cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/c9nwnswnyk
title: Data related to the study on patients perception of medication side effect descriptions in medication package inserts

description: The data set is for the 750 patients who purchased medicines from five selected pharmacies in Ibadan metropolis. These patients were divided into three groups based on the questionnaire variant they were administered. The three questionnaire variants were A - for participants who answered questions relating to the use of verbal descriptor in describing the risk of occurrence of medication side effects, B - for participants who responded to questions regarding the use of percentage descriptor in describing the risk of occurrence of medication side effects, and C - for participants who answered questions relating to the use of natural frequency descriptor in describing the risk of occurrence of medication side effects,
DOI: 10.6084/m9.figshare.27328107.v8
title: Dataset for "Deep-sea ascending predation by king penguin and its prey reaction observed by animal-borne video camera"

description: This is the data repository for the paper "Deep-sea ascending predation by king penguin and its prey reaction observed by animal-borne video camera"It contains data on dive depth, timing of foraging events.
DOI: 10.6084/m9.figshare.28327472.v2
title: <b>Genetically predicted mood disorders increased risk of Cisplatin-induced hearing loss in childhood cancer: Evidence from a Bidirectional Mendelian randomization study</b>

description: <i>It remains unclear whether the mood swings in children with cancer are causally related to the increased risk of hearing loss caused by cisplatin.</i><i>The first investigation into the interaction between mood swings and cisplatin-induced hearing loss.</i><i>It is a two-sample Mendelian randomization analysis. Mendelian randomization (MR) emerges as a valuable approach, employing genetic variations as instrumental variables to infer causal links between exposure factors and outcomes.</i>
DOI: 10.6084/m9.figshare.28755095.v1
title: data for figures in kagome paper

description: data for figures in kagome paper
DOI: 10.6084/m9.figshare.28830173.v1
title: WESAD Dataset

description: dfdffddf
DOI: 10.6084/m9.figshare.8082884.v1
title: Dados oficiais animais doentes modificado tabela oficial.xlsx

description: Canine Monocytic Ehrlichiosis (CME) is a systemic disease prevalent in the entire world caused by the obligate intracellular bacteria <i>Ehrlichia canis</i>. The occurrence of myocarditis with a high prevalence of arrhythmias in dogs affected by this disease in the cytopenic phase has already been proven. This study aims to evaluate the concentrations of CK MB, cTnI and NT-proBNP in dogs affected by Ehrlichia canis in the chronic phase since the intense stimulation of the immune system can lead to myocarditis; to evaluate if the condition can lead to arrhythmic events and, if so, define their frequency and classification through conventional and ambulatory electrocardiogram tests (Holter method) for a period of 24 hours; to analyze heart rate variability in the time domain and whether the condition can lead to autonomic imbalance; and to determine the survival rate of affected dogs, identifying possible risk factors for mortality at this stage of the disease.
DOI: 10.1594/pangaea.116101
title: Total carbon dioxide and total alkalinity at station TT043_7-20
DOI: 10.1594/pangaea.116171
title: Total carbon dioxide and total alkalinity at station TT049_18-4
DOI: 10.17632/6gp8m72s9p
title: Indonesian Query Answering Dataset for Online Essay Test System

description: This data is related to Indonesian query answering system research for online essay examination system. This data is derived from the bachelor thesis work of Trisna Ari Roshinta, under the supervision of Faisal Rahutomo.
DOI: 10.6084/m9.figshare.4620739.v2
title: Antigen receptor repertoires profiling from RNA-Seq

