cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.22099709.v1
title: Supplementary Material for: A Regional Survey of Awareness of Inflammatory Bowel Disease among the Saudi Population

description: Introduction: Inflammatory bowel disease (IBD) is common worldwide, including the gulf region. Early diagnosis of IBD can improve patients' outcomes. However, early diagnosis is dependent on patients' awareness of the disease to seek medical advice. This study aims to survey the awareness of IBD in the the general population of the western region of Saudi Arabia. Methods: A questionnaire about Crohn's disease (CD) and Ulcerative colitis (UC) was distributed to the general public. A score of 1 was given for the right response and 0 for an incorrect response giving a maximum of 3 and a miminum of 0 for the three questions in the questionnaire. Results: 1304 participants responded. 29% had not heard or read about CD, while 19% had not heard or read about UC. The mean awareness level score was 1.72±1.19. Females showed a significantly higher score compared to males (p &lt; 0.001). Age group 31 to 40 had the highest score (p =0.002). Moreover, responders, who had a Ph.D., showed significantly higher scores than other educational degrees (p &lt;0.001). Responders who dealt with CD or UC patients, showed significantly higher scores than their peers (p &lt; 0.001) for both. Conclusion: The general population in Saudi Arabia has an unacceptable level of awareness of IBD. Females, young adults (age group 31-40 years), educated individuals (with a PhD) and those who had dealt with IBD patients previously had better awareness compared to the rest of the population. National acts are essential to improve public awareness towards the disease.
DOI: 10.25345/c5j960k8b
title: MassIVE MSV000091424 - Reef boundary layers and macroalgae untargeted metabolomics
title: Untargeted metabolomics (LC-MS/MS) of water samples (SPE on Strata XL Phenomonex) and of macroalgal whole tissues (50% MeOH/DCM) and surface extracts (MeOH). Positive and negative ionization mode
DOI: 10.6084/m9.figshare.19383689
title: Interactive effects of nitrogen and phosphorus on growth and stoichiometry of lake phytoplankton

description: Two-way factorial experiment examining the interactive effects of nitrogen and phosphorus enrichment on phytoplankton variables.
DOI: 10.6084/m9.figshare.19383689.v1
title: Interactive effects of nitrogen and phosphorus on growth and stoichiometry of lake phytoplankton

description: Two-way factorial experiment examining the interactive effects of nitrogen and phosphorus enrichment on phytoplankton variables.
DOI: 10.35078/el8hg4
title: Replication Data for: Ficha epidemiológica de localidade: Distrito: Teófilo Otoni| Município: Sardoá

description: Levantamento Triatomínico com operações de campo ocorridas em Minas Gerais pela Superintendência de Campanhas de Saúde Pública (SUCAM) e FUNASA (Fundação Nacional de Saúde), no período de 1979 a 1999, objetivando a busca da presença de vetores da doença de Chagas ou de vestígios dessa presença. A partir dela são orientadas as operações com inseticida (pt) Triatomine survey with field operations carried out in Minas Gerais by the Public Health Campaigns Superintendence (SUCAM) and FUNASA (National Health Foundation), from 1979 to 1999, aiming to search for the presence of Chagas disease vectors or traces of that presence. From it, operations with insecticide are guided (en) Encuesta de triatominos con operaciones de campo realizada en Minas Gerais por la Superintendencia de Campañas de Salud Pública (SUCAM) y FUNASA (Fundación Nacional de Salud), de 1979 a 1999, con el objetivo de buscar la presencia de vectores de la enfermedad de Chagas o rastros de esa presencia. Desde ella se orientan las operaciones con insecticida (es)
DOI: 10.5281/zenodo.7705336
title: IODP Expedition 385 Alkalinity and pH

description: Alkalinity was determined by Gran titration with an autotitrator (Metrohm 794 basic Titrino) using 0.1 M HCl at 20 degrees C. Report includes alkalinity, correction factor (if applicable), and pH.
DOI: 10.5281/zenodo.7705335
title: IODP Expedition 385 Alkalinity and pH

description: Alkalinity was determined by Gran titration with an autotitrator (Metrohm 794 basic Titrino) using 0.1 M HCl at 20 degrees C. Report includes alkalinity, correction factor (if applicable), and pH.
DOI: 10.5281/zenodo.7705339
title: IODP Expedition 385 Hole drilling summary

description: Report includes detailed drilling data: BHA length, bit parameters, and primary depth measurements.
DOI: 10.5281/zenodo.7705338
title: IODP Expedition 385 Hole drilling summary

