cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5608503
title: cars_dataset

description: Dataset formado por 320 observaciones de coches con 11 variables. A continuación, se describe cada uno de estos 11 atributos: · Name: nombre del coche anunciado. Clase: carácter. · Location: ubicación del coche anunciado. Clase: carácter (Madrid, Barcelona, Valencia, Alicante, Málaga, Cantabria, Islas Baleares, Granada, Murcia, Vizcaya, Álava, Badajoz, Cáceres, Zamora, Sevilla, Córdoba, Pontevedra, Salamanca, La Coruña, León, Lleida, Cádiz, Asturias, Guipúzcoa, Las Palmas, Albacete, Ávila, Burgos, Valladolid, Almería, Huelva, Sta. C. de Tenerife, Jaén, Huesca, Castellón, Girona, Toledo, Tarragona, Zaragoza, Guadalajara, Ciudad Real, Ceuta). · Year: año de matriculación del coche anunciado. Clase: entero. · Combustible: tipo de combustible del coche anunciado. Clase: carácter (Diésel, Gasolina, Híbrido, Eléctrico, Híbrido Enchufable). · Kilometers: cantidad de kilómetros del coche anunciado. Clase: float. · Transmission: tipo de transmisión del coche anunciado. Clase: carácter (Automático, Manual). · Power [CV]: cantidad de caballos de potencia del coche anunciado. Clase: entero. · Price [€]: precio del coche anunciado. Clase: float. · Financed Price: financiación, o no, del precio del coche anunciado. Clase: carácter (Con financiación, Sin financiación). · Dealer: concesionario del coche anunciado. Clase: carácter. · Dealer Rating (over 5): puntuación sobre 5 del concesionario del coche anunciado. Clase: float.
DOI: 10.5281/zenodo.5608502
title: cars_dataset

description: Dataset formado por 320 observaciones de coches con 11 variables. A continuación, se describe cada uno de estos 11 atributos: · Name: nombre del coche anunciado. Clase: carácter. · Location: ubicación del coche anunciado. Clase: carácter (Madrid, Barcelona, Valencia, Alicante, Málaga, Cantabria, Islas Baleares, Granada, Murcia, Vizcaya, Álava, Badajoz, Cáceres, Zamora, Sevilla, Córdoba, Pontevedra, Salamanca, La Coruña, León, Lleida, Cádiz, Asturias, Guipúzcoa, Las Palmas, Albacete, Ávila, Burgos, Valladolid, Almería, Huelva, Sta. C. de Tenerife, Jaén, Huesca, Castellón, Girona, Toledo, Tarragona, Zaragoza, Guadalajara, Ciudad Real, Ceuta). · Year: año de matriculación del coche anunciado. Clase: entero. · Combustible: tipo de combustible del coche anunciado. Clase: carácter (Diésel, Gasolina, Híbrido, Eléctrico, Híbrido Enchufable). · Kilometers: cantidad de kilómetros del coche anunciado. Clase: float. · Transmission: tipo de transmisión del coche anunciado. Clase: carácter (Automático, Manual). · Power [CV]: cantidad de caballos de potencia del coche anunciado. Clase: entero. · Price [€]: precio del coche anunciado. Clase: float. · Financed Price: financiación, o no, del precio del coche anunciado. Clase: carácter (Con financiación, Sin financiación). · Dealer: concesionario del coche anunciado. Clase: carácter. · Dealer Rating (over 5): puntuación sobre 5 del concesionario del coche anunciado. Clase: float.
DOI: 10.6084/m9.figshare.16897045
title: May 2020 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>May, 2020</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.5281/zenodo.5593517
title: RNA dataset to train XGBoost model

