cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3751560
title: Data and Code to support COVID-19 - exploring the implications of long-term condition type and extent of multimorbidity on years of life lost: a modelling study

description: Data and code to support paper published in Wellcome Open research on years of life lost among people who died with COVID-19.
DOI: 10.5281/zenodo.3751561
title: Data and Code to support COVID-19 - exploring the implications of long-term condition type and extent of multimorbidity on years of life lost: a modelling study

description: Data and code to support paper published in Wellcome Open research on years of life lost among people who died with COVID-19.
DOI: 10.17632/274xbhc5rx.2
title: Tumour Infiltrating Lymphocytes in Follicular Lymphoma
DOI: 10.5281/zenodo.3751576
title: Wiese et al. rawdata geochemistry

description: Rawdata for geochemical analyses in the manuscript ""Lake highstands in the northern Kalahari, Botswana, during MIS 3b and LGM" by Wiese et al.
DOI: 10.5281/zenodo.3751575
title: Wiese et al. rawdata geochemistry

description: Rawdata for geochemical analyses in the manuscript ""Lake highstands in the northern Kalahari, Botswana, during MIS 3b and LGM" by Wiese et al.
DOI: 10.15468/vpapb9
title: Three New Species of Dendrogaster (Crustacea: Ascothoracida) Infecting Goniasterid Sea-Stars (Echinodermata: Asteroidea) from Japan

description: This dataset contains the digitized treatments in Plazi based on the original journal article Saito, Nobuhiro, Wakabayashi, Kaori, Moritaki, Takeya (2020): Three New Species of Dendrogaster (Crustacea: Ascothoracida) Infecting Goniasterid Sea-Stars (Echinodermata: Asteroidea) from Japan. Species Diversity 25: 75-87, DOI: 10.12782/specdiv.25.75
DOI: 10.5281/zenodo.3751754
title: Valores de cotización de empresas del mercado continuo español el 13/04/2020 y accionistas principales.

description: Los datos que se incluyen en el datset son los valores de cotización de empresas del mercado continuo español el 13/04/2020 y los accionistas principales de cada empresa en esa misma fecha. El Dataset contiene los siguientes ficheros: <strong>Company.csv</strong> - contiene el nombre de la emresa y el valor de cotización con fecha 13/04/2020 para todas las empresas del mercado continuo español. <strong>holders.csv</strong> - contiene los accionistas principales de cada empresa con el ticker de la empresa correspondiente.
DOI: 10.5281/zenodo.3751753
title: Valores de cotización de empresas del mercado continuo español el 13/04/2020 y accionistas principales.

description: Los datos que se incluyen en el datset son los valores de cotización de empresas del mercado continuo español el 13/04/2020 y los accionistas principales de cada empresa en esa misma fecha. El Dataset contiene los siguientes ficheros: <strong>Company.csv</strong> - contiene el nombre de la emresa y el valor de cotización con fecha 13/04/2020 para todas las empresas del mercado continuo español. <strong>holders.csv</strong> - contiene los accionistas principales de cada empresa con el ticker de la empresa correspondiente.
DOI: 10.5281/zenodo.3751756
title: Datasets Coronavirus (Cifras totales + Evolución de casos)

description: Se presentan 3 datasets obtenidos mediante técnicas de web scraping. En los datasets 'data_countries.csv' y 'data_continents.csv' se presentan datos absolutos sobre muertes, casos, test realizadors, casos críticis, etc En el dataset 'data_series_cases_countries.csv' se encuentra el número de casos nuevos por día desde el 22 de enero al 13 de abril de 2020
DOI: 10.15468/td8xj7
title: Upgrading of Three Subspecies of Eudigraphis takakuwai to the Species Rank (Diplopoda: Penicillata: Polyxenida: Polyxenidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Karasawa, Shigenori, Kawano, Keisuke, Fukaya, Shin-ichi, Tsurusaki, Nobuo (2020): Upgrading of Three Subspecies of Eudigraphis takakuwai to the Species Rank (Diplopoda: Penicillata: Polyxenida: Polyxenidae). Species Diversity 25: 89-102, DOI: 10.12782/specdiv.25.89
DOI: 10.5281/zenodo.3751734
title: Datasets Coronavirus (Cifras totales + Evolución de casos)

