cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11396055.v3
title: Sequences of mitochondrial contigs for Adineta vaga individuals L1-L11 v1

description: Sequences of mitochondrial contigs for <i>A. vaga </i>individuals L1-L11 extracted from whole-genome assemblies using the mitochondrial contig from the first published <i>A. vaga</i> genome assembly (Flot et al., 2013) as the query for blastn search. For L1, mitochondrial contigs were extracted from MiSeq-based L1 diploid assembly (N50=22,073 bp). For individuals L2-L11, highly fragmented assemblies (with N50 in the range 1,600-3,400 bp) were generated from HiSeq reads with SPAdes. Mitochondrial sequences for each individual, L1-L11, are in separate FASTA files.<br>
DOI: 10.6084/m9.figshare.11396055.v4
title: Sequences of mitochondrial contigs for Adineta vaga individuals L1-L11

description: Sequences of mitochondrial contigs for <i>A. vaga </i>individuals L1-L11 extracted from whole-genome assemblies using the mitochondrial contig from the first published <i>A. vaga</i> genome assembly (Flot et al., 2013) as the query for blastn search. For L1, mitochondrial contigs were extracted from MiSeq-based L1 diploid assembly (N50=22,073 bp). For individuals L2-L11, highly fragmented assemblies (with N50 in the range 1,600-3,400 bp) were generated from HiSeq reads with SPAdes. Mitochondrial sequences for each individual, L1-L11, are in separate FASTA files.<br>
DOI: 10.6084/m9.figshare.11396055.v5
title: Sequences of mitochondrial contigs for Adineta vaga individuals L1-L11 v1

description: Sequences of mitochondrial contigs for <i>A. vaga </i>individuals L1-L11 extracted from whole-genome assemblies using the mitochondrial contig from the first published <i>A. vaga</i> genome assembly (Flot et al., 2013) as the query for blastn search. For L1, mitochondrial contigs were extracted from MiSeq-based L1 diploid assembly (N50=22,073 bp). For individuals L2-L11, highly fragmented assemblies (with N50 in the range 1,600-3,400 bp) were generated from HiSeq reads with SPAdes. Mitochondrial sequences for each individual, L1-L11, are in separate FASTA files.<br>
DOI: 10.6084/m9.figshare.11394825
title: GRF_F_V_RAW_right

description: This file is structured as a matrix with N rows x M columns. Each row holds the data of one subject and trial. The first column identifies each subject "SUBJECT_ID", the second column each recording session "SESSION_ID", and the third column each single trial within a recording session "TRIAL_ID". Note that due to the non-normalized nature of the data and the resulting different vector lengths, non-available numbers have been replaced by "NaN" to maintain a constant matrix-dimension.
DOI: 10.6084/m9.figshare.11394822
title: GRF_F_V_RAW_left

description: This file is structured as a matrix with N rows x M columns. Each row holds the data of one subject and trial. The first column identifies each subject "SUBJECT_ID", the second column each recording session "SESSION_ID", and the third column each single trial within a recording session "TRIAL_ID". Note that due to the non-normalized nature of the data and the resulting different vector lengths, non-available numbers have been replaced by "NaN" to maintain a constant matrix-dimension.
DOI: 10.6084/m9.figshare.11394816
title: GRF_F_AP_PRO_left

description: This file is structured as a matrix with N rows x M columns. Each row holds the data of one subject and trial. The first column identifies each subject "SUBJECT_ID", the second column each recording session "SESSION_ID", and the third column each single trial within a recording session "TRIAL_ID".
DOI: 10.6084/m9.figshare.11394819
title: GRF_F_V_PRO_left

description: This file is structured as a matrix with N rows x M columns. Each row holds the data of one subject and trial. The first column identifies each subject "SUBJECT_ID", the second column each recording session "SESSION_ID", and the third column each single trial within a recording session "TRIAL_ID".
DOI: 10.6084/m9.figshare.11394810
title: GRF_F_ML_RAW_right

description: This file is structured as a matrix with N rows x M columns. Each row holds the data of one subject and trial. The first column identifies each subject "SUBJECT_ID", the second column each recording session "SESSION_ID", and the third column each single trial within a recording session "TRIAL_ID". Note that due to the non-normalized nature of the data and the resulting different vector lengths, non-available numbers have been replaced by "NaN" to maintain a constant matrix-dimension.
DOI: 10.6084/m9.figshare.11394813
title: GRF_F_ML_RAW_left