description: This repository includes the RNA-seq dataset from 27 GBM samples, as published in this manuscript: <strong>Topographic mapping of the glioblastoma proteome reveals a triple axis model of intra-tumoral heterogeneity</strong><br> <em>Lam KHB, Leon AJ, Hui W, Lee SCE, Batruch I, Faust K, Koritzinsky M, Richer M, Djuric U, Diamandis P</em> <strong>(under review)</strong>
description: Sample ID HISTOLOGICAL_DIAGNOSIS IDH1_STATUS-sequencing GENDER AGE OS_STATUS OS_MONTHS GBM_1012 GBM WT female 80 Dead 11 GBM_1017 GBM MUT male 45 Alive 54 GBM_1029 GBM MUT female 53 Alive 56 GBM_1044 GBM MUT male 47 Alive 52 GBM_1064 GBM WT female 62 Alive 49 GBM_1065 GBM WT male 48 Dead 5 GBM_1083 GBM WT male 60 Dead 4 GBM_1143 GBM WT female 61 Dead 10 GBM_1164 GBM WT male 66 Dead 11 GBM_1173 GBM WT male 74 Dead 5 GBM_1182 GBM WT male 54 Dead 9 GBM_1202 GBM WT male 54 Dead 7 GBM_1239 GBM WT female 62 Dead 6 GBM_1243 GBM WT female 67 Dead 4 GBM_1274 GBM WT male 75 Dead 7 GBM_1305 GBM WT male 74 Dead 9 GBM_351 GBM WT female 66 Dead 67 GBM_590 GBM WT female 64 Dead 44 GBM_753 GBM WT female 54 Dead 55 GBM_765 GBM WT female 51 Dead 60 GBM_778 GBM WT male 43 Dead 34 GBM_804 GBM WT female 72 Dead 5 GBM_810 GBM WT male 71 Dead 4 GBM_859 GBM WT female 63 Dead 33 GBM_866 GBM WT female 62 Dead 12 GBM_881 GBM WT male 60 Dead 11 GBM_893 GBM MUT female 30 Dead 40 GBM_931 GBM WT male 56 Dead 6 GBM_935 GBM WT female 71 Dead 6 GBM_943 GBM WT male 63 Alive 69 GBM_945 GBM WT female 63 Dead 12 GBM_986 GBM WT female 57 Alive 63 GBM_989 GBM WT male 56 Dead 8
DOI: 10.15468/5kbzsh
title: A new cynipid inquiline of the genus Lithosaphonecrus (Hymenoptera: Cynipidae Synergini) from India, with an updated key to all known species

description: This dataset contains the digitized treatments in Plazi based on the original journal article Melika, George, Ranjith, Avunjikkattu Parambil, Lobato-Vila, Irene, Priyadarsanan, Dharma Rajan, Pujade-Villar, Juli (2021): A new cynipid inquiline of the genus Lithosaphonecrus (Hymenoptera: Cynipidae Synergini) from India, with an updated key to all known species. Zootaxa 5060 (1): 124-136, DOI: https://doi.org/10.11646/zootaxa.5060.1.6
DOI: 10.6084/m9.figshare.16897060
title: March 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>March, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16897060.v1
title: March 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>March, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16897063
title: December 2020 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>December, 2020</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16897066
title: March 2021 Covid-19 Vaccines Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets containing at <b>least one vaccine-related word</b> (i.e., words that start with vaccin*, vacin*, or vax*) collected in <b>March, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16897069
title: October 2020 Covid-19 Vaccines Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets containing at <b>least one vaccine-related word</b> (i.e., words that start with vaccin*, vacin*, or vax*) collected in <b>October, 2020</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16897069.v1
title: October 2020 Covid-19 Vaccines Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets containing at <b>least one vaccine-related word</b> (i.e., words that start with vaccin*, vacin*, or vax*) collected in <b>October, 2020</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16897072
title: November 2020 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>November, 2020</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16897072.v1
title: November 2020 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>November, 2020</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16897075
title: June 2020 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>June, 2020</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16897075.v1
title: June 2020 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>June, 2020</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16897084
title: October 2020 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>October, 2020</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16897084.v1
title: October 2020 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>October, 2020</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.5255/ukda-sn-855010
title: Achieving Sustainability in Care Systems: The Potential of Technology, 2020-2021