description: <b>TCGA_SKCM_clinical_data_metadata_and_clonesets.xlsx</b> - Immune repertoires and sample metadata from the TCGA 48+48 nt paired-end RNA-Seq data for 460 patients with human skin cutaneous melanoma (SKCM).<br><br><b>CoverageStat.tsv</b> - data for calculation of TCR CDR3 extraction performance characteristics in respect to different TCR content, read-length and dataset type (single- or paired- end). Data for Supplementary Table 2.<b><br></b><b>Sorted_mice_TCR_clonesets.zip</b> - TCR clonesets for sorted mouse cell populations from section "Functional characterization of TRA and TRB repertoires based on RNA-Seq of sorted T-cells"<b><br></b><b>Data for comparison of MiXCR with TRUST and V'DJer on real data</b><br><b><br></b><b>SamplesForComparison.tsv</b> - list of all RNA-Seq samples used for MiXCR / V'DJer / TRUST comparison, including samples obtained by truncation of samples with longer read length. Summary analysis statistics for each sample. Tab-separated table. Data for <b>Figure 2e</b>.<br><b>analysis_results.zip</b> file contain all analysis results for all samples listed in SamplesForComparison.tsv. See <b>README.txt</b> file for exact commands used to generate this data. Archive has the following folder structure:<br><i>rnaseq/mixcr</i> - results of RNA-Seq samples using MiXCR. Individual files for each immunological chain: TRA, TRB, TRG, TRD, IGH, IGL, IGK.Combined data:TCR = TRA + TRB + TRG + TRDIG = IGH + IGL + IGK<br>ALL = all chainsTab-separated table. Unprocessed MiXCR output.<br><i>rnaseq/trust</i> - analysis results for TRUST. fasta files (some files are prepended with comment blocks, each line in comment block starts with #). Each file accumulate sequences for all chains (TRA + TRB + TRG + TRD). Unprocessed TRUST output.<br><i>rnaseq/vdjer</i> - analysis results for V'DJer. fasta files. Unprocessed V'DJer output.<br><br><i>repseq</i> - clonotypes from control TCR-Seq datasets assembled using MiXCR software with default settings.<br><br><i>notebooks</i> - contains IPython notebook for final data anaylsis and figure generation<br><br><i>scripts</i> - contains python and shell scripts to convert software-specific formatted data into msgpack format that is used by IPython notebook mentioned above<br><br><br>See also https://github.com/milaboratory/mixcr-rna-seq-paper repository for the code for <i>in-silico</i> experiments.
DOI: 10.1594/pangaea.116174
title: Total carbon dioxide and total alkalinity at station TT049_2-1
DOI: 10.1594/pangaea.116177
title: Total carbon dioxide and total alkalinity at station TT049_20-7
DOI: 10.17632/22msy7wv45
title: landslide susceptibility mapping, Cisangkuy

description: Landslide susceptibility mapping using frequency ratio, information value model, weight of evidence, and Shannon entropy
DOI: 10.17632/9sp99gt3r4
title: Central Bank Liquidity Reallocation and Bank Lending: Evidence from the Tiering System

description: Replication package for “Central Bank Liquidity Reallocation and Bank Lending: Evidence from the Tiering System” to fulfill the data and code disclosure requirement for accepted manuscripts in Journal of Financial Economics. This document provides a roadmap to the data and code used in the above-referenced paper
DOI: 10.17632/bh576fdcbm
title: LINF_170010000

description: EF-hand calcium-binding domain-containing protein; Leishmania infantum (strain JPCM5)
DOI: 10.17632/cy8b8kvfdf
title: ECG COVID-19 severity dataset

description: Note: When you use the dataset for your research please cite the related research paper:
All data have been anonymised 
It is worth noting that each patient signal alias file is assigned a unique identifier following the structure “Patient {#patient} {body posi- tion}”. This identifier was designed to safeguard patient anonymity.

Inside the designated “COVID-19 Database” folder, it can be found:
• An Excel file (.xlsx) named “COVID-19 Patients Information”, con- taining the following physiological data about patients:
1. Patient Information - Identifier number, Age, and Gender; 
2. Clinical Data - COVID-19 Severity Level, Comorbidities, Symptoms, Medication, Oxygen Therapy, Invasive Mechanical Ventilation, Non-Invasive Ventilation, Prone Positioning, Shock, Acute Respiratory Distress Syndrome (ARDS), PCR, Heart Injury, Kidney Injury, and Secondary Injury; 
3. Vital Signs - Heart Rate, Respiration Rate, SatO2, Systolic Blood Pressure, Diastolic Blood Pressure and BMI; 
4. Laboratory Exams - Red Cell Count, Hemoglobin, Hemat- ocrit, Neutrophil Count, Leukocyte Count, Lymphocyte Count, Platelets, Thrombin Time, Thromboplastin Time, Ferritin, Troponin, PCr, Urea, Creatinine, D-dimer, Aspartate Aminotrans- ferase, Alanine Aminotransferase, and Creatine Kinase.

