cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14272/spssddotezkoov-uhfffaoysa-n.3
title: 2,3-dichloroquinoxaline
title: InChI=1S/C8H4Cl2N2/c9-7-8(10)12-6-4-2-1-3-5(6)11-7/h1-4H

description: This dataset contains experimental data from a sample (2,3-dichloroquinoxaline, InChI=1S/C8H4Cl2N2/c9-7-8(10)12-6-4-2-1-3-5(6)11-7/h1-4H). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) mass spectrometry (MS) infrared absorption spectroscopy (IR)
DOI: 10.14272/spssddotezkoov-uhfffaoysa-n/chmo0000470.2
title: mass spectrometry (MS) (2,3-dichloroquinoxaline)
title: InChI=1S/C8H4Cl2N2/c9-7-8(10)12-6-4-2-1-3-5(6)11-7/h1-4H

description: dataset for mass spectrometry (MS) EI (m/z, 70 eV, 20 °C): 199 (11) [M+H]+, 202/200/198 (11/64/100) [M]+, 199 (11) [M]+, 165 (22), 163 (64), 102 (43), 75 (12). HRMS–EI (m/z): [M]+ calcd for C8H4N235Cl2, 197.9752; found, 197.9752.
DOI: 10.14272/spssddotezkoov-uhfffaoysa-n/chmo0000593.2
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) (2,3-dichloroquinoxaline)
title: InChI=1S/C8H4Cl2N2/c9-7-8(10)12-6-4-2-1-3-5(6)11-7/h1-4H

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, CDCl3, ppm) δ = 7.75–7.88 (m, 2H), 7.96–8.12 (m, 2H). Impurities: Spectra containing solvent (water) at 1.55 ppm and grease at 0.07 ppm and 0.84 ppm.
DOI: 10.14272/spssddotezkoov-uhfffaoysa-n/chmo0000595.2
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) (2,3-dichloroquinoxaline)
title: InChI=1S/C8H4Cl2N2/c9-7-8(10)12-6-4-2-1-3-5(6)11-7/h1-4H

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (100 MHz, CDCl3 ppm) δ = 128.4 (2C), 131.4 (2C), 140.8 (2C), 145.5 (2C).
DOI: 10.14272/spssddotezkoov-uhfffaoysa-n/chmo0000630.2
title: infrared absorption spectroscopy (IR) (2,3-dichloroquinoxaline)
title: InChI=1S/C8H4Cl2N2/c9-7-8(10)12-6-4-2-1-3-5(6)11-7/h1-4H

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 3104, 3040, 1955, 1847, 1652, 1606, 1579, 1555, 1530, 1482, 1343, 1295, 1265, 1241, 1211, 1176, 1116, 1018, 1006, 987, 969, 885, 785, 764, 671, 647, 596, 524, 492, 435, 377 cm–1.
DOI: 10.5281/zenodo.3749276
title: Web scraper on rental housing market

description: This data set includes a snapshot of the state of the rental housing market in Catalonia (Spain) on the 6th April 2020. The data was obtained using a web scraping tool on the real estate website www.tucasa.com. The web scraper collected data on price, square metres, price per square metre, number of rooms, number of bathrooms, neighbourhood and city; in the four main cities of Catalonia (Barcelona, Girona, Lleida and Tarragona).
DOI: 10.5281/zenodo.3749277
title: Web scraper on rental housing market

description: This data set includes a snapshot of the state of the rental housing market in Catalonia (Spain) on the 6th April 2020. The data was obtained using a web scraping tool on the real estate website www.tucasa.com. The web scraper collected data on price, square metres, price per square metre, number of rooms, number of bathrooms, neighbourhood and city; in the four main cities of Catalonia (Barcelona, Girona, Lleida and Tarragona).
DOI: 10.5281/zenodo.3748995
title: Airbnb Barcelona dataset

