cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28263002.v1
title: Supplementary Material for: Deep Learning-Based Detection of Ocular Surface Squamous Neoplasia from Ocular Surface Images

description: Purpose: Ocular Surface squamous neoplasia (OSSN) is a broad entity encompassing a spectrum of squamous neoplasms of conjunctiva and cornea. This study aims to explore the utility of Artificial Intelligence (AI) models in detecting OSSN from slit lamp (SL) images.
Methods: This is a retrospective observational study. Slit lamp (SL) images of OSSN disease, non-OSSN ocular surface lesions (OOSD) and normal ocular surfaces (N) were collected (2013-2023). Images with minimum resolution of 1024 x 1024 pixels, under diffuse illumination were included. Data was divided into training and testing sets (85:15). Deep learning (DL) algorithms were applied for ternary classification of the SL images (OSSN, OOSD and normal). Three AI models - MobileNetV2, Xception and DenseNet121 were used in the study. A five-fold cross-validation strategy was utilized for robust model evaluation.
Results: 163 images in OSSN group, 202 in OOSD group and 269 normal ocular surface images were included (n=634). Data augmentation was performed to increase and balance the data. The average accuracies for OSSN detection for DenseNet121, MobileNetV2 and Xception were 83%, 88.8%, and 84.5% respectively. MobileNetV2 and Xception had a similar average sensitivity for OSSN detection (74%) while MobileNetV2 was the most specific DL algorithm (96.25%) for OSSN detection.
Conclusions: AI models showed good performance in image-based OSSN detection. AI models may provide a promising tool for OSSN screening in primary health care centers and for teleconsultation from remote areas in the future.
DOI: 10.6084/m9.figshare.5117119.v2
title: Understanding Academic Research: Free and Low-Cost Tools & Workflows

description: ALCTS Scholarly Communications Interest Group: Understanding Academic Research: Free and Low-Cost Tools & Workflows <i></i><b>Saturday, June 24 </b><i></i><b>1:00 PM - 2:30 PM </b><i></i><b>Location: Hyatt Regency McCormick, Clark/CC 22AB </b><b><br></b>Much of the academic research world remains available only to institutions that are able to pay for subscriptions to specific tools and resources. However, over the last few years, vendors, funders, and other organizations have developed a number of free and low-cost tools targeted to a range of stakeholders. These tools and resources help support users inside and beyond institutions to discover, understand and use academic research.
DOI: 10.6084/m9.figshare.20272737.v1
title: Data for "The origin of placental mammal life histories"

description: This dataset contains images of palaeohistological slides and raw LA-ICP-MS geochemical data used in the study. Please consult the readme file for more information about what is contained within the zip archives. Each zip archive has its own readme file providing more information about the data within that archive.
DOI: 10.6084/m9.figshare.1561268.v1
title: BIOL 2050 Dataset 1: Danby Grassland Diversity in Vegetation

description: The data was collected at Danby Grassland at York University: Keele Campus on Thursday, September 24 2015 at approximately 3:00pm. The duration of the experiment took approximately 40 minutes and was completed at around 3:40pm. The temperature was 22°C and it was sunny with clear skies. The area site where the sampling took place was large, dry and open. The vegetation increased as you went further into the site. Species such as Common Milkweed, Common Plantain, Grass, and White Oak were predominate in the area of interest. The sampling was done in groups of four and was divided amongst the group members: Azeb, Eman, Bava and Saraswatie. This particular sampling was conducted by Azeb with the help of Eman. The purpose of this lab was to obtain experience in sampling and publishing data by becoming familiar with the tools and conducting fieldwork experiments. Overall, dataset one was meant to determine the level of diversity in vegetation in a given area. The sample size for this dataset is 20. The data was collected using 1m x 1m metal square quadrats which were placed randomly across the grassland. The plots were scattered randomly to obtain and accurate representation of the area. This was repeated and recorded 20 times using pen and paper in an organized chart. The total abundance was determined by counting every individual plant present within the quadrats with exception of grass which was counted as a whole instead of counting each individual blade. Total percent coverage and total grass coverage was determined by visually estimating the percentage of vegetation and grass occupied in the quadrats. The total number of different species was identified by analyzing the plant based on features such as colour, shape, and size.
DOI: 10.6084/m9.figshare.1245571.v1
title: Meta data lab report- Effect of density on abundance of crawling and flying insects

