cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10027950
title: Italian Gender Bias Dataset

description: Dataset described in the paper Badaloni S., Rodà A., Scant M., "An Italian dataset for the analysis of gender stereotypes in textual documents". Accepted for publication in the Proc. of the AIxIA Conference, Rome 6-9 November 2023.
description: Description and contents of files:
"textual-corpus.zip"
Contains all the collected italian text documents of the dataset in txt format. These are divided in "Female", "Male", and "Neutral" subdirectories depending on the hypothetical target audience of the source. This category is reflected also in the identification label given to the document which is a letter ("F", "M" or "N") followed by a unique number. The filename of each document is formed by its label and its title separated by an underscore. (e.g. "F2_Scarpe estive guida alla scelta dei modelli.txt").
"sources-textual-corpus.csv"
An index of all the documents of the dataset and information about the source. For each document ID label, title, author, source, and number of words are reported.
"scores-from-participants.csv"
Contains the results of the online survey where participants were asked to evaluate the bias towards a gender of the given text sections from the dataset. The answers could be given on a scale of integers from  -2 ("Completely female") to +2 ("Completely male") where the 0 indicates neutrality regarding gender. Each row represents a participant and all the answers given are reported. For a more accessible version of the results please refer to "labeled-dataset.csv. (For further details on the survey and this file please visit the digital appendix of the paper at https://github.com/m-scagnet/genere_ed_IA_APPENDICE )
"labeled-dataset.csv"
The elaborated results of the online survey:
each text section presented in the survey which received 5 or more different valid answers is reported together with its label and the average score obtained. The label of the section is obtained from the label of the document which contains it and a number which identifies the section in the text (e.g. "F2_6") The format used to store the information in this file is: "ID; text of the section; average score".
DOI: 10.6084/m9.figshare.23244212
title: Microgeographic speciation in a complex of Anatolian bush crickets facilitated by fast evolution of reproductive isolation

description: <b>Samples.csv: </b>Description of individual and population codes used in the different analyses and genomic datasets.<b>STACKS_process_radtags.txt</b>: Input file including all parameters used to run the <i>process_radtags</i> program in STACKS.<b>PYRAD.txt: </b>Input file including all parameters used to run PYRAD.<b>BPP.zip</b>: This ZIP folder contains the input files used to perform A00 and A01 analyses in BPP.<b>c85d5m40sh15.str</b>: Input file used to perform genetic clustering (STRUCTURE) and principal component (PCA) analyses including all genotyped individuals for the taxon <i>Poecilimon luschani</i>.<b>Chobanovi_c85d5m11sh15.str</b>: Input file used to perform genetic clustering (STRUCTURE) and principal component (PCA) analyses for the taxon <i>Poecilimon luschani chobanovi</i>.<b>Luschani_c85d5m16sh15.str</b>: Input file used to perform genetic clustering (STRUCTURE) and principal component (PCA) analyses for the taxon <i>Poecilimon luschani luschani</i>.<b>Birandi_c85d5m12sh15.str</b>: Input file used to perform genetic clustering (STRUCTURE) and principal component (PCA) analyses for the taxon <i>Poecilimon luschani birandi</i>.<b>c85d5m20sh15.txt</b>: Input file (NEXUS format) used to perform phylogenomic analyses in SVDQUARTETS.<b>c85d5m40sh15.arp</b>: Input file used to perform ARLEQUIN analyses.<b>DistanceMatrices.zip: </b>This ZIP folder contains distance matrices (FST, geographical distances and topographic distances) used to perform landscape genetic analyses.<b>FASTSIMCOAL2.zip</b>: This ZIP folder contains input files for demographic analyses in FASTSIMCOAL2.<b>StairwayPlot</b><b>.zip</b>: This ZIP folder contains input files for demographic reconstructions in STAIRWAYPLOT.
DOI: 10.6084/m9.figshare.23244212.v1
title: Microgeographic speciation in a complex of Anatolian bush crickets facilitated by fast evolution of reproductive isolation