description: Dataset que conté dades sobre els allotjaments disponibles a la web d'Airbnb a Barcelona per les dates següents: Allotjaments disponibles d'Airbnb a Barcelona entre el 12/04/2020 i el 18/04/2020. Allotjaments disponibles d'Airbnb a Barcelona entre el 19/04/2020 i el 25/04/2020. Camps de dades del dataset: <strong>id</strong>: identificador únic de l'allotjament a la web d'Airbnb. <strong>name</strong>: nom de l'allotjament. <strong>personCapacity</strong>: capacitat en número de persones de l'allotjament. <strong>neighborhood</strong>: barri on s'ubica l'allotjament. <strong>spaceType</strong>: tipus de vivenda. <strong>roomType</strong>: tipus d'habitació. <strong>pictureCount</strong>: quantitat d'imatges a Airbnb. <strong>starRating</strong>: valoració promig de l'allotjament entre 0-5 (en estrelles) <strong>avgRating</strong>: valoració promig exacte (dos decimals) de l'allotjament. <strong>priceString</strong>: preu per nit de l'allotjament. <strong>amount</strong>:<strong> </strong>preu total de l'estada. <strong>currency</strong>: moneda amb la que es comptabilitza el preu. <strong>reviewsCount</strong>: número de reviews o comentaris sobre l'allotjament a Airbnb. <strong>city</strong>: ciutat on es troba ubicada la propietat. <strong>country</strong>: pais on es troba ubicada la propietat. <strong>lat</strong>: latitud de la vivenda. <strong>lng</strong>: longitud de la vivenda. <strong>beds</strong>: quanitat de llits a l'allotjament. <strong>bedrooms</strong>: número d'habitacions de l'allotjament. <strong>bathrooms</strong>: nombre de lavabos de l'allotjament. <strong>checkin_date</strong>: data de check-in de la reserva. <strong>checkout_date</strong>: data de check-out de la reserva. <strong>created_at</strong>: data d'obtenció de les dades. -- <strong>No license specified</strong> No one can use, share, distribute, re-post, add to, transform or change this dataset.
DOI: 10.5281/zenodo.3748996
title: Airbnb Barcelona dataset

description: Dataset que conté dades sobre els allotjaments disponibles a la web d'Airbnb a Barcelona per les dates següents: Allotjaments disponibles d'Airbnb a Barcelona entre el 12/04/2020 i el 18/04/2020. Allotjaments disponibles d'Airbnb a Barcelona entre el 19/04/2020 i el 25/04/2020. Camps de dades del dataset: <strong>id</strong>: identificador únic de l'allotjament a la web d'Airbnb. <strong>name</strong>: nom de l'allotjament. <strong>personCapacity</strong>: capacitat en número de persones de l'allotjament. <strong>neighborhood</strong>: barri on s'ubica l'allotjament. <strong>spaceType</strong>: tipus de vivenda. <strong>roomType</strong>: tipus d'habitació. <strong>pictureCount</strong>: quantitat d'imatges a Airbnb. <strong>starRating</strong>: valoració promig de l'allotjament entre 0-5 (en estrelles) <strong>avgRating</strong>: valoració promig exacte (dos decimals) de l'allotjament. <strong>priceString</strong>: preu per nit de l'allotjament. <strong>amount</strong>:<strong> </strong>preu total de l'estada. <strong>currency</strong>: moneda amb la que es comptabilitza el preu. <strong>reviewsCount</strong>: número de reviews o comentaris sobre l'allotjament a Airbnb. <strong>city</strong>: ciutat on es troba ubicada la propietat. <strong>country</strong>: pais on es troba ubicada la propietat. <strong>lat</strong>: latitud de la vivenda. <strong>lng</strong>: longitud de la vivenda. <strong>beds</strong>: quanitat de llits a l'allotjament. <strong>bedrooms</strong>: número d'habitacions de l'allotjament. <strong>bathrooms</strong>: nombre de lavabos de l'allotjament. <strong>checkin_date</strong>: data de check-in de la reserva. <strong>checkout_date</strong>: data de check-out de la reserva. <strong>created_at</strong>: data d'obtenció de les dades. -- <strong>No license specified</strong> No one can use, share, distribute, re-post, add to, transform or change this dataset.
DOI: 10.5281/zenodo.3749286
title: Cruce de casos por Coronavirus con el PIB Per Cápita Mundial

