cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/vrbx9wrf3z.1
title: High-Quality Tea-Making Leaf Classification

description: This dataset contains a curated collection of tea leaf images sourced from Udila Tea Garden in Chittagong, Bangladesh, specifically designed to classify leaves based on their suitability for high-quality tea production. The dataset is categorized into two classes: "Tea-Making Leaf" and "Not Tea-Making Leaf" with a total of 747 samples. The "Good for Tea-Making" class includes fresh, vibrant leaves identified as ideal for producing flavorful, premium tea. In contrast, the "Not Good for Tea-Making" class contains leaves that are either aged, unhealthy, or otherwise unsuitable for quality tea production.

Data Collection: Images were captured under controlled natural lighting to accurately reflect the visual characteristics of each leaf. "Good" leaves are typically new, fresh, and exhibit a desirable green color and healthy structure. "Not Good" leaves may show signs of aging, poor health, or other qualities that would compromise the tea flavor and quality.

Applications: This dataset can be used for machine learning models aimed at quality assessment in tea production, enabling automated sorting and decision-making for optimal tea leaf selection in agricultural and commercial tea production processes.
DOI: 10.17632/trtrtn39sn.2
title: Real-world treatment outcomes of systemic treatments for moderate-to-severe atopic dermatitis in children aged less than 12 years: 2-year results from PEDISTAD [supplement]

description: Supplemental tables for "Real-world treatment outcomes of systemic treatments for moderate-to-severe atopic dermatitis in children aged less than 12 years: 2-year results from PEDISTAD", published in JAAD, Sept 2024
DOI: 10.17632/zsfz3chjzj.2
title: Side Profile Vehicle Binary Images

description: This is a dataset of vehicle's binary images obtained with an optical barrier. The main goal off this dataset is to train and test a vehicle classification method. Categories are defined according to the number of axles and the presence of double wheel or not. (See the vehicle_categories.png) This dataset is composed by 3 folders. Train: Images used to train a CNN. (Over 1000 images/cat) Total of 11232 images. Experiment1: Images used to validate.5389 images. Experiment2: Field test. Four days off images. 196018 images. Train and Experiment1 are selected images from the same lane, in different periods over 3 months. Experiment2 has images from several different lanes, and 3 different toll stations. We do not delete any image in this set.
DOI: 10.17632/8ckc3dfb2v.1
title: Papiliotrema terrestris strain PT22AV Genome Sequence

description: This dataset contains the genome sequence and annotation of Papiliotrema terrestris strain PT22AV. This dataset is associated with a publication named "A pipeline to predict the biosafety profile of putative biocontrol yeasts: Papiliotrema terrestris strain PT22AV as a case study".
DOI: 10.17632/3wyf3hgzdy.1
title: Data for: DOSE COEFFICIENTS OF MESH-TYPE ICRP REFERENCE COMPUTATIONAL PHANTOMS FOR IDEALIZED EXTERNAL EXPOSURES OF PHOTONS AND ELECTRONS

description: A dataset of dose coefficients calculated with the mesh-type ICRP reference computational phantoms for idealized external exposures of photons and electrons
DOI: 10.17632/sv8r6phkzt.1
title: Characterization of SARS-CoV-2 N protein reveals multiple functional consequences of the C-terminal domain

description: Data associated with doi: 10.1016/j.isci.2021.102681 CSV file includes peptides observed by HDX-MS with deuteration extents and HDX-MS summary table. PDF includes kinetic plots indicating deuterium uptake for unbound and RNA-bound peptides.
DOI: 10.17632/3cv8z3p43t.1
title: Tanzanian Bone Setter Interview Transcripts June-July 2017