description: <strong>Samples.xlsx</strong>: Description of individual and population codes used in the different analyses and genomic datasets. <br> <strong>STACKS_process_radtags.txt</strong>: Input file including all parameters used to run the <em>process_radtags</em> program in STACKS. <br> <strong>PYRAD.txt: </strong>Input file including all parameters used to run PYRAD. <br> <strong>BPP.zip</strong>: This ZIP folder contains the input files used to perform A00 and A01 analyses in BPP. <br> <strong>c85d5m40sh15.str</strong>: Input file used to perform genetic clustering (STRUCTURE) and principal component (PCA) analyses including all genotyped individuals for the taxon <em>Poecilimon luschani</em>. <br> <strong>Chobanovi_c85d5m11sh15.str</strong>: Input file used to perform genetic clustering (STRUCTURE) and principal component (PCA) analyses for the taxon <em>Poecilimon luschani chobanovi</em>. <br> <strong>Luschani_c85d5m16sh15.str</strong>: Input file used to perform genetic clustering (STRUCTURE) and principal component (PCA) analyses for the taxon <em>Poecilimon luschani luschani</em>. <br> <strong>Birandi_c85d5m12sh15.str</strong>: Input file used to perform genetic clustering (STRUCTURE) and principal component (PCA) analyses for the taxon <em>Poecilimon luschani birandi</em>. <br> <strong>c85d5m20sh15.txt</strong>: Input file (NEXUS format) used to perform phylogenomic analyses in SVDQUARTETS. <br> <strong>c85d5m40sh15.arp</strong>: Input file used to perform ARLEQUIN analyses. <br> <strong>DistanceMatrices.xlsx</strong>: Distance matrices (FST, geographical distances and topographic distances) used to perform landscape genetic analyses. <br> <strong>FASTSIMCOAL2.zip</strong>: This ZIP folder contains input files for demographic analyses in FASTSIMCOAL2. <br> <strong>StairwayPlot.zip</strong>: This ZIP folder contains input files for demographic reconstructions in STAIRWAYPLOT.
DOI: 10.6084/m9.figshare.24261226
title: The Readiness of the Asian Research Ethics Committees in Responding to the COVID-19 Pandemic Research Dataset

description: The survey data and supplementary figures to investigate the readiness of the Asian Research Ethics committees in responding to the COVID-19 Pandemic.
DOI: 10.6084/m9.figshare.24261226.v4
title: The Readiness of the Asian Research Ethics Committees in Responding to the COVID-19 Pandemic Research Dataset

description: The survey data and supplementary figures to investigate the readiness of the Asian Research Ethics committees in responding to the COVID-19 Pandemic.
DOI: 10.5281/zenodo.10115524
title: Albums top de CloundSound

description: This dataset provides a detailed view of the "SoundCloud Top Albums", offering a fairly rich perspective with information about album titles, artists, track count or popularity. With a diversity of records covering varied musical genres. This compilation captures the richness and diversity of emerging music available on the web.
DOI: 10.5281/zenodo.10115525
title: Albums top de CloundSound

description: This dataset provides a detailed view of the "SoundCloud Top Albums", offering a fairly rich perspective with information about album titles, artists, track count or popularity. With a diversity of records covering varied musical genres. This compilation captures the richness and diversity of emerging music available on the web.
DOI: 10.5281/zenodo.10115558
title: Evolución de la cotización de las empresas del S&P 500

description: Evolución diaria de la cotización de las empresas (valor a cierre de mercado) que componen el índice en diferentes momentos del tiempo.
DOI: 10.5281/zenodo.10115559
title: Evolución de la cotización de las empresas del S&P 500

description: Evolución diaria de la cotización de las empresas (valor a cierre de mercado) que componen el índice en diferentes momentos del tiempo.
DOI: 10.5281/zenodo.10115564
title: Evolución de la capitalización de las empresas del S&P 500