description: Esta práctica se ha realizado bajo el contexto de la asignatura Tipología y ciclo de vida de los datos, perteneciente al Máster en Ciencia de Datos de la Universitat Oberta de Catalunya. En ella, se aplican técnicas de web scraping mediante el lenguaje de programación Python para extraer información relevante al coronavirus a través de Wikipedia - Pandemia de enfermedad por coronavirus de 2019-2020 y del PIB de cada país con el periódico "Expansión". Se genera un dataset el cual sirve para buscar posibles correlaciones entre estos datos.
description: {"references": ["Subirats, L., Calvo, M. (2018). Web Scraping. Editorial UOC.", "Masip, D. El lenguaje Python. Editorial UOC.", "Lawson, R. (2015). Web Scraping with Python. Packt Publishing Ltd. Chapter 2. Scraping the Data.", "Simon Munzert, Christian Rubba, Peter Mei\u00dfner, Dominic Nyhuis. (2015). Automated Data Collection with R: A Practical Guide to Web Scraping and Text Mining. John Wiley & Sons.", "PIB Per C\u00e1pita -&gt; https://datosmacro.expansion.com/pib", "Casos confirmados de Coronavirus -&gt; https://es.wikipedia.org/wiki/Pandemia_de_enfermedad_por_coronavirus_de_2019-2020"]}
DOI: 10.5281/zenodo.3749290
title: Air contamination and covid-19 cases in Spain

description: This data provides values of the Air Quality Index (AQI) for the most populated city in each Spanish autonomous community since 2019. The compounds selected to evaluate AQI are PM10, O3, and NO2. This dataset is oriented to people that want to evaluate quarantine effects on air pollution during a COVID-19 disease outbreak. For this reason, a table with notified cases of the disease to the Public Administration at an autonomous community-scale is also supplied with the aim of providing a framework of disease evolution. The air_contamination.csv dataset contains 9 variables: - timestamp: date in format: yyyy/mm/dd<br> - ca: autonomous community (text)<br> - ciudad: city (text)<br> - pm10: AQI value of pm10 particle (number)<br> - pm10_level: Air quality base on pm10 particles (text)<br> - o3: AQI value of o3 particle (number)<br> - o3_level: Air quality based on o3 particles (text)<br> - no2: AQI value of no2 particle (number)<br> - no2_level: Air quality based on no2 particles (text) The casos_covid19.csv dataset contains 4 variables: - comunidad: autonomous community (text)<br> - casos: Cases notified by Ministerio de Sanidad/Healthcare minister (text) <br> - casos_notificados: Cases notified by RENAVE (Red Nacional de Vigilancia Epidemiológica) a través de la plataforma SiVIES.<br> - datetime: datetime in format yyyy/mm/dd For more information about the project visit the link on [Github](https://github.com/shiny-data-scientist/webscrap_pract_1/)
DOI: 10.5281/zenodo.3749289
title: Air contamination and covid-19 cases in Spain