description: A transect will place in the grasslands near stong pond, and pan traps and pitfalls will be placed within a distnace of the trasect. Traps will be placed in thre diferrent<br>distinct habitats; low amount of plants, medium, and high. The quadrant can be manipulated by pulling out plants to make it one of the three categories. Plant abudance and plant density will be recorded. Plant density will be measured on a scale of low, medium,a nd high.<br>The two traps will be placed near each other (within 10 cm) and then checked to group the insects as either flying or non-flying based on their primary means of travel.<br>The experiment will be done twice in the span of 2 weeks. The traps are set around 2:30, then checked the next day after minimum of8-10 hours have passed.<br>A total of 30 trials will be conducted, 10 trials in each habitat, of low, medium, and high.
DOI: 10.6084/m9.figshare.22580986.v1
title: Student-teachers interviews 1.docx

description: In this archive, there are the transcripts of the three interviews that student-teacher gave at the end of the teaching practicum.
DOI: 10.6084/m9.figshare.13258826.v1
title: Evidencing the need for psycho-socio-economic action to support the rural upskilled youth to cope with the COVID-19 health crisis: a state-wide audit

description: This data was collected in the context of a COVID-19 national lockdown from 14430 youth, who had been trained through the Deen Dayal Upadhyaya Grameen Kausalya Yojana (DDU-GKY). The aim was to understand the determine the grouping of modifiable impacts of COVID-19 among the rural poor youth working in unorganized sectors in Kerala, India <br>
DOI: 10.6084/m9.figshare.19330541.v4
title: Interpreting the importance of transcript features in protein abundance prediction models

description: Supplementary Tables S1-S8.<br>Protein and mRNA levels correlate only moderately across samples. The availability of proteogenomics data sets with protein and transcript measurements from matching samples is providing new opportunities to assess the degree to which protein levels in a system can be predicted from mRNA information. We analyzed large proteogenomics datasets from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) containing 13 637 proteins with matching transcriptome data from up to 958 tumor or normal adjacent tissue samples each, and trained models to predict protein abundance from the transcriptome of a sample using different algorithms and feature sets.<br>
DOI: 10.6084/m9.figshare.25855456.v1
title: FACTORS AFFECTING RESPONSIBILITY ACCOUNTING AND ITS IMPACT ON MANAGEMENT PERFORMANCE OF TEXTILE AND GARMENT ENTERPRISES IN VIETNAM

description: Data for this study mainly comes from responses of managers and accounting staff at large-scale garment enterprises in Vietnam via questionnaires. The collected answers are imported into SPSS and Amos software to calculate, and create data for the author to analyze. This dataset includes SPSS data imported, CFA and AMOS result. Respondents consented to publication of results from this study on the condition that their identity will not be revealed (stated in their consent form). Therefore, the dataset does not include their personal information.
DOI: 10.6084/m9.figshare.27992417.v5
title: CMAB-The World's First National-Scale Multi-Attribute Building Dataset

description: Rapidly acquiring three-dimensional (3D) building data, including geometric attributes like rooftop, height and orientations, as well as indicative attributes like function, quality, and age, is essential for accurate urban analysis, simulations, and policy updates. Current building datasets suffer from incomplete coverage of building multi-attributes. This paper presents the first national-scale Multi-Attribute Building dataset (CMAB) with artificial intelligence, covering 3,667 spatial cities, 31 million buildings, and 23.6 billion m² of rooftops with an F1-Score of 89.93% in OCRNet-based extraction, totaling 363 billion m³ of building stock. We trained bootstrap aggregated XGBoost models with city administrative classifications, incorporating morphology, location, and function features. Using multi-source data, including billions of remote sensing images and 60 million street view images (SVIs), we generated rooftop, height, structure, function, style, age, and quality attributes for each building with machine learning and large multimodal models. Accuracy was validated through model benchmarks, existing similar products, and manual SVI validation, mostly above 80%. Our dataset and results are crucial for global SDGs and urban planning.Data records: A building dataset with a total rooftop area of 23.6 billion square meters in 3,667 natural cities in China, including the attribute of building rooftop, height, structure, function, age, style, colour and quality, as well as the code files used to calculate these data. The deep learning models used are OCRNet, XGBoost, fine-tuned CLIP and Yolo-v8.Reference Format：Zhang, Y., Zhao, H. & Long, Y. CMAB: A Multi-Attribute Building Dataset of China. Sci Data 12, 430 (2025). https://doi.org/10.1038/s41597-025-04730-5.
DOI: 10.6084/m9.figshare.6148604.v1
title: Global χ Maps Dataset - Data Record A