description: nos muestra la evolución de la capitalización bursátil de las empresas que componen el S&P 500 en diferentes momentos del tiempo.
DOI: 10.5281/zenodo.10115565
title: Evolución de la capitalización de las empresas del S&P 500

description: nos muestra la evolución de la capitalización bursátil de las empresas que componen el S&P 500 en diferentes momentos del tiempo.
DOI: 10.5281/zenodo.10115534
title: Evolución de los pesos relativos de la empresas del S&P 500 sobre la capitalización total del índice

description: nos muestra la composición del S&P500 en diferentes momentos del tiempo, mostrando en cada punto de análisis las empresas que conforman el top x% seleccionado por el usuario. Los datos incluidos son:incluyendo: (1) ticker, (2) nombre de la empresa, (3) sector, (4) subsector, (5) peso relativo de la capitalización sobre la capitalización total del índice, (6) peso acumulado de la capitalización y (7) fecha valor de la capitalización utilizada para el cálculo de los pesos relativos.
DOI: 10.5281/zenodo.10115535
title: Evolución de la capitalización de las empresas del S&P 500

description: nos muestra la composición del S&P500 en diferentes momentos del tiempo, mostrando en cada punto de análisis las empresas que conforman el top x% seleccionado por el usuario. Los datos incluidos son:incluyendo: (1) ticker, (2) nombre de la empresa, (3) sector, (4) subsector, (5) peso relativo de la capitalización sobre la capitalización total del índice, (6) peso acumulado de la capitalización y (7) fecha valor de la capitalización utilizada para el cálculo de los pesos relativos.
DOI: 10.5281/zenodo.10115576
title: Evolución de los pesos relativos de la empresas del S&P 500 sobre la capitalización total del índice

description: nos muestra la composición del S&P500 en diferentes momentos del tiempo, mostrando en cada punto de análisis las empresas que conforman el top x% seleccionado por el usuario. Los datos incluidos son:incluyendo: (1) ticker, (2) nombre de la empresa, (3) sector, (4) subsector, (5) peso relativo de la capitalización sobre la capitalización total del índice, (6) peso acumulado de la capitalización y (7) fecha valor de la capitalización utilizada para el cálculo de los pesos relativos.
DOI: 10.5281/zenodo.10115582
title: Experimental Data of Air Heat Engine Characteristics

description: Air heat engine characteristic experiment is an experimental method used to study the working principle, performance, and efficiency of air heat engines. An air heat engine is a device that uses thermal energy to compress and expand gases, thereby driving mechanical motion. During the experimental process, a small piston air compressor is usually used as the experimental object.
DOI: 10.5281/zenodo.10115581
title: Experimental Data of Air Heat Engine Characteristics

description: Air heat engine characteristic experiment is an experimental method used to study the working principle, performance, and efficiency of air heat engines. An air heat engine is a device that uses thermal energy to compress and expand gases, thereby driving mechanical motion. During the experimental process, a small piston air compressor is usually used as the experimental object.
DOI: 10.21227/egd9-ab85
title: Empowering Non-Native Speakers in the 5G and 6G Era: The Vital Role of Intensive Listening Skills in Digital Communication and Professional Success

