cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/62kwttmkcv.1
title: C-OBGET (OBGET for Children)

description: • Psychological dataset for children can be used to train machine learning models which can classify and detect mental disorders in childhood, and prevents the progression of the disorders. • We collected Original Bender Gestalt Drawing Test dataset for children for automating Original Bender Gestalt Drawing Test for children. • There is no psychological drawing test dataset for children with related metadata. To the best of our knowledge most researchers focus on computerized psychological tests for adults, while many mental disorders in adults, initiate childhood problems. Therefore, by detecting mental disorders in childhood, it is possible to prevent many irreparable mental disorders in adulthood. • Collecting children hand sketch dataset is very challenging, children spatial visualization is incomplete and they cannot draw shapes correctly. • Collecting, analyzing, and labeling children's hand sketches are harder than adult hand sketches because it takes more work to explain test rules to children. Children do not sit for a certain period and draw specific shapes; spatial visualization is incomplete. • Children’s OBGET data collection steps and pre-processing are presented. • Children’s OBGET samples are labeled by the proposed semi-automatic labeling.
DOI: 10.17632/8gw56mt6c8.1
title: Data for: Automatic calculation of Mercuri grades from CT and MR muscle images

description: This Excel data contains pixel data of muscle CT or MR images. In this sheets, we set up formulas shown in our articles, and calculated the unknown constants using solver.
DOI: 10.17632/y5c3jnbjvs.1
title: GenEvaPa: A generic evaporation package for modeling evaporation in molecular dynamics simulations

description: This work presents a novel general tool for modeling the process of evaporation without the need for modifying existing software using Python. The tool was developed based on the MDAnalysis package, which is used to import a Molecular Dynamics trajectory. The tool then removes solvent molecules and outputs a new structure file to be used for further simulation and analysis. This process is designed to be iterated by using the resulting dynamic simulation trajectory as the input file. The evaporation is designed to randomly delete solvent molecules while preserving solvation shells around solutes. The evaporation rate can be controlled by the length of the MD simulations and the number of particles removed between dynamic simulations. Validity of the tool was tested extensively using the Gromacs suite. Advantages of this tool include its genericness, simplicity and user friendliness, as no significant modification of existing software platform or Gromacs specific tools are needed.
DOI: 10.17632/wm6w2fvkfj.1
title: Medical Appointments No-Show

description: The data are used for the study of medical appointments no-show. The dataset consists of data obtained and extracted from the University of Vale do Itajaí Center of Specialization in Physical and Intellectual Rehabilitation (CER). The clinic serves the Unified Health System (SUS) in southern Brazil.  The CER is an outpatient care service that performs diagnosis, assessment, guidance, early stimulation and specialized care. It has acted in functional rehabilitation and psychosocial qualification to encourage the autonomy and independence of people with disabilities.

Firstly, we collected the relevant information on the no-show problem in loco at the rehabilitation centre by transcribing 49,593 medical records from an electronic spreadsheet of 2016 to2022. In order to add more information to the collected data, some other databases were combined with the initial database, namely: ICD, weather conditions data and other related attributes. Finally, this dataset is composed of the following attributes:
1. "Specialty": specialty that patient received the treatment;
2. "Appointment Time": appointment time scheduled;
3. "Gender": male or female gender of the patient;
4. "Appointment Date": appointment date scheduled;
5. "No-show": given whether the patient attended the scheduled appointment or not;
6. "No-show Reason": description of the reason why the patient did not attend the scheduled appointment;
7. "Disability”: the patient’s motor or intellectual disability;
8. "Date of Birth": the patient’s date of birth;
9. "Date of Entry into the Service": date of the patient’s first appointment at the CER;
10. "City": city where the patient resides;
11. "ICD": identifier of the patient’s disease;
12. "Appointment Month";
13. "Appointment Year";
14. "Appointment Shift";
15. "Age": patient's age;
16. "Under 12 years old": patient's age under 12 years old;
17. "Over 60 years old": patient's age over 60 years old;
18. "Patient needs companion": patient's needs companion to go to the appointment;
19. "Patient needs companion": patient's needs companion to go to the appointment;
20. "Average Temperatura Day": Average temperatura in the day of the appointment;
21. "Average Rain Day": Average rain in the day of the appointment;
22. "Max Temperature Day": Maximum temperature in the day of the appointment;
23. "Max Rain Day": Maximum rainfall in the day of the appointment;
24. "Storm Day Before": Heavy rain in the day before the appointment;
25. "Rain Intensity": no rain, weak, moderate or heavy rain in the day of the appointment;
26. "Heat Intensity": cold, heavy cold, warm, heavy warm or mild in the day of the appointment;
DOI: 10.17632/t42ynjk936.1
title: EV Lithium Ion Battery Fault Diagnosis Dataset

