cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5522/04/12073611
title: Non-Invasive MRI of Blood-Cerebrospinal Fluid Barrier Function; Modulating Labelling Efficiency

description: This is the experiments where labelling efficiency was modulated data set from the work 'Non-invasive MRI of Blood-Cerebrospinal Fluid Barrier Function' (Evans et al., 2020, Figure 1F &G].<br>The experiments were designed to ensure there was negligible intra-vascular signal contributions from the BCSFB-ASL signal.<br>The data is in Matlab format. The BCSFB-ASL data is in a 4D array where the first two array elements are the 2D MRI images, the 3rd element is the interleaved tagged and control images (inner most loop) together with the B0 and diffusion weighted images (b=200 s/mm2) together with the number of repetitions (outer most loop, 20 repetitions) and the 4th element is the number of inflow times (750, 2750 and 6500 ms).<br>The traditional ASL data is in the same format but there is only 1 inflow time (750ms) and 5 rather then 20
DOI: 10.5522/04/12073611.v1
title: Non-Invasive MRI of Blood-Cerebrospinal Fluid Barrier Function; Modulating Labelling Efficiency

description: This is the experiments where labelling efficiency was modulated data set from the work 'Non-invasive MRI of Blood-Cerebrospinal Fluid Barrier Function' (Evans et al., 2020, Figure 1F &G].<br>The experiments were designed to ensure there was negligible intra-vascular signal contributions from the BCSFB-ASL signal.<br>The data is in Matlab format. The BCSFB-ASL data is in a 4D array where the first two array elements are the 2D MRI images, the 3rd element is the interleaved tagged and control images (inner most loop) together with the B0 and diffusion weighted images (b=200 s/mm2) together with the number of repetitions (outer most loop, 20 repetitions) and the 4th element is the number of inflow times (750, 2750 and 6500 ms).<br>The traditional ASL data is in the same format but there is only 1 inflow time (750ms) and 5 rather then 20
DOI: 10.6084/m9.figshare.12073596
title: South African Hospital Beds

description: These files were created from https://figshare.com/articles/SURGICAL_RESOURCES_latestmarch2016_xlsx/12066711. Hospital capacity information were extracted and tidied up. The readme explains the steps taken to go from the original data to the tidy data.<br># Note: Readme was updated to be more descriptive and in cleaner format on 3 April 2020<br>
DOI: 10.6084/m9.figshare.12073659.v1
title: Supplementary Material for: Continuous Ketamine Infusion for Pain as an Opportunity for Psychotherapy for PTSD: A Case Series of Ketamine-Enhanced Psychotherapy for PTSD and Pain (KEP-P2)

description: Medicine
DOI: 10.6084/m9.figshare.12073566
title: data.rar

description: Drug delivery in porcine skin10 times measurementeach contains 5 groups
DOI: 10.6084/m9.figshare.12073659.v3
title: Supplementary Material for: Continuous Ketamine Infusion for Pain as an Opportunity for Psychotherapy for PTSD: A Case Series of Ketamine-Enhanced Psychotherapy for PTSD and Pain (KEP-P2)

description: Medicine
DOI: 10.6084/m9.figshare.12073566.v1
title: data.rar

description: Drug delivery in porcine skin10 times measurementeach contains 5 groups
DOI: 10.6084/m9.figshare.12073659.v2
title: Supplementary Material for: Continuous Ketamine Infusion for Pain as an Opportunity for Psychotherapy for PTSD: A Case Series of Ketamine-Enhanced Psychotherapy for PTSD and Pain (KEP-P2)

description: Medicine
DOI: 10.6084/m9.figshare.12073500

description: See README.md
DOI: 10.25392/leicester.data.12073494
title: South Africa: Digital Stories.

description: This dataset contains 14 digital stories created by youth involved in the RYSE Project in South Africa. These stories reflect the young people’s perspectives and desires.<br>The digital stories are unedited. They were made by South African RYSE participants using tablets. The stories reflect participants' understanding of risk and resilience.
DOI: 10.6084/m9.figshare.12073500.v1
title: Gene annotation processing

description: See README.md
DOI: 10.6084/m9.figshare.12863567.v1
title: b6_y129_dec5_mm10.bw

description: descc
DOI: 10.25392/leicester.data.12073494.v1
title: South Africa: Digital Stories.