description:  translator AfrikaansAlbanian - shqipeArabic - &lrm;‫العربية‬&lrm;Armenian - ՀայերէնAzerbaijani - azərbaycancaBasque - euskaraBelarusian - беларускаяBengali - বাংলাBulgarian - българскиCatalan - catalàChinese - 中文（简体中文）Chinese - 中文 (繁體中文)Croatian - hrvatskiCzech - češtinaDanish - danskDutch - NederlandsEnglishEsperanto - esperantoEstonian - eestiFilipinoFinnish - suomiFrench - françaisGalician - galegoGeorgian - ქართულიGerman - DeutschGreek - ΕλληνικάGujarati - ગુજરાતીHaitian Creole - kreyòl ayisyenHebrew - &lrm;‫עברית‬&lrm;Hindi - हिन्दीHungarian - magyarIcelandic - íslenskaIndonesian - Bahasa IndonesiaIrish - GaeilgeItalian - italianoJapanese - 日本語Kannada - ಕನ್ನಡKorean - 한국어Latin - Lingua LatinaLatvian - latviešuLithuanian - lietuviųMacedonian - македонскиMalay - Bahasa MelayuMaltese - MaltiNorwegian - norskPersian - &lrm;‫فارسی‬&lrm;Polish - polskiPortuguese - portuguêsRomanian - românăRussian - русскийSerbian - СрпскиSlovak - slovenčinaSlovenian - slovenščinaSpanish - españolSwahili - KiswahiliSwedish - svenskaTamil - தமிழ்Telugu - తెలుగుThai - ไทยTurkish - TürkçeUkrainian - українськаUrdu - &lrm;‫اردو‬&lrm;Vietnamese - Tiếng ViệtWelsh - CymraegYiddish - יידיש  Double-click  Select to translate Intensive listening skills for non-native speakers in the era of 5Gs and 6Gs have become increasingly important. With the rapid advancement of technology and the widespread use of high-speed internet, the era of 5Gs and 6Gs has brought about a significant shift in the way we communicate and consume information.
DOI: 10.34740/kaggle/dsv/6950464
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.5066/p9gnen8s
title: Data to support Leveraging machine learning to automate regression model evaluations for large multi-site water-quality trend studies

description: This data release contains one dataset and one model archive in support of the journal article, "Leveraging machine learning to automate regression model evaluations for large multi-site water-quality trend studies," by Jennifer C. Murphy and Jeffrey G. Chanat. The model archive contains scripts (run in R) to reproduce the four machine learning models (logistic regression, linear and quadratic discriminant analysis, and k-nearest neighbors) trained and tested as part of the journal article. The dataset contains the estimated probabilities for each of these models when applied to a training and test dataset.
DOI: 10.5281/zenodo.10115156
title: SUPPLEMENTARY (For MD) An integrative pan-genome and subtractive proteomics approach for the identification of potential novel therapeutic drug target against antibiotic resistant honeybee pathogen Paenibacillus larvae

description: Parameters
Force field: AMBER ff19SB
Water type: TIP3P
Ions: NaCl  
Ligand topology force field: GAFF2
Temperature: 298k
Pressure: 1 bar
minimization step:  20000 on  5 nanoseconds
initial velocity is changed by changing "ntx" and "ig"
C2: ntx = 5 , ig = 8
C3: ntx = 2 , ig = 5
 
Uploads- 
1. Zip file of all 3 main files
2. Unzip file of C1 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)
3. Zip file of C1
4. Unzip file of C2 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)
5. Zip file of C2
6. Unzip file of C3 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)
7. Zip file of C3
8. Zip and unzip file of Initial PDB of complex prior to MD simulation with Post MD PDB (C1, C2, C3)
9. Zip file of topology files for C1, C2, and C3
DOI: 10.5281/zenodo.10115157
title: SUPPLEMENTARY (For MD) An integrative pan-genome and subtractive proteomics approach for the identification of potential novel therapeutic drug target against antibiotic resistant honeybee pathogen Paenibacillus larvae

description: Parameters
Force field: AMBER ff19SB
Water type: TIP3P
Ions: NaCl  
Ligand topology force field: GAFF2
Temperature: 298k
Pressure: 1 bar
minimization step:  20000 on  5 nanoseconds
initial velocity is changed by changing "ntx" and "ig"
C2: ntx = 5 , ig = 8
C3: ntx = 2 , ig = 5
 