description: To train and test two ANN models, two separate datasets are developed based on battery modelling in MATLAB. State of Charge (SOC), Temperature and Voltage are the input features in both datasets. Where as against output label column in first dataset 0 represents healthy battery and 1 represents unhealthy battery. Similarly in second dataset 0 represents healthy battery and 1 represents battery has over discharge fault in battery and 2 indicates short circuit fault in the battery.
DOI: 10.17632/n66rp59tr7.1
title: Priced risk in corporate bonds replication package

description: The code in this replication package generates Tables 1 to 6 and Figures 1 to 2 in the paper titled “Priced risk in corporate bonds,” by Dickerson, Mueller, and Robotti (2023, henceforth DMR). The code to generate Tables 1 to 5 and Figures 1 to 2 are written in Matlab. Table 6 is generated with Python only. The file RunAllResults.m, the ‘one-push-button’, takes about 2 minutes to execute, and will produce all of the results in the paper except for Table 6. These results are printed to the Matlab console, saved as AllTablesReport and also stored in Matlab arrays such that they can be exported to other file formats. The results for Table 6 are produced by executing the Python script Table6.py. The authors have set up a dedicated replication/companion website (Open Source Bond Asset Pricing) which can be accessed here: https://openbondassetpricing.com/. This README file guides the user through replicating the results of the paper as well as constructing the main database from scratch.
DOI: 10.17632/7j7n2r2zcm.1
title: EEG dataset of children with learning disabilities (LD)

description: Participants
Participants are 4 children with learning disabilities (LD) (boys and girls, aged 5-8 years).  The LD children were diagnosed by health professionals, and none of -them were under medications.  All of the children are right-handed.  

Hardware and dataset
EEG recordings were performed using 19-channels Brainmarker EEG machine with the sampling rate of 250Hz and Pz as the reference electrode. 

The presented dataset is for the following tasks: 1) eyes closed (1 minute), and 2) watching facial expressions for the emotion fear from the Radboud Faces Database (RafD).  The emotionally related facial expressions may help the children to change his/her feelings.  The experimental protocol was approved by the IIUM Research Ethics Committee No 419 (IREC 419).

The file names have the following format: {participant_id}_{task}.csv
DOI: 10.17632/jwfrz8238k.1
title: Global risk maps based on documented threats (1991-2016) for marine mammals: GIS files

description: In Avila et al. we conducted a systematic, spatially-explicit overview about currently, between 1991 and 2016, reported threats affecting marine mammal species (121 species), in specified locations around the world, by developing a series of risk maps. Risk maps were obtained by assigning threat to countries where they were reported, further refining geo-referencing by allocation to ocean basins and Longhurst biogeographical provinces and by intersection with mapped species’ distributions of AquaMaps using the core habitat (RES≥0.6, http://www.aquamaps.org/). Marine mammal species classification in AquaMaps follows the Taxonomy of Catalogue of Life (http://www.catalogueoflife.org/col/), and for the threats collection we used the Committee on Taxonomy (2016). As a result of these taxonomic discrepancies we obtained map risks for 114 different marine mammal species (SpID: code of the species; Sp_scName: name of the species). Here we present the GIS files (shapefiles) of the obtained risk maps for marine mammals. We also provide here the GIS file of the Species distribution of AquaMaps using the core habitat (Version 2016.8., http://www.aquamaps.org/) that we used to construct the risk maps. Risk maps differed by threat types (categories, attributes and sources), by taxa (odontocetes, mysticetes, pinnipeds and fissipeds, and sirenians) and by species. We identified 7 threat categories, 28 threat attributes and 13 threat sources affecting currently the marine mammals. The seven threat categories were based on the agent inflicting the actual harm and were: incidental catch & fishing gear interactions (BYCA), direct harvesting (DHAR), pollution (POLL), traffic (TRAF), pathogens & introduced species (INFE), resources depletion (HABI) and ocean-physics alteration (OCEA). Each of the threat categories was subdivided into a threat attribute with additional information such as type of pollution or reason for harvesting and others. Threat sources, defined as the ultimate origin of the threat, were defined following the IUCN threat classification scheme. We identified two types of threat sources, those related to direct human activities and those not. Threat sources, related to direct human activities were: aquaculture and agriculture (AQUA); fisheries (FISH), hunting and whaling (HUNT); energy production from oil, gas and mining (OGRE); energy production from nuclear power (NUCE); energy production from renewable resources (REWA); residential and industrial development (URBA); tourism and recreation (TOUR); scientific activities (SCIE); military activities (MILI); and unreported human activities (HUMA). Threat sources not related to direct human activities were climate change and geological events (CHAN). Also we identified threats resulted from sources that were unknown, unclear or unreported, but were not clearly related to direct human activities (UNKN).
DOI: 10.17632/n348fw6k85.1
title: Stated preference study with multiple myeloma patients

