cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/dc8dyrsfw4
title: Road Traffic Accident Determinant Factor Identification Data

description: Road Traffic Accident Determinant Factor Identification in case of East Gojjam, Ethiopia using Wrapper Feature Selection Algorithm data set
DOI: 10.17632/xpttrxfmzb
title: Dataset on Augmented reality smart batik (AR-SaBa) Application Assessment for helping batik marketing

description: This dataset comprises respondents' statements aimed at assessing Performance, Usefulness, Learnability, Accuracy, Suitability, Engagement, Novelty, Relevance, Feedback, and Flexibility and Control in the development of the Augmented Reality Smart Batik (AR-SaBa) application. The application was tested by AR developers, fashion lecturers, MSE owners, and consumers using a Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). The questionnaire was developed based on a literature review and validated by the research team.
DOI: 10.6084/m9.figshare.1309742.v18
title: raw fMRI data set

description: An fMRI data set used in "Boly et al. Stimulus set meaningfulness and neurophysiological differentiation: a functional magnetic resonance imaging study"
DOI: 10.6084/m9.figshare.18393689.v1
title: AGB_Mgha_10km_bias_adjusted_2000_2008_2010_2017_V1.tif

description: Global maps of Above-ground Biomass (AGB) from four epochs adjusted for map bias. See Araza et al. 2022 for details.
DOI: 10.6084/m9.figshare.24989073.v2
title: Phase 2 Students and Teachers Responses

description: Mathematics: Phase 2 data for Teachers and Students.
DOI: 10.6084/m9.figshare.25426150.v1
title: Metabolomics analysis-GCMS

description: Naïve CD4<sup>+</sup> T cells sorted by immunomagnetic beads from human PBMCs were cultured under Tfh cell skewing conditions for 5 days. The cells and untreated naïve CD4<sup>+</sup> T cells were snap-frozen and metabolites were extracted with 80% ice-cold methanol. Liquid chromatography-mass spectrometry (LC-MS) was performed on an Ultimate 3000-Velos Pro system equipped with a binary solvent delivery manager and a sample manager, coupled with an LTQ Orbitrap Mass Spectrometer equipped with an electrospray interface (ThermoFisher Scientific). The raw data were processed with progenesis QI (Waters Corporation) and Principal component analysis (PCA) and hierarchical clustering of metabolites were analyzed.
DOI: 10.6084/m9.figshare.26517325.v1
title: Real Estate Price Prediction Data

description: <b>Overview:</b> This dataset was collected and curated to support research on predicting real estate prices using machine learning algorithms, specifically Support Vector Regression (SVR) and Gradient Boosting Machine (GBM). The dataset includes comprehensive information on residential properties, enabling the development and evaluation of predictive models for accurate and transparent real estate appraisals.<b>Data Source:</b> The data was sourced from Department of Lands and Survey real estate listings.<b>Features:</b> The dataset contains the following key attributes for each property:<b>Area (in square meters):</b> The total living area of the property.<b>Floor Number:</b> The floor on which the property is located.<b>Location:</b> Geographic coordinates or city/region where the property is situated.<b>Type of Apartment:</b> The classification of the property, such as studio, one-bedroom, two-bedroom, etc.<b>Number of Bathrooms:</b> The total number of bathrooms in the property.<b>Number of Bedrooms:</b> The total number of bedrooms in the property.<b>Property Age (in years):</b> The number of years since the property was constructed.<b>Property Condition:</b> A categorical variable indicating the condition of the property (e.g., new, good, fair, needs renovation).<b>Proximity to Amenities:</b> The distance to nearby amenities such as schools, hospitals, shopping centers, and public transportation.<b>Market Price (target variable):</b> The actual sale price or listed price of the property.<b>Data Preprocessing:</b><b>Normalization:</b> Numeric features such as area and proximity to amenities were normalized to ensure consistency and improve model performance.<b>Categorical Encoding:</b> Categorical features like property condition and type of apartment were encoded using one-hot encoding or label encoding, depending on the specific model requirements.<b>Missing Values:</b> Missing data points were handled using appropriate imputation techniques or by excluding records with significant missing information.<b>Usage:</b> This dataset was utilized to train and test machine learning models, aiming to predict the market price of residential properties based on the provided attributes. The models developed using this dataset demonstrated improved accuracy and transparency over traditional appraisal methods.<b>Dataset Availability:</b> The dataset is available for public use under the [CC BY 4.0]. Users are encouraged to cite the related publication when using the data in their research or applications.<b>Citation:</b> If you use this dataset in your research, please cite the following publication:<br>[Real Estate Decision-Making: Precision in Price Prediction through Advanced Machine Learning Algorithms].
DOI: 10.6084/m9.figshare.5280679.v1
title: Deep Brain Stimulation in the Morris Water Maze