description: This dataset contains 14 digital stories created by youth involved in the RYSE Project in South Africa. These stories reflect the young people’s perspectives and desires.<br>The digital stories are unedited. They were made by South African RYSE participants using tablets. The stories reflect participants' understanding of risk and resilience.
DOI: 10.6084/m9.figshare.12863567
title: b6_y129_dec5_mm10.bw

description: descc
DOI: 10.6084/m9.figshare.12073437
title: Epigenetics_processing.zip

description: See README.md
DOI: 10.6084/m9.figshare.12073446

description: See README.md
DOI: 10.6084/m9.figshare.12073446.v1
title: Features.zip

description: See README.md
DOI: 10.6084/m9.figshare.12073431
title: Supplementary Material for: HT-29 and Caco2 Cell Lines Are Suitable Models for Studying the Role of Arachidonic Acid-Metabolizing Enzymes in Intestinal Cell Differentiation

description: <b><i>Introduction:</i></b> Cytochrome (CYP) epoxygenases (CYP2C and CYP2J) and soluble epoxide hydrolase (sEH) participate in the metabolism of arachidonic acid and may also have a potential role in enterocyte differentiation. The first critical step in the study of intestinal cell differentiation is the determination of a suitable in vitro model, which must be as similar as possible to the conditions of a living organism. It is known that HT-29 and Caco2 cell lines derived from human colorectal carcinomas can differentiate into enterocyte-like cells in appropriate culture conditions. <b><i>Material and Methods:</i></b> We tested 4 different approaches of enterocyte-like differentiation and determined the most appropriate culture conditions for each model. Subsequently, the changes in the expression of CYP epoxygenases and sEH in undifferentiated and differentiated cells were measured by In-Cell ELISA. These results were compared with immunohistochemical profiles of expression of CYP epoxygenases and sEH in samples of human embryonic and fetal intestines as well as adult duodenum and colon. <b><i>Results:</i></b> Our results show that sodium butyrate (NaBt)-differentiated HT-29 cells and spontaneously differentiated Caco2 cells resemble CYP epoxygenases and sEH profiles, corresponding with different types of intestines. <b><i>Conclusion:</i></b> Our study revealed that the most suitable models for the study of the role of CYP epoxygenases and sEH expression in differentiation of intestinal epithelium are NaBt-differentiated HT-29 cells and spontaneously differentiated Caco2 cells.
DOI: 10.6084/m9.figshare.12073431.v1
title: Supplementary Material for: HT-29 and Caco2 Cell Lines Are Suitable Models for Studying the Role of Arachidonic Acid-Metabolizing Enzymes in Intestinal Cell Differentiation

description: <b><i>Introduction:</i></b> Cytochrome (CYP) epoxygenases (CYP2C and CYP2J) and soluble epoxide hydrolase (sEH) participate in the metabolism of arachidonic acid and may also have a potential role in enterocyte differentiation. The first critical step in the study of intestinal cell differentiation is the determination of a suitable in vitro model, which must be as similar as possible to the conditions of a living organism. It is known that HT-29 and Caco2 cell lines derived from human colorectal carcinomas can differentiate into enterocyte-like cells in appropriate culture conditions. <b><i>Material and Methods:</i></b> We tested 4 different approaches of enterocyte-like differentiation and determined the most appropriate culture conditions for each model. Subsequently, the changes in the expression of CYP epoxygenases and sEH in undifferentiated and differentiated cells were measured by In-Cell ELISA. These results were compared with immunohistochemical profiles of expression of CYP epoxygenases and sEH in samples of human embryonic and fetal intestines as well as adult duodenum and colon. <b><i>Results:</i></b> Our results show that sodium butyrate (NaBt)-differentiated HT-29 cells and spontaneously differentiated Caco2 cells resemble CYP epoxygenases and sEH profiles, corresponding with different types of intestines. <b><i>Conclusion:</i></b> Our study revealed that the most suitable models for the study of the role of CYP epoxygenases and sEH expression in differentiation of intestinal epithelium are NaBt-differentiated HT-29 cells and spontaneously differentiated Caco2 cells.
DOI: 10.6084/m9.figshare.12073353

description: See README.md
DOI: 10.6084/m9.figshare.12073356

description: See README.md
DOI: 10.6084/m9.figshare.12073329
title: Test NWB data

description: Test data in NWB format<br>
DOI: 10.6084/m9.figshare.12073353.v1
title: Function_processing.zip

description: See README.md
DOI: 10.6084/m9.figshare.12073356.v1
title: Phenotype_processing.zip

description: See README.md
DOI: 10.6084/m9.figshare.12073302

description: See README.md
DOI: 10.6084/m9.figshare.12073314
title: Effects of hyperoxia on dynamic muscular endurance are associated with individual whole-body endurance capacity

