cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28746893.v1
title: Multimodal Speech Dataset Capturing Air and Bone Conduction

description: The corpus comprises 47,182 synchronized air-conducted (AC) and bone-conducted (BC) utterances collected from 100 speakers, totaling approximately 42 hours of speech. Each speaker contributed around 25 minutes of recorded material, with individual utterances ranging in duration from 1 to 5 seconds.Data collection was conducted in an anechoic chamber compliant with ISO 3745 standards, ensuring a controlled acoustic environment. The chamber measures 11.8 meters in length, 4.2 meters in width, and 3.8 meters in height. During the recording sessions, speakers wore a headset and read from prepared text transcriptions.
DOI: 10.4121/19487195.v1
title: Sonar Ping360 rosbag data files. Recorded at TU Delft 3ME pond on 25-03-2022

description: Sonar data recored in rosbag format using ROS. Recordings were performed using a BlueROV2 robot equipped with PIng360 sonar. <br> ROS setup: - ROS Kinetic - Ping360 sonar ROS packaged: ttps://github.com/CentraleNantesRobotics/ping360_sonar/tree/aa48eb6e5ae2650e5cf1d270e0fe4ce8fb3c8b0f <br> Data was recorded at TU Delft 3me pond 25-03-2022. This data will be used in the activities of REMARO (https://remaro.eu/) project. <br>
DOI: 10.6084/m9.figshare.21901545.v4
title: dataset1_p53

description: dataset1_p53
DOI: 10.6084/m9.figshare.25952857.v4
title: Data and scripts for the manuscript "Herbivory and water availability interact to shape the adaptive landscape in the perennial forb, <i>Boechera stricta</i>".

description: Data and scripts for the manuscript "Herbivory and water availability interact to shape the adaptive landscape in the perennial forb, <i>Boechera stricta</i>".
DOI: 10.6084/m9.figshare.8119058.v3
title: Mapping human microbiome drug metabolism by gut bacteria and their genes

description: Data sets for analysis and visualization pipelines for screening the capacity of common gut bacteria to metabolize oral drugs.############################################################Analysis schemes are provided in AnalysisSchemes subfolder.Subfolders Input and Output contain tables and figure files to reproduce tables and figures from the corresponding study. See analysis schemes and the project code readme files for further details. <br>############################################################The project code is deposited on GitHub (https://github.com/mszimmermann/drug-bacteria-gene_mapping) and consists of four pipelines:1) Drug Screen AnalysisData are in Raw_DrugScreenAnalysis subfolder. <br>Perform differential analysis and hierarchical clustering of the metabolism patterns of 271 oral drugs by 76 diverse human gut bacteria (and four pH controls).<br><br>2) Drug Metabolism Products AnalysisData files are in Raw_DrugMetabolismProducts subfolder. <br>Perform differential analysis of metabolites co-occuring with specific drugs measured by untargeted mass-spectrometry.<br>3) Drug-metabolizing Gene Products AnalysisData files are in Raw_DrugMetabolizingGeneProducts subfolder. <br>Perform protein identity analysis for the identified bacterial gene products.<br>4) Gain-of-Function Screen AnalysisData files are in Raw_GainOfFunction subfolder. <br>Reproduce other manuscript figures (including gain-of-function bacterial gene identification screen). <br><br>############################################################ This project is part of the work by Michael Zimmermann, Maria Zimmermann-Kogadeeva, Rebekka Wegmann and Andrew L. Goodman.
DOI: 10.6084/m9.figshare.24152733.v2
title: <b>Perception of Veracity and Believability in Human-Written and AI-Generated News Articles: A Comparative Analysis across Social Media Platforms</b>

description: Three sets are collected to compare the veracity and believability perceptions of Human-written and AI-generated news articles across social media platforms.
DOI: 10.6084/m9.figshare.26941669.v1
title: Data availibility Eighteen-month survival rate of advanced cervical cancer patients with comorbid Type 2 Diabetes Mellitus and Hypertension who underwent Urinary Diversion compared to without Urinary Diversion

description: Its a data that we then analysed in out journal with title: Eighteen-month survival rate of advanced cervical cancer patients with comorbid Type 2 Diabetes Mellitus and Hypertension who underwent Urinary Diversion compared to without Urinary Diversion
DOI: 10.6084/m9.figshare.29008412.v2
title: SRR3414635.uniq.bigWig

description: sfgsfgs
DOI: 10.6084/m9.figshare.13604153.v1
title: Brain Coronal HnE Image Crop

description: cite original dataset: https://support.10xgenomics.com/spatial-gene-expression/datasets/1.1.0/V1_Adult_Mouse_Brain
DOI: 10.6084/m9.figshare.3555702.v1
title: Supplement 1. R code for the SES (standardized effect sizes) bootstrapping procedures and the hierarchy of linear mixed models of individual tree growth.

