sample names include cell-type (NonNeuron or Neuron) and tissue (ACC or CA1) separated by dashes. GFF files can also be downloaded for local visualisation.<br>Figshare<br>The tables here contain all predicted cis-regulatory modules for each cell-type and tissue combination, where each table includes the CRM coordinates and the RFECS probabilities for each CRM.<br><br><b>DATA GENERATION</b><br>The binary file containing the RFECS trained forest used for the CRM predictions can be found in Figshare (https://dx.doi.org/10.6084/m9.figshare.3153406), and a clarification on how to run the analysis can be found in the reference mentioned below.<br><br>
DOI: 10.6084/m9.figshare.3146692.v15
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [Data Record 6: DHPTMs]

description: <b>DESCRIPTION</b><br>This data record contains tables of all differential histone post-translational modifications (DHPTMs), where each table contains different compared conditions for time point 1h, tissue CA1, and each cell type (Neu or Non) and histone mark (H3K27AC, H3K27ME3, H3K4ME1, H3K4ME3, H3K79ME3 or H3K9AC). Each mark is analysed for different DHPTM region set, where H3K27ac and H3K4me1 are analysed for peak regions, H3K27me3 and H3K79me3 are analysed for gene bodies and H3K4me3 and H3K9ac are analysed for TSS regions [-500,+1000] bases around the TSS. The compared conditions concern the different sets of mouse, N-C compares naïve (N) to context (C) mice, N-CS compares naive (N) to context shock (CS) mice, and C-CS compares context (C) to context shock (CS) mice.<br><br><b>TABLES</b><br>The table contains columns with HPTM coordinates and differential occupation statistics. Columns “chromosome”, “DHPTM start”, “DHPTM end” and “Strand” contain the position in the genome where the DHPTM was detected (mm10 assembly), where peak-based tests (marks H3K27ac and H3K4me1) were run as strand-independent, so for such tests column “Strand” contains “.” for all entries. Column “baseMean” shows the average normalised read counts in each DHPTM. Column “logFC” shows the fold change of the comparison between the conditions in log 2. Columns “pvalue” and “p-adjust” contain the pvalue and the adjusted p-value according to the FDR test.<br><br><b>DATA GENERATION</b><br>The code used to identify DHPTMs can be found in Figshare (https://dx.doi.org/10.6084/m9.figshare.3153400).<br><br>
DOI: 10.6084/m9.figshare.3146587.v1
title: mpg_metagenome_sample_metadata_050614.xlsx

description: Samples chosen for metagenomic sequencing from the MPG Ranch invasive species study: http://dx.doi.org/10.6084/m9.figshare.1504117
DOI: 10.6084/m9.figshare.3146590.v1
title: Graminoid data.xlsx

description: Graminoid data include number of tillers, tiller weight, leaf area and specific leaf measurements of four species of graminoids harvested from arctic tundra over two years.
DOI: 10.6084/m9.figshare.3146689.v13
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [Data Record 7: Predicted CRM regions]

description: <b>DESCRIPTION</b><br>Neuronal and non-neuronal cis-regulatory modules in tissues ACC and CA1 were predicted, together with their activity, using a discriminative random forest classifier RFECS that was trained on a published set of positive and negative transcription factor binding sites and histone post-translational modifications.<br><br><b>DATA REPOSITORIES<br></b>EPIGENOME BROWSER (https://memory-epigenome-browser.dzne.de/)<br>Contains the visualisation tracks for the CRMs (i.e. positions of CRMs) under category "ENHANCER-DATA". Sample names include cell-type (NonNeuron or Neuron) and tissue (ACC or CA1) separated by dashes. GFF files can also be downloaded for local visualisation.<br>Figshare<br>The tables here contain all predicted cis-regulatory modules for each cell-type and tissue combination, where each table includes the CRM coordinates and the RFECS probabilities for each CRM.<br><br><b>DATA GENERATION</b><br>The binary file containing the RFECS trained forest used for the CRM predictions can be found in Figshare (https://dx.doi.org/10.6084/m9.figshare.3153406), and a clarification on how to run the analysis can be found in the reference mentioned below.<br><br>
DOI: 10.6084/m9.figshare.3146692.v14
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [Data Record 8: DHPTMs]