Uploads- 
1. Zip file of all 3 main files
2. Unzip file of C1 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)
3. Zip file of C1
4. Unzip file of C2 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)
5. Zip file of C2
6. Unzip file of C3 (Trajectory, PDB complex after each 10 ns run, and Mp4 video of Complex)
7. Zip file of C3
8. Zip and unzip file of Initial PDB of complex prior to MD simulation with Post MD PDB (C1, C2, C3)
9. Zip file of topology files for C1, C2, and C3
DOI: 10.5281/zenodo.10115755
title: Job_offer/Companies_information
title: Dataset Job_Offer/Dataset Companies_information

description: Dataset Job_offer:
En este dataset hemos recopilado la información principal sobre 1251 ofertas de trabajo en diferentes ciudades (Madrid, Kiev, Um Bada, Buenos Aires, Tokio, Nueva Delhi) que incluye la ubicación, requisitos, puesto y salario de la oferta. Los datos que contiene el dataset corresponden a las ofertas de empleo del año 2023.
Dataset Companies_information:
En este dataset hemos recopilado la información principal sobre 
227 
empresas que ofrecen trabajo en la plataforma LinkedIn en diferentes ciudades/países (Madrid, Kiev, Um Bada, Buenos Aires, Tokio, Nueva Delhi), que incluye la ubicación, descripción, sector, número de empleados y etc. Los datos que contiene el dataset corresponden a los datos sobre las empresas en el año 2023.
 
Dataset Job_offer:
El dataset contiene las siguientes columnas:

Job_title - describe el nombre del puesto
Company - nombre de la empresa de la oferta
Location - ciudad y país de la oferta de trabajo
Presence - tipo de presencialidad (presencial/remoto/híbrido)
Workday - tipo de jornada
Responsibility - nivel de experiencia o responsabilidad
Num_application -  número de las solicitudes ya enviadas para la oferta
Description -  descripción de la oferta
Publish_date - dia de publicación
Salary - salario
Los datos han sido recopilados durante y para el año 2023.
Dataset Companies_information:
El dataset contiene las siguientes columnas:

Name -  nombre de la empresa
Sector -  industria/categoría a la que pertenece la empresa
Headquarters -  ubicación de la sede/oficina principal
Description - descripción de la empresa
Num_employees -  número de los empleados de la empresa
Website -  dirección al sitio web de la empresa
Specialties - áreas específicas de los servicios que ofrece la empresa
Founded_on - fecha de fundación
Los datos han sido recopilados durante y para el año 2023.
DOI: 10.5281/zenodo.10115756
title: Job_offer/Companies_information
title: Dataset Job_Offer/Dataset Companies_information

description: Dataset Job_offer:
En este dataset hemos recopilado la información principal sobre 1251 ofertas de trabajo en diferentes ciudades (Madrid, Kiev, Um Bada, Buenos Aires, Tokio, Nueva Delhi) que incluye la ubicación, requisitos, puesto y salario de la oferta. Los datos que contiene el dataset corresponden a las ofertas de empleo del año 2023.
Dataset Companies_information:
En este dataset hemos recopilado la información principal sobre 
227 
empresas que ofrecen trabajo en la plataforma LinkedIn en diferentes ciudades/países (Madrid, Kiev, Um Bada, Buenos Aires, Tokio, Nueva Delhi), que incluye la ubicación, descripción, sector, número de empleados y etc. Los datos que contiene el dataset corresponden a los datos sobre las empresas en el año 2023.
 
