cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11766231.v1
title: Vetter_et_al_GCB_2020_SIGNAL.xlsx

description: This data was collected during the SIGNAL experiment in 2014 and 2015. The data was collected in a multisite invader x biodiversity x drought manipulation experiment to examine the combined effects of biodiversity and invasion on drought resistance and recovery at three semi-natural grassland sites across Europe. The stability of biomass production to an extreme drought manipulation (100% rainfall reduction) was quantified in field mesocosms with a richness gradient of 1, 3, and 6 species and three invasion treatments (no invader, <i>Lupinus polyphyllus</i>, <i>Senecio inaequidens</i>).<br>A detailed description of the data, the methodology to analyze the data as well as the results gained can be found in the paper:<br>Vetter et al. (2020): Invader presence disrupts the stabilizing effect of species richness in plant community recovery after drought. Global Change Biology.
DOI: 10.6084/m9.figshare.13506711.v1
title: Raw IMU Data from Experiments

description: Raw IMU data from the different experimental conditions used in the thesis (mechanical, mechanical with soft-tissue, and human)
DOI: 10.6084/m9.figshare.21581232.v1
title: Supplementary Material for: Readability, Understandability and Actionability of Online Cardiovascular Risk Assessment Tools and Patient Educational Material: A Systematic Review

description: Background: Individuals with kidney diseases have increased risk of cardiovascular disease and death. Online cardiovascular risk assessment tools can educate patients on risks and modifiable factors. Since patients have variable health literacy, we evaluated the readability, understandability and actionability of publicly available online cardiovascular risk assessment tools. Methods: We systematically searched, reviewed, characterized and assessed English-language cardiovascular risk assessment tools online for readability (Flesch-Kincaid Grade Level (FKGL) score), understandability and actionability (Patient Education Materials Assessment Tool for printable materials (PEMAT-P)). Results: After screening 969 websites, 69 websites employing 76 risk tools were included. The most frequently used tools were the Framingham risk score (n= 13) and the Atherosclerotic Cardiovascular Disease score (n= 12), Most tools were intended for the general population and estimated the 10-year incident cardiovascular risk. Patient education was provided as targets for blood pressure (n= 17), lipids (n= 15), or glucose (n= 5); and advice regarding diet (n= 18), exercise (n= 19), and smoking cessation (n= 20). The median FKGL, PEMAT- understandability and actionability scores were 6.2 (4.7, 8.5), 84.6% (76.9%, 89.2%) and 60% (40%, 60%), respectively. Conclusion: The online cardiovascular risk tools were generally easy to read and understand, but only a third provided education on risk modification. Judicious selection of an online cardiovascular risk assessment tool may help patients in self-management.
DOI: 10.6084/m9.figshare.24631185.v1
title: Representaciones de manos del Paleolítico Superior: Catálogo

description: Catálogo completo de las representaciones de manos del Paleolítico Superior. Base de datos individualizada.
DOI: 10.6084/m9.figshare.26950900.v2
title: baloo-data

description: Data for paper "Baloo: A Large-Scale Hybrid Soft Robotic Torso for Whole-Arm Manipulation"
DOI: 10.6084/m9.figshare.28755044.v1
title: Image data of spatial transcriptome data depicting pRCC with IVCTT

description: Image data of spatial transcriptome data from patient No.1, include normal kidney tissues and primary tumor.
DOI: 10.17632/k2yhbng5dw
title: Best Fit for Complex Peaks Version 3.0  (BFCP V3.0) 

description: Best Fit for Complex Peaks (BFCP V3.0).
BFCP Matlab application, BFCP Standalone Desktop App  (Matlab Runtime), BFCP user manual for V3.0, BFCP scripts (Pseudo-Voigt only), and tested data. 
Detail is in the manuscript: Li et al. Best Fit for Complex Peaks (BFCP) in Matlab® for Quantitative Analysis of in situ 2D X-Ray Diffraction Data and Raman Spectra
DOI: 10.17632/pryxkv4ft4
title: Spiritual offences in relation to the Palestinian - Israeli Conflict

description: Parameters: The dataset would likely include values or ranges representing the severity of desecration and levels of indifference. These parameters might vary from 1 to 10, as mentioned in the code.