description: Report includes detailed drilling data: BHA length, bit parameters, and primary depth measurements.
DOI: 10.5281/zenodo.7705343
title: IODP Expedition 385 Core summary

description: Report includes detailed core data: drilling and coring depths, advancement, recovered core length measured on the catwalk and final curated length, core recovery, and sections cut.
DOI: 10.5281/zenodo.7705342
title: IODP Expedition 385 Core summary

description: Report includes detailed core data: drilling and coring depths, advancement, recovered core length measured on the catwalk and final curated length, core recovery, and sections cut.
DOI: 10.23708/pm58hp
title: Climatic dataset from ICIPE, Nairobi, Kenya, 2022

description: This dataset corresponds to climate data for the year 2022 at ICIPE, Nairobi, Kenya. The data includes temperature, relative humidity, atmospheric pressure in three separate CSV files with approximately one measurement every minute. The first column indicates the date and time in UTC and the second column the measurement made (column names are visible in the header). The measurements were made using a BME680 type sensor calibrated with a DS18B20 temperature probe, and connected to a Raspberry Pi. The dataset includes basic graphical visualizations. More information can be found here: https://pi2p.ird.fr/
DOI: 10.15131/shef.data.20480241.v1
title: Fluorescent dye traces in four UK sewer networks

description: This dataset describes experimental fluorescent dye traces (temporal concentration profiles) recorded in manholes within combined sewer networks located in four different cities across the United Kingdom. It accompanies the journal article entitled "Quantifying mixing in sewer networks for source localisation" (Sonnenwald et al., submitted). This dataset was collected by Professor Ian Guymer and colleagues. This archive was funded by EPSRC grant EP/P012027/1 and the UK Health Security Agency.
DOI: 10.5525/gla.researchdata.1410
title: Rotational properties of two interacting cold polar molecules: linear, symmetric, and asymmetric tops

description: Energy spectra and projections of the dipole moments for two interacting rotating molecules. This dataset includes all the results presented in arXiv:2303.02199
DOI: 10.17638/datacat.liverpool.ac.uk/2178
title: Statistics anxiety and predictions of exam performance in UK Psychology students

description: Dataset of variables used in analysis of statistics anxiety and role of self-prediction.
DOI: 10.18112/openneuro.ds003468.v1.1.0
title: IQSEC2_sMRI_fMRI
DOI: 10.6084/m9.figshare.14769732
title: Genomic annotation of BMA64-1A, SEY6210, BY4742, BY4741, X2180-1A and S288C strains: bed format

description: - Here is presented the full annotation of genomic sequences used in the differential expression analysis.- These data also appraised annotations of tRNAs, TEs, and lncRNAs.- The annotation of the <i>de novo</i> assembled BMA64-1A strain (NCBI BioProject number PRJNA727478) is also here presented.
DOI: 10.18434/mds2-2788
title: CyRSoXS: A GPU-accelerated virtual instrument for Polarized Resonant Soft X-ray Scattering (P-RSoXS)
DOI: 10.5281/zenodo.5513051
title: ASE2021 vulnerability fix dataset

description: The dataset of "Finding A Needle in a Haystack: Automated Mining of Silent Vulnerability Fixes", which was accepted in the 36th IEEE/ACM Automated Software Engineering (<em>ASE</em>) Conference. Followings are the descriptions of columns: commit_id: The commit ID/hash. repo: The Github Author and repository (e.g., "apache/hive"). filename: The name of the file changed in the commit. partition: Which dataset the commit information belongs to (i.e., "train", "val", or "test"). PL: Programming Language (PL) (i.e., "java" or "py"). label: Label of the commit, 0 for non-vulnerability fixing commit and 1 for vulnerability fixing commit. diff: The entire code change information of the file in this commit. committer_date: The date of the commit (e.g., 2015-03-02 13:48:25+13:00) msg: The commit message (NA if empty). MOD_DIFF: The code change of the file in this commit after preprocessing: filtering out lines that are not added lines or removed lines, and removing refactoring information and comments. BPE_MOD_DIFF: BPE processing applied to MOD_DIFF information (using codeprep Python package). ADD_DIFF: The added lines from the MOD_DIFF information (indicated as a line starting with '+' character). REM_DIFF: The removed lines from the MOD_DIFF information (indicated as a line starting with '-' character). LOC_ADD: Total lines of code added in this file change. LOC_REM: Total lines of code removed in this file change. LOC_MOD: Total lines of code modified in this file change (LOC_ADD + LOC_REM). commit_repo: The commit ID and repository concatenated. cve_list: A list of CVEs which the commit fixes (e.g., CVE-2015-5348, CVE-2016-8902).
DOI: 10.5281/zenodo.5516636
title: ASE2021 vulnerability fix dataset