description: χ maps in shapefile format
DOI: 10.6084/m9.figshare.7284803.v1
title: Data from: "Spatial ecology of a range-expanding bumble bee pollinator"

description: .CSV files containing microsattelite genotypes; sampling locations of <i>Bombus hypnorum </i>workers sampled in Norwich, UK in 2014 and 2015; and microsattelite genotypes of collected queens collected in 2014 and their dissected spermathecae.<br>Datta associated with the paper: ....
DOI: 10.6084/m9.figshare.5197123.v2
title: POLAR UVI images

description: Permanent FTP link at cdaweb.gsfc.nasa.gov and instructions for downloading and pre-processing of POLAR UVI images used in the papers "Data-derived optimization of sensitivity requirements for upcoming auroral imaging missions" by Eric Donovan, Vadim Uritsky, Craig Unick, and Vladimir Troyan (JGR, 2017)<br>
DOI: 10.6084/m9.figshare.22191193.v1
title: Zhang_Zagha_code_data_NatureComm

description: A. What is this? Codes and data in this folder are used in the article titled "Motor Cortex Gates Distractor Stimulus Encoding in Sensory Cortex" by Zhaoran Zhang and Dr. Edward Zagha. Data in this folder are collected by Zhaoran Zhang. Scripts in this folder are written by Zhaoran Zhang. <br> B. What does it include? This folder contains 4 parts: 1.example sessions of original data; 2.preprocessed data; 3.MatLab scripts for plotting figures using original data or preprocessed data, depending on certain plots; 4.MatLab functions needed for running the scripts. <br> C. How to use it? 1. Software: The code in this folder is based on Matlab (R2019b). Certain toolbox might be required to run the code. Other version of Matlab should also be fine, but there might be small errors relating to Matlab functions. 2. Code and data: 2.1 After downloading the folder, please unzip it and save it directly in C drive. (If C drive is not available, data pathways in the code need to be modified accordingly.) 2.2 Open Matlab. Copy and paste "C:\Zhang_Zagha_code_data_NatureComm\functions" in the Current Directory, then press "Enter". 2.3 Right click the blank space in current folder. Click "Add to Path", then click "Current Folder". 2.4 Open any code script in Matlab. 2.5 Use "Run and Advance" to obtain all the figures. No need to make any change in the code.
DOI: 10.6084/m9.figshare.22329079.v1
title: 2023_03_16 figure source data - Estimating coastal flood damage costs to transit infrastructure under future SLR.xlsx

description: Source data for generating figures presented in an upcoming journal publication: <br> Martello, M. V. & Whittle, A. J. (2023). Estimating coastal flood damage costs to transit infrastructure under future sea level rise. Communications Earth & Environment.
DOI: 10.6084/m9.figshare.19134989.v1
title: gellan gum raw data.xlsx

description: <b>Gellan gum prevent non-alcoholic fatty liver disease via improving gut microbiota and metabolites</b>
DOI: 10.6084/m9.figshare.21219926.v1
title: LV Pedestrian Bridge.xlsx

description: Data set of all the auto vs. ped collisions and injuries that occured on four intersections of the the Las Vegas Strip between July 1997 and March 2019. The intersections are as follows. 1) Las Vegas Boulevard & Sahara Avenue 2) Las Vegas Boulevard & Flamingo Road 3) Las Vegas Boulevard & Spring Mountain Road 4) Las Vegas Boulevard & Harmon Avenue
DOI: 10.6084/m9.figshare.22817651.v1
title: arxiv_summary

description: https://github.com/usnistgov/chemnlp
DOI: 10.6084/m9.figshare.21066223.v1
title: Supplementary Tables