Calculation of Spiritual Offence: Using the defined model (likely a mathematical formula involving the severity and indifference parameters), the dataset would compute the spiritual offence for different combinations of these values. It could involve calculating the ratio of severity to indifference raised to the power of 'n' and scaled by a constant 'k', as specified in the code.

3D Plot Generation: The dataset might contain data points for a 3D plot where the x-axis represents the severity of desecration, the y-axis represents indifference, and the z-axis corresponds to the calculated spiritual offence. Each data point would likely correspond to a specific combination of severity and indifference values, and the dataset would include enough points to form a comprehensive visualisation.

Color Mapping: There might be additional information in the dataset related to color mapping for the generated 3D plot. The color variation on the plot's surface would represent the severity of spiritual offense, with cooler colors indicating lower values and warmer colors representing higher values.

Symbolic Representation: The dataset may include specific coordinates representing a heart symbol (❤️) within the 3D plot, often placed at particular coordinates (5, 5, 10) to symbolically depict the hurt or pain caused to the divine heart due to calculated spiritual offense.

Overall, the dataset generated from this code is likely structured to facilitate the visualisation and analysis of spiritual offence based on varying degrees of desecration and indifference, with a specific focus on understanding the impact of these parameters on the perceived anguish or spiritual harm.
DOI: 10.17632/z93rb2m4fk
title: NYA-Crack-Data: A High Variability Concrete Crack Dataset for Enhanced Model Generalisation

description: Purpose: 
The NYA-Crack-DATA dataset was created to address an identified gap of limited variability in existing concrete crack datasets. The crack dataset is intended to enhance the robustness of existing concrete crack datasets, thereby improving the generalisability of crack detection models. 

Characteristics and variability:
The images were captured from the following locations and concrete specimens: two concrete bridges in South Wales (UK), concrete buildings around the University of South Wales (USW) Treforest Campus, indoor and outdoor concrete slabs and concrete beam, cube and cylinder specimens from laboratory experiments.

The NYA-Crack-DATA dataset was created from 297 images, captured using an iPhone 11 Pro Max, DJI Mini 3 Pro and Nikon D3400 DSLR. The images were split into smaller images (227 x 227 pixels) resulting in a total of 5026 images. The dataset consists of two classes: 'Crack' and 'No Crack', which consist of 2167 and 2859 images, respectively. The images in the dataset have not undergone any data augmentation. 

The images have the following variability: 
- Image quality/resolution (8000 x 6000 pixels, 6000 x 4000 pixels and 4032 x 3024 pixels)
- Lighting and weather conditions (poor and good lighting, rainy, cloudy, sunny)
- Graffiti markings
- Background noise
- Foreground occlusions such as vegetation
DOI: 10.6084/m9.figshare.12805643.v2
title: Air Pollution Data for Seoul

description: The data was adapted from https://www.kaggle.com/bappekim/air-pollution-in-seoul. The original data was from Seoul Open Data Plaza.<br><br>The data consisted of 25 different zones in Seoul; however, the Jung district located in the center of Seoul was modeled with PFM and served as a model for Seoul air pollution. The 26,241 hourly values were manually processed on Excel to fit the input requirements for the model. The dates and hours were changed to a M-D-Y HH:MM:SS format to satisfy PFM’s formatting requirements. Any error reading from the sensors denoted by “-1” was replaced with “NA” as PFM would interpolate the missing values. <br>https://creativecommons.org/licenses/by-sa/4.0/<br>
DOI: 10.6084/m9.figshare.1572291.v2
title: 'Dataset1' - Who Tweets with Their Location? Understanding the Relationship Between Demographic Characteristics and the Use of Geoservices and Geotagging on Twitter

description: Data associated with the paper: Who Tweets with Their Location? Understanding the Relationship Between Demographic Characteristics and the Use of Geoservices and Geotagging on Twitter Luke Sloan & Jeffrey Morgan
DOI: 10.6084/m9.figshare.2199646.v10
title: TV Series - Networks of characters

