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142 Research products, page 1 of 15

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  • Open Access English
    Authors: 
    Bardi, Alessia; Kuchma, Iryna; Brobov, Evgeny; Truccolo, Ivana; Monteiro, Elizabete; Casalegno, Carlotta; Clary, Erin; Romanowski, Andrew; Pavone, Gina; Artini, Michele; +19 more
    Publisher: Zenodo
    Countries: Germany, Italy
    Project: EC | OpenAIRE Nexus (101017452), EC | OpenAIRE-Advance (777541)

    This dump provides access to the metadata records of publications, research data, software and projects that may be relevant to the Corona Virus Disease (COVID-19) fight. The dump contains records of the OpenAIRE COVID-19 Gateway, identified via full-text mining and inference techniques applied to the OpenAIRE Research Graph. The Graph is one of the largest Open Access collections of metadata records and links between publications, datasets, software, projects, funders, and organizations, aggregating 12,000+ scientific data sources world-wide, among which the Covid-19 data sources Zenodo COVID-19 Community, WHO (World Health Organization), BIP! FInder for COVID-19, Protein Data Bank, Dimensions, scienceOpen, and RSNA. The dump consists of a tar archive containing gzip files with one json per line. Each json is compliant to the schema available at https://doi.org/10.5281/zenodo.4723499.

  • English
    Authors: 
    Bardi A.; Kuchma I.; Pavone G.; Artini M.; Atzori C.; Backer A.; Baglioni M.; Czerniak A.; De Bonis M.; Dimitropoulos H.; +13 more
    Country: Italy
    Project: EC | OpenAIRE-Advance (777541)

    This dump provides access to the metadata records of publications, research data, software and projects that may be relevant to the Corona Virus Disease (COVID-19) fight. The dump contains records of the OpenAIRE COVID-19 Gateway (https://covid-19.openaire.eu/), identified via full-text mining and inference techniques applied to the OpenAIRE Research Graph (https://explore.openaire.eu/). The Graph is one of the largest Open Access collections of metadata records and links between publications, datasets, software, projects, funders, and organizations, aggregating 12,000+ scientific data sources world-wide, among which the Covid-19 data sources Zenodo COVID-19 Community, WHO (World Health Organization), BIP! FInder for COVID-19, Protein Data Bank, Dimensions, scienceOpen, and RSNA. The dump consists of a gzip file containing one json per line. Each json is compliant to the schema available at https://doi.org/10.5281/zenodo.3974226

  • Open Access English
    Authors: 
    Lalas, Dimitri; Gakis, Nikolaos; Mirasgedis, Sebastian; Georgopoulou, Elena; Sarafidis, Yannis; Doukas, Haris;
    Publisher: Zenodo
    Project: EC | PARIS REINFORCE (820846)

    This dataset contains the underlying data for the following publication: Lalas, D., Gakis, N., Mirasgedis, S., Georgopoulou, E., Sarafidis, Y., & Doukas, H. (2021). Energy and GHG Emissions Aspects of the COVID Impact in Greece. Energies, 14(7), 1955. https://doi.org/10.3390/en14071955.

  • Open Access English
    Authors: 
    Guillaume Bernard;
    Publisher: Zenodo
    Project: EC | NewsEye (770299)

    This is a publication of the FibVid dataset originaly dedicated to fake news detection. We changed here the purpose of this dataset in order to use it in the context of event tracking in press documents. Kim, Jisu, Jihwan Aum, SangEun Lee, Yeonju Jang, Eunil Park, et Daejin Choi. 2021. « FibVID: Comprehensive Fake News Diffusion Dataset during the COVID-19 Period ». Telematics and Informatics 64 (novembre): 101688. https://doi.org/10.1016/j.tele.2021.101688. In this dataset, we provide multiple features extracted from the text itself. Please note the text is missing from the dataset published in the CSV format for copyright reasons. You can download the original datasets and manually add the missing texts from the original publications. Features are extracted using: - A corpus of reference articles in multiple languages languages for TF-IDF weighting. (features_news) [1] - A corpus of tweets reporting news for TF-IDF weighting. (features_tweets) [1] - A S-BERT model [2] that uses distiluse-base-multilingual-cased-v1 (called features_use) [3] - A S-BERT model [2] that uses paraphrase-multilingual-mpnet-base-v2 (called features_mpnet) [4] References: [1]: Guillaume Bernard. (2022). Resources to compute TF-IDF weightings on press articles and tweets (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6610406 [2]: Reimers, Nils, et Iryna Gurevych. 2019. « Sentence-BERT: Sentence Embeddings Using Siamese BERT-Networks ». In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 3982‑92. Hong Kong, China: Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1410. [3]: https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1 [4]: https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2