description: This file is structured as a matrix with N rows x M columns. Each row holds the data of one subject and trial. The first column identifies each subject "SUBJECT_ID", the second column each recording session "SESSION_ID", and the third column each single trial within a recording session "TRIAL_ID". Note that due to the non-normalized nature of the data and the resulting different vector lengths, non-available numbers have been replaced by "NaN" to maintain a constant matrix-dimension.
DOI: 10.6084/m9.figshare.11394801
title: GRF_F_ML_PRO_left

description: This file is structured as a matrix with N rows x M columns. Each row holds the data of one subject and trial. The first column identifies each subject "SUBJECT_ID", the second column each recording session "SESSION_ID", and the third column each single trial within a recording session "TRIAL_ID".
DOI: 10.6084/m9.figshare.11394804
title: GRF_F_V_PRO_right

description: This file is structured as a matrix with N rows x M columns. Each row holds the data of one subject and trial. The first column identifies each subject "SUBJECT_ID", the second column each recording session "SESSION_ID", and the third column each single trial within a recording session "TRIAL_ID".
DOI: 10.6084/m9.figshare.11394798
title: GRF_F_AP_RAW_right

description: This file is structured as a matrix with N rows x M columns. Each row holds the data of one subject and trial. The first column identifies each subject "SUBJECT_ID", the second column each recording session "SESSION_ID", and the third column each single trial within a recording session "TRIAL_ID". Note that due to the non-normalized nature of the data and the resulting different vector lengths, non-available numbers have been replaced by "NaN" to maintain a constant matrix-dimension.
DOI: 10.6084/m9.figshare.11394795
title: GRF_COP_ML_RAW_left

description: This file is structured as a matrix with N rows x M columns. Each row holds the data of one subject and trial. The first column identifies each subject "SUBJECT_ID", the second column each recording session "SESSION_ID", and the third column each single trial within a recording session "TRIAL_ID". Note that due to the non-normalized nature of the data and the resulting different vector lengths, non-available numbers have been replaced by "NaN" to maintain a constant matrix-dimension.
DOI: 10.6084/m9.figshare.11394789
title: GRF_COP_ML_RAW_right

description: This file is structured as a matrix with N rows x M columns. Each row holds the data of one subject and trial. The first column identifies each subject "SUBJECT_ID", the second column each recording session "SESSION_ID", and the third column each single trial within a recording session "TRIAL_ID". Note that due to the non-normalized nature of the data and the resulting different vector lengths, non-available numbers have been replaced by "NaN" to maintain a constant matrix-dimension.
DOI: 10.6084/m9.figshare.11394783
title: GRF_COP_ML_PRO_left

description: This file is structured as a matrix with N rows x M columns. Each row holds the data of one subject and trial. The first column identifies each subject "SUBJECT_ID", the second column each recording session "SESSION_ID", and the third column each single trial within a recording session "TRIAL_ID".
DOI: 10.6084/m9.figshare.11394786.v1
title: GRF_F_ML_PRO_right

description: This file is structured as a matrix with N rows x M columns. Each row holds the data of one subject and trial. The first column identifies each subject "SUBJECT_ID", the second column each recording session "SESSION_ID", and the third column each single trial within a recording session "TRIAL_ID".
DOI: 10.6084/m9.figshare.11394777
title: GRF_COP_AP_RAW_right

description: This file is structured as a matrix with N rows x M columns. Each row holds the data of one subject and trial. The first column identifies each subject "SUBJECT_ID", the second column each recording session "SESSION_ID", and the third column each single trial within a recording session "TRIAL_ID". Note that due to the non-normalized nature of the data and the resulting different vector lengths, non-available numbers have been replaced by "NaN" to maintain a constant matrix-dimension.
DOI: 10.6084/m9.figshare.11394774
title: GRF_F_AP_RAW_left

description: This file is structured as a matrix with N rows x M columns. Each row holds the data of one subject and trial. The first column identifies each subject "SUBJECT_ID", the second column each recording session "SESSION_ID", and the third column each single trial within a recording session "TRIAL_ID". Note that due to the non-normalized nature of the data and the resulting different vector lengths, non-available numbers have been replaced by "NaN" to maintain a constant matrix-dimension.
DOI: 10.6084/m9.figshare.11394780.v1
title: GRF_COP_ML_PRO_right

description: This file is structured as a matrix with N rows x M columns. Each row holds the data of one subject and trial. The first column identifies each subject "SUBJECT_ID", the second column each recording session "SESSION_ID", and the third column each single trial within a recording session "TRIAL_ID".
DOI: 10.6084/m9.figshare.11394771
title: GRF_COP_AP_RAW_left