description: The following contains behavioural data from a study, in which rats underwent theta-burst deep-brain stimulation, inside a Morris Water Maze. Matlab is required for accessing and displaying this data. The following files are included:<br><b>README.txt</b> Instructions of use<br><b>MWMperf.mat</b>All of the rat performance data, as extracted from the video-tracking software "Smart" by Panlab. The data is stored inside a structure, which can be accessed manually in Matlab.<br><b>MWM_proc_data.m</b>Run this program in Matlab, for displaying the data and performing statistics. For instructions of use, type the following into the Matlab command window.<br>help MWM_proc_data<br>When displaying the data in Matlab, the following labels are used to refer to the rat groups:<br><b>SHAM</b>Sham group (n=21). <br><b>BEF</b>Rats stimulated for 30-minutes before an acquisition session (n=11).<br><b>DUR</b>Rats stimulated during an acquisition session (n=12).
DOI: 10.6084/m9.figshare.28200323.v1
title: Slide-seqV2

description: Dataset nameAbbreviationNo. of spots/cellsNo. of genesNo. of clustersMouse kidney diabete (Puck_191206_03)Slide-seqV2_MKD185422019014Mouse kidney normal (Puck_200104_03)Slide-seqV2_MKN160271928313Mouse embryo E8.5Slide-seqV2_embryo84252755416
DOI: 10.6084/m9.figshare.29144546.v1
title: ITEMV

description: It is a dataset for my new article.
DOI: 10.6084/m9.figshare.29148449.v1
title: Functional Reorganization and Taxonomic Shifts in the Symbiotic Microbiota of the Temperate Soft Coral <i>Eleutherobia rubra</i> Under Heat Stress

description: This dataset was generated during an investigation into the thermal stress response of the temperate soft coral <i>Eleutherobia rubra</i>, focusing on taxonomic and functional profiling of its symbiotic microbiota. Using 16S rRNA gene sequencing and PICRUSt2-based functional prediction, we show that short-term exposure to elevated temperature induces marked changes in microbial functional potential before compositional shifts are observed. These findings highlight the early and sensitive role of microbial functions in coral stress adaptation. Although most studies of coral microbiomes under climate change focus on tropical reef-building corals, this study expands current understanding by examining an azooxanthellate octocoral from a temperate ecosystem.
DOI: 10.6084/m9.figshare.11106575.v1
title: LUCM Map Ratser File

description: LUCM map generated in "Dorber, M.; Kuipers, K.; Verones, F., Global characterization factors for terrestrial biodiversity impacts of future land inundation in Life Cycle Assessment. Science of The Total Environment 2019. (https://doi.org/10.1016/j.scitotenv.2019.134582)" to calculate Area (Ai,j) and area shares (pi,j). Values for land use types: 1= Natural habitat; 2= Managed forest; 3 Pasture; 4 Agriculture; 5= Urban; 210 = Water<br><br><br>
DOI: 10.6084/m9.figshare.12336773.v1
title: Horse dataset

description: plink binary files of phenotype and genotype
DOI: 10.17632/gjttfcy4m6
title: Data for: Variety, frequency, and type of Internet use and its association with depression in middle- and older-aged Chinese: evidence from the China Health and Retirement Longitudinal Study, 2015

description: Here are the raw data we used to analyse, and the urban, rural and whole samples were included repectively in the three datasets.
DOI: 10.17632/p5yvtms2yx
title: Tumour Infiltrating Lymphocytes in Follicular Lymphoma - additional data 4

description: Part 4/10 Description provided in the dataset linked below.
DOI: 10.17632/zg92v4bxk9
title: Data on evaluation of depressive disorder in lung cancer patients

description: Data using for examine the prevalence of the depressive disorder in a lung cancer patient who plans to receive chemotherapy conducted at a one-day chemotherapy unit
DOI: 10.6084/m9.figshare.1019859_d8
title: Table2_SNP_genotyping.xls