description: <b>DESCRIPTION</b><br>This data record contains tables of all differential histone post-translational modifications (DHPTMs), where each table contains different compared conditions for time point 1h, tissue CA1, and each cell type (Neu or Non) and histone mark (H3K27AC, H3K27ME3, H3K4ME1, H3K4ME3, H3K79ME3 or H3K9AC). Each mark is analysed for different DHPTM region set, where H3K27ac and H3K4me1 are analysed for peak regions, H3K27me3 and H3K79me3 are analysed for gene bodies and H3K4me3 and H3K9ac are analysed for TSS regions [-500,+1000] bases around the TSS. The compared conditions concern the different sets of mouse, N-C compares naïve (N) to context (C) mice, N-CS compares naive (N) to context shock (CS) mice, and C-CS compares context (C) to context shock (CS) mice.<br><br><b>TABLES</b><br>The table contains columns with HPTM coordinates and differential occupation statistics. Columns “chromosome”, “DHPTM start”, “DHPTM end” and “Strand” contain the position in the genome where the DHPTM was detected (mm10 assembly), where peak-based tests (marks H3K27ac and H3K4me1) were run as strand-independent, so for such tests column “Strand” contains “.” for all entries. Column “baseMean” shows the average normalised read counts in each DHPTM. Column “logFC” shows the fold change of the comparison between the conditions in log 2. Columns “pvalue” and “p-adjust” contain the pvalue and the adjusted p-value according to the FDR test.<br><br><b>DATA GENERATION</b><br>The code used to identify DHPTMs can be found in Figshare (https://dx.doi.org/10.6084/m9.figshare.3153400).<br><br>
DOI: 10.6084/m9.figshare.3146416
title: Candidate proteomic biomarkers for non-alcoholic fatty liver disease (steatosis and non-alcoholic steatohepatitis) discovered with mass-spectrometry: a systematic review

description: <i>Context</i>: Non-alcoholic fatty liver disease (NAFLD) is characterized by lipid accumulation in the liver which is accompanied by a series of metabolic deregulations. There are sustained research efforts focusing upon biomarker discovery for NAFLD diagnosis and its prognosis in order investigate and follow-up patients as minimally invasive as possible. <i>Objective</i>: The objective of this study is to critically review proteomic studies that used mass spectrometry techniques and summarize relevant proteomic NAFLD candidate biomarkers. <i>Methods</i>: Medline and Embase databases were searched from inception to December 2014. <i>Results</i>: A final number of 22 records were included that identified 251 candidate proteomic biomarkers. Thirty-three biomarkers were confirmed – 14 were found in liver samples, 21 in serum samples, and two from both serum and liver samples. <i>Conclusion</i>: Some of the biomarkers identified have already been extensively studied regarding their diagnostic and prognostic capacity. However, there are also more potential biomarkers that still need to be addressed in future studies.
DOI: 10.6084/m9.figshare.3146434
title: opinion_timestamps.csv.big

description: timestamps of courtlistener opinions
DOI: 10.6084/m9.figshare.3146410
title: Biomarkers of oxidative-stress and inflammation in exhaled breath condensate from hospital cleaners

description: <i>Objective</i>: We studied the impact of chlorinated agents exposure on exhaled breath condensate (EBC) biomarkers in cleaners. <i>Methods</i>: Malondialdehyde (MDA), 4-hydroxynonenal (4-HNE), nitrites (), nitrates (), pH, hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) and ammonium () were tested in EBC of 40 cleaners and 40 non-exposed controls. Pentraxin-3 (PTX3) and soluble type II receptor of IL-1 (sIL-1RII) were analyzed also in plasma. <i>Results</i>: Levels of MDA-EBC, 4-HNE-EBC and -EBC were higher, while pH-EBC values were lower, in cleaners. MDA-EBC was associated with 4-HNE-EBC, -EBC and pH. 4-HNE-EBC correlated with PTX3. <i>Conclusion</i>: Professional exposure to chlorinated agents increases EBC biomarkers of oxidative stress and inflammation.
DOI: 10.6084/m9.figshare.3146410.v1
title: Biomarkers of oxidative-stress and inflammation in exhaled breath condensate from hospital cleaners

