cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5940017
title: VTSCat: The VERITAS Catalog of Gamma-Ray Observations

description: <strong>VTSCat</strong> is the catalog of high-level data products from all publications of the VERITAS collaboration. The <strong>VTSCat</strong> data collection contains: high-level data like spectral flux points, light curves, spectral fits in human- and machine-readable yaml and ecsv file format tabled data like upper limits tables from dark matter searches or results on the extragalactic background in ecsv file format sky maps (wherever available) in FITS file format The data collection contains results from gamma-ray measurements only. This is a pre-release for testing and early publications. A forthcoming research note will provide more details on the catalog. Please check the README file and all documentation linked to the README. VTSCat supplements the HEASARC catalogue of VERITAS results (to be published). VTSCat is inspired and derived from gamma-cat. <strong>Access</strong>: GitHub: https://github.com/VERITAS-Observatory/VERITAS-VTSCat <strong>References</strong>: VERITAS: https://veritas.sao.arizona.edu/ VER Dictionary of Nomenclature: https://cds.u-strasbg.fr/cgi-bin/Dic-Simbad?/17350620
description: {"references": ["https://github.com/VERITAS-Observatory/VERITAS-VTSCat/tree/pre-release-v01a", "Patel, S., Maier, G, Kaaret, P. (the VERITAS Collaboration), VTSCat - The VERITAS Catalog of Gamma Ray Observations, arXiv:2108.06424"]}
DOI: 10.5281/zenodo.4723219
title: VTSCat: The VERITAS Catalog of Gamma-Ray Observations

description: <strong>VTSCat</strong> is the catalog of high-level data products from all publications of the VERITAS collaboration. <strong>Most recent versions of VTSCat are available through https://doi.org/10.5281/zenodo.6988967</strong> The <strong>VTSCat</strong> data collection contains: high-level data like spectral flux points, light curves, spectral fits in human- and machine-readable yaml and ecsv file format tabled data like upper limits tables from dark matter searches or results on the extragalactic background in ecsv file format sky maps (wherever available) in FITS file format The data collection contains results from gamma-ray measurements only. This is a pre-release for testing and early publications. A forthcoming research note will provide more details on the catalog. Please check the README file and all documentation linked to the README. VTSCat supplements the HEASARC catalogue of VERITAS results (to be published). VTSCat is inspired and derived from gamma-cat. If you are a previous VERITAS author and would like to be associated with this repository, please send an email to G. Maier. <strong>Access</strong>: GitHub: https://github.com/VERITAS-Observatory/VERITAS-VTSCat <strong>References</strong>: VERITAS: https://veritas.sao.arizona.edu/ VER Dictionary of Nomenclature: https://cds.u-strasbg.fr/cgi-bin/Dic-Simbad?/17350620
description: {"references": ["https://github.com/VERITAS-Observatory/VERITAS-VTSCat/tree/pre-release-v01a", "Patel, S., Maier, G, Kaaret, P. (the VERITAS Collaboration), VTSCat - The VERITAS Catalog of Gamma Ray Observations, arXiv:2108.06424"]}
DOI: 10.5281/zenodo.6163391
title: VTSCat: The VERITAS Catalog of Gamma-Ray Observations

description: <strong>VTSCat</strong> is the catalog of high-level data products from all publications of the VERITAS collaboration. <strong>Most recent versions of VTSCat are available through https://doi.org/10.5281/zenodo.6988967</strong> The <strong>VTSCat</strong> data collection contains: high-level data like spectral flux points, light curves, spectral fits in human- and machine-readable yaml and ecsv file format tabled data like upper limits tables from dark matter searches or results on the extragalactic background in ecsv file format sky maps (wherever available) in FITS file format The data collection contains results from gamma-ray measurements only. This is a pre-release for testing and early publications. A forthcoming research note will provide more details on the catalog. Please check the README file and all documentation linked to the README. VTSCat supplements the HEASARC catalogue of VERITAS results (to be published). VTSCat is inspired and derived from gamma-cat. If you are a previous VERITAS author and would like to be associated with this repository, please send an email to G. Maier. <strong>Access</strong>: GitHub: https://github.com/VERITAS-Observatory/VERITAS-VTSCat <strong>References</strong>: VERITAS: https://veritas.sao.arizona.edu/ VER Dictionary of Nomenclature: https://cds.u-strasbg.fr/cgi-bin/Dic-Simbad?/17350620
description: {"references": ["https://github.com/VERITAS-Observatory/VERITAS-VTSCat/tree/pre-release-v01a", "Patel, S., Maier, G, Kaaret, P. (the VERITAS Collaboration), VTSCat - The VERITAS Catalog of Gamma Ray Observations, arXiv:2108.06424"]}
DOI: 10.5281/zenodo.6686404
title: Code and data accompanying Palmeirim et al. (2022) Emergent properties of species-habitat networks in an insular forest landscape. Science Advances