description: Supplementary Table 1-7
DOI: 10.6084/m9.figshare.23604333.v1
title: Global <em>Mallocybe</em> 4-locus alignment (supermatrix)

description: Supermatrix of 4 gene regions (ITS, 28S, <em>rpb1</em>, and <em>rpb2</em>) with a focus on global sampling of the genus <em>Mallocybe</em> (Inocybaceae, Agaricales). Nucleotide datset, including RAxML and IQ-TREE tree files and gene and codon partition scheme.
DOI: 10.25405/data.ncl.11534988
title: Critical transport and vortex dynamics in a thin atomic Josephson junction

description: The description of the data contained in different files can be found in the file Description_data.txt. The simulations were performed on Newcastle's HPC service Topsy while the experimental data are taken by the experimental group of Li6 at European Laboratory for Non-Linear Spectroscopy (LENS), Florence.
DOI: 10.6084/m9.figshare.1048760.v1
title: Gaussian Job Archive for C16H16(3)

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.28816496.v1
title: Figure 14 Dataset

description: Comparison of the average path length, number of nodes, and step count of the algorithms in the test environments.
DOI: 10.6084/m9.figshare.3436388.v2
title: The diagnostic performance of thyroid US in each category of the Bethesda System for Reporting Thyroid Cytopathology

description: Raw data, which were analyzed for the diagnostic performance of thyroid US in each category of the Bethesda System for Reporting Thyroid Cytopathology.
DOI: 10.6084/m9.figshare.5620651.v4
title: Empirical Analysis of Ranking Models for an Adaptable Dataset Search: complementary material

description: This repository contains performance measures of dataset ranking models.<br>- Usage: from Results/src run <i>Python results m<sub>1</sub> m<sub>2</sub> ...</i><br>such that <i>m<sub>i</sub></i> can be omitted, or be any element of the list of model labels ['bayesian-12C', 'bayesian-5L', 'bayesian-5L12C', 'cos-12C', 'cos-5L', 'cos-5L5C', 'j48-12C', 'j48-5L', 'j48-5L5C', 'jrip-12C', 'jrip-5L', 'jrip-5L5C', 'sn-12C', 'sn-5L', 'sn-5L12C']. Results of selected models will be plotted in a 2D line plot. If no model is provided all models will be listed.<br>
DOI: 10.6084/m9.figshare.11743572.v1
title: Supplementary Material for: Examination of Treatment Satisfaction Instruments in Psoriasis: 2017 Results from the Psoriasis Working Group of the International Dermatology Outcome Measures (IDEOM)

description: <b><i>Background/Aims:</i></b> In dermatology clinical trials, assessment of patients’ treatment satisfaction is crucial but often lacking. To address this need, IDEOM’s Psoriasis Working Group seeks to evaluate, develop, and validate treatment satisfaction instruments for the psoriasis population. The Psoriasis Working Group aimed to determine (1) factors affecting psoriasis patients’ satisfaction with their therapies, (2) adequacy of two commonly used generic treatment satisfaction instruments in reflecting the psoriasis patients’ perspective, and (3) whether a need exists to develop a new treatment satisfaction instrument. <b><i>Methods:</i></b> Patient perspectives on satisfaction with treatment efficacy, safety, convenience, and overall satisfaction were elicited.<b><i></i></b>Stakeholders were presented with information regarding the feasibility and content validity of two generic treatment instruments, the Treatment Satisfaction Questionnaire for Medication (TSQM) and the Treatment Satisfaction with Medicines Questionnaire (SATMED-Q). We conducted a nominal group discussion and survey to determine whether stakeholders considered these instruments feasible and adequate to address treatment satisfaction for psoriasis therapies. <b><i>Results:</i></b> Forty-five stakeholders participated in the nominal group discussion and survey. 53% of participants voted that the TSQM and SATMED-Q are not adequate and that we should create a new dermatology-specific treatment satisfaction instrument. Patients and other stakeholders also provided feedback on aspects of treatment satisfaction important to them. These include speed of onset and durability of therapeutic effect of a medication, permanence of side effects, and convenience of administering the medication. <b><i>Conclusion:</i></b> Stakeholders, including patients and providers, determined that generic treatment satisfaction questionnaires are not adequate to evaluate treatment satisfaction in psoriasis patients.
DOI: 10.6084/m9.figshare.19651746.v3
title: AbEMap results: Confusion Matrix for BM5.5 Ab crystal inputs