description: File List BootstrapSES.R: R code demonstrating how to compute diversity effects as standardized effect size (SES) following Potvin and Gotelli (2008) for both (i) plot-level overyielding and (ii) comparison among canopy statuses.<br> (md5: 0b9c6fc3752d601e7660515eb0505159) MultilevelModeling.R: R code for fitting and comparing the relative support for a hierarchy of linear-mixed models of individual tree growth. R code demonstrating how to compute confidence intervals with a bias-corrected bootstrap method.<br> (md5: c73e3dec4e1059c7c518fe1a07a416fa) Description BootstrapSES.R generates random data conforming to our experimental design (part 0) and tests for overyielding (part 1) using our modified version of the bootstrap methods developed by Potvin and Gotelli (2008). The bootstrap procedure is then used to compare the magnitude of overyielding among canopy statuses (part 2). Part 1 and part 2 could be directly applied to any data.frame containing columns similar to that of “data”, which is fully described at the end of part 0. MultilevelModeling.R requires the lme4 package to fit linear-mixed models. First, methods are defined to generate random tree growth data (simulateData()), to perform bootstrap likelihood ratio tests (bootLRT()), and to compute confidence intervals following a bias-corrected percentile method (see Efron and Tibishrani, 1986; BCconf()). The rest of the code generates random data, fits a hierarchy of linear-mixed model of tree growth, compares the models based on AIC and computes confidence intervals for the most complex example model.
DOI: 10.17632/rv6scbpd7v
title: Student Skill Gap Analysis

description: This dataset is designed for skill gap analysis, focusing on evaluating the skill gap between students’ current skills and industry requirements. It provides insights into technical skills, soft skills, career interests, and challenges, helping in skill gap analysis to identify areas for improvement.

By leveraging this dataset, educators, recruiters, and researchers can conduct skill gap analysis to assess students’ job readiness and tailor training programs accordingly. It serves as a valuable resource for identifying skill deficiencies and skill gaps improving career guidance, and enhancing curriculum design through targeted skill gap analysis.

Following is the column descriptors: Name - Student's full name. email_id - Student's email address. Year - The academic year the student is currently in (e.g., 1st Year, 2nd Year, etc.). Current Course - The course the student is currently pursuing (e.g., B.Tech CSE, MBA, etc.). Technical Skills - List of technical skills possessed by the student (e.g., Python, Data Analysis, Cloud Computing). Programming Languages - Programming languages known by the student (e.g., Python, Java, C++). Rating - Self-assessed rating of technical skills on a scale of 1 to 5. Soft Skills - List of soft skills (e.g., Communication, Leadership, Teamwork). Rating - Self-assessed rating of soft skills on a scale of 1 to 5. Projects - Indicates whether the student has worked on any projects (Yes/No). Career Interest - The student's preferred career path (e.g., Data Scientist, Software Engineer). Challenges - Challenges faced while applying for jobs/internships (e.g., Lack of experience, Resume building issues).
DOI: 10.6084/m9.figshare.24993546.v1
title: self-efficacy

description: The data are related to the study of the effect of education on the self-efficacy of palliative care of nursing students
DOI: 10.6084/m9.figshare.28428731.v1
title: Geoprocessing Data in QGIS (training)

description: This repo contains a series of datasets connected to training on geoprocessing.Within the zipped folder there are two subfolder, one containing raster data and the second one containing vector data.
DOI: 10.6084/m9.figshare.3827151.v1
title: Supplementary Material for: Renal Interstitial Fibrosis: An Imperfect Predictor of Kidney Disease Progression in Some Patient Cohorts