description: File of a panel of SNP genotypes (fingerprinting assays) per cell line for quality control purposes.  This also includes the identity with either a pre-screen fingerprint of each cell line or the SNP genotype from the CCLE SNP6.0 array data for that cell line.
DOI: 10.6084/m9.figshare.21760586.v1
title: Barley9Kplate3Project. (96 sample_Raw data).csv

description: Barley9Kplate3Project. (96 sample_Raw data)
DOI: 10.6084/m9.figshare.28566488.v1
title: raw data for figures

description: raw data of figures for "Optically driven spin angular momentum transport in paramagnetic insulator NiO"
DOI: 10.6084/m9.figshare.5129674.v1
title: Supplementary Material for: Chronic Obstructive Pulmonary Disease and Cardiac Repolarization: Data from a Randomized Controlled Trial

description: <b><i>Background:</i></b> Altered cardiac repolarization is a risk factor for sudden cardiac death and seems to be increased in chronic obstructive pulmonary disease (COPD) patients. <b><i>Objective:</i></b> Lung volume reduction surgery (LVRS) has been shown to improve breathing mechanics and lung function in patients with severe COPD and emphysema and possibly also improve altered cardiac repolarization. <b><i>Methods:</i></b> Thirty patients scheduled for LVRS were randomized to LVRS or to the control group. We investigated the treatment effect 3 months after LVRS on measures of cardiac repolarization and dispersion of repolarization (QTc interval, QT dispersion) derived from electrocardiography. Univariable and multivariable analyses were used to identify possible confounders influencing the treatment effect. <b><i>Results:</i></b> LVRS<b> </b>was associated with an improvement in lung function (mean ± SD residual volume/total lung capacity of -9 ± 11% and forced expiratory volume in 1 s of +30 ± 29%). LVRS did not significantly reduce QTc (median -5.3 ms, 95% confidence interval, CI -15.5 to 3.7, p = 0.214) and QT dispersion (median -3.0 ms, 95% CI -13.0 to 7.0, p = 0.536) compared to the control group. No significant association between change in QTc and change in QT dispersion, respectively, and change in possible confounders was found. <b><i>Conclusion:</i></b> LVRS seems to have no effect on cardiac repolarization in patients with COPD. Thus, lung hyperinflation seems not to be a causal mechanism for altered cardiac repolarization in COPD patients.
DOI: 10.17632/6cx6k6k4yn
title: Tropical Dry Forest climatic edaphic and vegetation Database

description: Selection of sample points
We created a 1 km2 grid for each of the five biogeographic regions and identified the centroid of each cell of the grid, which we used as a sample point. We selected 100 sample points in a random block design from the full population of centroids for a given ecoregion and repeated this for each of the 80 studied ecoregions. We then used the HILDA+ Global Land Use Change layer (HILDAplus_vGLOB-1.0; Winkler, Fuchs, Rounsevell, & Herold, 2021) to evaluate the land use types for the resulting grid of 8,000 points. The HILDA+ layer contains land cover information from 1960 to 2019, which allowed us to mask points defined as urban, cropland, ocean, water, or as missing data. We used the remaining unmasked points, defined as forest, shrubland, pasture, rangeland, or unmanaged grassland, in our analyses. 
 We used a randomized block design to assess relationships between NDVI and climatic and edaphic variations found across TDL. All spatial analyses were conducted in ArcGIS 10.2.1.

Normalized difference vegetation index
We used 12 years (2005 to 2016) of NDVI data (250 m pixel resolution) collected with Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard US NASA’s Terra and Aqua satellites. The MODIS product was used because of its longevity, resolution, and frequent observations. Data analyses relied on 12-yr mean NDVI conditions for each of the 80 ecoregions sampled here. While data are collected sub-weekly, we used averaged monthly data to calculate mean annual NDVI for the 12-yr period. NDVI is dimensionless and ranges from -0.1 to 0.9, with higher values associated with denser vegetation and lower values associated with sparser vegetation. The lowest values represent bare ground.