description: The dataset of "Finding A Needle in a Haystack: Automated Mining of Silent Vulnerability Fixes", which was accepted in the 36th IEEE/ACM Automated Software Engineering (<em>ASE</em>) Conference. Followings are the descriptions of columns: commit_id: The commit ID/hash. repo: The Github Author and repository (e.g., "apache/hive"). filename: The name of the file changed in the commit. partition: Which dataset the commit information belongs to (i.e., "train", "val", or "test"). PL: Programming Language (PL) (i.e., "java" or "py"). label: Label of the commit, 0 for non-vulnerability fixing commit and 1 for vulnerability fixing commit. diff: The entire code change information of the file in this commit. committer_date: The date of the commit (e.g., 2015-03-02 13:48:25+13:00) msg: The commit message (NA if empty). MOD_DIFF: The code change of the file in this commit after preprocessing: filtering out lines that are not added lines or removed lines, and removing refactoring information and comments. BPE_MOD_DIFF: BPE processing applied to MOD_DIFF information (using codeprep Python package). ADD_DIFF: The added lines from the MOD_DIFF information (indicated as a line starting with '+' character). REM_DIFF: The removed lines from the MOD_DIFF information (indicated as a line starting with '-' character). LOC_ADD: Total lines of code added in this file change. LOC_REM: Total lines of code removed in this file change. LOC_MOD: Total lines of code modified in this file change (LOC_ADD + LOC_REM). commit_repo: The commit ID and repository concatenated. cve_list: A list of CVEs which the commit fixes (e.g., CVE-2015-5348, CVE-2016-8902). Following is the code snippet to reproduce Table 1. <pre><code class="language-python">import pandas as pd all_commits = pd.read_csv('./ase_dataset_sept_19_2021.csv') #Separate by language, since the Java commits are missing some info which we will add later on. py = all_commits[all_commits.PL == 'python'] java = all_commits[all_commits.PL == 'java'] #Java first: partition into train/val/test and check # of commits print("Java VF vs NVF for train/val/test") java_train = java[java.partition =="train"] java_val = java[java.partition == "val"] java_test = java[java.partition == "test"] print(java_train.drop_duplicates(subset='commit_id').label.value_counts()) print(java_val.drop_duplicates(subset='commit_id').label.value_counts()) print(java_test.drop_duplicates(subset='commit_id').label.value_counts()) #Python: partition into train/val/test and check # of commits print("Py VF vs NVF for train/val/test") py_train = py[py.partition =="train"] py_val = py[py.partition == "val"] py_test = py[py.partition == "test"] print(py_train.drop_duplicates(subset='commit_id').label.value_counts()) print(py_val.drop_duplicates(subset='commit_id').label.value_counts()) print(py_test.drop_duplicates(subset='commit_id').label.value_counts())</code></pre>
DOI: 10.5281/zenodo.5565182
title: ASE2021 vulnerability fix dataset

description: The dataset of "Finding A Needle in a Haystack: Automated Mining of Silent Vulnerability Fixes", which was accepted in the 36th IEEE/ACM Automated Software Engineering (<em>ASE</em>) Conference. Followings are the descriptions of columns: commit_id: The commit ID/hash. repo: The Github Author and repository (e.g., "apache/hive"). filename: The name of the file changed in the commit. partition: Which dataset the commit information belongs to (i.e., "train", "val", or "test"). PL: Programming Language (PL) (i.e., "java" or "py"). label: Label of the commit, 0 for non-vulnerability fixing commit and 1 for vulnerability fixing commit. diff: The entire code change information of the file in this commit. committer_date: The date of the commit (e.g., 2015-03-02 13:48:25+13:00) msg: The commit message (NA if empty). MOD_DIFF: The code change of the file in this commit after preprocessing: filtering out lines that are not added lines or removed lines, and removing refactoring information and comments. BPE_MOD_DIFF: BPE processing applied to MOD_DIFF information (using codeprep Python package). ADD_DIFF: The added lines from the MOD_DIFF information (indicated as a line starting with '+' character). REM_DIFF: The removed lines from the MOD_DIFF information (indicated as a line starting with '-' character). LOC_ADD: Total lines of code added in this file change. LOC_REM: Total lines of code removed in this file change. LOC_MOD: Total lines of code modified in this file change (LOC_ADD + LOC_REM). commit_repo: The commit ID and repository concatenated. cve_list: A list of CVEs which the commit fixes (e.g., CVE-2015-5348, CVE-2016-8902). Following is the code snippet to reproduce Table 1. <pre><code class="language-python">import pandas as pd all_commits = pd.read_csv('./ase_dataset_sept_19_2021.csv') #Separate by language, since the Java commits are missing some info which we will add later on. py = all_commits[all_commits.PL == 'python'] java = all_commits[all_commits.PL == 'java'] #Java first: partition into train/val/test and check # of commits print("Java VF vs NVF for train/val/test") java_train = java[java.partition =="train"] java_val = java[java.partition == "val"] java_test = java[java.partition == "test"] print(java_train.drop_duplicates(subset='commit_id').label.value_counts()) print(java_val.drop_duplicates(subset='commit_id').label.value_counts()) print(java_test.drop_duplicates(subset='commit_id').label.value_counts()) #Python: partition into train/val/test and check # of commits print("Py VF vs NVF for train/val/test") py_train = py[py.partition =="train"] py_val = py[py.partition == "val"] py_test = py[py.partition == "test"] print(py_train.drop_duplicates(subset='commit_id').label.value_counts()) print(py_val.drop_duplicates(subset='commit_id').label.value_counts()) print(py_test.drop_duplicates(subset='commit_id').label.value_counts())</code></pre>
DOI: 10.5281/zenodo.5513050
title: ASE2021 vulnerability fix dataset