description: This data provides values of the Air Quality Index (AQI) for the most populated city in each Spanish autonomous community since 2019. The compounds selected to evaluate AQI are PM10, O3, and NO2. This dataset is oriented to people that want to evaluate quarantine effects on air pollution during a COVID-19 disease outbreak. For this reason, a table with notified cases of the disease to the Public Administration at an autonomous community-scale is also supplied with the aim of providing a framework of disease evolution. The air_contamination.csv dataset contains 9 variables: - timestamp: date in format: yyyy/mm/dd<br> - ca: autonomous community (text)<br> - ciudad: city (text)<br> - pm10: AQI value of pm10 particle (number)<br> - pm10_level: Air quality base on pm10 particles (text)<br> - o3: AQI value of o3 particle (number)<br> - o3_level: Air quality based on o3 particles (text)<br> - no2: AQI value of no2 particle (number)<br> - no2_level: Air quality based on no2 particles (text) The casos_covid19.csv dataset contains 4 variables: - comunidad: autonomous community (text)<br> - casos: Cases notified by Ministerio de Sanidad/Healthcare minister (text) <br> - casos_notificados: Cases notified by RENAVE (Red Nacional de Vigilancia Epidemiológica) a través de la plataforma SiVIES.<br> - datetime: datetime in format yyyy/mm/dd For more information about the project visit the link on [Github](https://github.com/shiny-data-scientist/webscrap_pract_1/)
DOI: 10.5281/zenodo.3749002
title: Cruce de casos por Coronavirus con el PIB Per Cápita Mundial

description: Esta práctica se ha realizado bajo el contexto de la asignatura Tipología y ciclo de vida de los datos, perteneciente al Máster en Ciencia de Datos de la Universitat Oberta de Catalunya. En ella, se aplican técnicas de web scraping mediante el lenguaje de programación Python para extraer información relevante al coronavirus a través de Wikipedia - Pandemia de enfermedad por coronavirus de 2019-2020 y del PIB de cada país con el periódico "Expansión". Se genera un dataset el cual sirve para buscar posibles correlaciones entre estos datos.
description: {"references": ["Subirats, L., Calvo, M. (2018). Web Scraping. Editorial UOC.", "Masip, D. El lenguaje Python. Editorial UOC.", "Lawson, R. (2015). Web Scraping with Python. Packt Publishing Ltd. Chapter 2. Scraping the Data.", "Simon Munzert, Christian Rubba, Peter Mei\u00dfner, Dominic Nyhuis. (2015). Automated Data Collection with R: A Practical Guide to Web Scraping and Text Mining. John Wiley & Sons.", "PIB Per C\u00e1pita -&gt; https://datosmacro.expansion.com/pib", "Casos confirmados de Coronavirus -&gt; https://es.wikipedia.org/wiki/Pandemia_de_enfermedad_por_coronavirus_de_2019-2020"]}
DOI: 10.25345/c5t39d
title: MassIVE MSV000085276 - TESTESTESTESTESTESTESTESTESTESTESTESTESTESTESTESTEST
title: TESTESTESTESTESTESTESTESTTESTESTESTESTESTESTESTESTTESTESTESTESTESTESTESTESTTESTESTESTESTESTESTESTESTTESTESTESTESTESTESTESTESTTESTESTESTESTESTESTESTESTTESTESTESTESTESTESTESTESTTESTESTESTESTESTESTESTESTTESTESTESTESTESTESTESTEST
DOI: 10.21227/7zb1-m907
title: Methods for reducing the amount of data transmitted and stored in IoT systems

description: In this paper we present method for reducing the amount of data transmitted and stored in IoT systems. Instead of expensive and complex network devices, developers can apply cheap and proven low-speed solutions (ZigBee, NB IoT, BLE). This approach is focused on on-the-sensor data processing. Correlation and autocorrelation methods for detecting events, depending on shape of a wave, were described in details and proposed endpoint architecture implementation. Novelty and another feature of this approach is the use of not full shape of waves, but their components. This greatly reduces the number of operations and implementation complexity. Simulation results show that when the data rate from the sensor ~ 10 MSamples/s the proposed method makes it possible to transfer and store 280 bytes in 70 minutes instead of 157 GB with the bypass method. Reducing the requirements for the volume of transmitted and stored data will allow 1) to simplify and reduce the cost of IoT systems, increase performance, and apply additional precision sensors to get more accurate object data.Current solutions are focused on low-powered and FPGA/ASIC implementations.
DOI: 10.5281/zenodo.3723424
title: Spatial coding for memory-guided reaching in visual and pictorial spaces