description: This file is structured as a matrix with N rows x M columns. Each row holds the data of one subject and trial. The first column identifies each subject "SUBJECT_ID", the second column each recording session "SESSION_ID", and the third column each single trial within a recording session "TRIAL_ID". Note that due to the non-normalized nature of the data and the resulting different vector lengths, non-available numbers have been replaced by "NaN" to maintain a constant matrix-dimension.
DOI: 10.6084/m9.figshare.11394768
title: GRF_COP_AP_PRO_left

description: This file is structured as a matrix with N rows x M columns. Each row holds the data of one subject and trial. The first column identifies each subject "SUBJECT_ID", the second column each recording session "SESSION_ID", and the third column each single trial within a recording session "TRIAL_ID".
DOI: 10.6084/m9.figshare.11394765
title: GRF_COP_AP_PRO_right

description: This file is structured as a matrix with N rows x M columns. Each row holds the data of one subject and trial. The first column identifies each subject "SUBJECT_ID", the second column each recording session "SESSION_ID", and the third column each single trial within a recording session "TRIAL_ID".
DOI: 10.6084/m9.figshare.11954748
title: all patients data.xlsx

description: We included 71 patients with primary open-angle glaucoma and assessed their IOP measurements obtained with the GAT, NCT, and Corvis ST.
DOI: 10.6084/m9.figshare.11394696
title: Supplementary Material for: Association between Dialysis Modality and Cardiovascular Diseases: A Comparison between Peritoneal Dialysis and Hemodialysis

description: <b><i>Introduction:</i></b> In patients with end-stage kidney disease (ESKD), cardiovascular diseases (CVDs) are the most common causes of hospitalization and death. <b><i>Objective:</i></b> We aimed to determine the association between dialysis modality and CVDs. <b><i>Methods:</i></b> This retrospective observational cohort study compared the emergency hospitalization and mortality of patients with CVDs who underwent peritoneal dialysis (PD) versus hemodialysis (HD). After propensity score matching, the risk factors were evaluated using Cox proportional hazards regression models. <b><i>Results:</i></b> A total of 260 patients were matched: 130 of 135 PD (75 men; age, 65.4 years; dialysis vintage, 3.3 years) and 130 of 706 HD (70 men [<i>p</i> = 0.5]; 66.6 years [<i>p</i> = 0.4]; dialysis vintage, 3.1 years [<i>p</i> = 0.5]) patients. Emergency hospitalization rates (hospitalizations/person-years) for overall CVDs (0.138 vs. 0.066, <i>p</i> = 0.002) and pulmonary edema (0.048 vs. 0.019, <i>p</i> = 0.03) were significantly higher in patients who underwent PD than those who underwent HD. The log-rank test revealed that all-cause and CVD mortalities were significantly higher in PD (both <i>p</i> &lt; 0.001). Mortality rates (deaths/person-years) for overall CVDs (0.058 vs. 0.015, <i>p</i> &lt; 0.002), cerebrovascular disease (0.019 vs. 0.004, <i>p</i> = 0.03), and ischemic heart disease (0.010 vs. 0, <i>p</i> = 0.02) were significantly higher in PD. The Cox proportional hazards regression model showed that PD and age were significant predictors of emergency hospitalization (hazard ratio [HR] 2.70; 95% CI 1.53–4.77; <i>p</i> = 0.001) and mortality (HR 4.41; 95% CI 1.66–11.72; <i>p</i> = 0.003) for CVDs. <b><i>Conclusions:</i></b> PD is a risk factor for emergency hospitalization and mortality associated with CVDs in dialysis patients with ESKD. Strict control of body fluid balance may prevent cardiovascular events in patients undergoing PD.
DOI: 10.6084/m9.figshare.11394687
title: Napf_data

description: The folder contains the landslide inventory data (landslide.xlsx), the rainfall time series of the triggering storm (rainfall.xlsx), the DEM (dem.txt), and the map of vegetation (vegetation.txt) in the Napf catchment, Switzerland. The recorded landslide attributes include: coordinates and elevation (measured at the top of each landslide), average slope, maximum length, maximum width, and volume. The triggering rainfall storm on 15-16 July 2002 lasted for 6 hours. Rainfall intensity at each minute is recorded in the rainfall.txt file. The DEM and vegetation map are projected to the spatial reference of CH1903_LV03. A value of 0 in the vegetation.txt file represents bare soil/meadow and a value of 1 represents vegetation (shrubs and small trees).
DOI: 10.6084/m9.figshare.11394657
title: GRF_metadata