description: Dataset containing species distribution in insular forest fragments at Balbina and full R code for analyses and figures. For deatails, please see the original publication: "Emergent properties of species-habitat networks in an insular forest landscape". Ana Filipa Palmeirim, Carine Emer, Maíra Benchimol, Danielle Storck-Tonon, Anderson S. Bueno, Carlos A. Peres. Science Advances (2022). 10.1126/sciadv.abm0397.
DOI: 10.5281/zenodo.6686403
title: Code and data accompanying Palmeirim et al. (2022) Emergent properties of species-habitat networks in an insular forest landscape. Science Advances

description: Dataset containing species distribution in insular forest fragments at Balbina and full R code for analyses and figures. For deatails, please see the original publication: "Emergent properties of species-habitat networks in an insular forest landscape". Ana Filipa Palmeirim, Carine Emer, Maíra Benchimol, Danielle Storck-Tonon, Anderson S. Bueno, Carlos A. Peres. Science Advances (2022). 10.1126/sciadv.abm0397.
DOI: 10.5281/zenodo.6473291
title: Sediment datasets of Lake Taihu

description: Dediment datasets, including bed sediments, turbidity, meteorological data 2018 and 2020, sediment setting experiments, model data, suspended sediment concentration.
DOI: 10.5281/zenodo.6473290
title: Sediment datasets of Lake Taihu

description: Dediment datasets, including bed sediments, turbidity, meteorological data 2018 and 2020, sediment setting experiments, model data, suspended sediment concentration.
DOI: 10.5281/zenodo.6989083
title: Sediment datasets of Lake Taihu

description: Dediment datasets, including bed sediments, turbidity, meteorological data 2018 and 2020, sediment setting experiments, model data, suspended sediment concentration.
DOI: 10.34740/kaggle/dsv/4066894
title: UFC Events 1997-2022
DOI: 10.5281/zenodo.6989097
title: All data of the manuscript "A self-sustained charge neutrality intracloud lightning parameterization containing channel decay and reactivation" submitted to Geophysical Research Letters

description: The data support the manuscript entitled "A self-sustained charge neutrality intracloud lightning parameterization containing channel decay and reactivation". Microsoft Notepad can open the *.txt files, they contain the channel information of two intracloud flashes (IC1 and IC2) and the channel elctrical parameters at the first fork of positive or negative leader channels. A normal video player software can open Movie_S1.avi, and it shows the entire development process of IC1 discharge. The data can be used freely for scientific purposes with the appropriate citation.
DOI: 10.5281/zenodo.6989096
title: All data of the manuscript "A self-sustained charge neutrality intracloud lightning parameterization containing channel decay and reactivation" submitted to Geophysical Research Letters

description: The data support the manuscript entitled "A self-sustained charge neutrality intracloud lightning parameterization containing channel decay and reactivation". Microsoft Notepad can open the *.txt files, they contain the channel information of two intracloud flashes (IC1 and IC2) and the channel elctrical parameters at the first fork of positive or negative leader channels. A normal video player software can open Movie_S1.avi, and it shows the entire development process of IC1 discharge. The data can be used freely for scientific purposes with the appropriate citation.
DOI: 10.5281/zenodo.6983429
title: GRACED2021-Near-real-time global gridded daily CO2 emissions 2021