description: Se presentan 3 datasets obtenidos mediante técnicas de web scraping. En los datasets 'data_countries.csv' y 'data_continents.csv' se presentan datos absolutos sobre muertes, casos, test realizadors, casos críticis, etc En el dataset 'data_series_cases_countries.csv' se encuentra el número de casos nuevos por día desde el 22 de enero al 13 de abril de 2020
DOI: 10.5281/zenodo.3751735
title: Datasets Coronavirus (Cifras totales + Evolución de casos)

description: Se presentan 3 datasets obtenidos mediante técnicas de web scraping. En los datasets 'data_countries.csv' y 'data_continents.csv' se presentan datos absolutos sobre muertes, casos, test realizadors, casos críticis, etc En el dataset 'data_series_cases_countries.csv' se encuentra el número de casos nuevos por día desde el 22 de enero al 13 de abril de 2020
DOI: 10.5281/zenodo.3751103
title: Vortex interaction in the wake of a two- and three-bladed wind turbine

description: Here, the rotor design specifications + experimental results from the article "Vortex interaction in the wake of a two- and three-bladed wind turbine" (Journal of Physics, Conference Series, EERA Deepwind 2020) authored by Jan Bartl, Thomas H. Hansen, W. Ludwig Kuhn, Franz Mühle and Lars Sætran are documented.
DOI: 10.5281/zenodo.3751104
title: Vortex interaction in the wake of a two- and three-bladed wind turbine

description: Here, the rotor design specifications + experimental results from the article "Vortex interaction in the wake of a two- and three-bladed wind turbine" (Journal of Physics, Conference Series, EERA Deepwind 2020) authored by Jan Bartl, Thomas H. Hansen, W. Ludwig Kuhn, Franz Mühle and Lars Sætran are documented.
DOI: 10.6073/pasta/35ee6b0a732a48fbaac77ac76ab9b9df
title: Biogeochemical data collected from Northeast Shark River Slough (FCE LTER), Everglades National Park from September 2006 to November 2018

description: This project was established in 2006 to document the pattern of abundance of key ecological indicators (e.g., surface water, soil, floc, periphyton and sawgrass) across the NESRS landscape (Gaiser et al. 2009). A total of 30 sites were established and monitored in 2006, 2007 and 2008 .After the completion of 1-mile bridge in 2012, additional 10 new sites were established to observe the ecological impact of 1-mile bridge ( Known as Bridge & Census sites). In 2015, additional 40 sites were established along eight transects (T1-T8, known as near canal sites) in ENP marshes starting at, and roughly perpendicular to the L-29 canal. The purpose of these sites was to monitor the potential effects of Modified Water Deliveries (MWD) operations on changing nutrient concentrations and ratios in key ecological compartments due to increased downstream discharges from the L-29 canal beneath the 1-mile and 2.6-mile bridges and culverts along Tamiami Trail.
DOI: 10.5281/zenodo.3751703
title: World Health Indicators

description: <strong>Dataset Description:</strong> The <strong>World Health Indicators</strong> dataset has been built merging data obtained from the United Nations website (http://data.un.org/) and from The World Bank site (https://data.worldbank.org/indicator). The dataset gives indicators regarding public health, including economic and social data for different countries and regions worldwide for the years 2005, 2010 and 2019. <strong>Attribute description:</strong> <strong>country_code</strong> – ISO3 country code <strong>country</strong> – Country name <strong>year </strong>– Year <strong>population</strong> – Total population <strong>population_grow</strong> – Population growth (% annual) <strong>population_under_14</strong> – Population between 0-14 years old (% total population) <strong>population_above_65</strong> – Population above 65 years old (% total population) <strong>gdp </strong>- GDP: Gross domestic product (million current US$) <strong>gdp_growth_rate</strong> – GDP growth rate (annual %, constant 2010 prices) <strong>unemployment</strong> – Unemployment (% of labour force) <strong>education_gov_expenditure</strong> – Government expenditure in education (% of GDP) <strong>health_expenditure</strong> – Current expenditure in health (% of GDP) <strong>life_expectancy_fem</strong> – Life expectancy, female (years) <strong>life_expectancy_male</strong> – Life expectancy, male (years) <strong>non_commun_disease_death</strong> – Cause of death, by non-communicable diseases (% of total) <strong>commun_disease_death</strong> – Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions (% of total) <strong>tuberculosis</strong> – Incidence of tuberculosis (per 100,000 people) <strong>hiv </strong>– Prevalence of HIV, total (% of population ages 15-49) <strong>infant_mortality </strong>– Infant mortality rate (per 1,000 live births) <strong>undernourishment</strong> – Prevalence of undernourishment (% of population) <strong>hospital_beds</strong> – Hospital beds (per 1,000 people) <strong>physicians</strong> – Physicians (per 1,000 people)
DOI: 10.5281/zenodo.3751704
title: World Health Indicators