description: Between June and July 2017, we conducted semi-structured interviews with six self-described traditional bone setters, two each in the Northern Tanzanian regions of Kilimanjaro, Arusha, and Manyara. The bone setters sampled were considered to be key informants based on community perceptions and reputation. Bone setters were interviewed at either their place of work or their home. The interview teams consisted of two to four people, always including at least one foreign and one native investigator and up to two foreign and two native investigators. Interviews were conducted in English by a foreign investigator and live-translated into Swahili or M’aa by a native investigator. Both native and foreign contextual note takers were present during interviews. Interviews lasted between one and two hours and were semi-structured around the topics of background, practices, and views on allopathic medicine for musculoskeletal injuries. Audio and video were recorded for later analysis. We transcribed verbatim each interview and one investigator subsequently translated them into English. Consent to interview was obtained orally prior to the day of interview and written on the day of the interview. Written consent was obtained by initials or thumbprint depending on literacy. Institutional board review approval was obtained from Tumaini University and Kilimanjaro Christian Medical College.
DOI: 10.17632/ky6zfyf669.1
title: Appeal Cases heard at the Supreme Court of Nigeria Dataset

description: The dataset contains information about appeal cases heard at the Supreme Court of Nigeria (SCN) between the years 1962 to 2022. The dataset was extracted from case files that were provided by The Prison Law Pavillion; a data archiving firm in Nigeria. The dataset originally consisted of documentation of the various appeal cases alongside the outcome of the judgment of the SCN. Feature extraction techniques were used to generate a structured dataset containing information about a number of annotated features. Some of the features were stored as string values while some of the features were stored as numeric values. The dataset consists of information about 14 features including the outcome of the judgment. 13 features are the input variables among which 4 are stored as strings while the remaining 9 were stored as numeric values. Missing values among the numeric values were represented using the value -1. Unsupervised and Supervised machine learning algorithms can be applied to the dataset for the purpose of extracting important information required for gaining a better understanding of the relationship that exists among the features and with respect to predicting the target class which is the outcome of the SCN judgment.
DOI: 10.17632/9y6tzg9pj5.1
title: Understanding Learning Style Preferences of Zillennial Junior Doctors in Singapore

description: This manuscript investigates the learning styles of junior doctors in Singapore and proposes tailored teaching methods based on Grasha's Integrated Model.
DOI: 10.17632/8jh8nrzc55.1
title: Handwritten Lontara Numerals (0-9) Image Dataset

description: This dataset contains images of handwritten Lontara numerals ranging from 0 to 9. It comprises a total of 10890 samples, with 1089 images for each numeral class. The images were collected from various individuals to ensure diversity in handwriting styles.

Key Features:

Classes: 10 (Lontara numerals 0-9)
Total Samples: 10890
Samples per Class: 1089
Image Format: Grayscale
Data Collection and Labeling:

The dataset was created by collecting handwritten numerals from participants with different handwriting styles.
Each image was manually labeled to ensure accurate and consistent annotations.
The data collection and labeling process was meticulously carried out by one of the authors.
Usage:

This dataset is suitable for training and testing machine learning models for handwritten numeral recognition. It can be used in various applications such as optical character recognition (OCR) systems, pattern recognition, and other related fields.

Contributors:

Author 1: Conducted the data collection and labeling process, ensuring accurate and consistent annotations for all samples.
Author 2: Handled the data preprocessing, including image normalization and augmentation.
Author 3: Developed the script for data collection and managed the overall project coordination.
Author 4: Performed the quality check and validation of the dataset.
Acknowledgments:

We would like to thank all the participants who contributed their handwritten numerals for this dataset.

License:
CC BY NC 3.0
You are free to adapt, copy or redistribute the material, providing you attribute appropriately and do not use the material for commercial purposes.
DOI: 10.17632/nk4n3jcsk6.3
title: Gait Assessment of  Chinese Elderly in Daily Environment Using the "Pocket Gait" App

description: This data set corresponds to the paper entitled "Gait Assessment of the Elderly with a Smart-Phone App: Impacts of Walking States, Exercise Amount and Age". The dataset includes demographic information of the participants and gait parameters (step frequency, max acceleration, min acceleration, acceleration root mean square, step variability, step regularity and symmetry) in three walking states (normal, fast-walking and simulated visually impaired).
Please find the dataset of 100 older adults, the app and the sample of a .csv data collected by the app in the following files.
DOI: 10.17632/czgpmp8cr7.2
title: Temperature Data Loggers Network