description: <strong>Description:</strong> GRACED2021-Near-real-time global gridded daily CO<sub>2</sub> emissions 2021 <strong>The website: https://carbonmonitor-graced.com</strong> In an era of rapid upheavals, global near-real-time and fine-grid carbon dioxide (CO<sub>2</sub>) emissions data are essential to monitoring the distribution of emissions in space and time and achieving carbon neutrality worldwide. To address this need, we presented the near-real-time Global Gridded Daily CO<sub>2</sub> Emissions Dataset (GRACED) throughout 2021. GRACED provides gridded CO<sub>2</sub> emissions at a 0.1° × 0.1° spatial resolution and 1-day temporal resolution from cement production, and from fossil fuel combustion over seven sectors (power; industry; residential consumption; ground transportation; international aviation; international shipping; domestic aviation). GRACED data are made freely available to the public and the scientific community in the belief that their wide dissemination will lead to greater understanding and new scientific insights. The availability of these data does not constitute publication of the data. The data providers rely on the ethics and integrity of the user to ensure that they receive fair credit for their work. If the data are obtained for potential use in a publication or presentation, we kindly ask you to inform us at the outset of the nature of this work. If the GRACED data are essential to the work, or if an important result or conclusion depends on the GRACED data, co-authorship may be appropriate. This should be discussed at an early stage in the work. Manuscripts using the GRACED data should be sent for review before they are submitted for publication so we can ensure that the quality and limitations of the data are accurately represented. Because GRACED2021 has a large amount of data and the Zenodo platform has a limit on the total amount of uploaded data, we recommend that data users download GRACED2021 data for free through the cloud disk link- <em><strong>https://cloud.tsinghua.edu.cn/d/24d9ed6e225d455a834e/.</strong></em> For more detailed display and introduction of data, as well as download of more GRACED data, please refer to our official website of GRACED - <strong>https://carbonmonitor-graced.com</strong>. Please check the README.txt file for more details.
description: This research has been supported by the National Natural Science Foundation of China (grant 41921005 and 71874097), Beijing Natural Science Foundation (JQ19032), and the Qiu Shi Science & Technologies Foundation.
DOI: 10.5281/zenodo.6983430
title: GRACED2021-Near-real-time global gridded daily CO2 emissions 2021

description: <strong>Description:</strong> GRACED2021-Near-real-time global gridded daily CO<sub>2</sub> emissions 2021 <strong>The website: https://carbonmonitor-graced.com</strong> In an era of rapid upheavals, global near-real-time and fine-grid carbon dioxide (CO<sub>2</sub>) emissions data are essential to monitoring the distribution of emissions in space and time and achieving carbon neutrality worldwide. To address this need, we presented the near-real-time Global Gridded Daily CO<sub>2</sub> Emissions Dataset (GRACED) throughout 2021. GRACED provides gridded CO<sub>2</sub> emissions at a 0.1° × 0.1° spatial resolution and 1-day temporal resolution from cement production, and from fossil fuel combustion over seven sectors (power; industry; residential consumption; ground transportation; international aviation; international shipping; domestic aviation). GRACED data are made freely available to the public and the scientific community in the belief that their wide dissemination will lead to greater understanding and new scientific insights. The availability of these data does not constitute publication of the data. The data providers rely on the ethics and integrity of the user to ensure that they receive fair credit for their work. If the data are obtained for potential use in a publication or presentation, we kindly ask you to inform us at the outset of the nature of this work. If the GRACED data are essential to the work, or if an important result or conclusion depends on the GRACED data, co-authorship may be appropriate. This should be discussed at an early stage in the work. Manuscripts using the GRACED data should be sent for review before they are submitted for publication so we can ensure that the quality and limitations of the data are accurately represented. Please check the README.txt file for more details.
description: This research has been supported by the National Natural Science Foundation of China (grant 41921005 and 71874097), Beijing Natural Science Foundation (JQ19032), and the Qiu Shi Science & Technologies Foundation.
DOI: 10.5281/zenodo.6984058
title: GRACED2021-Near-real-time global gridded daily CO2 emissions 2021