• A folder named “ECG Signals” contains participants’ raw ECGs in .mat extension files. After being loaded in Matlab, each ECG .mat file includes 3 variables:
1. DATA - The date that ECG has been collected; 
2. sinalf1 - ECG raw signal;
3. TEMPO1 - Duration of the raw signal in seconds.
DOI: 10.5522/04/13366271.v1
title: Bantu witchcraft accusations

description: This dataset was collected from accounts of witchcraft accusations that took place in 44 Bantu societies from sub-Saharan Africa. The accusations are historic. Source materials were originally identified from societies listed in the Ethnographic Atlas (Murdock 1967), a large cross-cultural database. These were acquired from the website d-place.org (Kirby et al. 2016).The dataset includes information on the sex of the accused witch and their relationship to their accusers and purported victims. There are also variables taken from the Ethnographic Atlas that were amended for the analysis by combining categories. These include information on post-marital residence patterns (where couples live after marriage) and levels of polygamous marriage.
DOI: 10.6084/m9.figshare.13580693.v1
title: ESAC database

description: Database with codebook for the European Alcohol Use and COVID-19 Survey – 1st survey wave conducted between April and July 2020.
DOI: 10.1594/pangaea.116103
title: Total carbon dioxide and total alkalinity at station TT043_7-9
DOI: 10.1594/pangaea.116105
title: Total carbon dioxide and total alkalinity at station TT043_8-3
DOI: 10.17632/ysnmgygmmn
title: eVIN assessment

description: Electronic Vaccine Intelligence Network (eVIN) assessment was conducted in 12 states (Assam, Chhattisgarh, Gujarat, Jharkhand, Manipur, Nagaland, Odisha, Bihar Himachal Pradesh, Madhya Pradesh, Rajasthan and Uttar Pradesh) where eVIN was launched initially. A pre-post comparison was used on key performance indicators for programmatic assessment. As obtaining data for one-year prior to implementation of eVIN was challenging, therefore, six months period was chosen as the reference period for pre-eVIN phase. The pre-eVIN reference period was of six months for all CCPs, however it varied for different states and districts due to different timeframe of eVIN rollout. The specific duration of pre-eVIN phase are as mentioned: most of the CCPs in a district were covered either in between the period of April 2015 to September 2015 (13 districts), April 2016 to September 2016 (12 districts), October 2015-March 2016 (11 districts) and October 2011- March 2012 (1 district). The post eVIN reference period was from October 2017 to March 2018 for the assessment. Minimum number of CCPs required for the study was calculated to be 502 considering 43% of PHCs reported instances of stock-out, 10% non-response rate and 1.2 design effect. It was further increased to 617 CCPs in order to draw valid conclusions at the state levels. Selection of CCPs in the eVIN states were done using two-stage sampling design. In the first stage, districts were selected followed by selection of CCPs in the second stage. In each eVIN state, number of sampled districts was decided based on Probability Proportion to Size of CCPs. In total 37 districts were selected using systematic random sampling technique after arranging the districts in ascending order based on the proportionate share of cold chain point in the total cold chain point in state. Further, CCPs were randomly selected in each of the selected district. A detailed methodology is available in the larger study document. Quantitative data was obtained using structured questionnaire from CCPs pertaining to stock management, temperature monitoring, cold chain equipment, and documentation aspects of vaccine supply chain. The primary data for pre-eVIN phase was done from stock registers, vaccine distribution registers, temperature log books and other important registers. Completeness and accuracy were analyzed in the assessment. Completeness was seen of Indent form, vaccine stock register, and temperature log book. Accuracy was assessed through stock register and eVIN record, eVIN record and physical count. Computer Assisted Personal Interviewing (CAPI) technique was employed using tablets/mobiles for real-time data collection and data entry.
DOI: 10.25442/hku.27924057.v1
title: Support Data for Deciphering spatio-temporal variations in Holocene relative sea level in the South China Sea: from local reconstructions to a regional database