  • English
    Authors: 
    Yoon, Ji-seong; Kim, Gyudong; Jarhad, Dnyandev B.; Kim, Hong-Rae; Shin, Young-Sup; Qu, Shuhao; Sahu, Pramod K.; Kim, Hea Ok; Lee, Hyuk Woo; Wang, Su Bin; +8 more
    Publisher: Cambridge Crystallographic Data Centre
    Project: EC | ANTIVIRALS (642434)

    Related Article: Ji-seong Yoon, Gyudong Kim, Dnyandev B. Jarhad, Hong-Rae Kim, Young-Sup Shin, Shuhao Qu, Pramod K. Sahu, Hea Ok Kim, Hyuk Woo Lee, Su Bin Wang, Yun Jeong Kong, Tong-Shin Chang, Natacha S. Ogando, Kristina Kovacikova, Eric J. Snijder, Clara C. Posthuma, Martijn J. van Hemert, Lak Shin Jeong|2019|J.Med.Chem.|62|6346|doi:10.1021/acs.jmedchem.9b00781

  • Open Access English
    Authors: 
    Fontanesi, Luca; Bovo, Samuele; Schiavo, Giuseppina; Utzeri, Valerio Joe;
    Publisher: Zenodo
    Project: EC | EOSCsecretariat.eu (831644)

    VirAnimalOne is a project focused on large scale mining of publicly deposited genomic and transcriptomic datasets available from ENA/SRA and derived from pets, livestock and wild animal species: to identify unexpected coronavirus sequences; to mine the host animal genomes for potential variants that might confer resistance or susceptibility to SARS-CoV-2 and other coronaviruses known to infect both humans and animals; to phylogenetically and structurally evaluate host receptor conformations and infer potential animal susceptibility to coronavirus infections, with particular attention for SARS-Cov-2. Here, we report the pipeline, preliminary datasets and results produced in this project and related to the VirAnimalOne Technical Report: Technical_Report_VirAnimalOne_Pipeline. It reports and describes the bioinformatic pipeline used for the mining of the omics datasets. Tables S1-S6. Genome datasets used for mining activities. Table S7. List of host genes involved in coronavirus infection and used for mining polymorphisms in the host genome using the retrieved datasets. Table S8-S13. List of annotated variants in the selected host genes involved in coronavirus infection. Tables S14-S10. Viral sequences identified in the next-generation sequencing datasets generated from the selected domestic species. Warning: Please, check for updated versions as data can present some inaccuracies. Fundings: EOSCsecretariat.eu has received funding from the European Union's Horizon Programme call H2020-INFRAEOSC-05-2018-2019, grant Agreement number 831644.

  • Open Access English
    Authors: 
    Leitão, Catarina; Shumba, Jefrey; Quinn, Marian;
    Publisher: Zenodo
    Project: EC | PEAR_EC (890925)

    Data files related to the manuscript Perspectives and experiences of Covid-19: Two Irish studies of families in disadvantaged communities. The manuscript includes two studies. The following materials are shared below. Study 1: - Qualitative data (Microsoft Office Excel file) - Codebook for coding the qualitative data developed through content analysis (pdf file) Study 2: - Qualitative data (Microsoft Office Excel file) Data are named using the following naming convention: Project acronym_Date (YYYYMMDD)_Study_Type of data_Type of participant_Version number of the file. Both studies in the manuscript were developed by the Childhood Development Initiative (CDI), Dublin, Ireland. Study 1 was conducted within the project PEAR_EC, that has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 890925. Study 2 was conducted within the Child Poverty research project, funded by Tusla under the Area Based Childhood funding and the Child and Youth Participation Initiatives grant.