description: <strong>Dataset Description:</strong> The <strong>World Health Indicators</strong> dataset has been built merging data obtained from the United Nations website (http://data.un.org/) and from The World Bank site (https://data.worldbank.org/indicator). The dataset gives indicators regarding public health, including economic and social data for different countries and regions worldwide for the years 2005, 2010 and 2019. <strong>Attribute description:</strong> <strong>country_code</strong> – ISO3 country code <strong>country</strong> – Country name <strong>year </strong>– Year <strong>population</strong> – Total population <strong>population_grow</strong> – Population growth (% annual) <strong>population_under_14</strong> – Population between 0-14 years old (% total population) <strong>population_above_65</strong> – Population above 65 years old (% total population) <strong>gdp </strong>- GDP: Gross domestic product (million current US$) <strong>gdp_growth_rate</strong> – GDP growth rate (annual %, constant 2010 prices) <strong>unemployment</strong> – Unemployment (% of labour force) <strong>education_gov_expenditure</strong> – Government expenditure in education (% of GDP) <strong>health_expenditure</strong> – Current expenditure in health (% of GDP) <strong>life_expectancy_fem</strong> – Life expectancy, female (years) <strong>life_expectancy_male</strong> – Life expectancy, male (years) <strong>non_commun_disease_death</strong> – Cause of death, by non-communicable diseases (% of total) <strong>commun_disease_death</strong> – Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions (% of total) <strong>tuberculosis</strong> – Incidence of tuberculosis (per 100,000 people) <strong>hiv </strong>– Prevalence of HIV, total (% of population ages 15-49) <strong>infant_mortality </strong>– Infant mortality rate (per 1,000 live births) <strong>undernourishment</strong> – Prevalence of undernourishment (% of population) <strong>hospital_beds</strong> – Hospital beds (per 1,000 people) <strong>physicians</strong> – Physicians (per 1,000 people)
DOI: 10.21227/30ca-eg80
title: Spotting political social bots in Twitter: a dataset for the 2019 Spanish general election

description: While social media has been proved as an exceptionally useful tool to interact with other people and massively and quickly spread helpful information, its great potential has been ill-intentionally leveraged as well to distort political elections and manipulate constituents. In the paper at hand, we analyzed the presence and behavior of social bots on Twitter in the context of the November 2019 Spanish general election. Throughout our study, we classified involved users as social bots or humans, and examined their interactions from a quantitative (i.e., amount of traffic generated and existing relations) and qualitative (i.e., user's political affinity and sentiment towards the most important parties) perspectives. Results demonstrated that a non-negligible amount of those bots actively participated in the election, supporting each of the five principal political parties. The dataset at hand presents the data collected during the observation period (from October 4th, 2019 to November 11th, 2019). It includes both the anonymized tweets and the users' data.
DOI: 10.21227/5j3j-8647
title: Spotting political social bots in Twitter: a dataset for the 2019 Spanish general election

description: While social media has been proved as an exceptionally useful tool to interact with other people and massively and quickly spread helpful information, its great potential has been ill-intentionally leveraged as well to distort political elections and manipulate constituents. In the paper at hand, we analyzed the presence and behavior of social bots on Twitter in the context of the November 2019 Spanish general election. Throughout our study, we classified involved users as social bots or humans, and examined their interactions from a quantitative (i.e., amount of traffic generated and existing relations) and qualitative (i.e., user's political affinity and sentiment towards the most important parties) perspectives. Results demonstrated that a non-negligible amount of those bots actively participated in the election, supporting each of the five principal political parties. The dataset at hand presents the data collected during the observation period (from October 4th, 2019 to November 11th, 2019). It includes both the anonymized tweets and the users' data.
DOI: 10.5281/zenodo.3751764
title: Epigenomic profiling of TASOR in Hela cells using ChIP, CUT&RUN and CUT&Tag