description: Analysis dataset with the fitted preference models and sample R code to allow for further stochastic multi-criteria decision analyses
DOI: 10.17632/vzrs8n8pk6.1
title: DScribe: Library of descriptors for machine learning in materials science

description: DScribe is a software package for machine learning that provides popular feature transformations (“descriptors”) for atomistic materials simulations. DScribe accelerates the application of machine learning for atomistic property prediction by providing user-friendly, off-the-shelf descriptor implementations. The package currently contains implementations for Coulomb matrix, Ewald sum matrix, sine matrix, Many-body Tensor Representation (MBTR), Atom-centered Symmetry Function (ACSF) and Smooth Overlap of Atomic Positions (SOAP). Usage of the package is illustrated for two different applications: formation energy prediction for solids and ionic charge prediction for atoms in organic molecules. The package is freely available under the open-source Apache License 2.0.
DOI: 10.17632/v4w72bsts5.1
title: Potato Leaf (Healthy and Late Blight)

description: The potato crop is found at fourth as a major crop in the world, after rice, wheat, and maize. Nonetheless, in Ethiopia, the yield per unit area of potato is very low compared to other countries. There are a plethora of reasons, one of them is the disease. The major disease, which affects the major potato production area is Late Blight, according to researchers the disease caused 100% crop loss on the unimproved local cultivar and 67.1% on a susceptible variety. Not to take early Late Blight disease management would destroy the whole farm within a few days. For decades many researchers have experimented on plant disease detection and classification using computer vision via different approaches and algorithms. Many researchers used traditional machine learning algorithms which require a handcrafted feature extraction to detect a given image. Besides, the data collected were under a laboratory setup which makes it less reliable while testing in real cultivation farms captured images of the potato. Heterogeneous image datasets were collected from (Holeta, Ethiopia) potato farm with the help of two plant pathologists. The dataset correctly labeled with two classes as ‘Healthy’ and ‘Late Blight’, and the image has variety meaning some of the images captured with less noisy background image and others with a highly noisy environment. Under ‘Late Blight’ class 63 images were collected and under ‘Healthy’ class 363 images were collected. Finally, the prepared dataset could be used for different researches that aimed at plant disease detection and classification.
DOI: 10.17632/hc8gxwks3p.1
title: Cancer-Associated Fibroblasts Regulate Patient-Derived Organoid Drug Responses

description: This dataframe contains the full PDO + CAF screening developed and detailed in "Cancer-Associated Fibroblasts Regulate Patient-Derived Organoid Drug Responses", 2022 (https://www.biorxiv.org/content/10.1101/2022.10.19.512668v1). 
We developed a highly multiplexed thiol-reactive organoid barcoding in situ mass cytometry (TOBis MC) platform to perform single-cell post translational modification (PTM) signalling analysis of colorectal cancer (CRC) PDOs and cancer associated fibroblasts (CAFs) in response to clinical therapies. 