description: Low-intensity training involving high repetitions is recommended to enhance muscular endurance. Hyperoxic conditions could increase the number of repetitions until exhaustion and thereby improve the results of muscular endurance training. This study aimed to investigate the acute effects of hyperoxia on dynamic muscular endurance, and determine individual factors that may be related to these effects. A single-blinded, counterbalanced crossover design was used. Twenty-five young men performed repetitions of the one-arm preacher curl at 30% of their 1-repetition maximum until exhaustion under hyperoxic and normoxic conditions. The maximum number of repetitions was recorded as an index of muscular endurance. Electromyogram (EMG) and near-infrared spectroscopy parameters were measured in the biceps brachii. The maximum number of repetitions was greater under hyperoxic conditions than under normoxic conditions. The root mean square amplitude of EMG and oxygenated hemoglobin concentration for the last five repetitions under normoxic conditions were greater than those under hyperoxic conditions. The percent change in the maximum number of repetitions between hyperoxic and normoxic conditions had significant positive correlations with individual maximal oxygen uptake measured using an incremental cycle ergometer test, but not with muscular endurance or muscle strength. The results suggest that hyperoxia can acutely enhance dynamic muscular endurance, with delayed elevation of EMG amplitude due to fatigue, and the effects are associated with individual whole-body endurance capacity.
DOI: 10.6084/m9.figshare.12073302.v1
title: Genomics_processing.zip

description: See README.md
DOI: 10.6084/m9.figshare.12073314.v1
title: Effects of hyperoxia on dynamic muscular endurance are associated with individual whole-body endurance capacity

description: Low-intensity training involving high repetitions is recommended to enhance muscular endurance. Hyperoxic conditions could increase the number of repetitions until exhaustion and thereby improve the results of muscular endurance training. This study aimed to investigate the acute effects of hyperoxia on dynamic muscular endurance, and determine individual factors that may be related to these effects. A single-blinded, counterbalanced crossover design was used. Twenty-five young men performed repetitions of the one-arm preacher curl at 30% of their 1-repetition maximum until exhaustion under hyperoxic and normoxic conditions. The maximum number of repetitions was recorded as an index of muscular endurance. Electromyogram (EMG) and near-infrared spectroscopy parameters were measured in the biceps brachii. The maximum number of repetitions was greater under hyperoxic conditions than under normoxic conditions. The root mean square amplitude of EMG and oxygenated hemoglobin concentration for the last five repetitions under normoxic conditions were greater than those under hyperoxic conditions. The percent change in the maximum number of repetitions between hyperoxic and normoxic conditions had significant positive correlations with individual maximal oxygen uptake measured using an incremental cycle ergometer test, but not with muscular endurance or muscle strength. The results suggest that hyperoxia can acutely enhance dynamic muscular endurance, with delayed elevation of EMG amplitude due to fatigue, and the effects are associated with individual whole-body endurance capacity.
DOI: 10.6084/m9.figshare.12073233.v1
title: Mutation_processing.zip

description: See README.md
DOI: 10.6084/m9.figshare.12073278.v1
title: A satellite-derived database for stand-replacing windthrows in boreal forests of the European Russia in 1986–2017

description: The GIS database contains the data of stand-replacing windthrows in the forest zone of the European Russia (ER) for the 1986–2017 period, and the ArcGIS 10.1/10.2 and Qgis 3* projects to view and analyze the data. The database contains three shapefiles (.shp), corresponding to three hierarchical levels of the data: • “Elementary damaged area”, that is a single-part polygon of wind-damaged forest; • “Windthrow”, that represent a group of closely spaced forest disturbances, i.e., a multipart polygon, associated with one storm event; • “Storm event track”, that is a cluster of windthrows with identical direction and having the same date (or date range) of occurrence, which was most likely induced by one convective or non-convective storm. The key fields ID and storm_ID associates each damaged area with the features in the datasets of windthrows and storm event tracks respectively using one-to-many relation. A single value “‒9999” was used for storing NoData in all fields of the attribute table. All data and projects have WGS 1984 coordinate system (without projection). Delineation of windthrows was based on the full archive of Landsat images and two Landsat-derived products on forest cover change, namely the Global Forest Change and the Eastern’ Europe Forest Cover Change. Subsequent verification and analysis of each windthrow was carried out to determine a type of storm events with a degree of event certainty, dates (date ranges) and time (time ranges) of an event, windthrow geometrical characteristics. The database contains 102,747 elementary areas of damaged forest that were combined into 700 windthrows caused by 486 convective or non-convective storm events. The database includes stand-replacing windthrows only, which an area &gt; 5 ha or &gt; 25 ha for events caused by tornadoes or other storms, respectively. Additional information contained weather station reports and event description from media sources is also provided.. The compiled database provides a valuable source of spatial and temporal information on windthrows in the ER and can be successfully used both in forestry and severe storm studies.
DOI: 10.6084/m9.figshare.12073281.v1
title: Untitled Item