description: A network of a modular number of temperature sensors communicating through LoRa that stores data local or remotely
DOI: 10.17632/fh5xv7d3zg.1
title: Numerical code (Python) for transient analysis of step-drawdown test

description: This program (TASHAn, Transient Analysis Step-drawdown test by Hyowon An) was developed for the interpretation of the step-drawdown test data. This program was created using Python. You can download the software from https://www.python.org/. This program was developed to calculate hydraulic parameters (T, r2S, P, C, a, RMSE) of the transient step-drawdown test. (T: aquifer transmissivity, r: well radius, S: aquifer storativity, P: well loss power, C: well loss coefficient, a: transient well loss shape factor.) Governing equations explaining T, r, S, P and C can be found from previous studies including Jacob (1947). For more information of transient well loss shape factor, please contact eunheelee@kigam.re.kr. This datasets include three files (input.xlsx, TASHAn.py, output.xlsx). The data in the input.xlsx was referred from Clark (1977). Description of each file is as follows: 1. input.xlsx 1) The 'input.xlsx' includes observed drawdowns[m], elapsed time[min] from the start of step-drawdown test, the number of total test steps[-] and pumping rate[m3/day] for each step. For example, the column 'B' indicates the elapsed time during the first step of the step-drawdown test and the column 'C' is for the observed drawdown during step 1. Column 'D' and 'E' are reserved for the elapsed time and drawdowns during the step 2. 2) The cell 'P1' is for assigning the number of total steps of the test and the cell 'P2~P7' is for assigning the average pumping rate for each step. 3) This version of code can evaluate the step-drawdown test with the maximum step number of six. If you have test data with five steps, you can leave as blank for the column 'L' and 'M'. 4) If the step-drawdown test includes some bad data and 'null' data, please fill these cells with value of zero '0'. Then, the code automatically skips these values. If you fill any cells outside the black line, the code could have errors. 2. TASHAn.py 1) If you had to install Python or Editor, run it and load the source code (TASHAn.py) 2) There are several modules included in this Code (numpy, lnfit and openpyxl). You can install these modules before running the code. 3) For the parameter optimization, this code uses iterative solver. Therefore, the ranges and initial values for each parameter should be assigned before running the model. If you wish to adjust these parameters, you can revise them in the source code ('line 346 ~ 349'.). If the initial values of the model is too high or low, the analysis result could be unreasonable. *When you running the 'TASHAn.py', the 'input.xlsx' and 'output.xlsx' must be closed. 3. output.xlsx If you can change the file name in 'line 399' Reference. Clark L (1977) The analysis and planning of step drawdown tests, Q J Eng Geol Hydrogeol. 10(2):125-143 Jacob CE (1947) Drawdown test to determine effective radius of artesian well, Trans Am Soc Civ Eng. 112(1):1047-1064
DOI: 10.17632/3drdhrxjm7.1
title: Benign and Malicious JavaScript Dataset

description: The URLs are sourced from benchmarks containing both malicious and benign JavaScript. Benign URLs are sourced from the Alexa Top sites, while malicious URLs are obtained from the PhishTank database, which includes verified phishing pages. Malicious JavaScript samples are extracted from PhishTank and GeeksOnSecurity-GitHub. Additionally, samples of malicious JavaScript are collected from HynekPetrak/malware-jail. The dataset comprises 77 features extracted from both benign (taken from Alexa Top sites) and malicious samples (collected from PhishTank, GeeksOnSecurity-GitHub and HynekPetrak/malwarejail). It is in SVMlight (.svm) format and includes 4,500 benign JavaScript samples and 2,225 malicious JavaScript samples, totaling 6,725 samples.
DOI: 10.17632/vj8bwtwynb.1
title: Distância entre os municípios brasileiros