description: The metadata file contains annotations and additional subject-related information. It is structured as a matrix with N rows x M columns. Here, the first two columns hold the SUBJECT_ID and SESSION_ID, the other columns hold information such as class labels, sex, body mass, age, shod-condition, etc.. Note that this information is available in all records. Missing values are identified as „NaN“. A particularly notable field is AFFECTED_SIDE, which indicates which leg is affected by a certain impairment (e.g. left knee) or if both sides are affected.
DOI: 10.6084/m9.figshare.11394648
title: Supplementary Material for: Causes that Contribute to the Excess Mortality Risk in Multiple Sclerosis: A Population-Based Study

description: <b><i>Background:</i></b> Lifespan is 6–10 years shorter in multiple sclerosis (MS), but the reasons remain unclear. Using linked clinical- and population-based administrative health databases, we compared cause-specific mortality in an MS cohort to the general population. <b><i>Methods:</i></b> MS patients in British Columbia (BC), Canada, were followed from the later of first MS clinic visit or January 1, 1986, to the earlier of death, emigration, or December 31, 2013. Comprehensive mortality information was obtained by linkage to BC’s multiple-cause-of-death mortality data. Causes were grouped using International Classification of Disease codes. Standardized mortality ratios (SMRs) were calculated for underlying cause, and relative mortality ratios (RMRs) for any mention cause, by comparison to mortality rates in the age-, sex-, and calendar year-matched general population. Cause-specific relative mortality was explored by sex and disease course (relapsing onset and primary progressive). <b><i>Results:</i></b> Among 6,629 MS patients with 104,236 patient-years of follow-up, 1,416 died. The all-cause mortality risk was increased relative to the general population (SMR 2.71; 95% CI 2.55–2.87). MS was the underlying cause in 50.4%, and a mentioned cause in 77.9%, of deaths. Mortality by underlying cause was higher than expected for genitourinary disorders/infections (SMR 3.55; 95% CI 2.25–5.32), respiratory diseases/infections (SMR 2.69; 95% CI 2.17–3.28), suicide (SMR 2.40; 95% CI 1.61–3.45), cardiovascular disease (SMR 1.57; 95% CI 1.36–1.81), and other infections/septicemia (SMR 1.83; 95% CI 1.15–2.78). Risks of death due to overall cancer, accidents, digestive system disorders, and endocrine/nutritional diseases as underlying causes were similar to the general population. However, mortality with any mention of accidents (RMR 2.71; 95% CI 2.22–3.29) or endocrine/nutritional diseases (RMR 1.75; 95% CI 1.46–2.09) was greater. Bladder cancer mortality was increased in women (SMR 3.87; 95% CI 1.42–8.42) but not men. No notable differences were observed by disease course. <b><i>Conclusions:</i></b> MS itself was the most frequent underlying cause of death. Infections (genitourinary, respiratory, and septicemia), suicides, cardiovascular disease, and accidents contributed significantly to the increased risk of death. Some findings differed by sex, but not disease course. Multiple-cause death data offer advantages over “traditional” use of underlying cause only.
DOI: 10.6084/m9.figshare.11394618
title: Metadata and datasets supporting the published article: A simple ATAC-seq protocol for population epigenetics

description: In this study, the authors describe a protocol for the generation of sequence-ready libraries for population epigenomics studies.<br><b>Data access</b>: All the datasets that support the findings of this study are publicly available as supplementary files that accompany the main article. Dataset qPCR cycles.xlsx is available in figshare, as part of this data record (<b>https://doi.org/10.6084/m9.figshare.11394618</b>).<br><br><b>Study aims and methodology: </b>The purpose of this study was to describe a streamlined and robust method for Assay for Transposase Accessible Chromatin (ATAC-Seq) for individuals of the crustacea <i>Daphnia pulex</i> and also for the trematode <i>Schistosoma mansoni</i>.The following procedures are described in more detail in the published article:preparation of animal samples and transposase mixture, chromatin tagmentation, DNA library amplification, animal sampling and transposase mixture, AMPure XP beads double side purification, libraries check. <br><b>Datasets supporting the figures in the published article:</b><b><br></b><b>Data supporting figure 1</b>: Data file <b>qPCR plot 29-05-18.jpg</b> shows an example qPCR amplification profile for four samples.Dataset <b>qPCR cycles.xlsx</b> is an Excel table with the number of library amplification PCR cycles based on qPCR.<br><b>Data supporting figure 2</b>: Data files <b>Agarose picture.Tif</b>, <b>Agarose profile.png</b> and <b>BioAnalyser.pdf </b>show examples of fragmentation profiles. <b>Data supporting figure 3</b>: Data files <b>BioAnalyser 2.pdf</b>, <b>BioAnalyser 3.pdf </b>and <b>BioAnalyser 4.pdf</b> show examples of BioAnalyser profiles of ATAC-seq libraries after fragment size selection.<br><br><br>
DOI: 10.6084/m9.figshare.11394618.v1
title: Metadata and datasets supporting the published article: A simple ATAC-seq protocol for population epigenetics