description: Current social care systems face challenges to their sustainability due in part to population ageing and the changing nature of care needs. These have led to a mismatch between care supply and demand and more complexity, as many people, especially those over 75, experience co-morbid chronic conditions. Geography and population diversity also pose challenges for the design and delivery of social care. Familial networks of care are increasingly dispersed and complex, rural populations are ageing, and in some urban settings populations are very diverse. Rising numbers of older adults live alone (some experiencing loneliness, isolation and the associated, negative, impacts on their wellbeing and health) and a growing minority of older people have no children who could support them. At the same time, social care is underfunded and failing to keep pace with the escalating and changing nature of care demand, with some local authorities struggling to deliver wellbeing outcomes (both for those in need of care and their carers, as required in England’s Care Act 2014). Some are also finding it challenging to contract with care providers able to deliver reliable services of good quality. Wider, more systematic use of technology in care at home, and advanced technologies in development, are often seen as offering promising solutions to these challenges, yet the place of technology as a source of future sustainability in the care system is far from clear. 'Achieving sustainability in care systems: the potential of technology' aims to: • Map the changing role of technologies within care systems in: a) planning/organisation of social care; b) delivery of social care through the support received by people living in their own homes; c) collection and communication of data between different parts of the care system, including its integration with health care. • Explore the potential role of technologies in developing sustainable care systems: a) using a future-oriented perspective and broad view of advances in technology; b) focusing on areas of promise for sustainability / wellbeing in care at home) using cross-national comparisons and stakeholder input to address issues of practical, attitudinal and ethical acceptability, and design and other requirements needed to embed technologies in care systems. 'Achieving sustainability in care systems: the potential of technology' is part of the Sustainable Care research programme.
DOI: 10.5281/zenodo.5606460
title: Establishing a Dual Generational Modality Dataset: Comparing the Ride-Sharing Adoption Trends and Perspectives of Consumers from two Generational Cohorts, Millennials and Gen-Xers - E2

description: Ride-hailing services such as Uber or Lyft are the latest tool in sustainable transportation strategies to come under scrutiny. Originally thought to be a way to reduce congestion, these services have actually been shown to increase it in some cases. Although the number of individuals driving around urban centers to find parking appears to decrease with the adoption of ride-hailing, Uber or Lyft drivers are instead circling around waiting for riders. Additionally, ride-hailing services have not led to the abandonment of personal vehicles, but rather to the abandonment of public transit in some cases. The purpose of this study is to evaluate the use of ride-hailing services in the two largest age cohorts in the United States: Millennials and Generation X-ers, focusing on the Southeastern states of Florida and North Carolina. This study seeks to determine how each generation has adopted these methods to help planners learn how to incorporate these strategies in transportation planning. The data provides information about the mode of transportation, trip details, and important socio-economic indicators of 1903 respondents from Florida and North Carolina.
DOI: 10.5281/zenodo.5606459
title: Establishing a Dual Generational Modality Dataset: Comparing the Ride-Sharing Adoption Trends and Perspectives of Consumers from two Generational Cohorts, Millennials and Gen-Xers - E2

description: Ride-hailing services such as Uber or Lyft are the latest tool in sustainable transportation strategies to come under scrutiny. Originally thought to be a way to reduce congestion, these services have actually been shown to increase it in some cases. Although the number of individuals driving around urban centers to find parking appears to decrease with the adoption of ride-hailing, Uber or Lyft drivers are instead circling around waiting for riders. Additionally, ride-hailing services have not led to the abandonment of personal vehicles, but rather to the abandonment of public transit in some cases. The purpose of this study is to evaluate the use of ride-hailing services in the two largest age cohorts in the United States: Millennials and Generation X-ers, focusing on the Southeastern states of Florida and North Carolina. This study seeks to determine how each generation has adopted these methods to help planners learn how to incorporate these strategies in transportation planning. The data provides information about the mode of transportation, trip details, and important socio-economic indicators of 1903 respondents from Florida and North Carolina.
DOI: 10.5519/qd.366v0hhl
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (221699 records)