The dataframe is structured in rows that correspond to each single cell, and columns that include information on the PTM or protein measured, and the metadata (information on the provenance of each cell). 
Metadata columns include:
-Date: in format YYYMMDD, correspond to the date those rows were acquired.
-Patient: PDO number (5, 11, 21, 23, 27, 75, 99, 109, 141, 216)
-Culture: indicates the nature (monoculture/co-culture of the sample). PDO (PDO monoculture), F (CAF monoculture), PDOF (PDO+CAF co-culture).
-Treatment: drug(s) administered to each culture. The key for the drug labels appear detailed in Table S2 in the article referenced.
-Concentration: concentration used for each treatment. The key for the drug concentrations appear detailed in Table S2 in the article referenced.
-Replicate: treatment conditions were performed in triplicate (A, B, C) and control conditions in triplicate or duplicate (A, B, C, AA, BB, CC)
-Cell_type: refers to the cell-type of the cell gated (PDOs, or CAFs). In monocultures there is one cell type, and in co-cultures 2. 
-Plate: since different treatments contain different vehicle controls (DMSO, AH, O), the plate column helps to specify which vehicle corresponds to which treatments. This is important for pairing. 
-Batch: the data was acquired along 10 months, but the acquisition was performed in 2 general batches, with 5 months in between. However, for a more accurate batch representation, we recommend using the columns "Date" and "Patient".
DOI: 10.17632/pwgt6nbpjt.1
title: COLD6-OSM1 module senses chilling for cold tolerance via 2',3'-cAMP signaling in rice

description: These files contain Gel images, microscopy images and phenotypic images.
DOI: 10.17632/z8s6w86tr3.2
title: Suicidal Ideation Detection Reddit Dataset

description: 1. I failed to find any current public datasets while I was thinking about developing a text classifier to identify suicide ideation. I hope this will save time and be helpful to anyone searching for suicide detection datasets. 2. This dataset is a collection of posts from the Reddit online platform. The posts are collected using PRAW. PRAW, an acronym for "Python Reddit API Wrapper", is a Python package that allows for simple access to Reddit's API. 3. Suicidal texts have been collected from the following Reddit online community:  SuicideWatch 4. Non-suicidal texts have been collected from the following Reddit online communities:  CasualConversation  BenignExistence and  CongratsLikeImFive 5. There are a total of 15477 records and 3 attributes in this dataset, and the data were collected from June 1, 2023, to November 13, 2023. 6. All posts collected from suicidal texts are labeled as suicidal, while posts collected from non-suicidal texts are labeled as non-suicidal.
DOI: 10.17632/yg6rhtfrr5.1
title: 3D model of Shakespeare's first Globe Theatre and geometric study of the plan layout

description: This repository contains a virtual reproduction of Shakespeare's first Globe Theatre, which was destroyed by fire in 1613, and a plausible study of its plan using the ad quadratum method. The works collected here were created by the authors following a careful study of most of the available iconographic and bibliographic information on the subject. The planimetric scheme has been realised using the AutoCAD software by Autodesk Inc. showing the drawing of the plan step by step. The three-dimensional model was also created in CAD software, modelling the structure in solids and trying to represent most of the building elements. The internal part of the Tiring House, the element on which we have the least information, was deliberately left incomplete, although it is assumed that there should have been at least one staircase block connecting the various floors of the structure, as suggested by the authors in the respective article.
DOI: 10.17632/63rszpyfns.2
title: normal and stroke

description: raw fnirs data for normal and stroke
DOI: 10.17632/cxz82jh92m.1
title: ESG and Energy data

description: Thwaw data are about china energy and china ESG
DOI: 10.17632/wpnxnxtxpv.1
title: EXPERIENCIA DE USO DOCENTE DE LA APLICACIÓN DE MEDICIÓN DE AUDIENCIA DE RESPUESTA INMEDIATA KAHOOT! CON FINES PEDAGÓGICOS