  • Open Access English
    Authors: 
    Jones, Matthew William; Andrew, Robbie M.; Peters, Glen P.; Janssens-Maenhout, Greet; De-Gol, Anthony J.; Dou, Xinyu; Liu, Zhu; Ciais, Philippe; Patra, Prabir K.; Chevallier, Frederic; +1 more
    Publisher: Zenodo
    Project: EC | VERIFY (776810), EC | CHE (776186)

    See Jones et al. (2021) for a detailed description of this dataset and the core methods used to produce it. Key details are provided below. Product Description GCP-GridFED (version 2021.2) is a gridded fossil emissions dataset that is consistent with the national CO2 emissions reported by the Global Carbon Project (GCP). GCP-GridFEDv2021.2 provides monthly fossil CO2 emissions for the period 1959-2020 at a spatial resolution of 0.1° × 0.1°. The gridded emissions estimates are provided separately for fossil CO2 emitted by the oxidation of oil, coal and natural gas, with mixed international bunker fuels considered separately, as well as for the calcination of limestone during cement production. GCP-GridFED also includes gridded uncertainties in CO2 emission, incorporating differences in uncertainty across emissions sectors and countries, and gridded estimates of corresponding O2 uptake based on oxidative ratios for oil, coal and natural gas. Core Methodology in Brief GCP-GridFEDv2021.2 was produced by scaling monthly gridded emissions for the year 2010, from the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2; Janssens-Maenhout et al., 2019), to the national annual emissions estimates compiled as part of the 2020 global carbon budget (GCP-NAE) for the years 1959-2020 (Friedlingstein et al., in preparation [Earth System Science Data]), an update from the 2020 global carbon budget (Friedlingstein et al., 2020). GCP-GridFEDv2021.2 uses a preliminary release of GCP-NAE covering the years 1959-2020 (timestamp 31st July 2021). The GCP-NAE estimates for year 2020 are based on data available on 31st July 2021 and the estimates are thus expected to differ somewhat from those that will be presented by Friedlingstein et al. (in preparation [Earth System Science Data]), which will adopt updates to GCP-NAE since 31st July 2021. From October 2021, Andrew and Peters (2021) began to publish regular updates of their GCP-NAE dataset, including regular updates. For full details of the core methodology, see Jones et al. (2021). New Methodology for Years 2019-2020 using Carbon Monitor Data GCP-GridFEDv2021.2 features methodological changes beyond the core methods presented by Jones et al. (2021) in this version of GCP-GridFED because the seasonality of CO2 emissions was drastically affected by the lockdowns implemented to deal with the COVID-19 pandemic. Hitherto, GCP-GridFED has adopted the seasonality (that is, the monthly distribution of emissions) from EDGAR v4.3.2 (year 2010; Janssens-Maenhout et al., 2019) and applied a small correction based on heating/cooling degree days to account for inter-annual climate variability which effects emissions in some sectors (see Jones et al., 2021). Due to international responses to the COVID-19 pandemic, the typical seasonal pattern was broken in the year 2020. Hence, we now adopt the seasonality of CO2 emissions at the national level from Carbon Monitor (Liu et al., 2020), with state-level data for large countries. We apply this change in methodology to the years included in the Carbon Monitor dataset; 2019-2020. Nonetheless, the national annual emissions remain consistent with GCP-NAE (timestamp 31st July 2021) in 2019 and 2020.