Dataset Job_offer:
El dataset contiene las siguientes columnas:

Job_title - describe el nombre del puesto
Company - nombre de la empresa de la oferta
Location - ciudad y país de la oferta de trabajo
Presence - tipo de presencialidad (presencial/remoto/híbrido)
Workday - tipo de jornada
Responsibility - nivel de experiencia o responsabilidad
Num_application -  número de las solicitudes ya enviadas para la oferta
Description -  descripción de la oferta
Publish_date - dia de publicación
Salary - salario
Los datos han sido recopilados durante y para el año 2023.
Dataset Companies_information:
El dataset contiene las siguientes columnas:

Name -  nombre de la empresa
Sector -  industria/categoría a la que pertenece la empresa
Headquarters -  ubicación de la sede/oficina principal
Description - descripción de la empresa
Num_employees -  número de los empleados de la empresa
Website -  dirección al sitio web de la empresa
Specialties - áreas específicas de los servicios que ofrece la empresa
Founded_on - fecha de fundación
Los datos han sido recopilados durante y para el año 2023.
DOI: 10.13026/1crd-qk14
title: BioNLP Workshop 2023 Shared Task 1A: Problem List Summarization

description: Automatically summarizing patients' main problems from the daily care notes in
the electronic health record can help mitigate information and cognitive
overload for clinicians and provide augmented intelligence via computerized
diagnostic decision support at the bedside. The task of Problem List
Summarization aims to generate a list of diagnoses and problems in a patient's
daily care plan using input from the provider's progress notes during
hospitalization.

This task aims to promote NLP model development for downstream applications in
diagnostic decision support systems that could improve efficiency and reduce
diagnostic errors in hospital care. The task contains 768 hospital daily
progress notes and 2783 diagnoses in the training set, and a new set of 237
daily progress notes are recently annotated as the test set. The annotation
methods and annotation quality have previously been reported.

The dataset supports a more complex summarization task to generate a list of
relevant diagnoses/problems given the information in the Subjective,
Objective, and Assessment sections of the note. Only diagnoses/problems that
are available in the progress note were labeled for the task.
DOI: 10.13026/mg5c-x807
title: BioNLP Workshop 2023 Shared Task 1A: Problem List Summarization

description: Automatically summarizing patients' main problems from the daily care notes in
the electronic health record can help mitigate information and cognitive
overload for clinicians and provide augmented intelligence via computerized
diagnostic decision support at the bedside. The task of Problem List
Summarization aims to generate a list of diagnoses and problems in a patient's
daily care plan using input from the provider's progress notes during
hospitalization.

This task aims to promote NLP model development for downstream applications in
diagnostic decision support systems that could improve efficiency and reduce
diagnostic errors in hospital care. The task contains 768 hospital daily
progress notes and 2783 diagnoses in the training set, and a new set of 237
daily progress notes are recently annotated as the test set. The annotation
methods and annotation quality have previously been reported.

The dataset supports a more complex summarization task to generate a list of
relevant diagnoses/problems given the information in the Subjective,
Objective, and Assessment sections of the note. Only diagnoses/problems that
are available in the progress note were labeled for the task.
DOI: 10.13026/s8wk-ja78
title: BioNLP Workshop 2023 Shared Task 1A: Problem List Summarization

description: Automatically summarizing patients' main problems from the daily care notes in
the electronic health record can help mitigate information and cognitive
overload for clinicians and provide augmented intelligence via computerized
diagnostic decision support at the bedside. The task of Problem List
Summarization aims to generate a list of diagnoses and problems in a patient's
daily care plan using input from the provider's progress notes during
hospitalization.

This task aims to promote NLP model development for downstream applications in
diagnostic decision support systems that could improve efficiency and reduce
diagnostic errors in hospital care. The task contains 768 hospital daily
progress notes and 2783 diagnoses in the training set, and a new set of 237
daily progress notes are recently annotated as the test set. The annotation
methods and annotation quality have previously been reported.

The dataset supports a more complex summarization task to generate a list of
relevant diagnoses/problems given the information in the Subjective,
Objective, and Assessment sections of the note. Only diagnoses/problems that
are available in the progress note were labeled for the task.
DOI: 10.6084/m9.figshare.24548557
title: Sequensing result of <i>Acanthopsoides molobrion </i>based on COI gene

description: Sequensing result of Cytochrome C Oxidase Subunit 1 (CO1) gene