cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19773049.v1
title: Dataset for "Higher immigration and lower land take rates are driving a new densification wave in European cities"

description: Dataset for the article: Cortinovis C., Geneletti D., Haase D. (2022). Higher immigration and lower land take rates are driving a new densification wave in European cities. npj Urban Sustainability 2<strong>,</strong> 19. doi:10.1038/s42949-022-00062-0 <br> The dataset includes three excel workbooks. The main workbook "<strong>EU_cities_data.xlsx</strong>" calculates residential density in 2006, 2012 and 2018 and its trends in the two periods 2006-2012 and 2012-2018 for 331 European cities and greater cities. The worksheet "CITIES_GREATER_sel" is the basis to reproduce the analyses and figures presented in the paper and related Supplementary Material, using the code R code "EU_cities_plot" available at doi:10.6084/m9.figshare.19773151. <br> The main workbook is linked to two datasets containing demographic data for the selected cities and greater cities: - <strong>EU_cities_population.xlsx</strong>: total population at the 1st of January of the three reference years; - <strong>EU_cities_demo_balance.xlsx</strong>: data on births and deaths in each city during the analysed period, and calculation of net migration figures. <br> Population data are based on the Eurostat Urban Audit (https://ec.europa.eu/eurostat/web/cities/data/database) and on datasets from national statistical offices. The numerous corrections to the original Urban Audit database and the final source of each value are indicated in the two datasets (see worksheet "sources and legend"). Residential area and land take for residential use are based on the Copernicus Urban Atlas (https://land.copernicus.eu/local/urban-atlas) version 021 for the reference year 2012, version 012 for the reference year 2018, and the consolidated “Revised” version available in 2021 for the reference year 2006. <br> Cities’ and greater cities’ boundaries were retrieved from the GISCO Eurostat spatial database linked to the Urban Audit, version 2018 ( https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/urban-audit).
DOI: 10.6084/m9.figshare.21639749.v1
title: Interview data for the near-peer teaching project

description: This is the interview data for the manuscript <strong>Near-peer Teaching in Problem-based Learning: Perspectives from Tutors and Tutees. </strong>
DOI: 10.6084/m9.figshare.22712365.v1
title: Maximum and Minimum temperature at a coarser resolution (PISCOt v1.2)

description: Maximum and Minimum air temperature from PISCOt v1.2. at coarser resolution: 0.05° and 0.10° spatial resolution. Daily values from 1981 to 2020.
DOI: 10.17632/b83hmrzth8
title: Sun Flower Fruits and Leaves dataset for Sunflower Disease Classification through Machine Learning and Deep Learning

description: (1) The world economy largely depends on vegetable production, whereas The sunflower (Helianthus annuus) is an annual plant with a sizeable daisy-like flower face.  Because of the early disease recognition of sunflower, the farmer cannot produce high yield and face economic loss.
(2) In the recent era, image classification and detection is crucial application of Computer Vision. 
(3) In this dataset, two types files of Sunflower images are included, namely the original image file and the augmented image file. Three types of diseases, namely downy mildew, leaf scars, and Gray Mold, exist in every file. Besides, the fresh leaves image of a sunflower is also included.  
(4) A total of four hundred and sixty-seven images (467) are collected from the sunflower garden contained in the original image file. Then the augmentation operation is performed to boost the images, and a total of one thousand and six hundred sixty-six (1668) images are contained in the augmented image file. 
(5) The images of sunflower (disease-affected and disease-free) are assembled from the Bangladesh Agricultural Research Institute (BARI) demonstration farm at Gazipur with the help of domain expects.
DOI: 10.6084/m9.figshare.26382538.v2
title: Supplemental Materials_072924.pdf

description: This is the supplemental materials for AJP-Lung
DOI: 10.6084/m9.figshare.28811573.v1
title: Physical Performance Data Tigres UANL. 2024

description: Analysis of external load obtained through the WIMU Pro system in Liga MX players from Tigres UANL, Mexico's first division, during the 2021–2023 seasons. (Postdoctoral project on football performance)"
DOI: 10.17632/d24fmr56db
title: Fe-3%Si cold-rolled silicon steel data: EBSD, tensile test, and X-ray diffraction.