description: Results of the three experiments.
DOI: 10.5281/zenodo.3723425
title: Spatial coding for memory-guided reaching in visual and pictorial spaces

description: Results of the three experiments.
DOI: 10.21227/7pzg-p109
title: Methods for reducing the amount of data transmitted and stored in IoT systems

description: In this paper we present method for reducing the amount of data transmitted and stored in IoT systems. Instead of expensive and complex network devices, developers can apply cheap and proven low-speed solutions (ZigBee, NB IoT, BLE). This approach is focused on on-the-sensor data processing. Correlation and autocorrelation methods for detecting events, depending on shape of a wave, were described in details and proposed endpoint architecture implementation. Novelty and another feature of this approach is the use of not full shape of waves, but their components. This greatly reduces the number of operations and implementation complexity. Simulation results show that when the data rate from the sensor ~ 10 MSamples/s the proposed method makes it possible to transfer and store 280 bytes in 70 minutes instead of 157 GB with the bypass method. Reducing the requirements for the volume of transmitted and stored data will allow 1) to simplify and reduce the cost of IoT systems, increase performance, and apply additional precision sensors to get more accurate object data.Current solutions are focused on low-powered and FPGA/ASIC implementations.
DOI: 10.5281/zenodo.3749323
title: Data file for paper: Highly-cited papers in software engineering: the top-100

description: Data file for paper: Highly-cited papers in software engineering: the top-100 https://doi.org/10.1016/j.infsof.2015.11.003
DOI: 10.5281/zenodo.3749324
title: Data file for paper: Highly-cited papers in software engineering: the top-100

description: Data file for paper: Highly-cited papers in software engineering: the top-100 https://doi.org/10.1016/j.infsof.2015.11.003
DOI: 10.26300/yr5e-r103
title: Chapter 2 Table Data for "Mitonuclear genotype, tissue type and sex modify the effects of rapamycin on transcription and metabolism"
DOI: 10.5519/qd.80fp7o6t
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (16668 records)
DOI: 10.5519/qd.gdar4rpt
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4543426 records)
DOI: 10.26300/g715-1768
title: Chapter 3 Figure Data for "Mitonuclear genotype, tissue type and sex modify the effects of rapamycin on transcription and metabolism"
DOI: 10.26300/mbtd-ve79
title: Chapter 2 Figure Data for "Mitonuclear genotype, tissue type and sex modify the effects of rapamycin on transcription and metabolism"
DOI: 10.26300/0rws-n059
title: Chapter 04 Figure Data for "Mitonuclear genotype, tissue type and sex modify the effects of rapamycin on transcription and metabolism"
DOI: 10.26300/p6tr-kt04
title: Chapter 04 Table Data for "Mitonuclear genotype, tissue type and sex modify the effects of rapamycin on transcription and metabolism"
DOI: 10.17632/pty9ryd3br.1
title: Data for: Meiofauna improve oxygenation and accelerate sulfide removal in the seasonally hypoxic seabed

description: Supplementary Information file is made of graphs that support and help understanding the results and their interpretations (see manuscript text). Table S1 is a supplementary table with all DADA2 amplicon sequence variants, the taxonomic classifications and sequence counts.
DOI: 10.5281/zenodo.3749345
title: PreusVivendaEspanya

description: Evolució del preu de la vivenda en les ciutats més importants d'Espanya.
DOI: 10.5281/zenodo.3749346
title: PreusVivendaEspanya

description: Evolució del preu de la vivenda en les ciutats més importants d'Espanya.
DOI: 10.21227/gx43-ks25
title: Methods for reducing the amount of data transmitted and stored in IoT systems