description: Base de dados com a distância geográfica entre os municípios brasileiros, em metros, não é a distância viária. base de dados compactada (zip) em formato CSV (separador vírgula e decimal ponto) Campos nome_municipio1, nome_municipio2 - nomes do município cod_municipio1, cod_municipio2 - código do município segundo o IBGE distanciaGeo - distância geodésica considerando o elipsoide WGS84 distanciaCos - distância esférica utilizando a lei dos cossenos distanciaHsin - distância esférica utilizando o método de haversine distanciaMeeus - distância geodésica utilizando o método Meeus distanciaSvince - distância geodésica utilizando o método Vincenty fonte das coordenadas dos municípios: https://github.com/kelvins/Municipios-Brasileiros
DOI: 10.17632/y5z7wjfw8j.1
title: Replication_Package_Chor-Li_JIE_2024

description: This material replicates the Figures and Tables in Chor and Li (2024), "Illuminating the Effects of the US-China Tariff War on China's Economy", Journal of International Economics.
DOI: 10.17632/922v98p227.1
title: Data for: Prioritization of Pesticide Usage in the Upper Citarum River Basin, Indonesia

description: This dataset is associated with the article "Prioritization of Pesticide Usage in the Upper Citarum River Basin, Indonesia" by .R. Utami, G. W. Geerling, I.R.S. Salami, S. Notodarmojo and A.M.J. Ragas to be considered for publication as a research paper in Science of the Total Environment in March 2020. This research involves environmental data of anthroposphere (i.e. pesticide used by the farmers), hydrosphere (i.e. pesticide concentration in surface water, precipitation data and river discharge data for the runoff modelling), and partly lithosphere (i.e. half-life of the pesticide in soil, sorption coefficient of the pesticide to organic carbon and the fraction organic carbon in soil for the runoff modelling). The study combines several subjects such as agriculture, environmental modelling, ecotoxicology and human health risk assessment, and also emerging contaminants in the surface water. Please see the research manuscript for more information about this dataset.
DOI: 10.17632/7cp325hk29.1
title: Single and joint impact of type 2 diabetes and of congestive heart failure on albuminuria: Data from subgroup analysis and on moderate albuminuria

description: We investigated 180 consecutive patients with congestive heart failure (CHF), of whom 83 had type 2 diabetes (T2DM) and 97 did not have diabetes as well as 223 controls without CHF, of whom 39 had T2DM and 184 did not have diabetes. Data was recorded by standardized interviews and by standardized examination protocols at our institution and were extracted from medical records. Here, we analyzed data on gender differences. Further, we examined the effect of CHF and T2DM on moderate albuminuria, i.e. on an albumin-creatinine ratio (ACR) of 30–300 mg/g. Table 1 shows baseline characteristics of our patients stratified by gender. Table 2 gives ACRs and prevalence rates of albuminuria separately for men and women.

The data set presented here could be reused with similar patient cohorts for pooled analysis. 

Data description
Table 1: Shows the baseline characteristics in men and women of our study population with regard to the presence of both CHF and T2DM [1]. 
Table 2: Shows ACRs and the prevalence of albuminuria in in men and women with regard to the presence of both CHF and T2DM.  
Dataset: Features the data of our study population including the presence of CHF and T2DM, gender, ACR and prevalence of albuminuria. The data are pseudonymized as each participant was assigned to consecutive numbers (“ID”) on the basis of date of inclusion. The variables “Diabetes_mellitus_type_2” and “congestive_heart_failure” distinguish if subjects are suffering from T2DM and/or CHF, whereas “0” encodes “no” and “1” stands for “yes”. “Sex” describes the biological sex of subjects, whereas “0” stands for “female” and “1” for “male” patients. The variable “albumine_creatinine_ratio” describes the quantity of albumin-creatinine ratio (ACR) in mg/g. Finally, the variable “moderate_albuminuria” states if subjects suffer from moderate albuminuria, which is defined as an ACR of 30–300 mg/g, and as before “0” stands for “no” and “1” for yes. 
Questionnaire: The questionnaire used for the standardized interview and examination of every subject.
DOI: 10.17632/gs8sh8h4ds.1
title: CAE model of Total Knee Replacement with Femur and Tibia bones at hyperflexion