description: Chapter Two investigates the application of microfossils and δ13C and C/N geochemistry to develop high-resolution late Holocene RSL records from sediments in Deep Bay, located on the eastern side of the PRD. This chapter characterises the contemporary distribution of these proxies across three transects encompassing subtidal, intertidal (mudflat and mangrove), and upland environments. Partitioning Around Medoids (PAM) analysis is employed to characterise distinct biozones based on microfossil assemblages, while CCA examines the relationships between contemporary diatom and foraminiferal communities and environmental variables. Bayesian transfer functions (BTFs) are developed using modern foraminiferal and diatom assemblages, incorporating priors informed by δ13C and C/N geochemistry. These BTFs are applied to a sediment core obtained from a contemporary mangrove setting in Mai Po, Deep Bay, to evaluate their efficacy. The reconstructions are subsequently validated against local tide gauge data to assess the relative effectiveness of foraminiferal and diatom-based proxies in reconstructing RSL changes. This chapter has been submitted to Marine Geology.<br>Chapter Three establishes the indicative meaning of S. cuccullata examining its contemporary vertical distribution along rocky coasts in Hong Kong, including periodic inundated thin S. cuccullata beds in elevated tidal pools. The elevation of the ancient oyster bed is resurveyed, and its morphology and depositional context are documented. Petrographic analysis and X-ray diffraction are used to evaluate the lithology and mineralogy of the ancient oyster bed, and therefore classify the reef and reconstruct the environment where it formed. Additionally, two new radiocarbon dates are obtained from samples collected at different elevations within the ancient bed to reconstruct mid-Holocene RSL in Hong Kong, constraining the timing and magnitude of the highstand in the region. This chapter has been prepared to be submitted to Palaeogeography, Palaeoclimatology, Palaeoecology.Chapter Four develops a quality-controlled Holocene RSL database for the SCS coast, incorporating SLIPs and limiting points derived from sedimentary, geomorphic, coral, fixed biological, and archaeological indicators spanning 14 ka to the present. Uncertainties associated with tidal range changes over the Holocene are addressed using a paleotidal model. Spatio-temporal fields of RSL change are reconstructed, with RSL and rates of RSL change estimated by applying SPTEHM to the database. Sensitivity tests are performed to examine the effects of global and Antarctic ice sheet deglaciation timing and variations in 3D Earth structure, including lateral heterogeneity, on GIA model predictions. The results are used to interpret regional and local processes driving spatio-temporal variability in RSL change. This chapter has been prepared to be submitted to Quaternary Science Reviews.
DOI: 10.6084/m9.figshare.13480317.v1
title: Protein composition of circulating extracellular vesicles immediately changed by particular short time of high intensity interval training exercise

description: The changes in circulating proteomic profile of EVs to assess the effect of High intensity interval training @font-face {font-family:"Cambria Math"; panose-1:2 4 5 3 5 4 6 3 2 4; mso-font-charset:0; mso-generic-font-family:roman; mso-font-pitch:variable; mso-font-signature:-536870145 1107305727 0 0 415 0;}@font-face {font-family:"ＭＳ Ｐゴシック"; panose-1:2 11 6 0 7 2 5 8 2 4; mso-font-charset:128; mso-generic-font-family:swiss; mso-font-pitch:variable; mso-font-signature:-536870145 1791491579 134217746 0 131231 0;}@font-face {font-family:"\@ＭＳ Ｐゴシック"; mso-font-charset:128; mso-generic-font-family:swiss; mso-font-pitch:variable; mso-font-signature:-536870145 1791491579 134217746 0 131231 0;}p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-unhide:no; mso-style-qformat:yes; mso-style-parent:""; margin:0mm; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"ＭＳ Ｐゴシック",sans-serif; mso-bidi-font-family:"ＭＳ Ｐゴシック";}.MsoChpDefault {mso-style-type:export-only; mso-default-props:yes; font-family:"游明朝",serif; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}div.WordSection1 {page:WordSection1;} @font-face {font-family:"Cambria Math"; panose-1:2 4 5 3 5 4 6 3 2 4; mso-font-charset:0; mso-generic-font-family:roman; mso-font-pitch:variable; mso-font-signature:-536870145 1107305727 0 0 415 0;}@font-face {font-family:"ＭＳ Ｐゴシック"; panose-1:2 11 6 0 7 2 5 8 2 4; mso-font-charset:128; mso-generic-font-family:swiss; mso-font-pitch:variable; mso-font-signature:-536870145 1791491579 134217746 0 131231 0;}@font-face {font-family:"\@ＭＳ Ｐゴシック"; mso-font-charset:128; mso-generic-font-family:swiss; mso-font-pitch:variable; mso-font-signature:-536870145 1791491579 134217746 0 131231 0;}p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-unhide:no; mso-style-qformat:yes; mso-style-parent:""; margin:0mm; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"ＭＳ Ｐゴシック",sans-serif; mso-bidi-font-family:"ＭＳ Ｐゴシック";}.MsoChpDefault {mso-style-type:export-only; mso-default-props:yes; font-family:"游明朝",serif; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}div.WordSection1 {page:WordSection1;}
DOI: 10.6084/m9.figshare.13691239.v1
title: Results of compression test of single corn grain

description: In this data the results of compression test for 100 grain was presented, also the summary of size and shape determination of grain was presented.
DOI: 10.6084/m9.figshare.22126553.v1
title: Global gridded aerosol models established for atmospheric correction over inland and nearshore coastal waters: aerosol LUTs