 Climatic and edaphic metrics
For each sample point, we obtained rainfall, temperature, and evapotranspiration data from repositories including: NASA Earth Observations (https://neo.gsfc.nasa.gov/); Climatic Hazards Center - UC Santa Barbara (www.chc.ucsb.edu/about); Numerical Terradynamic Simulation Group (NTSG)-University of Montana (www.ntsg.umt.edu/); Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) for Biogeochemical Dynamics (www.daac.ornl.gov/); and SOILGRID (www.soilgrids.org/). We examined a total of 17 climate variables: four temperature metrics, 10 precipitation metrics, an evapotranspiration metric, and two aridity metrics that integrate annual precipitation and temperature measures. All climate variables were summarized into mean annual averages representing from 1 up to 12 years of available data (Table S3). We also considered 17 edaphic metrics involving physical, chemical and biological properties of soils. These were all represented by a single point in time measurement, and so have no temporal averaging component. Each of the 34 metrics was processed and normalized to standard units.
DOI: 10.17632/cgnx2cs665
title: Road Traffic Incident (RTI) Data for Sydney Greater Metropolitan Area

description: The curated Road Traffic Incident (RTI) Data includes 85,611 incidents (crashes, vehicle breakdowns, and others) that took place in the entire Sydney Greater Metropolitan Area, New South Wales, Australia from 1st January 2017 to 31st July 2022.

Here, the datasets are presented in the form of Excel files, shapefiles, Python codes (APIs)

The raw (File name: "Road Traffic Incident Dataset-Raw.csv") and curated RTI  (File name: "Final Road Traffic Incidents.xlsx") datasets are presented in the form of Excel files. Further, metadata (File name: Metadata for final road traffic incidents.xlsx) explain the variables in the curated dataset and is presented in the form of an Excel file. The curated RTI dataset is linked with the zonal information as well such that analysis can be performed at both microscopic and macroscopic levels.

The Independent variables such as road network characteristics, public transport network characteristics, socioeconomic characteristics, and travel characteristics at statistical area level-2 (SA2) were curated and presented in the form of an Excel file (File name: Sydney Zones SA2 variables_4.xlsx). Further, metadata (File name: Metadata for Sydney Zones SA2 variables_4.xlsx) that explains the independent variables in the curated dataset is presented in the form of an Excel file. 

In the shape files folder, 
1. "Breakdowns at Sydney zones SA2.zip" is the file where all the breakdown incidents that took place in Sydney MSA are linked to the zones at SA2 
2. "Crashes at Sydney zones SA2.zip" is the file where all the crash incidents that took place in Sydney MSA are linked to the zones at SA2 
3. "Incidents at Sydney zones SA2.zip" is the file where all the all incidents that took place in Sydney MSA are linked to the zones at SA2 
4. "Sydney zones SA2 breakdown count.zip" is the breakdown count at SA2 zones in Sydney MSA
5. "Sydney zones SA2 crash counts.zip" is the crash count at SA2 zones in Sydney MSA
6. "Sydney zones SA2 incident counts.zip" is the count of all incidents at SA2 zones in Sydney MSA
7. "Sydney zones SA2 Edges.zip" - all the edges in the Sydney MSA are linked to SA2 zones
8. "Sydney zones SA2 Nodes.zip"- all the nodes in the Sydney MSA are linked to SA2 zones
9. "Sydney zones SA2 MeshBlocks.zip" - the mesh blocks that tell about the land use in the Sydney MSA are linked to SA2 zones
10. "Sydney zones SA2.zip" - Sydney MSA zonal details for SA2

In the Python code for the API folder, 
"API code for RTI Data.py" is the Python script for API. The code can be used for extracting incident data from OpenDataHub. Interested users may use the code by simply replacing their own API key for retrieving the data.
DOI: 10.6084/m9.figshare.12996920.v1
title: Cheetah_WGS_poolseq_VCF

description: While recent advances in genomics has enabled vast improvements in the quantification of genome-wide diversity and the identification of adaptive and deleterious alleles in model species, wildlife and non-model species have largely not reaped the same benefits. Here, we demonstrate the utility of cross-species genome alignment methods in gaining insights into population structure and functional genomic features in the cheetah, snow leopard and Sumatran tiger, relative to the domestic cat. <br><br>Alignment of the big cats to the domestic cat reference assembly yielded 38M variants in cheetah, 15.5M variants in snow leopards and 13.4M variants in the Sumatran tiger. These were annotated to delineate population structure and reveal insights into adaptive traits, evolutionary history and the parthenogenesis of heritable diseases. <br>
DOI: 10.6084/m9.figshare.13157687.v2
title: Embryonic SMG Integrated.rds

description: Redy-to-use Seurat objects associated with GSE150327 dataset (Salivary gland scRNAseq) The analysis code is available as supplementary material in the manuscript and in github repository: https://github.com/chiblyaa/Salivary-Gland-DevelopmentPlease cite the associated manuscript when using this resource: http://dx.doi.org/10.2139/ssrn.3651506<br> Direct questions to chiblyaa@nih.gov
DOI: 10.6084/m9.figshare.1541133.v2
title: The height and age of fellow classmates