description: This database contains results of Fe-3%Si cold-rolled silicon steel characterizations. There are folders containing Electron Backscatter Diffraction (EBSD), tensile tests at 25 and 150 ºC, and X-ray diffraction results available. For EBSD data, there is information about the crystallography directions and grain sizes. For tensile tests at 25 and 150 ºC, there are the strain-stress curves, and for X-ray diffraction, there are the data to get the diffraction graph and scan parameters.
DOI: 10.17632/grtwryxfbd
title: DATASET - Prevalence of musculoskeletal disorders among dental students A systematic review and meta-analysis

description: The aim of this study was to determine the prevalence of work-related musculoskeletal disorders (WRMSD) in dental students and associated factors. We hypothesized that we would find high global prevalence rates, especially affecting the neck, low back, and shoulder regions.
DOI: 10.17632/sv5kv6tk94
title: Dataset for unrevealing the application of MGIDI and FAI-BLUP models in the identification of high-yielding and stable barley genotypes

description: Raw dataset for calculation of stability parameters related to the report entitled "Dataset for unrevealing the application of MGIDI and FAI-BLUP models in the identification of high-yielding and stable barley genotypes"
DOI: 10.25452/figshare.plus.24160713.v1
title: scPerturb Single-Cell Perturbation Data: RNA and protein h5ad files

description: This repository contains the single-cell RNA and protein datasets as h5ad files scRNA-seq and protein datasets within the scperturb database as h5ad files (saved with scanpy v1.9.1.) from the scperturb database.<br>In order to facilitate development and benchmarking of computational methods in systems biology, we collected a set of 44 publicly available single-cell perturbation-response datasets with molecular readouts, including transcriptomics, proteomics and epigenomics. We applied uniform quality control pipelines and harmonize feature annotations. The resulting information resource enables efficient development and testing of computational analysis methods, and facilitates direct comparison and integration across datasets. In addition, we describe E-statistics for perturbation effect quantification and significance testing, and demonstrate E-distance as a general distance measure for single-cell data as both a python (scperturb on PyPI) and R (scperturbR on CRAN) package.<br>See the associated publication for info on how the data was handled. We also have an interactive table (Data Explorer) on our website with metadata per dataset.
DOI: 10.6084/m9.figshare.28942505.v1
title: MM-TBA

description: MM-TBA is a comprehensive multi-modal dataset designed for analyzing teacher behavior in offline classroom settings. MM-TBA consists of three sub-datasets: the Teacher Teaching Action Detection Sub-Dataset, the Teacher Lecture Evaluation Sub-Dataset, and the Teaching Design Sub-Dataset. The Teacher Teaching Action Detection Sub-Dataset includes visual feature files of teachers' teaching actions and action classification annotation files. The Teacher Lecture Evaluation Sub-Dataset comprises the text of teachers' lectures and corresponding evaluation reports. The Teaching Design Sub-Dataset contains the original teaching designs and their respective annotation files. The aim of introducing MM-TBA is to promote scientific research in teacher behavior and cognitive science, provide new research tools for educational science, and foster the development of intelligent education technology.
DOI: 10.17632/mprsccwxb7
title: Biglycan breast cancer dataset

description: Images of histological sections with and without breast cancer, using the Biglycan biomarker.
DOI: 10.6084/m9.figshare.22689751.v18
title: Mapping annual 10-m maize cropland changes in China during 2017-2021

description: Mapping annual 10-m maize cropland changes in China during 2017-2021<br><b>The format of data is: [adcode]_[year].tif</b><b>the [adcode] is a unique code for province of China. (</b> More detail: DataV.GeoAtlas地理小工具系列 (aliyun.com) <b>)</b><b>另有国内备份：</b>链接：https://pan.baidu.com/s/1vs_yXtg4H7PkexJfYHZGBw?pwd=3g0b 提取码：3g0b <br>
DOI: 10.6084/m9.figshare.28823045.v1
title: alldata