description: The data presented here is a Computer Aided Engineering (CAE) model of Total Knee Replacement (TKR) components with Femur and Tibia bones at hyperflexion (120 degrees of knee). This is the CAE model of the same CAD as published in [1]. The CAE model (ASSEMBLY.hm) presented here can be opened in Hypermesh, a CAE software for defining material properties, section definitions, boundary conditions, loads etc. Then the complete model can be imported in a supported format to a CAE software like Abaqus, Ansys etc. to execute a finite element analysis which might be very crucial for throwing light into knee kinematics and mechanics at hyperflexion of the knee.
DOI: 10.17632/3m778636p2.1
title: example

description: example for mendeley data
DOI: 10.17632/77p6rzb73n.2
title: Software code quality and source code metrics dataset

description: The dataset contains quality, source code metrics information of 60 versions under 10 different repositories. The dataset is extracted into 3 levels: (1) Class (2) Method (3) Package. The dataset is created upon analyzing 9,420,246 lines of code and 173,237 classes. The provided dataset contains one quality_attributes folder and three associated files: repositories.csv, versions.csv, and attribute-details.csv. The first file (repositories.csv) contains general information(repository name, repository URL, number of commits, stars, forks, etc) in order to understand the size, popularity, and maintainability. File versions.csv contains general information (version unique ID, number of classes, packages, external classes, external packages, version repository link) to provide an overview of versions and how overtime the repository continues to grow. File attribute-details.csv contains detailed information (attribute name, attribute short form, category, and description) about extracted static analysis metrics and code quality attributes. The short form is used in the real dataset as a unique identifier to show value for packages, classes, and methods.
DOI: 10.17632/c2293m3yjf.1
title: Rare Earth Elements (REE) recycling analysis of patents' data

description: The patent dataset analyzed is based on search criteria aimed at differentiating worldwide patent data regarding applications dealing with REE recycling, and a ‘control’ sample where patent data concern a more generical technical context dealing with recycling. Such differentiation is essentially achieved by two different selections of patent data from the EPO database Patstat (Spring 2022 ed.), each selection based on IPC/CPC classification codes with the addition of keywords specific for Rare Earth Elements/Lanthanoid in case the REE specific patent documents are looked for.
In a first phase a dataset has been obtained by means of a pool of classification codes devoid of the CPC “green” code Y02P10/20, while in a second search the CPC Y02P10/20 has been included in the search query.
This code concerns specifically the metal recycling methodologies, thus determining a consistent increase of the patent documents retrieved. When the search strategy is subsequently modified, i.e. by simply replacing the CPC code Y02P10/20 with the combination of IPC/CPC C22B59, specifically referring to the Rare Earth Elements, almost 86% of the data are present in both patent collections, even though the C22B59 is not recognised as a “green” classification code.

Further information can be gathered from the trend of patent applications filed worldwide (i.e. including either national authorities or supranational authorities, such as the EPO or the WIPO) considering the timeframe starting from yr. 2010 to yr. 2022. Such trends can also be assessed in order to normalize the amount of filing events, i.e. by comparing, for each of the most representative patent authorities, the trend of REE specific applications with the trend resulting from the control sample. Additional information is based on proxies that have been dealt with in an earlier publication regarding a similar approach used to investigate about the WEEE/RAEE recycling (“WEEE recycling patents' dataset”, Mendeley Data, V2, doi: 10.17632/4ysw32c9fy.2). The assessment in the ‘REE’ dataset entails the analysis of the patent families based on the ranking of IPC and CPC codes, respectively, to detect those most frequently assigned considering each of the two classification’s systems. Subsequently, qualitative and quantitative information is retrieved concerning the most frequently addressed patent authorities, the most representative players and their affiliation kind, as well as the technology concepts ranking, performed using the Orbit Intelligence patent database (available from Questel). Moreover, such source of patent data has been used to either focus the search on specific technical procedures aimed at recovering the REE from waste materials or to select the patent documents specifically regarding the recovery of chemical elements such as Yttrium and Samarium.