description: The dataset of "Finding A Needle in a Haystack: Automated Mining of Silent Vulnerability Fixes", which was accepted in the 36th IEEE/ACM Automated Software Engineering (<em>ASE</em>) Conference. Followings are the descriptions of columns: commit_id: The commit ID/hash. repo: The Github Author and repository (e.g., "apache/hive"). filename: The name of the file changed in the commit. partition: Which dataset the commit information belongs to (i.e., "train", "val", or "test"). PL: Programming Language (PL) (i.e., "java" or "py"). label: Label of the commit, 0 for non-vulnerability fixing commit and 1 for vulnerability fixing commit. diff: The entire code change information of the file in this commit. committer_date: The date of the commit (e.g., 2015-03-02 13:48:25+13:00) msg: The commit message (NA if empty). MOD_DIFF: The code change of the file in this commit after preprocessing: filtering out lines that are not added lines or removed lines, and removing refactoring information and comments. BPE_MOD_DIFF: BPE processing applied to MOD_DIFF information (using codeprep Python package). ADD_DIFF: The added lines from the MOD_DIFF information (indicated as a line starting with '+' character). REM_DIFF: The removed lines from the MOD_DIFF information (indicated as a line starting with '-' character). LOC_ADD: Total lines of code added in this file change. LOC_REM: Total lines of code removed in this file change. LOC_MOD: Total lines of code modified in this file change (LOC_ADD + LOC_REM). commit_repo: The commit ID and repository concatenated. cve_list: A list of CVEs which the commit fixes (e.g., CVE-2015-5348, CVE-2016-8902). Following is the code snippet to reproduce Table 1. <pre><code class="language-python">import pandas as pd all_commits = pd.read_csv('./ase_dataset_sept_19_2021.csv') #Separate by language, since the Java commits are missing some info which we will add later on. py = all_commits[all_commits.PL == 'python'] java = all_commits[all_commits.PL == 'java'] #Java first: partition into train/val/test and check # of commits print("Java VF vs NVF for train/val/test") java_train = java[java.partition =="train"] java_val = java[java.partition == "val"] java_test = java[java.partition == "test"] print(java_train.drop_duplicates(subset='commit_id').label.value_counts()) print(java_val.drop_duplicates(subset='commit_id').label.value_counts()) print(java_test.drop_duplicates(subset='commit_id').label.value_counts()) #Python: partition into train/val/test and check # of commits print("Py VF vs NVF for train/val/test") py_train = py[py.partition =="train"] py_val = py[py.partition == "val"] py_test = py[py.partition == "test"] print(py_train.drop_duplicates(subset='commit_id').label.value_counts()) print(py_val.drop_duplicates(subset='commit_id').label.value_counts()) print(py_test.drop_duplicates(subset='commit_id').label.value_counts())</code></pre>
DOI: 10.5281/zenodo.7705363
title: ASE2021 vulnerability fix dataset