description: Sets of files related to the <b>social networks of characters</b> in the following episodes of <b>3 TV series</b>:<br>- <i>Breaking Bad</i> (BB): S01 - S02 - S03 (E01--05).<br>- <i>Game of Thrones</i> (GoT): S01--05.<br>- <i>House of Cards</i> (HoC): S01 - S02.<br><br>The <b>three separate graphml files</b> (.graphml extension) contain the static, <b>cumulative</b>, conversational networks at the <b>end</b> of the set of episodes considered.<br><br>Three <b>images</b> (.jpg extension) of the resulting static<b>, cumulative</b>, conversational networks at the end of the <b>first two seasons</b> are also provided. <br><br>The <b>compressed archives</b> (.tgz extension) provide <b>snapshots</b> of the conversational network of characters (<b>graphml</b> format) <b>in every scene</b>. These files come in <b>three</b> flavours:<br>1/ '<b>Narrative smoothing</b>' based networks (denoted '<b>ns</b>'). <br>2/ <b>Time-slice </b>based networks<b>,</b> where all interactions are agglomerated every <b>10</b> scenes (denoted '<b>ts10'</b>).<br>3/ <b>Time-slice </b>based networks, where all interactions are agglomerated every <b>40</b> scenes (denoted '<b>ts40'</b>).<br><br>The <b>video files</b> (.mp4 extension) contain short <b>animations</b> of the <b>dynamic</b> networks of characters as they evolve over the whole set of episodes considered:<br>- The <b>nodes</b> are represented by the <b>names</b> of the corresponding characters and the <b>distance</b> between two character names is inversely proportional to the <b>weight</b> of the corresponding edge: the closer they are at some point of the story, the more they interact then.<br>- The <b>size</b> of each <b>character name</b> is proportional to the <b>local strength</b> of the corresponding character at the moment considered.<br>- The <b>color</b> of each <b>character name </b>corresponds to his <b>community</b> at any moment. <br>Each <b>snapshot</b> in these animations is based on our '<b>narrative smoothing</b>' approach.<br><br><b>Reference</b>:<br>Xavier Bost, Vincent Labatut, Georges Linarès. <i>Narrative smoothing: dynamic conversational network for the analysis of TV Series plots</i>, 2016.<br>https://hal.archives-ouvertes.fr/hal-01276708<br>http://arxiv.org/abs/1602.07811<br><br> <br>
DOI: 10.6084/m9.figshare.c.3918607_d3.v1
title: Additional file 3: Table S2. of Widespread activation of antisense transcription of the host genome during herpes simplex virus 1 infection

description: Oligos. (XLSX 43 kb)
DOI: 10.60893/figshare.apl.28270490.v1
title: Supplemental Materials

description: Supplemental Materials after revision
DOI: 10.17632/b3gw6hx98v
title: Techno-economic analysis of PV + battery storage systems in commercial buildings with different electric tariffs

description: This repository contains the data for the paper: "Making the Case for Time-of-Use Electric Rates to Boost the Value of Battery Storage in Commercial Buildings with Grid Connected PV Systems".
DOI: 10.6084/m9.figshare.21685412.v1
title: Pg and Human proteomes

description: Combined proteomes for UP000005640 and UP00000884 from UniProt
DOI: 10.6084/m9.figshare.25664949.v1
title: Budburst phenology data and field temperature data

description: Abstract of the article:Climate change is expected to increase the frequency and intensity of winter thaws, which could affect leaf phenology. Phenology could either be advanced through the acceleration of forcing accumulation or chilling completion, or be postponed through a reduction in chilling associated with warming air temperature. We tested the influence of winter thaws on budburst phenology by exposing 300 tree cuttings of sugar maple and yellow birch trees to five different frequencies and durations of winter thaws in the lab. In spring, half of the cuttings were exposed to air temperature in two cities representing an air temperature gradient of + 2.0 °C to mimic the ongoing climate warming and bud phenology was monitored three times a week. Irrespective of thaw treatment, yellow birch bud phenology occurred earlier in the warmer city, showing the importance of spring temperature in triggering budburst. The treatment with the highest frequency and duration of thawing increased bud mortality and delayed the onset of spring budburst whereas low frequency treatments did not, thereby identifying a tipping point (3 days twice a month) in the impact of winter thaws on bud phenology. Past this point, winter thaws could slow down bud phenology induced by warmer spring temperature and limit carbon uptake by delaying the closure of the canopy. Climate change simulations projected by the CMIP6 Canadian downscaled climate scenario show that winter thaws will increase in frequency Hence the expected advance in spring leaf emergence associated with warmer spring is not necessarily as straightforward as previously thought.<br>Licence for non-commercial use.
DOI: 10.6084/m9.figshare.28695305.v1
title: data and R code-bladder cancer SEER and TCGA