description: <b>Social Exclusion’s Effect on Sense of Agency, Moral Cognition and Psychological Needs from an Embodied Cognition Perspective</b>
DOI: 10.6084/m9.figshare.7007090.v1
title: Caltech101 Image Dataset

description: <br>Pictures of objects belonging to 101 categories. About 40 to 800 images per category. <br>http://www.vision.caltech.edu/Image_Datasets/Caltech101/<br>
DOI: 10.6084/m9.figshare.7222634.v2
title: The processed data of bright-field images of hematopoietic tumor cell lines for machine learning

description: Morphological images of cells contain extensive information, which help biologists to infer the type and state of cells to some degree based on their morphology. Convolutional Neural Network (CNN), a neural network architecture, is a powerful tool used for image recognition. However, whether it can be used to classify cells on the basis of their morphology remains unclear.<br>We have obtained bright-field images of 10 human hematopoietic tumor-derived cell lines (including acute myeloid leukemia, chronic myeloid leukemia, B-cell acute lymphoblastic leukemia, and myeloma) by imaging flow cytometer (90,000 images per group). These data have been processed for use in Keras. Thus, we believe that these data could be useful for research in the field of cell biology using machine learning.<br><br>
DOI: 10.60893/figshare.apl.28466249.v1
title: Supplementary Material for: Ultra-Narrowband Dielectric Metasurfaces for Surface-Enhanced Infrared Absorption

description: Section 1-11 including Figure S1 to Figure S19 for supplemental material
DOI: 10.17605/osf.io/3cmht
title: Data from: Pollen exploitation by non-native honey bees in a hotspot of native bee diversity

description: Raw data associated with study on pollen exploitation by honey bees (Apis mellifera) in San Diego, CA, USA.
DOI: 10.17632/94f5fzgsrh
title: GNSS Dataset (with Interference and Spoofing) Part V

description: GNSS Dataset (with Interference and Spoofing) Part X

GNSS Dataset (with Interference and Spoofing) consists of three parts: 
Part I (Raw data of 2023 12 to 20, Sept, 2023, clean data), 
Part II (Raw data of 2023 21 to 30, Sept, 2023, clean data) and 
Part III (Processed data 12 to 30, Sept, 2023; data collected with spoofing and jamming on 21 Dec, 2023; Scripts and Material) .
Part IV (Supplementary Raw data and Processed data, 15 to 24, March, 2024).
Part V (Supplementary Raw data and Processed data, 24 to 30, March, 2024).

Part I, II, and III are provided in detail by the paper entitled "GNSS Interference and Spoofing Dataset" in Elsevier, and IV and V use the same collection method supplemented with more spoofing data.


The data were recorded by a GNSS receiver installed on the 5th floor of the Science Hall of Yunnan University. HackRF One emits spoofing signals and the commercial jammer emits suppression jamming to attack the receiver. The provided datasets are interesting for the GNSS monitoring, GNSS security, anti-jamming and anti-spoofing mechanisms based scientific communities.

These data provide the most comprehensive information available on the spatial and temporal patterns of GNSS satellites, observation and receiver parameters, referring to five constellations (GPS, Campass, Galileo, GLONASS and QZSS) and eight signal bands (L1C/A, L2C, E1, E5b, B1, B2, L1, L2). The dataset provides observations of the receiver three scenarios: normal state, affected by commercial jammers, and spoofed by SDR HackRF One. These observations include more details such as carrier-to-noise density ratio (C/N0), signal spectrum, Doppler shift, pseudorange, carrier phase, satellite health indicator, real-time position data and dilution of precision (DOP), etc. These data can be used to analyze navigation satellite operation rules, satellite covering time above the receiver, satellite overhead time prediction and GNSS monitoring system construction, provides a large amount of fine-grained data that can be used as an example to study safeguards at civil aviation airports, monitoring for harmful radio interference.
DOI: 10.6084/m9.figshare.20391693.v1
title: Syntax data analysis SPSS detect sexism

description: Syntax for data analysis in SPSS
DOI: 10.6084/m9.figshare.22826636.v1
title: <strong>Dataset for exploring genomic-epigenetic configurations in lung adenocarcinoma without clinically actionable genetic alterations </strong>