description: Data of CSHRS documents with topic modeling and sentiment analysis results
DOI: 10.6084/m9.figshare.12073278.v2
title: A satellite-derived database for stand-replacing windthrows in boreal forests of the European Russia in 1986–2017

description: The GIS database contains the data of stand-replacing windthrows in the forest zone of the European Russia (ER) for the 1986–2017 period, and the ArcGIS 10.1/10.2 and Qgis 3* projects to view and analyze the data. The database contains three shapefiles (.shp), corresponding to three hierarchical levels of the data: • “Elementary damaged area”, that is a single-part polygon of wind-damaged forest; • “Windthrow”, that represent a group of closely spaced forest disturbances, i.e., a multipart polygon, associated with one storm event; • “Storm event track”, that is a cluster of windthrows with identical direction and having the same date (or date range) of occurrence, which was most likely induced by one convective or non-convective storm. The key fields ID and storm_ID associates each damaged area with the features in the datasets of windthrows and storm event tracks respectively using one-to-many relation. A single value “‒9999” was used for storing NoData in all fields of the attribute table. All data and projects have WGS 1984 coordinate system (without projection). Delineation of windthrows was based on the full archive of Landsat images and two Landsat-derived products on forest cover change, namely the Global Forest Change and the Eastern’ Europe Forest Cover Change. Subsequent verification and analysis of each windthrow was carried out to determine a type of storm events with a degree of event certainty, dates (date ranges) and time (time ranges) of an event, windthrow geometrical characteristics. The database contains 102,747 elementary areas of damaged forest that were combined into 700 windthrows caused by 486 convective or non-convective storm events. The database includes stand-replacing windthrows only, which an area &gt; 5 ha or &gt; 25 ha for events caused by tornadoes or other storms, respectively. Additional information contained weather station reports and event description from media sources is also provided.. The compiled database provides a valuable source of spatial and temporal information on windthrows in the ER and can be successfully used both in forestry and severe storm studies.
DOI: 10.6084/m9.figshare.12073140

description: # Codes used for Gene annotation<br> Steps Dependency: Java<br>1. Ensembl synonyms to gene symbol```javac step1_ensembl_synonyms_to_symbol.javajava step1_ensembl_synonyms_to_symbol```<br> Input: external_synonym.txt #downloaded from ensembl-annotation Input: xref.txt #Downloaded from The Ensembl Xref System Output: ensembl_synonymous_to_symbol.txt<br><br>2. Genes annotated by COSMIC annotation```javac step2_gene_merge.javajava step2_gene_merge```Input: H3K4me3_width.all_in_one.xlsInput: TUSON_all_genelist.txtOutput: compiled_genelist.txt #(Gene list compiled from TUSON and Kaifu Chen's Paper)<br>3. Gene name list generation```javac step3_gene_name_generation.javajava step3_gene_name_generation``` Input: Gene_set_new.txt<br> Input: TUSON_input_new_sorted.txt # From COSMIC gene list Output: compiled_genelist_genename.txt # (Gene symbol list consistent with Gene_set_new.txt) Output: gene_set_sorted.txt #(Gene symbol list consistent with mutation with same sequence)<br>4. Gene promoter and genebody BED file generation```javac Step4_gene_promoter_genebody_BED_generation.javajava Step4_gene_promoter_genebody_BED_generation``` Input: compiled_genelist_genename.txt #(Gene symbol list consistent with Gene_set_new.txt) Input: ensembl_synonymous_to_symbol.txt #(Gene alias conversion to HGNC gene names) Input: gencode.v25lift37.annotation.gtf #(GENCODE annotation) Input: gencode.v19.annotation.gtf #(GENCODE annotation)<br> Output: compiled_genelist_promoter_hg19.bed #(Gene promoters defined as [-500, 1000] around TSSs)<br> Output: compiled_genelist_promoter_250_hg19.bed #(Gene promoters defined as [-500, 250] around TSSs)<br> Output: compiled_genelist_genebody_hg19.bed #(Gene promoters defined as 500 downstream of TSSs towards to TTSs)<br><br>5. Gene full-length BED file generation```javac Step5_gene_fulllength_BED_generation.javajava Step5_gene_fulllength_BED_generation```<br> Input: Gene_set_new.txt #(Gene annotation information used by this study) Input: compiled_genelist_genename.txt #(Gene symbol list consistent with Gene_set_new.txt) Input: ensembl_synonymous_to_symbol.txt #(Gene alias conversion to HGNC gene names) Input: gencode.v25lift37.annotation.gtf #(Gencode v25 hg19 GTF annotation) Input: gencode.v19.annotation.gtf #(Gencode v19 hg19 GTF annotation)<br> Output: compiled_gene_pos_hg19.bed<br>6. Gene name and alias mapping file generation```javac Step6_COSMIC_to_Gene_Symbol.javajava Step6_COSMIC_to_Gene_Symbol``` Input: compiled_genelist_genename.txt #(Gene symbol list consistent with Gene_set_new.txt) Input: CosmicTranscripts.tsv #(Transcript anntation file downloaded from COSMIC website) Input: Ensembl_TransID_v87_GeneSymbol.txt #(Annotation file: Gene Symbol to Ensembl IDs for some outdated genes) Input: Ensembl_TransID_v92_GeneSymbol.txt #(Annotation file: Gene Symbol to Ensembl IDs for some outdated genes) Output: Cosmic2Gene_name.txt #(Gene name and alias mapping file)
DOI: 10.6084/m9.figshare.12073140.v1
title: Gene_annotation.zip