description: bladder cancer
DOI: 10.15468/dl.hu3fen
title: Occurrence Download

description: A dataset containing 1558560 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is one of (Canada, Mexico, United States of America)",
 "EventDate is greater than or equal to 2025-04-05",
 "EventDate is less than or equal to 2025-05-05",
 {
 "not" : {
 "Issue" : [
 "is one of (Basis of record invalid, Coordinate reprojection failed, Coordinate reprojection suspicious, Coordinate precision invalid, Coordinate uncertainty meters invalid, Country mismatch, Country invalid, Geodetic datum invalid, Presumed swapped coordinate, Presumed negated longitude, Presumed negated latitude, Country coordinate mismatch)"
 ]
 }
 }
 ]
}

 The dataset includes 1558560 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013031-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/7kxf3bdbjz
title: Media Coverage of Startups and Venture Capital Investments Replication Code

description: [1] Run R script: "VC_Media_V2.R" to generate key variables such as media coverage and venture capital investments
[2] Run Stata codes in the folder "Code for Media Coverage of Startups and Venture Capital Investments.zip" to generate summary statistics and regression results in the paper
DOI: 10.17632/vf9n9f2z9z
title: CM chondrite equilibrium modelling

description: Geochemical modelling with PHREEQC (equilibrium models) to model the aqueous alteration of CM chondrites (see Haller&Lee "Controls on the petrologic type of CM carbonaceous chondrites evaluated by geochemical equilibrium modelling"). 
diagenesis_equi.dat: database with which to run the input script. Compiled from core10.dat (Neveu et al. 2017) and diagenesis.dat (Zhang et al. 2020)
.phr files: input files for each scenario (default, NoGas, NH3CO2, HighP, Asuka)
Model_results: Output from PHREEQC (see ReadMe in first sheet)
DOI: 10.25442/hku.25844470.v1
title: Supporting data for “Crowded Minds: The Psychological Impact of Social Crowding"

description: This dataset is the supporting data for the thesis titled “Crowded Minds: The Psychological Impact of Social Crowding". This dataset delves into the realm of social psychology, specifically exploring the psychological impact of social crowding. It includes data from 10 studies that covers behavioral data, natural language data and survey data. The dataset include the demographic information such as gender, age, race and education level. <br>
DOI: 10.6084/m9.figshare.12991403.v1
title: What explains life satisfaction? Relative income or rank income? The case of Ecuador

description: Data set and do-file for paper What explains life satisfaction? Relative income or rank income? The case of Ecuador
DOI: 10.17632/c6d6x5kny4
title: Lyap: A fortran 90 program to compute the Lyapunov exponents of a dynamical system from a time series

description: Abstract The program presented computes all the Lyapunov exponents of an unknown dynamical system from a time series. The method used is based on a fitting of the time series with an analytical function through a least square minimization, after which the Lyapunov exponents are determined from the stability of the trajectories calculated using this analytical function. Title of program: LYAP Catalogue Id: ADHC_v1_0 Nature of problem The program calculates all the Lyapunov exponents of a dynamical system when the law of evolution is unknown, as is the case for most of the systems studied in applied science (astronomy, biology, ...), and only a set of data in the form of a time series is available. Versions of this program held in the CPC repository in Mendeley Data ADHC_v1_0; LYAP; 10.1016/S0010-4655(97)00124-0 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)
DOI: 10.17632/xtrjd3g5ys
title: Good and Bad classification of Marsh Barbel Leaves (Hygrophila auriculata)

description: Data Description for Marsh Barbel Leaves Classification Project
Overview
This dataset contains images of Marsh Barbel Leaves (Hygrophila auriculata) categorized into two classes: good and bad. Each class contains 500 samples, totaling 1000 images. The primary objective of this project is to develop a classification model that can accurately distinguish between good and bad leaves based on visual features.