description: The purpose of this figshare is to make available all data from the peak-to-gene links analysis presented in Kaneko S. et al., "Mechanism of<em> ERBB2</em> gene overexpression by the formation of super-enhancer with genomic structural abnormalities in lung adenocarcinoma without clinically actionable genetic alterations", as well as files for genome-wide visualization of ChIP-seq and Hi-C datasets. The ChIP-seq and Hi-C data file formats are as follows: The bigWig format is used in ChIP-seq studies for efficient storage and visualization of the genomic locations of the H3K27Ac modification. These files can be easily loaded into genome browsers like the Integrative Genomics Viewer (IGV) [1], allowing for interactive exploration and visualization of these active enhancer and promoter regions. The mcool file format is a specialized file type used to visualize and analyze Hi-C data. Hi-C is a technique for investigating chromatin interactions within the genome, and its results generate complex and large-scale datasets that require specialized formats for efficient management and manipulation. "mcool" stands for "multi-resolution cooler," and it contains multiple "resolutions" or levels of detail for the same Hi-C dataset. These resolutions represent different degrees of binning of the original Hi-C interaction matrix. This multi-resolution format allows efficient zooming and panning across different scales when visualizing Hi-C data in genome browsers such as HiGlass [2]. 
<br>
References
1. Thorvaldsdottir H, Robinson JT, Mesirov JP: Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. <em>Brief Bioinform </em>2013, 14:178-192.
2. Kerpedjiev P, Abdennur N, Lekschas F, McCallum C, Dinkla K, Strobelt H, Luber JM, Ouellette SB, Azhir A, Kumar N, et al: HiGlass: web-based visual exploration and analysis of genome interaction maps. <em>Genome Biol </em>2018, 19:125.
DOI: 10.6084/m9.figshare.3464357.v2
title: Rainfall Data, manually assigned labels indicating presence of rainfall

description: Contains labels and associated rainfall predictions for audio captured over 20 days at the Michelin Ecological Reserve in Bahia, Brazil.dataDesc.xlsx contains the data labels for each 5 minute section, both manually assigned and the average predicted class.feats.xlsx contains the raw features extracted in 10s windows. Presented in blocks of 30 rows representing each 5 minute section in the dataDesc.xlsx. Features in columns, first 10 columns are the 1st 10 central moments, next 1023 columns are wavelet packet energy features, last 241 colums is an amplitude histogram, -60 to 60 dB in 0.5 dB bins. Normalized to rms level of 10 secs.<br><br>
DOI: 10.6084/m9.figshare.12624614.v1
title: Fig 7 (panels A, B, C, D)

description: FGB, CD45+ cells, GFAP and CDH5/IB4 expression level in Ctrl vs EAE spinal cord tissues from Dhh<sup>ECKO</sup> mice and Ctrl littermates. <br>
DOI: 10.6084/m9.figshare.14151563.v3
title: 210113-Mapping DB Digital Transformation Media literacy.xlsx

description: The data set includes the systematic mapping literature review of digital transformation and media literacy in higher education institutions from 2015 to 2020.The searched was carried out in Scopus and Web of Science data bases.This research is part of a doctoral thesis on Universities' Digital Transformation.
DOI: 10.6084/m9.figshare.21485946.v1
title: SPIRIT-Checklist

description: SPIRIT guidelines
DOI: 10.6084/m9.figshare.25479699.v1
title: Mechanistic insights into sodium ion-mediated ligand binding affinity and modulation of 5-HT2B GPCR activity: implications for drug discovery and development