description: The age and height of each classmate collected in lab 1 of ecology, monday 2:30 to 5:30. There were 20 subjects with the units of height measured in centimetres and the unit of age measured in years. Coauthors Dylan Cairns, Felicia Oyedepo and Richer Lai.
DOI: 10.6084/m9.figshare.28644923.v1
title: Identifying Climate and Environmental Determinants of Spatial Disparities in Wheat Production Using a Geospatial Machine Learning Model

description: Data and code for a geospatial machine learning model.
DOI: 10.6084/m9.figshare.28719911.v1
title: Family Communication dataset

description: Family communication dataset
DOI: 10.17632/h552f4wnjv
title: A determinant based full configuration interaction program

description: Abstract The program FCI solves the Full Configuration Interaction (Full CI) problem of quantum chemistry, in which the electronic Schrödinger equation is solved exactly within a given one particle basis set. The Slater determinant based algorithm leads to highly efficient implementation on a vector computer, and has enabled Full CI calculations of dimension more than 10^7 to be performed. Title of program: FCI Catalogue Id: ABHV_v1_0 Nature of problem Benchmark calculations for quantum chemistry. Versions of this program held in the CPC repository in Mendeley Data ABHV_v1_0; FCI; 10.1016/0010-4655(89)90033-7 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)
DOI: 10.6084/m9.figshare.20443788.v1
title: By-Products of the Timber Industries as Raw-Material for the Production of MDP (Medium Density Particleboard)

description: Abstract This study aimed to verify technical potential of using waste from wood veneers and plywood as a raw material for the production of MDP. The variance analysis of the panels density was carried out. Physical-mechanical properties were evaluated by comparing them with the international standards and by analysis of variance. The averages were subjected to the analysis of covariance, at the level of 5% probability. Finally, Pearson’s correlation analysis between properties was performed. The panels produced with “log core” and “veneer clippings” residues showed equivalent averages for physical properties and higher for mechanical properties of internal bond and surface screw withdrawal, when compared the averages of panels produced with commercial particles. Panels produced with “plywood cutting” showed unsatisfactory mechanical properties. It’s concluded that the “log core” and “veneer clippings” residues show potential for use as a raw material in the composition of MDP.
DOI: 10.6084/m9.figshare.21971690.v1
title: MATEO HPTML model data

description: In this Figsahre repository we release the dataset used to train/validate the MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization Web Server. Supporting Info File: <strong>SI01_HPTML model.xlsx</strong> Content: Data used to train and validate the model. <br> Summary: The enantioselective Brønsted acid-catalyzed α-amidoalkylation reaction is a useful procedure is for the production of new drugs and natural products (products) or chiral catalysts (tools). The enantioselectivity is sensitive to many factors, from the nature of the nucleophile and the catalyst to the experimental conditions (solvent, temperature, etc.). Although computational chemistry has been used to rationalize experimental results, it is still difficult to understand the influence of different parameters (solvent, temperature, etc.) on the quantitative reaction outcome (as yield or regio- and stereoselectivities).Both experimental and computational (Quantum Chemistry) study of a large number of reactions may become costly in terms of resources and time. Thus, the development of fast-track public computational tools to predict the enantioselectivity [enantiomeric excess <em>ee</em>(%)obs] would be very useful. Furthermore, making the new tool available online could save time and experimental resources in many labs worldwide. We used an Heuristic Perturbation-Theory and Machine Learning (HPTML) algorithm to seek a predictive model with R2 = 0.91 in training and validation series has been developed. It involves a Monte Carlo sampling of&gt;100,000 pairs of query and reference reactions. In addition, the computational and experimental investigation of a new set of intermolecular α-amidoalkylation reactions using BINOL-derived <em>N</em>-trifylphosphoramides as chiral catalysts is reported as a case of study. After validation of the model, it was implementedin a web server called MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization. This tool is available online at:https://cptmltool.rnasa-imedir.com/CPTMLTools-Web/mateo.This new user-friendly online computational tool may become useful to explore a large number of combinations of reactants, catalysts, and experimental conditions. This public tool would enable sustainable optimization of reaction conditions that could lead to the design of new catalysts, substrates, nucleophiles, and/or products.
DOI: 10.6084/m9.figshare.3528290.v1
title: Appendix A. Proofs 1–4.

description: Proofs 1–4.