  • Open Access English
    Authors: 
    Palmer, John R.B.; Ottow, Ramona; Bartumeus, Frederic;
    Publisher: Zenodo
    Country: Spain
    Project: EC | H-MIP (853271), EC | VEO (874735)

    This data was generated from the survey implemented through the project "Impacto de las medidas de distanciamiento social sobre la expansión de la epidemia de Covid-19 en España," funded by the Spanish National Research Council (Consejo Superior de Investigaciones Científicas, CSIC). Processing and analysis was done with support from the Human-Mosquito Interaction Project (H-MIP) funded by European Research Council Starting Grant No. 853271, and the Versatile emerging infectious disease observatory (VEO) funded by the European Union's Horizon 2020 research and innovation programme under grant agreement No. 874735. Estimates of age-specific contact patterns in Spain during the Covid-19 pandemic. This data was generated from the CSIC Distancia-Covid survey (https://distancia-covid.csic.es/). It includes estimated mean numbers of coresidents and non-coresident contacts by age group during 2020 and 2021, for all of Spain and disaggregated by autonomous community. (See `data/distancia_covid_contact_estimates_spain_metadata_dictionary.csv` for variable descriptions.) This repository also includes the survey instrument used in each wave. File Descriptions - distancia_covid_contact_estimates_spain_metadata_dictionary: Data dictionary - distancia_covid_contact_estimates_spain.csv: Contact estimates - distancia_covid_instrument_wave_1.xlsx: Survey instrument used in Wave 1 - distancia_covid_instrument_wave_2.xlsx: Survey instrument used in Wave 2 - distancia_covid_instrument_wave_3_4.xlsx: Survey instrument used in Waves 3 and 4 - CITATION.cff: Citation information Peer reviewed

  • Research data . 2022
    Open Access English
    Authors: 
    Cassetti, Gabriele; Boitier, Baptiste; Elia, Alessia; Le Mouël, Pierre; Gargiulo, Maurizio; Zagamé, Paul; Nikas, Alexandros; Koasidis, Konstantinos; Doukas, Haris; Chiodi, Alessandro;
    Publisher: Zenodo
    Project: EC | PARIS REINFORCE (820846)

    This dataset contains the underlying input and output data (model assumptions and results) for the manuscript Cassetti et al. ("The interplay among COVID-19 economic recovery, behavioural changes, and the European Green Deal: an energy-economic modelling perspective"), submitted to Energy in 2022.

Advanced search in Research products
Research products
arrow_drop_down
Searching FieldsTerms
Any field
arrow_drop_down
includes
arrow_drop_down
Include:
The following results are related to COVID-19. Are you interested to view more results? Visit OpenAIRE - Explore.
142 Research products, page 1 of 15
  • Open Access English
    Authors: 
    Bardi, Alessia; Kuchma, Iryna; Brobov, Evgeny; Truccolo, Ivana; Monteiro, Elizabete; Casalegno, Carlotta; Clary, Erin; Romanowski, Andrew; Pavone, Gina; Artini, Michele; +19 more
    Publisher: Zenodo
    Countries: Germany, Italy
    Project: EC | OpenAIRE Nexus (101017452), EC | OpenAIRE-Advance (777541)

    This dump provides access to the metadata records of publications, research data, software and projects that may be relevant to the Corona Virus Disease (COVID-19) fight. The dump contains records of the OpenAIRE COVID-19 Gateway, identified via full-text mining and inference techniques applied to the OpenAIRE Research Graph. The Graph is one of the largest Open Access collections of metadata records and links between publications, datasets, software, projects, funders, and organizations, aggregating 12,000+ scientific data sources world-wide, among which the Covid-19 data sources Zenodo COVID-19 Community, WHO (World Health Organization), BIP! FInder for COVID-19, Protein Data Bank, Dimensions, scienceOpen, and RSNA. The dump consists of a tar archive containing gzip files with one json per line. Each json is compliant to the schema available at https://doi.org/10.5281/zenodo.4723499.