description: <strong>Description:</strong> GRACED2021-Near-real-time global gridded daily CO<sub>2</sub> emissions 2021 <strong>The website: https://carbonmonitor-graced.com</strong> In an era of rapid upheavals, global near-real-time and fine-grid carbon dioxide (CO<sub>2</sub>) emissions data are essential to monitoring the distribution of emissions in space and time and achieving carbon neutrality worldwide. To address this need, we presented the near-real-time Global Gridded Daily CO<sub>2</sub> Emissions Dataset (GRACED) throughout 2021. GRACED provides gridded CO<sub>2</sub> emissions at a 0.1° × 0.1° spatial resolution and 1-day temporal resolution from cement production, and from fossil fuel combustion over seven sectors (power; industry; residential consumption; ground transportation; international aviation; international shipping; domestic aviation). GRACED data are made freely available to the public and the scientific community in the belief that their wide dissemination will lead to greater understanding and new scientific insights. The availability of these data does not constitute publication of the data. The data providers rely on the ethics and integrity of the user to ensure that they receive fair credit for their work. If the data are obtained for potential use in a publication or presentation, we kindly ask you to inform us at the outset of the nature of this work. If the GRACED data are essential to the work, or if an important result or conclusion depends on the GRACED data, co-authorship may be appropriate. This should be discussed at an early stage in the work. Manuscripts using the GRACED data should be sent for review before they are submitted for publication so we can ensure that the quality and limitations of the data are accurately represented. Because GRACED2021 has a large amount of data and the Zenodo platform has a limit on the total amount of uploaded data, we recommend that data users download GRACED2021 data for free through the cloud disk link- <em><strong>https://cloud.tsinghua.edu.cn/d/24d9ed6e225d455a834e/.</strong></em> For more detailed display and introduction of data, as well as download of more GRACED data, please refer to our official website of GRACED - <strong>https://carbonmonitor-graced.com</strong>. Please check the README.txt file for more details.
description: This research has been supported by the National Natural Science Foundation of China (grant 41921005 and 71874097), Beijing Natural Science Foundation (JQ19032), and the Qiu Shi Science & Technologies Foundation.
DOI: 10.5281/zenodo.6989134
title: GRACED2021-Near-real-time global gridded daily CO2 emissions 2021

description: <strong>Description:</strong> GRACED2021-Near-real-time global gridded daily CO<sub>2</sub> emissions 2021 <strong>The website: https://carbonmonitor-graced.com</strong> In an era of rapid upheavals, global near-real-time and fine-grid carbon dioxide (CO<sub>2</sub>) emissions data are essential to monitoring the distribution of emissions in space and time and achieving carbon neutrality worldwide. To address this need, we presented the near-real-time Global Gridded Daily CO<sub>2</sub> Emissions Dataset (GRACED) throughout 2021. GRACED provides gridded CO<sub>2</sub> emissions at a 0.1° × 0.1° spatial resolution and 1-day temporal resolution from cement production, and from fossil fuel combustion over seven sectors (power; industry; residential consumption; ground transportation; international aviation; international shipping; domestic aviation). GRACED data are made freely available to the public and the scientific community in the belief that their wide dissemination will lead to greater understanding and new scientific insights. The availability of these data does not constitute publication of the data. The data providers rely on the ethics and integrity of the user to ensure that they receive fair credit for their work. If the data are obtained for potential use in a publication or presentation, we kindly ask you to inform us at the outset of the nature of this work. If the GRACED data are essential to the work, or if an important result or conclusion depends on the GRACED data, co-authorship may be appropriate. This should be discussed at an early stage in the work. Manuscripts using the GRACED data should be sent for review before they are submitted for publication so we can ensure that the quality and limitations of the data are accurately represented. Because GRACED2021 has a large amount of data and the Zenodo platform has a limit on the total amount of uploaded data, we recommend that data users download GRACED2021 data for free through the cloud disk link- <em><strong>https://cloud.tsinghua.edu.cn/d/24d9ed6e225d455a834e/.</strong></em> For more detailed display and introduction of data, as well as download of more GRACED data, please refer to our official website of GRACED - <strong>https://carbonmonitor-graced.com</strong>. Please check the README.txt file for more details.
description: This research has been supported by the National Natural Science Foundation of China (grant 41921005 and 71874097), Beijing Natural Science Foundation (JQ19032), and the Qiu Shi Science & Technologies Foundation.
DOI: 10.34740/kaggle/dsv/4066960
title: Mahanagar Yatayat Dataset
DOI: 10.5281/zenodo.6969287
title: SPEAR ATS data (March-April)

description: Data for supporting the finding in the manuscript "Skillful forecasts of springtime CONUS tornado activity up to a year in advance" by Tseng et al. (2022)
DOI: 10.5281/zenodo.6969286
title: SPEAR ATS data (March-April)

description: Data for supporting the finding in the manuscript "Skillful forecasts of springtime CONUS tornado activity up to a year in advance" by Tseng et al. (2022)
DOI: 10.5281/zenodo.6914898
title: SEN12 Global Urban Mapping Dataset