description: The G-protein coupled receptor (GPCR) family, implicated in neurological disorders and drug targets, includes the sensitive serotonin receptor subtype, 5-HT2B. The influence of sodium ions on ligand binding at the receptor’s allosteric region is being increasingly studied for its impact on receptor structure. High-throughput virtual screening of three libraries, specifically the Asinex-GPCR library, which contains 8,532 compounds and FDA-approved (2466 compounds) and investigational compounds (2731)) against the modeled receptor [4IB4-5HT<sub>2B</sub>R<sub>M</sub>] using the standard agonist/antagonist (Ergotamine/Methysergide), as previously selected from our studies based on ADMET profiling, and further on basis of binding free energy a single compound – dihydroergotamine is chosen. This compound displayed strong interactions with the conserved active site. Ions influence ligand binding, with stronger interactions (3-H-bonds and 1-π-bond around 3.35 Å) observed when an agonist and ions are present. Ions entry is guided by conserved motifs in helices III, IV, and VII, which regulate the receptor. Dihydroergotamine, the selected drug, showed binding variance based on ions presence/absence, affecting amino acid residues in these motifs. DCCM and PCA confirmed the stabilization of ligands, with a greater correlation (∼46.6%-PC1) observed with ions. Dihydroergotamine-modified interaction sites within the receptor necessary for activation, serving as a potential 5HT<sub>2B</sub>R<sub>M</sub> agonist. RDF analysis showed the sodium ions density around the active site during dihydroergotamine binding. Our study provides insights into sodium ion mobility’s role in controlling ligand binding affinity in 5HT<sub>2B</sub>R, offering therapeutic development insights. Summary of computational drug discovery workflow using molecular dynamics (MD) simulations and subsequent sodium ion-binding analysis to evaluate potential drug candidates interacting with a target protein.
DOI: 10.6084/m9.figshare.26977525.v1
title: genotype_and_phenotype_data.xls

description: Raw and filtered genotype data and phenotype data.
DOI: 10.6084/m9.figshare.28078376.v1
title: Dynamic Predictive Pattern of Non-fungible Tokens: Insight from Uncertainties, Geopolitical Risk, and Market Sentiments

description: This data comprises of the return series used in the estimations of the study with the above title
DOI: 10.6084/m9.figshare.28578668.v1
title: Machine Learning for Cloud Security Metrics.pdf

description: The growing complexity of cloud environments has increased the need for advanced security mechanisms to detect and mitigate cyber threats effectively. Machine learning (ML) has emerged as a transformative approach in cloud security, enabling organizations to analyze vast amounts of security data, identify patterns, and automate threat response mechanisms. This paper explores how ML-driven security metrics enhance threat detection, improve incident response, and strengthen overall cloud security. Key ML-based security metrics, such as anomaly detection rates, false positive reduction, and response time optimization, are examined. Furthermore, established frameworks for integrating ML into cloud security, including supervised and unsupervised learning models, deep learning techniques, and reinforcement learning, are discussed. The paper concludes with best practices for deploying ML-driven security solutions, ensuring compliance, and mitigating cyber risks in cloud environments.
DOI: 10.6084/m9.figshare.5572789.v1
title: Sturgeon figures

description: The dataset contains figures of different numbers of sturgeons collected in aquaculture ponds and can be used for sturgeon counting.
DOI: 10.6084/m9.figshare.5937268.v3
title: Global Marine Environment Dataset (GMED)

description: The Global Marine Environment Datasets (GMED) is a compilation of publicly available climatic, biological and geophysical environmental layers featuring present, past and future environmental conditions. GMED covers the widest available range of environmental layers from a variety of sources and depths from the surface to the deepest part of the ocean. It has a uniform spatial extent, high-resolution land mask (to eliminate land areas in the marine regions), and high spatial resolution (5 arc-minute, c. 9.2 km near equator). The free online availability of GMED enables rapid map overlay of species of interest (e.g. endangered or invasive) against different environmental conditions of the past, present and the future, and expedites mapping distribution ranges of species using popular SDM algorithms. This archive features a snapshot of GMED dataset in 2019 as a single archive.
DOI: 10.17632/wcz7n878cf
title: Data for: Diagenetic facies prediction using a LDA-assisted SSOM method for the Eocene beach-bar sandstones of Dongying Depression, East China

description: The training samples for LDA and SSOm method, and the core routine analysis results of fractured diagenetic facies.
DOI: 10.6084/m9.figshare.28638626.v1
title: Direct characterisation of <i>m</i><sub><em>J</em></sub> = ± 15/2 ground state in octahedral Dy(III) single-molecule magnets

description: Characterisation data for two novel THF-solvated octahedral dysprosium aryloxide halide complexes.