  • English
    Authors: 
    Bardi A.; Kuchma I.; Pavone G.; Artini M.; Atzori C.; Backer A.; Baglioni M.; Czerniak A.; De Bonis M.; Dimitropoulos H.; +13 more
    Country: Italy
    Project: EC | OpenAIRE-Advance (777541)

    This dump provides access to the metadata records of publications, research data, software and projects that may be relevant to the Corona Virus Disease (COVID-19) fight. The dump contains records of the OpenAIRE COVID-19 Gateway (https://covid-19.openaire.eu/), identified via full-text mining and inference techniques applied to the OpenAIRE Research Graph (https://explore.openaire.eu/). The Graph is one of the largest Open Access collections of metadata records and links between publications, datasets, software, projects, funders, and organizations, aggregating 12,000+ scientific data sources world-wide, among which the Covid-19 data sources Zenodo COVID-19 Community, WHO (World Health Organization), BIP! FInder for COVID-19, Protein Data Bank, Dimensions, scienceOpen, and RSNA. The dump consists of a gzip file containing one json per line. Each json is compliant to the schema available at https://doi.org/10.5281/zenodo.3974226

  • Open Access English
    Authors: 
    Lalas, Dimitri; Gakis, Nikolaos; Mirasgedis, Sebastian; Georgopoulou, Elena; Sarafidis, Yannis; Doukas, Haris;
    Publisher: Zenodo
    Project: EC | PARIS REINFORCE (820846)

    This dataset contains the underlying data for the following publication: Lalas, D., Gakis, N., Mirasgedis, S., Georgopoulou, E., Sarafidis, Y., & Doukas, H. (2021). Energy and GHG Emissions Aspects of the COVID Impact in Greece. Energies, 14(7), 1955. https://doi.org/10.3390/en14071955.

  • Open Access English
    Authors: 
    Guillaume Bernard;
    Publisher: Zenodo
    Project: EC | NewsEye (770299)

    This is a publication of the FibVid dataset originaly dedicated to fake news detection. We changed here the purpose of this dataset in order to use it in the context of event tracking in press documents. Kim, Jisu, Jihwan Aum, SangEun Lee, Yeonju Jang, Eunil Park, et Daejin Choi. 2021. « FibVID: Comprehensive Fake News Diffusion Dataset during the COVID-19 Period ». Telematics and Informatics 64 (novembre): 101688. https://doi.org/10.1016/j.tele.2021.101688. In this dataset, we provide multiple features extracted from the text itself. Please note the text is missing from the dataset published in the CSV format for copyright reasons. You can download the original datasets and manually add the missing texts from the original publications. Features are extracted using: - A corpus of reference articles in multiple languages languages for TF-IDF weighting. (features_news) [1] - A corpus of tweets reporting news for TF-IDF weighting. (features_tweets) [1] - A S-BERT model [2] that uses distiluse-base-multilingual-cased-v1 (called features_use) [3] - A S-BERT model [2] that uses paraphrase-multilingual-mpnet-base-v2 (called features_mpnet) [4] References: [1]: Guillaume Bernard. (2022). Resources to compute TF-IDF weightings on press articles and tweets (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6610406 [2]: Reimers, Nils, et Iryna Gurevych. 2019. « Sentence-BERT: Sentence Embeddings Using Siamese BERT-Networks ». In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 3982‑92. Hong Kong, China: Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1410. [3]: https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1 [4]: https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2

  • English
    Authors: 
    Yoon, Ji-seong; Kim, Gyudong; Jarhad, Dnyandev B.; Kim, Hong-Rae; Shin, Young-Sup; Qu, Shuhao; Sahu, Pramod K.; Kim, Hea Ok; Lee, Hyuk Woo; Wang, Su Bin; +8 more
    Publisher: Cambridge Crystallographic Data Centre
    Project: EC | ANTIVIRALS (642434)

    Related Article: Ji-seong Yoon, Gyudong Kim, Dnyandev B. Jarhad, Hong-Rae Kim, Young-Sup Shin, Shuhao Qu, Pramod K. Sahu, Hea Ok Kim, Hyuk Woo Lee, Su Bin Wang, Yun Jeong Kong, Tong-Shin Chang, Natacha S. Ogando, Kristina Kovacikova, Eric J. Snijder, Clara C. Posthuma, Martijn J. van Hemert, Lak Shin Jeong|2019|J.Med.Chem.|62|6346|doi:10.1021/acs.jmedchem.9b00781

  • Open Access English
    Authors: 
    Fontanesi, Luca; Bovo, Samuele; Schiavo, Giuseppina; Utzeri, Valerio Joe;
    Publisher: Zenodo
    Project: EC | EOSCsecretariat.eu (831644)