description: <b><i>Background:</i></b> The extent of interstitial fibrosis on kidney biopsy is regarded as a prognostic indicator and guide to treatment. Patients with extensive fibrosis are assigned to supportive treatments with the expectation that they have advanced beyond the point at which immunosuppressive or other disease-modifying therapies would be of benefit. Our study highlights some of the limitations of using interstitial fibrosis to predict who will develop end-stage renal disease (ESRD). <b><i>Methods:</i></b> Analysis of 434 consecutive renal biopsies performed between 2001 and 2012 at a single center. We assessed the influence of various clinical factors along with fibrosis as predictors of ESRD and dialysis-free survival in various patient groups. <b><i>Results:</i></b> Interstitial fibrosis performed well overall as a predictor of progression to dialysis. On average, patients with &gt;50% fibrosis progressed more rapidly than those with either 25-49 or 0-24% fibrosis with a median time to dialysis of 1.2, 6.5 and &gt;10 years, respectively. In contrast, interstitial fibrosis was of less value as a predictor of disease progression in a subset of cases that included patients over the age of 70 and those with diabetic nephropathy on biopsy. Surprisingly, 13.9% of patients with normal renal function had 25-49% fibrosis and 5% had more than 50% fibrosis on biopsy, and 5 years after undergoing biopsy 21% of patients with &gt;50% fibrosis still remained dialysis free. <b><i>Conclusion:</i></b> Renal fibrosis is an imperfect prognostic indicator for the development of ESRD and caution should be exercised in applying it too rigidly, especially in elderly or diabetic patients.
DOI: 10.17632/bcnv3n43wg
title: Classification of Microorganisms of Sukhna and Dhanas Lakes

description: The dataset consists of morphological features that map the body structure for ten different classes of microorganisms. These microorganisms are found in the lakes of Sukhna and Dhanas, Chandigarh, India. The images of microorganisms were captured by taking microscopic images of whole mounted glass slides. This dataset is used to design and construct a generic intelligent machine learning model and a pipeline of algorithms for classifying multiple microorganisms with a high level of accuracy. Following are the features extracted along with their descriptions. 1. Solidity: It is the ratio of area of an object to the area of a convex hull of the object. Computed as Area/ConvexArea. 2. Eccentricity: The eccentricity is the ratio of length of major to minor axis of an object. 3. EquivDiameter: Diameter of a circle with the same area as the region. 4. Extrema: Extrema points in the region. The format of the vector is [top-left top-right right-top right-bottom bottom-right bottom-left left-bottom left-top]. 5. Filled Area: Number of on pixels in FilledImage, returned as a scalar. 6. Extent: Ratio of the pixel area of a region with respect to the bounding box area of an object. 7. Orientation: The overall direction of the shape. The value ranges from -90 degrees to 90 degrees. 8. Euler number: Number of objects in the region minus the number of holes in those objects. 9. Bounding box: Position and size of the smallest box (rectangle) which bounds the object. 10. Convex hull: Smallest convex shape/polygon that contains the object. 11. Major axis: The major axis is the endpoints of the longest line that can be drawn through the object. Length (in pixels) of the major axis is the largest dimension of the object. 12. Minor axis: The axis perpendicular to the major axis is called the minor axis. Length (in pixels) of the minor axis is the smallest line connecting a pair of points on the contour. 13. Perimeter: Number of pixels around the border of the region. 14. Centroid: Centre of mass of the region. It is a measure of object’s location in the image. 15. Area: Total number of pixels in a region/shape.
DOI: 10.17632/nmg8p8jrhb
title: Mendeley Supplemental Figure 1

description: Eosinophils in traditionally non-eosinophil rich dermatoses
DOI: 10.6084/m9.figshare.15170736.v2
title: AbstractAnalysisMetadata

description: Includes an abstract database and various text files used for curation of MGE protein function
DOI: 10.6084/m9.figshare.8279618.v3
title: Accurate inference of tree topologies from multiple sequence alignments using deep learning

description: Each directory contains Training, Validation and Test datasets in fasta format. Directory names contain the following information about the MSAs: <br>1) Gap (w/ indels) or nogap (w/o indels) MSAs2) Number of MSAs per topology generated for training (i.e. 50k MSA * 3 possible topologies = 150k MSAs in TRAIN.fasta file) Order of topologies: 50k x ((D,A),B,C); 50k x ((D,B),A,C); 50k x ((D,C),A,B);3) MSA length (500, 1000 or 10000)4) archive3 contains Keras models and its associated trained weights. They will generate identical CNN accuracies reported in Fig. 2 <br>List of all directories:<b>archive1_gap.tar.gz</b> gap50k_1000 gap50k_10000 gap50k_500<b>archive2_nogap.tar.gz</b> nogap150k_1000 nogap300k_1000 nogap50k_1000 nogap50k_10000 nogap50k_500<b>archive3_trained_keras.tar.gz</b><br>gap50k_1000<br>nogap150k_1000nogap300k_1000nogap50k_1000<br>
DOI: 10.6084/m9.figshare.26318407.v1
title: Non-muscle powered actuation inspired by cellular morphology and hydraulics of the Venus flytrap