description: Example bigwig coverage tracks illustrating TASOR-regulated H3K9me3 and TASOR genome binding. Mapped to hg38. For detailed methods, see linked preprint.
DOI: 10.5281/zenodo.3751765
title: Epigenomic profiling of TASOR in Hela cells using ChIP, CUT&RUN and CUT&Tag

description: Example bigwig coverage tracks illustrating TASOR-regulated H3K9me3 and TASOR genome binding. Mapped to hg38. For detailed methods, see linked preprint.
DOI: 10.17632/xtnhs8f5zy.1
title: Movie for Fine Friction Grinding of Thermoplastics

description: This is the movie for showing the process of grinding of plastics in a high speed friction grinder
DOI: 10.5281/zenodo.3751742
title: Datasets Coronavirus (Cifras totales + Evolución de casos)

description: Se presentan 3 datasets obtenidos mediante técnicas de web scraping. En los datasets 'data_countries.csv' y 'data_continents.csv' se presentan datos absolutos sobre muertes, casos, test realizadors, casos críticis, etc En el dataset 'data_series_cases_countries.csv' se encuentra el número de casos nuevos por día desde el 22 de enero al 13 de abril de 2020
DOI: 10.5281/zenodo.3751714
title: SALAMI - Subjective Assessments of Legibility in Ancient Manuscript Images

description: <strong>This is just a preview containing data for 5 test images (out of 250) for review purposes; the full dataset will be published when the accompanying paper is accepted.</strong> We introduce a novel dataset of Subjective Assessments of Legibility in Ancient Manuscript Images (SALAMI) to serve as a ground truth for the development of quantitative evaluation metrics in the field of digital text restoration.<br> This dataset consists of 250 images of 50 manuscript regions with corresponding spatial maps of mean legibility and uncertainty, which are based on a study conducted with 20 experts of philology and paleography. <strong>Description of files:</strong> images input - rated test images mean_score_maps - spatial maps of mean legibility std_maps - spatial maps of uncertainty (standard deviation of legibility) src images.json - definition of source images contained in the dataset users.json - list of participants with their respective properties assessments.json - the main data generated by our experiments. salami_proc.py - contains python functions to process the .json files named above salami_proc_usage.py - uses the functions from salami_proc.py to reproduce the output images and statistical results described in the accompanying paper salami_llm.R - documents the linear mixed models analysis performed in R
description: PREVIEW
DOI: 10.3886/e117929v2
title: Paper_Bank efficiency and leasing in U.S.A. banking system

description: We obtain the data of our research using the SDI (Statistics on Depository Institutions) report created by FDIC[1] (Federal Deposit Insurance Corporation). This report provides banks’ financial statements, ratios, types, ownership structure and information for USA banks. Therefore, it is the reference database for USA samples that offer data capturing the whole spectrum of banks loans and leases products. <br> [1] Our data are from the Statistics on Depository Institutions (https://www5.fdic.gov/sdi/download), which provides branch-level information. <br><br><br><br><br>
DOI: 10.3886/e117929
title: Paper_Bank efficiency and leasing in U.S.A. banking system

description: We obtain the data of our research using the SDI (Statistics on Depository Institutions) report created by FDIC[1] (Federal Deposit Insurance Corporation). This report provides banks’ financial statements, ratios, types, ownership structure and information for USA banks. Therefore, it is the reference database for USA samples that offer data capturing the whole spectrum of banks loans and leases products. <br> [1] Our data are from the Statistics on Depository Institutions (https://www5.fdic.gov/sdi/download), which provides branch-level information. <br><br><br><br><br>
DOI: 10.5281/zenodo.3749825
title: A Benchmark Dataset for Semi-Automatic Seismic Interpretation Based on a New Zealand's Seismic Survey

description: In the past decades, machine learning algorithms have been developed for accelerating the seismic interpretation process. For this purpose, benchmark availability for algorithm improvement in computational geophysics is imperative. However, in geosciences, the supply of open benchmark datasets is very limited. The purpose of our study was to evaluate a CNN algorithm response in comparison to an OpendTect tool for seismic horizon interpolation, and in addition, offer a benchmark dataset for further algorithms evaluation.
DOI: 10.21227/dm6h-yq63
title: Spotting political social bots in Twitter: a dataset for the 2019 Spanish general election