description: <i>Objective</i>: We studied the impact of chlorinated agents exposure on exhaled breath condensate (EBC) biomarkers in cleaners. <i>Methods</i>: Malondialdehyde (MDA), 4-hydroxynonenal (4-HNE), nitrites (), nitrates (), pH, hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) and ammonium () were tested in EBC of 40 cleaners and 40 non-exposed controls. Pentraxin-3 (PTX3) and soluble type II receptor of IL-1 (sIL-1RII) were analyzed also in plasma. <i>Results</i>: Levels of MDA-EBC, 4-HNE-EBC and -EBC were higher, while pH-EBC values were lower, in cleaners. MDA-EBC was associated with 4-HNE-EBC, -EBC and pH. 4-HNE-EBC correlated with PTX3. <i>Conclusion</i>: Professional exposure to chlorinated agents increases EBC biomarkers of oxidative stress and inflammation.
DOI: 10.6084/m9.figshare.3146416.v1
title: Candidate proteomic biomarkers for non-alcoholic fatty liver disease (steatosis and non-alcoholic steatohepatitis) discovered with mass-spectrometry: a systematic review

description: <i>Context</i>: Non-alcoholic fatty liver disease (NAFLD) is characterized by lipid accumulation in the liver which is accompanied by a series of metabolic deregulations. There are sustained research efforts focusing upon biomarker discovery for NAFLD diagnosis and its prognosis in order investigate and follow-up patients as minimally invasive as possible. <i>Objective</i>: The objective of this study is to critically review proteomic studies that used mass spectrometry techniques and summarize relevant proteomic NAFLD candidate biomarkers. <i>Methods</i>: Medline and Embase databases were searched from inception to December 2014. <i>Results</i>: A final number of 22 records were included that identified 251 candidate proteomic biomarkers. Thirty-three biomarkers were confirmed – 14 were found in liver samples, 21 in serum samples, and two from both serum and liver samples. <i>Conclusion</i>: Some of the biomarkers identified have already been extensively studied regarding their diagnostic and prognostic capacity. However, there are also more potential biomarkers that still need to be addressed in future studies.
DOI: 10.6084/m9.figshare.3146584.v1
title: 14 SB - e-luk.co.uk.xlsx

description: 14 SB - e-luk.co.uk.xlsx
DOI: 10.6084/m9.figshare.3146401
title: crydata_20160402.xlsx

description: cryptosporidium infeciton
DOI: 10.6084/m9.figshare.3146434.v2
title: opinion_timestamps.csv.big

description: timestamps of courtlistener opinions
DOI: 10.6084/m9.figshare.3146398
title: Light Field Imaging Based Accurate Image Specular Highlight Removal

description: This is the dataset for paper: Light Field Imaging Based Accurate Image Specular Highlight Removal
DOI: 10.6084/m9.figshare.3146401.v1
title: crydata_20160402.xlsx

description: cryptosporidium infeciton
DOI: 10.6084/m9.figshare.3146404
title: Meals on wheels? A decade of megafaunal visual and acoustic observations from offshore oil & gas rigs and platforms in the North and Irish Seas

description: Marine Mammal Observer (MMO )and Passive Acoustic Monitoring (PAM) data of marine mammal sightings around offshore oil and gas platforms<br>
DOI: 10.6084/m9.figshare.3146434.v1
title: opinion_timestamps.csv.big

description: timestamps of courtlistener opinions
DOI: 10.6084/m9.figshare.3146380
title: Black Pete through the eyes of Dutch children.sav

description: SPSS dataset and syntax of the project 'Black Pete through the eyes of Dutch children'.
DOI: 10.6084/m9.figshare.3146317
title: Area of Occupancy and Extent of Occurrence of Qualea insignis (Vochysiaceae), using GeoCAT

description: .kml file of <i>Qualea insignis</i> (Vochysiaceae) distribution data, using GeoCAT (http://geocat.kew.org/).<br>
DOI: 10.6084/m9.figshare.3146380.v1
title: Black Pete through the eyes of Dutch children.sav

description: SPSS dataset and syntax of the project 'Black Pete through the eyes of Dutch children'.
DOI: 10.6084/m9.figshare.3146398.v1
title: light_field_dataset.rar

description: This is the dataset for paper: Light Field Imaging Based Accurate Image Specular Highlight Removal
DOI: 10.6084/m9.figshare.3146404.v1
title: PONE-D-15-45387R2 Data

description: Marine Mammal Observer (MMO )and Passive Acoustic Monitoring (PAM) data of marine mammal sightings around offshore oil and gas platforms<br>