description: In this study, the authors describe a protocol for the generation of sequence-ready libraries for population epigenomics studies.<br><b>Data access</b>: All the datasets that support the findings of this study are publicly available as supplementary files that accompany the main article. Dataset qPCR cycles.xlsx is available in figshare, as part of this data record (<b>https://doi.org/10.6084/m9.figshare.11394618</b>).<br><br><b>Study aims and methodology: </b>The purpose of this study was to describe a streamlined and robust method for Assay for Transposase Accessible Chromatin (ATAC-Seq) for individuals of the crustacea <i>Daphnia pulex</i> and also for the trematode <i>Schistosoma mansoni</i>.The following procedures are described in more detail in the published article:preparation of animal samples and transposase mixture, chromatin tagmentation, DNA library amplification, animal sampling and transposase mixture, AMPure XP beads double side purification, libraries check. <br><b>Datasets supporting the figures in the published article:</b><b><br></b><b>Data supporting figure 1</b>: Data file <b>qPCR plot 29-05-18.jpg</b> shows an example qPCR amplification profile for four samples.Dataset <b>qPCR cycles.xlsx</b> is an Excel table with the number of library amplification PCR cycles based on qPCR.<br><b>Data supporting figure 2</b>: Data files <b>Agarose picture.Tif</b>, <b>Agarose profile.png</b> and <b>BioAnalyser.pdf </b>show examples of fragmentation profiles. <b>Data supporting figure 3</b>: Data files <b>BioAnalyser 2.pdf</b>, <b>BioAnalyser 3.pdf </b>and <b>BioAnalyser 4.pdf</b> show examples of BioAnalyser profiles of ATAC-seq libraries after fragment size selection.<br><br><br>
DOI: 10.6084/m9.figshare.11394312
title: Supplementary Material for: Asymmetric Slow-Spike-Wave Patterns with Maximal Discharges Contralateral to MRI Lesions Predict Better Surgical Prognosis in Symptomatic Lennox-Gastaut Syndrome or Lennox-Gastaut Phenotypes

description: <b><i>Introduction:</i></b> Lennox-Gastaut syndrome (LGS) is a severe subtype of childhood-onset epileptic encephalopathy with drug-resistant and poor surgical prognosis. However, electroencephalogram (EEG) patterns of symptomatic LGS or LG phenotypes with structural brain lesions including focal abnormalities or asymmetric slow-spike-wave (SSW) patterns remain largely unknown. Due to the contradictory lateralization difference between MRI lesions and EEG pattern in symptomatic LGS or LG phenotypes, it is difficult to determine the precise lateralization of epileptic lesions, which is crucial to better surgical prognosis. This study is aim to ascertain the clinical characteristics of the EEG patterns, and its relationship with MRI lesions and to evaluate its prognostic value of surgical treatment in symptomatic LGS or LG phenotypes. <b><i>Methods:</i></b> Twenty-four symptomatic LGS cases with asymmetric EEG SSW patterns and contralaterally independent or contralaterally dominant MRI lesions were collected, and their clinical features were analyzed retrospectively. <b><i>Results:</i></b> In this cohort, most of lesions were perinatal or acquired during the first 6 months of life. The most common etiology was intracerebral hemorrhage. The LGS patients with both asymmetric SSW and focal sporadic epileptic waves (SEW) patterns showed the best surgical outcome with Engel class I level. Asymmetric SSW patterns with maximal discharges contralateral to MRI lesions were frequently observed in most of symptomatic LGS or LG phenotypes. Predominantly diffuse destructive lesions led to an attenuated voltage of ipsilateral scalp EEG producing an asymmetric SSW pattern in those patients with symptomatic LGS or LG phenotypes. <b><i>Conclusions:</i></b> Our study reveals a special SEW EEG pattern in symptomatic LG patients with asymmetric SSW and MRI lesions contralateral to the dominant EEG patterns. Contradictory lateralization difference between MRI and EEG probably arises from the relative voltage attenuation presenting in EEG ipsilateral to huge destructive lesions from early life. Our study suggests that the independent focal SEW activity remaining ipsilateral to the MRI lesion can potentially predict better surgical prognosis in symptomatic LGS or LG phenotypes.