description: In this paper we present method for reducing the amount of data transmitted and stored in IoT systems. Instead of expensive and complex network devices, developers can apply cheap and proven low-speed solutions (ZigBee, NB IoT, BLE). This approach is focused on on-the-sensor data processing. Correlation and autocorrelation methods for detecting events, depending on shape of a wave, were described in details and proposed endpoint architecture implementation. Novelty and another feature of this approach is the use of not full shape of waves, but their components. This greatly reduces the number of operations and implementation complexity. Simulation results show that when the data rate from the sensor ~ 10 MSamples/s the proposed method makes it possible to transfer and store 280 bytes in 70 minutes instead of 157 GB with the bypass method. Reducing the requirements for the volume of transmitted and stored data will allow 1) to simplify and reduce the cost of IoT systems, increase performance, and apply additional precision sensors to get more accurate object data.Current solutions are focused on low-powered and FPGA/ASIC implementations.
DOI: 10.25934/00005444
title: Lipid Research Clinics (LRC) Coronary Primary Prevention Trial (CPPT)

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.5281/zenodo.3749361
title: Covid-19 - impact on evolution of energy demand

description: <strong>Consumo_elect_COVID_2020 & Consumo elect_COVID_2019</strong> Dataset with register of energy demand in Spain. Timeframe: January to March (2019 & 2020) <strong>Casos_COVID_ESPAÑA</strong> Dataset with register of the evolution of the spread of Covid-19 in Spain. Break-down of data per region (CCAA). Timeframe: January to March 2020 <strong>Casos_COVID_mundo</strong> Dataset with register of the evolution of the spread of Covid-19 in Spain. Break-down of data per country. Timeframe: January to March 2020
description: Data realted to Energy Demand in Spain is property of Red Eléctrica (https://www.ree.es/es). Use of this data is forbidden for comercial purposes.
DOI: 10.26300/h4wh-c644
title: Data from "Mitonuclear genotype, tissue type and sex modify the effects of rapamycin on transcription and metabolism"
DOI: 10.3886/e118861
title: Special Education Teachers in California

description: The shortage of special education teachers (SETs) is a challenge in the United States, but less is known about two other issues that affect students with disabilities: racial diversity within the SET workforce and the distribution of SETs. Using data on all teachers in California from 1997 to 2014, we examined the racial composition and distribution of teachers with special education credentials. Our results show that while teachers with special education credentials remain majority White, the number of teachers of color with special education credentials has increased at a rate more than twice that of general education teachers and special education students of color. Much of the distribution of teachers with special education credentials occurs across districts within the same region, while disparities in teacher qualifications are larger by school poverty, racial composition, and student achievement. The results have implications for diversity and educational equity within the special education workforce.
DOI: 10.3886/e118861v1
title: Special Education Teachers in California

description: The shortage of special education teachers (SETs) is a challenge in the United States, but less is known about two other issues that affect students with disabilities: racial diversity within the SET workforce and the distribution of SETs. Using data on all teachers in California from 1997 to 2014, we examined the racial composition and distribution of teachers with special education credentials. Our results show that while teachers with special education credentials remain majority White, the number of teachers of color with special education credentials has increased at a rate more than twice that of general education teachers and special education students of color. Much of the distribution of teachers with special education credentials occurs across districts within the same region, while disparities in teacher qualifications are larger by school poverty, racial composition, and student achievement. The results have implications for diversity and educational equity within the special education workforce.
DOI: 10.5281/zenodo.3749384
title: Evolución nuevos casos de cáncer y mortalidad de 1975 a 2016

description: Datasets con la evolución de los nuevos casos de cáncer y la tasa de mortalidad de 1975 a 2016 en los Estados Unidos desglosado por raza y sexo para los tipos de cáncer más importantes.
DOI: 10.5281/zenodo.3749385
title: Evolución nuevos casos de cáncer y mortalidad de 1975 a 2016

description: Datasets con la evolución de los nuevos casos de cáncer y la tasa de mortalidad de 1975 a 2016 en los Estados Unidos desglosado por raza y sexo para los tipos de cáncer más importantes.
DOI: 10.5519/qd.zu2wtc8c
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (34254 records)