description: This is the sharing file for the journal paper Non-muscle powered actuation inspired by cellular morphology and hydraulics of the Venus flytrap.
DOI: 10.6084/m9.figshare.27901797.v2
title: MEE2025-Integrated movement models for individual tracking and species distribution data-Supplement

description: Simulated data and code to fit the integrated movement model described in: <i>Buderman et al. 2025. </i><i>Integrated movement models for individual tracking and species distribution data. Methods in Ecology and Evolution.</i>supplement.data.rdata is an R workspace containing simulated data<br>supplement.code.R is an R script to fit the model<br>mcmc.jmm.ou.R is the MCMC sampler for the model, called by supplement.code.Rout.sim.rdata contains samples from fitting the MCMC algorithm to the simulated data<br>
DOI: 10.17632/3j83sv5zdv
title: RAW DATA SPSS AND QUESTIONNAIRE (MALAY AND ENGLISH VERSION)

description: The data set provides insightful information based on a survey related to the socioeconomic demographics of drug-free parents living in high-risk environments and their involvement in drug prevention programs. The survey involved 200 families living in high-risk drug environments located in East Coast states of Peninsular Malaysia. The data includes a significant group of variables (A) socioeconomic demographic including; gender, age, race, marital status, place of current residents, number of children, number of children in primary school, number of children in secondary school, number of working children, number of children with disabilities, number of household members, level of education and employment categories, (B) parents involvement in drug prevention programs organised by various agencies including National Anti-Drug Agency, Ministry of Education, Ministry of Youth and Sport, The National Population and Family Development Board (LPPKN), Department of Information (Ministry of Communications and Multimedia), Department of National Unity and National Integration (Ministry of National Unity) and Non-Government Organisation (NGOs), parents’ interest towards drug prevention program and reasons for involvement. Each question regarding parent involvement is rated on a nominal scale in such a way that scores are given for 'Yes' for parents who involved in drug prevention programs and 'No' for parents who never involved in any drug prevention programs. Further question for parents’ interest towards drug prevention programs is rated by Yes because of seek new information, increase knowledge, availability and No because of commitment and health condition
DOI: 10.6084/m9.figshare.14115515.v1
title: SNA Is is conspiracy or truth.xlsx

description: This data set has:- Comments manually collected from a YouTube video containing the 5G conspiracy theory articulated as legiitmate truth <br>- Number of followers and followed Twitter users found on posts that shared the aforementioned video- Number of posts identified on Facebook sharing the same video and their respective number of followers<br>
DOI: 10.6084/m9.figshare.19633404.v25
title: Code snippet suggestions for exploratory programming - User study

description: This dataset contains participant notebooks, scripts for analysis, study instruments and pilot study results.
DOI: 10.6084/m9.figshare.28861451.v1
title: SEM-HR-IPMA

description: Human Resources-IPMA Analysis via SEM PLS
DOI: 10.6084/m9.figshare.29134814.v1
title: 细菌原始序列读数

description: 比较各组细菌多样性
DOI: 10.6084/m9.figshare.14935983.v1
title: Predator kairomone triggers sexual reproduction of Daphnia population via increasing population density

description: Original data of the paper.
DOI: 10.6084/m9.figshare.26112346.v1
title: Sample dataset associated with the manuscript: "Ungated, plug-and-play preclinical cardiac CEST-MRI using radial FLASH with segmented saturation"

description: An example dataset used in the paper "Ungated, plug-and-play preclinical cardiac CEST-MRI using radial FLASH with segmented saturation." This dataset contains ECG gated and ungated acquisitions with a saturation power of 1.1uT, and ungated acquisitions with saturation powers of 1.8, 2.4, and 3.1uT. The experiment numbers are as follows:E14 - 1.1uT, 4 traj/seg, 300 projections, ECGE12 - 1.1uT, 20 traj/seg, 1000 projections, ungatedE8 - 1.8uT, 20 traj/seg, 1000 projections, ungatedE9 - 2.4uT, 20 traj/seg, 1000 projections, ungatedE10 - 3.1uT, 20 traj/seg, 1000 projections, ungatedThese data can be processed using the code at https://github.com/jweigandwhittier/cestsegCSUTE_scripts.
DOI: 10.6084/m9.figshare.26355529.v1
title: Sharks in Scotland - Shark Sightings over 145 years

description: The dataset encompassing 3844 observations over 145 years (1879-2024) provides a comprehensive historical perspective on shark sightings in Scotland. Despite the wide range of years, the diversity is represented by 12 distinct species. This longitudinal view underscores the stability and persistence of certain species over time, while highlighting potential shifts in distribution or abundance due to environmental factors or human impacts. Continued monitoring and public engagement are crucial to further understanding and safeguarding Scotland's diverse shark populations amid ongoing environmental challenges.
DOI: 10.6084/m9.figshare.4658728.v1
title: Data Set 3: Transcription factors from the network based on statistically validated interactions missing in MTBC main lineages or sublineages.