description: This repository contains the relevant data for the paper <strong> Global gridded aerosol models established for atmospheric correction over inland and nearshore coastal waters.</strong> <br> The <strong>aerosol_modisa.zip</strong> file includes the aerosol LUTS (.hdf) for MODIS Aqua. Please refer to the README file for more detailed information.
DOI: 10.6084/m9.figshare.29135063.v1
title: <b>Autonomous Physical Learning</b>

description: This study examines the role of social support in promoting adolescents' autonomous physical learning behavior, considering the mediating effects of self-efficacy and exercise motivation. As part of the ongoing "Double Reduction" policy and the "Healthy China" strategy, improving adolescents' physical fitness and sports literacy has become a central focus of national education reform. Autonomous physical learning is crucial for promoting both physical and mental health, and fostering lifelong engagement in physical activity. However, existing research has typically focused on average relationships between variables, neglecting the psychological pathways and individual differences that underpin autonomous physical learning.This study uses Social Cognitive Theory and Self-Determination Theory to develop a chain mediation model, exploring how social support influences autonomous physical learning behavior through self-efficacy and exercise motivation. Latent Profile Analysis (LPA) was employed to identify different latent types of students' autonomous physical learning behaviors, and Multi-group Structural Equation Modeling (SEM) was used to analyze differences in psychological pathways across these types. The study surveyed 2,359 students from three middle schools in Shandong Province, collecting data for structural and latent class modeling analysis.The findings reveal that social support positively predicts autonomous physical learning behavior, with self-efficacy and exercise motivation serving as partial and chain mediators. Four distinct latent behavior types were identified: "Highly Engaged," "Positively Regulated," "Selectively Participative," and "Passively Participative." These groups exhibited significant differences in their psychological pathways, with the "Highly Engaged" group relying more on intrinsic motivation, while the "Passively Participative" group was more responsive to external activation.This research advances the understanding of autonomous physical learning by incorporating multiple pathway models and considering individual differences. It provides valuable insights for designing more targeted interventions in school physical education, offering a multidimensional theoretical framework for further studies on adolescent physical behavior.
DOI: 10.1594/pangaea.116107
title: Total carbon dioxide and total alkalinity at station TT043_9-8
DOI: 10.1594/pangaea.116151
title: Total carbon dioxide and total alkalinity at station TT049_1-1
DOI: 10.6084/m9.figshare.24056658.v1
title: Data and code for the paper Detecting Road Network Errors from Trajectory Data with Partial Map Matching and Bidirectional Recurrent Neural Network Model

description: <pre>This repository contains data and codes for the manuscript _Detecting Road Network Errors from Trajectory Data with Partial Map Matching and Bidirectional Recurrent Neural Network Model_ submitted to IJGIS. <br><br>Requirements (tested with Python 3.8.8):<br><br>- Numpy &gt;= 1.22.4<br>- PyTorch &gt;= 2.0.0<br>- Sklearn &gt;= 0.24.1 <br>- Shapely &gt;= 1.8.2<br>- rtree &gt;= 1.0.0<br>- Networkx &gt;= 2.8.4<br>- Geopandas &gt;= 0.10.2<br><br>### Code structure <br><br>- lib: codes for partial map matching, context feature extraction and BiRNN model<br>- data: a sample of training and test dataset in npz format, which contains "xy" and "label" attribute storing the trajectory geometry<br>and manually labels. It also contains a shapefile of road network. <br><br>### Run the program with two steps <br><br>```bash<br># 1. Generate context features<br><br>python feature_extract.py<br><br># 2. Training a BiRNN model for classification<br><br>python train_birnn.py<br>```<br><br>For demonstration, data folder contains a small trajectory dataset with labels and road network downloaded from OSM. Accessing the complete dataset can be applied at https://outreach.didichuxing.com/. </pre><br>
DOI: 10.6084/m9.figshare.5481565.v2
title: Dataset 1: Anti-H3 Stalk Reactive Antibodies in Human Plasma

description: Plasma was obtained from 13 donors, 12 of which had been previously vaccinated within the past 5 years with trivalent or quadrivalent seasonal influenza vaccines. Plasma baseline anti-influenza IgG was measured by mPlex-Flu assay. (A) Levels of IgG against distinct H3N2 strains. Each column depicts mean fluorescence intensity (MFI), representing an individual donor. (B) Levels of H3 stalk-reactive IgG. Each symbol and line represents one donor. (C) IgG-binding to chimeric cH5/1 and cH5/3 proteins. This analysis was performed at a 1:5000 dilution.