description: While social media has been proved as an exceptionally useful tool to interact with other people and massively and quickly spread helpful information, its great potential has been ill-intentionally leveraged as well to distort political elections and manipulate constituents. In the paper at hand, we analyzed the presence and behavior of social bots on Twitter in the context of the November 2019 Spanish general election. Throughout our study, we classified involved users as social bots or humans, and examined their interactions from a quantitative (i.e., amount of traffic generated and existing relations) and qualitative (i.e., user's political affinity and sentiment towards the most important parties) perspectives. Results demonstrated that a non-negligible amount of those bots actively participated in the election, supporting each of the five principal political parties. The dataset at hand presents the data collected during the observation period (from October 4th, 2019 to November 11th, 2019). It includes both the anonymized tweets and the users' data.
DOI: 10.21227/6hxh-p990
title: Spotting political social bots in Twitter: a dataset for the 2019 Spanish general election

description: While social media has been proved as an exceptionally useful tool to interact with other people and massively and quickly spread helpful information, its great potential has been ill-intentionally leveraged as well to distort political elections and manipulate constituents. In the paper at hand, we analyzed the presence and behavior of social bots on Twitter in the context of the November 2019 Spanish general election. Throughout our study, we classified involved users as social bots or humans, and examined their interactions from a quantitative (i.e., amount of traffic generated and existing relations) and qualitative (i.e., user's political affinity and sentiment towards the most important parties) perspectives. Results demonstrated that a non-negligible amount of those bots actively participated in the election, supporting each of the five principal political parties. The dataset at hand presents the data collected during the observation period (from October 4th, 2019 to November 11th, 2019). It includes both the anonymized tweets and the users' data.
DOI: 10.25345/c5cx1m
title: MassIVE MSV000085287 - IL-22 signaling drives erythroid dysfunction in stress-induced anemias
title: TMT-based proteomic analysis of FAC-sorted mouse erythroid progenitors with Riok2 mutations
DOI: 10.21227/gxq5-b212
title: Spotting political social bots in Twitter: a dataset for the 2019 Spanish general election

description: While social media has been proved as an exceptionally useful tool to interact with other people and massively and quickly spread helpful information, its great potential has been ill-intentionally leveraged as well to distort political elections and manipulate constituents. In the paper at hand, we analyzed the presence and behavior of social bots on Twitter in the context of the November 2019 Spanish general election. Throughout our study, we classified involved users as social bots or humans, and examined their interactions from a quantitative (i.e., amount of traffic generated and existing relations) and qualitative (i.e., user's political affinity and sentiment towards the most important parties) perspectives. Results demonstrated that a non-negligible amount of those bots actively participated in the election, supporting each of the five principal political parties. The dataset at hand presents the data collected during the observation period (from October 4th, 2019 to November 11th, 2019). It includes both the anonymized tweets and the users' data.
DOI: 10.5281/zenodo.3751797
title: COVID-19, evolución y repercusión.

description: La temática elegida para este dataset ha sido la actual sobre el Coronavirus o COVID-19. Este virus ha entrado en la vida de todas las personas del planeta desde hace unos pocos meses, llevando su letalidad a parar las actividades normales de todo tipo (trabajos, rutinas…). En la actualidad, este es un tema recurrente y sobre el que se tiene información casi las 24 horas del día, debido a su notoriedad y magnitud. En cuanto a las fuentes elegidas, se ha seleccionado la web oficial del gobierno de España y la web Worldometers como fuentes estadísticas. Ambas webs proporcionan datos actualizados del virus, la primera de España y la segunda a nivel mundial. De esta forma, se consideran fuentes fiables debido a que, en la primera, el gobierno es el que proporcionaría los datos; y la segunda debido a que, tras realizar un proceso de investigación, se ha visto que los datos que utilizan se corresponden con los de los gobiernos correspondientes. Además, hemos utilizado Twitter como fuente textual, una red sociales en la que se puede recolectar datos de forma más sencilla y, sobre todo, inmediata, necesario para establecer correlaciones temporales entre la evolución del virus y la opinión del mismo de la sociedad. Esta plataforma permite él envió de mensajes en texto plano de corta longitud por parte de los usuarios, con un máximo de 280 caracteres. Estos mensajes, llamados tweets, se muestran en la página principal del usuario y pueden ser capturados a través de una API proporcionada por la propia red social.