    VirAnimalOne is a project focused on large scale mining of publicly deposited genomic and transcriptomic datasets available from ENA/SRA and derived from pets, livestock and wild animal species: to identify unexpected coronavirus sequences; to mine the host animal genomes for potential variants that might confer resistance or susceptibility to SARS-CoV-2 and other coronaviruses known to infect both humans and animals; to phylogenetically and structurally evaluate host receptor conformations and infer potential animal susceptibility to coronavirus infections, with particular attention for SARS-Cov-2. Here, we report the pipeline, preliminary datasets and results produced in this project and related to the VirAnimalOne Technical Report: Technical_Report_VirAnimalOne_Pipeline. It reports and describes the bioinformatic pipeline used for the mining of the omics datasets. Tables S1-S6. Genome datasets used for mining activities. Table S7. List of host genes involved in coronavirus infection and used for mining polymorphisms in the host genome using the retrieved datasets. Table S8-S13. List of annotated variants in the selected host genes involved in coronavirus infection. Tables S14-S10. Viral sequences identified in the next-generation sequencing datasets generated from the selected domestic species. Warning: Please, check for updated versions as data can present some inaccuracies. Fundings: EOSCsecretariat.eu has received funding from the European Union's Horizon Programme call H2020-INFRAEOSC-05-2018-2019, grant Agreement number 831644.

  • Open Access English
    Authors: 
    Leitão, Catarina; Shumba, Jefrey; Quinn, Marian;
    Publisher: Zenodo
    Project: EC | PEAR_EC (890925)

    Data files related to the manuscript Perspectives and experiences of Covid-19: Two Irish studies of families in disadvantaged communities. The manuscript includes two studies. The following materials are shared below. Study 1: - Qualitative data (Microsoft Office Excel file) - Codebook for coding the qualitative data developed through content analysis (pdf file) Study 2: - Qualitative data (Microsoft Office Excel file) Data are named using the following naming convention: Project acronym_Date (YYYYMMDD)_Study_Type of data_Type of participant_Version number of the file. Both studies in the manuscript were developed by the Childhood Development Initiative (CDI), Dublin, Ireland. Study 1 was conducted within the project PEAR_EC, that has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 890925. Study 2 was conducted within the Child Poverty research project, funded by Tusla under the Area Based Childhood funding and the Child and Youth Participation Initiatives grant.

  • Open Access English
    Authors: 
    Jones, Matthew William; Andrew, Robbie M.; Peters, Glen P.; Janssens-Maenhout, Greet; De-Gol, Anthony J.; Dou, Xinyu; Liu, Zhu; Ciais, Philippe; Patra, Prabir K.; Chevallier, Frederic; +1 more
    Publisher: Zenodo
    Project: EC | VERIFY (776810), EC | CHE (776186)