description: The summary confusion matrix (residue based) for the epitope mapping done by AbEMap on 21 of the new antigens added to BM5.5 with antibody crystal structures as input. The text file contains the True Positive (TP), False Positive (FP), False Negative (FN) and True Negative (TN) counts for each antibody-antigen complex at different residue ranking thresholds (Top1, Top5, Top10, ... Top120).
DOI: 10.6084/m9.figshare.13228301.v1
title: Comprehensive Inventory of Thriving - dataset

description: the dataset contains data collected from 483 Italian participants who filled in different psychometrics scales measuring thriving and well-being.
DOI: 10.6084/m9.figshare.25019903.v2
title: Single-cell integrative analysis reveals consensus cancer cell states and clinical relevance in breast cancer

description: High heterogeneity and complex interactions of malignant cells in breast cancer has been recognized as a driver of cancer progression and therapeutic failure. However, complete understanding of common cancer cell states and their underlying driver factors remain scarce and challenging. Here, we revealed seven consensus cancer cell states recurring cross patients by integrative analysis of single-cell RNA sequencing data of breast cancer. The distinct biological functions, the subtype-specific distribution, the potential cells of origin and the interrelation of consensus cancer cell states were systematically elucidated and validated in multiple independent datasets. We further uncovered the internal regulons and external cell components in tumor microenvironments, which contribute to the consensus cancer cell states. Using the state-specific signature, we also inferred the abundance of cells with each consensus cancer cell state by deconvolution of large breast cancer RNA-seq cohorts, revealing the association of immune-related state with better survival. Our study provides new insights for the cancer cell state composition and potential therapeutic strategies of breast cancer.
DOI: 10.6084/m9.figshare.1406903.v1
title: Microbiota data level 6 ~ species

description: Level 6 data (species) microbiota data of jejunum content from two divergent chicken broiler lines at day 0, 4, and 16. Sequenced by Illumina MiSeq technology.
DOI: 10.6084/m9.figshare.13513548.v3
title: A comparative study on dispersed doses during photon and proton radiation therapy in pediatric applications

description: The Monte Carlo method was employed to simulate realistic treatment situations for photon and proton radiation therapy for a set of Oak Ridge National Laboratory (ORNL) pediatric phantoms for 15, 10, 5 and 1-year olds as well as newborns. Complete radiotherapy situations were simulated using the previously developed NRUrad input code for Monte Carlo N-Particle (MCNP) code package. Each pediatric phantom was irradiated at five different positions, namely, the testes, colon, liver, left lung and brain, and the doses in targeted organs (<i>D<sub>t</sub></i>) were determined using the track length estimate of energy. The dispersed photon and proton doses in non-targeted organs (<i>D<sub>d</sub></i>), namely, the skeleton, skin, brain, spine, left and right lungs were computed. The conversion coefficients (<i>F = D<sub>d</sub>/D<sub>t</sub></i>) of the dispersed doses were used to study the dose dispersion in different non-targeted organs for phantoms for 15, 10, 5 and 1-year olds as well as newborns. In general, the <i>F</i> values were larger for younger patients. The <i>F</i> values for non-targeted organs for phantoms for 1-year olds and newborns were significantly larger compared to those for other phantoms. The dispersed doses from proton radiation therapy were also found to be significantly lower than those from conventional photon radiation therapy. For example, the largest <i>F</i> values for the brain were 65.6% and 0.206% of the dose delivered to the left lung (<i>P<sub>4</sub></i>) for newborns during photon and proton radiation therapy, respectively. The present results demonstrated that dispersion of photons and generated electrons significantly affected the absorbed doses in non-targeted organs during pediatric photon therapy, and illustrated that proton therapy could in general bring benefits for treatment of pediatric cancer patients.
DOI: 10.6084/m9.figshare.c.3471585_d62.v1
title: Mace Head, Ireland, particle number concentration, 2011-2012

description: particle number concentration, Mace Head, Ireland, 2011-2012
DOI: 10.6084/m9.figshare.27922932.v1
title: Data of L1MFPC

description: Some data downloaded from UCI for training clustering models.