description: Resumen
Experiencia de uso  docente de la aplicación de medición de audiencia de respuesta inmediata Kahoot! con fines pedagógicos.Este informe presenta los resultados de una intervención  cuyo principal objetivo  fue  caracterizar  la  experiencia  de  uso    docente  de  la  aplicación  de medición de  audiencia  de  respuesta  inmediata  Kahoot!  con fines pedagógicos, en el área de educación general básica, para describir los discursos docentes al respecto. La  investigación,  de  carácter  cualitativo, se  realizó desde  un  enfoque fenomenológico, a través de un diseño emergente.En elestudio participaron un grupo  de  seis  docentes  voluntariosde  un  colegio  de  dependencia  particular subvencionada, los  cuales,  a  través  del  uso  del sistema  de  medición  de audienciaKahoot!,    desarrollaronactividades,    según    las    indicaciones curriculares de  sus  respectivos  cursos.  Las  actividades  fueron  realizadas durante el segundo semestre del año 2016,obteniendo como resultado una alta participación  de  la  audiencia,debido  ala  motivación  lograda, además  de  la obtención  deun  registrode  informaciónútil  para  los  fines  establecidos  por  el objetivo de la intervención.A partir de lainformaciónrecopilada a través de dos entrevistas en profundidad  realizadas  a  docentes  participantes  de  la  experiencia,  un  grupo focal  y  la  observación  participante  plasmada  en  el  diario  del  investigador, emergen  cuatro categorías:  Propósitos,  Efectos,  Estrategias  al  utilizar  Kahoot! según  el  momento  en  relación  a  la  clase  y Disposición  que  los  sujetos  de investigación  asignan  de  manera  subjetiva  al  trabajo  con  Kahoot!,  constructos que representan el consenso de los discursos docentes.
Palabras clave: m-learning, dispositivos móviles, Kahoot!, discursos docentes.
DOI: 10.17632/y7hyt83vxr.1
title: Sprime results for 1000 Genomes non-African populations and SGDP Papuans
DOI: 10.17632/zp5drxxnz6.2
title: The position of Iranian left-wing parties regarding the White Revolution project in Iran

description: Academic studies have not given adequate attention to the history of political parties and organizations in Iran, despite Iran being an important neighboring country with significant sensitivity and importance to various regions in the Arab world.
Since the early 1960s, Iran has become entangled in significant political developments that have had a profound impact on its overall situation. It has witnessed important transformations, including the maturation of opposition political thought. This thought was able to move beyond theoretical ideological frameworks and engage in practical realities, opposing the authoritarian rule of the Pahlavi regime and foreign presence. The authorities attempted to establish party organizations loyal to them in order to limit the activities of opposition parties. However, these opposition parties were weak in the face of the dominance of the authorities and their security forces.
Opposition to the Pahlavi family's rule was not a futile endeavor aimed solely at overthrowing their rule. It was a prelude to new leadership that the Iranian street perceived as having a higher and more noble position. The feeling of reassurance towards these leadership figures generated a desire for comprehensive change that encompassed all aspects of the existing state.
The opposition was not characterized by a single ideological color. It consisted of various nationalist, secular, religious, materialistic communist, and Western liberal parties. However, the Islamic current dominated the Iranian political scene. In response, the authorities attempted to establish political parties loyal to them, known as the "power parties," in order to suppress public discontent. However, these power parties themselves contributed to the downfall of the regime due to their detachment from the Iranian street and the prevalence of administrative and financial corruption within their leadership ranks.
DOI: 10.17632/4m3w368g4m.1
title: Metal transfer modes for wire arc additive manufacturing with wire aristorod 69

description: This database demonstrates the modes of metal transfer with short-circuited transfer (CMT) and drop transfer (C-GMAW).
DOI: 10.17632/r2wwzgdybs.2
title: Sustainable Impact of the Shopee Pay E-Wallet Digital Payment System on Consumer Buying Interest: A Case Study of the Golden Hour T-Shirt Business Data

description: Sustainable Impact of the Shopee Pay E-Wallet Digital Payment System on Consumer Buying Interest: A Case Study of the Golden Hour T-Shirt Business Data
DOI: 10.17632/c749xw3c9w.1
title: Leadership Styles and Employee Performance

description: The data collected from the hospital employee and leaders to access the impact of leadership styles on employee performance.
DOI: 10.17632/55jb65dx3p.1
title: RESinvANFIS v1.0 - A Versatile MATLAB Tool for Inverting Geoelectrical Resistivity Sounding Data using Neuro Fuzzy Technique

description: RESinvANFIS is a standalone application that provides the output subsurface layer model (e.g. True resistivity and Thickness). The software module runs in MATLAB 2012 or newer version installation. If MATLAB software is not available then install MCR (Matlab run time environment) installer to run the software for the system without MATLAB.