description: # Codes used for Gene annotation<br> Steps Dependency: Java<br>1. Ensembl synonyms to gene symbol```javac step1_ensembl_synonyms_to_symbol.javajava step1_ensembl_synonyms_to_symbol```<br> Input: external_synonym.txt #downloaded from ensembl-annotation Input: xref.txt #Downloaded from The Ensembl Xref System Output: ensembl_synonymous_to_symbol.txt<br><br>2. Genes annotated by COSMIC annotation```javac step2_gene_merge.javajava step2_gene_merge```Input: H3K4me3_width.all_in_one.xlsInput: TUSON_all_genelist.txtOutput: compiled_genelist.txt #(Gene list compiled from TUSON and Kaifu Chen's Paper)<br>3. Gene name list generation```javac step3_gene_name_generation.javajava step3_gene_name_generation``` Input: Gene_set_new.txt<br> Input: TUSON_input_new_sorted.txt # From COSMIC gene list Output: compiled_genelist_genename.txt # (Gene symbol list consistent with Gene_set_new.txt) Output: gene_set_sorted.txt #(Gene symbol list consistent with mutation with same sequence)<br>4. Gene promoter and genebody BED file generation```javac Step4_gene_promoter_genebody_BED_generation.javajava Step4_gene_promoter_genebody_BED_generation``` Input: compiled_genelist_genename.txt #(Gene symbol list consistent with Gene_set_new.txt) Input: ensembl_synonymous_to_symbol.txt #(Gene alias conversion to HGNC gene names) Input: gencode.v25lift37.annotation.gtf #(GENCODE annotation) Input: gencode.v19.annotation.gtf #(GENCODE annotation)<br> Output: compiled_genelist_promoter_hg19.bed #(Gene promoters defined as [-500, 1000] around TSSs)<br> Output: compiled_genelist_promoter_250_hg19.bed #(Gene promoters defined as [-500, 250] around TSSs)<br> Output: compiled_genelist_genebody_hg19.bed #(Gene promoters defined as 500 downstream of TSSs towards to TTSs)<br><br>5. Gene full-length BED file generation```javac Step5_gene_fulllength_BED_generation.javajava Step5_gene_fulllength_BED_generation```<br> Input: Gene_set_new.txt #(Gene annotation information used by this study) Input: compiled_genelist_genename.txt #(Gene symbol list consistent with Gene_set_new.txt) Input: ensembl_synonymous_to_symbol.txt #(Gene alias conversion to HGNC gene names) Input: gencode.v25lift37.annotation.gtf #(Gencode v25 hg19 GTF annotation) Input: gencode.v19.annotation.gtf #(Gencode v19 hg19 GTF annotation)<br> Output: compiled_gene_pos_hg19.bed<br>6. Gene name and alias mapping file generation```javac Step6_COSMIC_to_Gene_Symbol.javajava Step6_COSMIC_to_Gene_Symbol``` Input: compiled_genelist_genename.txt #(Gene symbol list consistent with Gene_set_new.txt) Input: CosmicTranscripts.tsv #(Transcript anntation file downloaded from COSMIC website) Input: Ensembl_TransID_v87_GeneSymbol.txt #(Annotation file: Gene Symbol to Ensembl IDs for some outdated genes) Input: Ensembl_TransID_v92_GeneSymbol.txt #(Annotation file: Gene Symbol to Ensembl IDs for some outdated genes) Output: Cosmic2Gene_name.txt #(Gene name and alias mapping file)