    See Jones et al. (2021) for a detailed description of this dataset and the core methods used to produce it. Key details are provided below. Product Description GCP-GridFED (version 2021.2) is a gridded fossil emissions dataset that is consistent with the national CO2 emissions reported by the Global Carbon Project (GCP). GCP-GridFEDv2021.2 provides monthly fossil CO2 emissions for the period 1959-2020 at a spatial resolution of 0.1° × 0.1°. The gridded emissions estimates are provided separately for fossil CO2 emitted by the oxidation of oil, coal and natural gas, with mixed international bunker fuels considered separately, as well as for the calcination of limestone during cement production. GCP-GridFED also includes gridded uncertainties in CO2 emission, incorporating differences in uncertainty across emissions sectors and countries, and gridded estimates of corresponding O2 uptake based on oxidative ratios for oil, coal and natural gas. Core Methodology in Brief GCP-GridFEDv2021.2 was produced by scaling monthly gridded emissions for the year 2010, from the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2; Janssens-Maenhout et al., 2019), to the national annual emissions estimates compiled as part of the 2020 global carbon budget (GCP-NAE) for the years 1959-2020 (Friedlingstein et al., in preparation [Earth System Science Data]), an update from the 2020 global carbon budget (Friedlingstein et al., 2020). GCP-GridFEDv2021.2 uses a preliminary release of GCP-NAE covering the years 1959-2020 (timestamp 31st July 2021). The GCP-NAE estimates for year 2020 are based on data available on 31st July 2021 and the estimates are thus expected to differ somewhat from those that will be presented by Friedlingstein et al. (in preparation [Earth System Science Data]), which will adopt updates to GCP-NAE since 31st July 2021. From October 2021, Andrew and Peters (2021) began to publish regular updates of their GCP-NAE dataset, including regular updates. For full details of the core methodology, see Jones et al. (2021). New Methodology for Years 2019-2020 using Carbon Monitor Data GCP-GridFEDv2021.2 features methodological changes beyond the core methods presented by Jones et al. (2021) in this version of GCP-GridFED because the seasonality of CO2 emissions was drastically affected by the lockdowns implemented to deal with the COVID-19 pandemic. Hitherto, GCP-GridFED has adopted the seasonality (that is, the monthly distribution of emissions) from EDGAR v4.3.2 (year 2010; Janssens-Maenhout et al., 2019) and applied a small correction based on heating/cooling degree days to account for inter-annual climate variability which effects emissions in some sectors (see Jones et al., 2021). Due to international responses to the COVID-19 pandemic, the typical seasonal pattern was broken in the year 2020. Hence, we now adopt the seasonality of CO2 emissions at the national level from Carbon Monitor (Liu et al., 2020), with state-level data for large countries. We apply this change in methodology to the years included in the Carbon Monitor dataset; 2019-2020. Nonetheless, the national annual emissions remain consistent with GCP-NAE (timestamp 31st July 2021) in 2019 and 2020.

  • Open Access English
    Authors: 
    Palmer, John R.B.; Ottow, Ramona; Bartumeus, Frederic;
    Publisher: Zenodo
    Country: Spain
    Project: EC | H-MIP (853271), EC | VEO (874735)

    This data was generated from the survey implemented through the project "Impacto de las medidas de distanciamiento social sobre la expansión de la epidemia de Covid-19 en España," funded by the Spanish National Research Council (Consejo Superior de Investigaciones Científicas, CSIC). Processing and analysis was done with support from the Human-Mosquito Interaction Project (H-MIP) funded by European Research Council Starting Grant No. 853271, and the Versatile emerging infectious disease observatory (VEO) funded by the European Union's Horizon 2020 research and innovation programme under grant agreement No. 874735. Estimates of age-specific contact patterns in Spain during the Covid-19 pandemic. This data was generated from the CSIC Distancia-Covid survey (https://distancia-covid.csic.es/). It includes estimated mean numbers of coresidents and non-coresident contacts by age group during 2020 and 2021, for all of Spain and disaggregated by autonomous community. (See `data/distancia_covid_contact_estimates_spain_metadata_dictionary.csv` for variable descriptions.) This repository also includes the survey instrument used in each wave. File Descriptions - distancia_covid_contact_estimates_spain_metadata_dictionary: Data dictionary - distancia_covid_contact_estimates_spain.csv: Contact estimates - distancia_covid_instrument_wave_1.xlsx: Survey instrument used in Wave 1 - distancia_covid_instrument_wave_2.xlsx: Survey instrument used in Wave 2 - distancia_covid_instrument_wave_3_4.xlsx: Survey instrument used in Waves 3 and 4 - CITATION.cff: Citation information Peer reviewed

  • Research data . 2022
    Open Access English
    Authors: 
    Cassetti, Gabriele; Boitier, Baptiste; Elia, Alessia; Le Mouël, Pierre; Gargiulo, Maurizio; Zagamé, Paul; Nikas, Alexandros; Koasidis, Konstantinos; Doukas, Haris; Chiodi, Alessandro;
    Publisher: Zenodo
    Project: EC | PARIS REINFORCE (820846)

    This dataset contains the underlying input and output data (model assumptions and results) for the manuscript Cassetti et al. ("The interplay among COVID-19 economic recovery, behavioural changes, and the European Green Deal: an energy-economic modelling perspective"), submitted to Energy in 2022.