description: The dataset of "Finding A Needle in a Haystack: Automated Mining of Silent Vulnerability Fixes", which was accepted in the 36th IEEE/ACM Automated Software Engineering (<em>ASE</em>) Conference. Followings are the descriptions of columns: commit_id: The commit ID/hash. repo: The Github Author and repository (e.g., "apache/hive"). filename: The name of the file changed in the commit. partition: Which dataset the commit information belongs to (i.e., "train", "val", or "test"). PL: Programming Language (PL) (i.e., "java" or "py"). label: Label of the commit, 0 for non-vulnerability fixing commit and 1 for vulnerability fixing commit. diff: The entire code change information of the file in this commit. committer_date: The date of the commit (e.g., 2015-03-02 13:48:25+13:00) msg: The commit message (NA if empty). MOD_DIFF: The code change of the file in this commit after preprocessing: filtering out lines that are not added lines or removed lines, and removing refactoring information and comments. BPE_MOD_DIFF: BPE processing applied to MOD_DIFF information (using codeprep Python package). ADD_DIFF: The added lines from the MOD_DIFF information (indicated as a line starting with '+' character). REM_DIFF: The removed lines from the MOD_DIFF information (indicated as a line starting with '-' character). LOC_ADD: Total lines of code added in this file change. LOC_REM: Total lines of code removed in this file change. LOC_MOD: Total lines of code modified in this file change (LOC_ADD + LOC_REM). commit_repo: The commit ID and repository concatenated. cve_list: A list of CVEs which the commit fixes (e.g., CVE-2015-5348, CVE-2016-8902). Following is the code snippet to reproduce Table 1. <pre><code class="language-python">import pandas as pd all_commits = pd.read_csv('./ase_dataset_sept_19_2021.csv') #Separate by language, since the Java commits are missing some info which we will add later on. py = all_commits[all_commits.PL == 'python'] java = all_commits[all_commits.PL == 'java'] #Java first: partition into train/val/test and check # of commits print("Java VF vs NVF for train/val/test") java_train = java[java.partition =="train"] java_val = java[java.partition == "val"] java_test = java[java.partition == "test"] print(java_train.drop_duplicates(subset='commit_id').label.value_counts()) print(java_val.drop_duplicates(subset='commit_id').label.value_counts()) print(java_test.drop_duplicates(subset='commit_id').label.value_counts()) #Python: partition into train/val/test and check # of commits print("Py VF vs NVF for train/val/test") py_train = py[py.partition =="train"] py_val = py[py.partition == "val"] py_test = py[py.partition == "test"] print(py_train.drop_duplicates(subset='commit_id').label.value_counts()) print(py_val.drop_duplicates(subset='commit_id').label.value_counts()) print(py_test.drop_duplicates(subset='commit_id').label.value_counts())</code></pre>
DOI: 10.6084/m9.figshare.19948535
title: Genomic annotation of BMA64-1A, SEY6210, BY4742, BY4741, X2180-1A and S288C strains: GFF format

description: - Here is presented the full annotation of genomic sequences used in the differential expression analysis. - These data also appraised annotations of tRNAs, TEs, and lncRNAs. - The annotation of the <em>de novo</em> assembled BMA64-1A strain (NCBI BioProject number PRJNA727478) is also here presented.
DOI: 10.17036/researchdata.aston.ac.uk.00000010
title: Phospholipid oxidation and carotenoid supplementation in Alzheimer’s disease patients
DOI: 10.6084/m9.figshare.16831973
title: Rare variant association study of veteran twin whole-genomes links severe depression with a nonsynonymous change in the neuronal gene <i>BHLHE22</i>

description: Major Depressive Disorder (MDD) is a complex neuropsychiatric disease with known genetic associations, but without known links to rare variation in the human genome. Here we aim to identify rare genetic variants associated with MDD using deep whole-genome sequencing data in an independent population. We report the sequencing of 1,688 whole genomes in a large sample of male-male Veteran twins. Depression status was classified based on a structured diagnostic interview according to <i>DSM-III-R</i> diagnostic criteria. Searching only rare variants in genomic regions from recent GWAS on MDD, we used the optimised sequence kernel association test and Fisher's Exact test to fine map loci associated with severe depression. Our analysis identified one gene associated with severe depression, basic helix loop helix e22 (<i>P</i><sub>Adjusted</sub> = 0.03) via SKAT-O test between unrelated severely depressed cases compared to unrelated non-depressed controls. The same gene <i>BHLHE22</i> had a non-silent variant rs13279074 (<i>P</i><sub>Adjusted</sub> = 0.032) based on a single variant Fisher’s Exact test between unrelated severely depressed cases compared to unrelated non-depressed controls. The gene <i>BHLHE22</i> shows compelling genetic evidence of directly impacting the severe depression phenotype. Together these results advance understanding of the genetic contribution to major depressive disorder in a new cohort and link a rare variant to severe forms of the disorder.