Data Structure
Images: High-resolution images of Marsh Barbel Leaves.
Labels: Each image is labeled as either "good" or "bad".
Features
Leaf Color: Variations in shades of green, indicating health or disease.
Leaf Texture: Surface texture, which may show signs of damage or disease.
Shape and Size: Differences in leaf morphology, which can suggest health status.
Spots or Discoloration: Presence of spots or other markings that may indicate disease or poor health.
Good Leaves
Healthy appearance with uniform green color.
Smooth texture and consistent shape.
No visible damage or discoloration.
Bad Leaves
May have visible spots, discoloration, or irregular shapes.
Rough texture or signs of wilting.
Indicators of disease or poor environmental conditions.
Usage
The dataset can be used for training and testing machine learning models aimed at classifying leaves. It is suitable for image classification tasks and can be used in various algorithms, such as Convolutional Neural Networks (CNNs).

Applications
Agricultural Monitoring: Helps in identifying unhealthy plants for early intervention.
Environmental Studies: Assists in assessing plant health in different environmental conditions.
Educational Tools: Useful for teaching plant pathology and classification techniques.
DOI: 10.17632/xv5j5zfdc8
title: Microbiome data 

description: Count data from 16S metagenomic analysis.
DOI: 10.6084/m9.figshare.12946988.v5
title: Fatal Police Violence

description: Police killings dataset (not just shooting, but by any means) is from https://mappingpoliceviolence.org/ that covers the period 2013-2023. This includes the Washington Post (WaPo) dataset on shooting victims.<br><br>The curated datasets are included here along with a research question and guiding questions.<br>See the codebook for full details.
DOI: 10.6084/m9.figshare.13365893.v1
title: Fig5-aln.fasta

description: Alignment for Fig5 (fasta format)<br>
DOI: 10.6084/m9.figshare.13893887.v3
title: Appendix K

description: Appendix K Genes that significantly changed in frequency during breeding.
DOI: 10.6084/m9.figshare.14074451.v1
title: Metadata record for the manuscript: Panels and models for accurate prediction of tumor mutation burden in tumor samples

description: <b>Summary</b><br> This metadata record provides details of the data supporting the claims of the related manuscript: “Panels and models for accurate prediction of tumor mutation burden in tumor samples”. The related study presents a bioinformatics-based method to select panels and mathematical models for accurate tumor mutation burden (TMB) prediction and propose cancer-specific panels for 14 malignancies which can offer reliable, clinically relevant estimates of TMBs. Type of data: tumour mutation burden as data for R language (.Rdata) and R script (.R) Subject of data: whole genome (WGS) and whole exon sequencing (WES) datasets of the Cosmic version 84 (Cosmic_v84) for human grch38 assembly Sample size: 24,726 samples of 42 cancer types for the training dataset <b>Data access</b> The datasets and code generated and/or analysed during the current study are available as part of this data record, and also via https://gitlab.com/bioinformatics-fil/predict_tmb. A list of the files underlying the figures, tables and supplementary tables of the related manuscript is available as part of this figshare record in the file ‘Martinez-Perez_et_al_underlying_data_list.xlsx’. For details about the software used, see the supplementary methods of the related publication. <b>Corresponding author(s) for this study</b> Cristina Marino-Buslje. Fundacion Instituto Leloir. Avda. Patricias Argentinas, 435. C1405BWE. Ciudad Autonoma de Buenos Aires, Argentina. E-mail: cmb@leloir.org.ar. Tel: +541123457500. Miguel Ángel Molina-Vila, Laboratorio de Oncología/Pangaea Oncology, Hospital Universitario Quirón Dexeus, C. Sabino Arana 5, 08203 Barcelona, Spain. E-mail: mamolina@panoncology.com; Tel: +34935460140. <br> <b>Study approval </b> Not applicable