description: Each row of the table corresponds to a mutation found in a TF of the regulatory network. Each row number agree with the number set in the Fig. S1A. Strain names came from a previous work (1). The RD column reffers to the Region of Difference (if any) associated to this TF's deletion. The regulatory influence in the network refers to the number of genes regulated by the mutated TF in network derived from H37Rv network. The column principal GO terms overrepressented on the subnetwork came from the Gene Set Enrichment Analysis performed over the genes regulated by the TF (see Material and Methods for details).
DOI: 10.17632/9rwbkf5p4g
title: Designing Cost-Effective Open-Source Multihead 3D Bioprinters

description: Over the past decade, additive manufacturing has resulted in significant advances toward fabricating anatomic-size, patient-specific scaffolds for tissue models and regenerative medicine. This can be attributed to the development of advanced bioinks capable of precise deposition of cells and biomaterials. The combination of additive manufacturing with advanced bioinks is enabling researchers to fabricate intricate tissue scaffolds that recreate the complex spatial distributions of cells and bioactive cues found in the human body. However, the expansion of this promising technique has been hampered by the high cost of commercially available bioprinters and proprietary software. In contrast, conventional 3D printing has become increasingly popular with home hobbyists and caused an explosion of both low-cost thermoplastic 3D printers and open-source software to control the printer. In this work, we bring these benefits into the field of bioprinting by converting widely available and cost-effective 3D printers into fully functional, open-source, and customizable multi-head bioprinters. These bioprinters utilize computer-controlled volumetric extrusion, allowing bioinks with a wide range of flow properties to be bioprinted, including non-Newtonian bioinks. We demonstrate the practicality of this approach by designing bioprinters customized with multiple extruders, automatic bed leveling, and temperature controls for approximately $400. These bioprinters were then used for in vitro and ex vivo bioprinting to demonstrate their utility for tissue engineering.
DOI: 10.6084/m9.figshare.28451660.v1
title: <b>Role of galectin-9 in the development of gestational diabetes mellitus</b>

description: Galectin-9 (Gal-9) is highly expressed in trophoblasts in placenta. Interaction between Gal-9 and T-cell immunoglobulin and mucin-domain containing-3 (Tim-3) is important for the differentiation of tissue resident natural killer (trNK) cells in placenta and maintenance of normal pregnancy. Furthermore, the enhanced maternal systemic inflammation associated with increased proinflammatory cytokines in preeclampsia is mediated by enhanced interaction between Gal-9 and Tim-3. However, the role of Gal-9 in gestational diabetes (GDM) remains unexplored. Plasma Gal-9 levels were elevated at 3rd trimester in pregnant women with GDM and positively correlated with placenta and newborn weight. <i>Lgals9</i> knockout pregnant mice fed with high fat diet (HFD KO) demonstrated maternal glucose intolerance and fetus macrosomia compared with wild pregnant mice (HFD WT). In HFD KO, increased proliferating cells, reduced apoptosis, and autophagy impairment were observed in junctional zones. The number of trNK cells and percentage of Tim-3+ trNK increased, while early apoptosis percentage in Tim-3+ trNK was reduced in placenta of HFD KO. The elevation of plasma Gal-9 may be a biomarker for prediction of maternal glucose intolerance and fetal macrosomia in pregnant women with GDM and Gal-9 functions as a compensation factor for GDM by inducing apoptosis in Tim-3+ trNK cells.
DOI: 10.6084/m9.figshare.28740974.v2
title: Supplementary Table 2 for Ghias et al.

description: Supplemental Table 2 for Ghias et al.
DOI: 10.17632/28jgtynrb4
title: Performance of 11C-FMZ versus 18F-FDG in PET

description: File name: 18F-database Description: 18F-FDG-PET outcomes in the articles included in this meta-analysis: absolute numbers of true positive, false positive, true negative and false negative. File name: 11C-database Description: 11C-FMZ-PET outcomes in the articles included in this meta-analysis: absolute numbers of true positive, false positive, true negative and false negative. File name: roc Description: Outcomes of 18F-FDG-PET, 11C-FMZ-PET and gold standard in localization of epileptogenic areas, “1” stands for positive, and “0” stands for negative.