description: The SEN12 Global Urban Mapping (SEN12_GUM) dataset consists of Sentinel-1 SAR (VV + VH band) and Sentinel-2 MSI (10 spectral bands) satellite images acquired over the same area for 96 training and validation sites and an additional 60 test sites covering unique geographies across the globe. The satellite imagery was acquired as part of the European Space Agency's Earth observation program Copernicus and was preprocessed in Google Earth Engine. Built-up area labels for the 30 training and validation sites located in the United States, Canada, and Australia were obtained from Microsoft's open-access building footprints. The other 66 training sites located outside of the United States, Canada, and Australia are unlabeled but can be used for semi-supervised learning. Labels obtained from the SpaceNet7 dataset are provided for all 60 test sites.
description: This work was supported in part by the Swedish National Space Agency under Grant dnr 155/15, the Digital Futures under the grant for the EO-AI4GlobalChange project, and in part by ESA-China Dragon 5 Program under the EO-AI4Urban Project.
DOI: 10.5281/zenodo.6914897
title: SEN12 Global Urban Mapping Dataset

description: The SEN12 Global Urban Mapping (SEN12_GUM) dataset consists of Sentinel-1 SAR (VV + VH band) and Sentinel-2 MSI (10 spectral bands) satellite images acquired over the same area for 96 training and validation sites and an additional 60 test sites covering unique geographies across the globe. The satellite imagery was acquired as part of the European Space Agency's Earth observation program Copernicus and was preprocessed in Google Earth Engine. Built-up area labels for the 30 training and validation sites located in the United States, Canada, and Australia were obtained from Microsoft's open-access building footprints. The other 66 training sites located outside of the United States, Canada, and Australia are unlabeled but can be used for semi-supervised learning. Labels obtained from the SpaceNet7 dataset are provided for all 60 test sites.
description: This work was supported in part by the Swedish National Space Agency under Grant dnr 155/15, the Digital Futures under the grant for the EO-AI4GlobalChange project, and in part by ESA-China Dragon 5 Program under the EO-AI4Urban Project.
DOI: 10.34740/kaggle/dsv/4066976
title: Marriage and Divorce Dataset
DOI: 10.6073/pasta/97939ef97c7daae015f03d382805a16f
title: Intertidal Crab Data in Midcoast Maine: 2018-2021

description: Invasive species have caused major disruptions to ecosystems worldwide. The European green crab invaded North America in the 1800s and is considered one of the world’s 100 worst invaders by the IUCN. Observations of spatiotemporal green crab population dynamics are essential for predicting and managing the ecological and economic impacts of this harmful invasive species. These data come from a standardized method for assessing green crab population dynamics in the rocky intertidal zone of New England and Atlantic Canada. Data was collected from 10 survey sites located along the coast of Maine from Yarmouth, ME (furthest point south), to Walpole, ME (furthest site north) from January, 2018 to November, 2021. Data was collected following the survey design described in McMahan (2020), using a 1m^2 quadrat to sample rocky intertidal habitat. The resulting data collected using this protocol has a wide range of uses, including to inform ecological research, conservation efforts, mitigation strategies, and fishery development, as well as for educational outreach purposes.
DOI: 10.5281/zenodo.6554831
title: A simple model for daily basin-wide thermodynamic sea ice thickness growth retrieval: Data

description: Data for: Anheuser, J., Liu, Y., and Key, J.: A daily basin-wide sea ice thickness retrieval methodology: Stefan's Law Integrated Conducted Energy (SLICE), The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-333, in review, 2021. Scripts for producing data and figures can be found at: https://doi.org/10.5281/zenodo.6561431
DOI: 10.5281/zenodo.6554832
title: A simple model for daily basin-wide thermodynamic sea ice thickness growth retrieval: Data

description: Data for: Anheuser, J., Liu, Y., and Key, J.: A daily basin-wide sea ice thickness retrieval methodology: Stefan's Law Integrated Conducted Energy (SLICE), The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-333, in review, 2021. Scripts for producing data and figures can be found at: https://doi.org/10.5281/zenodo.6561431
DOI: 10.5281/zenodo.6985505
title: A simple model for daily basin-wide thermodynamic sea ice thickness growth retrieval: Data

description: Data for: Anheuser, J., Liu, Y., and Key, J.: A daily basin-wide sea ice thickness retrieval methodology: Stefan's Law Integrated Conducted Energy (SLICE), The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-333, in review, 2021. Scripts for producing data and figures can be found at: https://doi.org/10.5281/zenodo.6561431
DOI: 10.34740/kaggle/dsv/4067112
title: all models weights classification pfe