cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7905628
title: MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 1km SIN Grid in 2016

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 1 km spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2016. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2015</strong></em>, <em>and <strong>click here</strong> to download the MUSES FVC product <strong>in 2017</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2016 Spatial Resolution: 1 km Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.004 Valid Range: 0 – 250 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7905627
title: MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 1km SIN Grid in 2016

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 1 km spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2016. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2015</strong></em>, <em>and <strong>click here</strong> to download the MUSES FVC product <strong>in 2017</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2016 Spatial Resolution: 1 km Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.004 Valid Range: 0 – 250 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.6084/m9.figshare.22678759
title: Priming effect stimulates carbon release from thawed permafrost

description: This dataset is composed of two datasets. Dataset1 includes geographic location, soil layer thickness, soil organic carbon, bulk density, soil organic carbon density, basal mirobial respiration, cumulative priming effect and relative priming effect across 24 sampling sites on the Tibetan Plateau. Dataset2 includes CO2 emission rate and δ13C values across 24 sampling sites during the 60-day incubation. At each site, both CO2 emission rate and δ13C values of three parallel samples were measured for 9 times on days 1, 2, 4, 9, 15, 22, 30, 45, and 60.
DOI: 10.6084/m9.figshare.22678759.v5
title: Priming effect stimulates carbon release from thawed permafrost

description: This dataset is composed of two datasets. Dataset1 includes geographic location, soil layer thickness, soil organic carbon, bulk density, soil organic carbon density, basal mirobial respiration, cumulative priming effect and relative priming effect across 24 sampling sites on the Tibetan Plateau. Dataset2 includes CO2 emission rate and δ13C values across 24 sampling sites during the 60-day incubation. At each site, both CO2 emission rate and δ13C values of three parallel samples were measured for 9 times on days 1, 2, 4, 9, 15, 22, 30, 45, and 60.
DOI: 10.6084/m9.figshare.22783469
title: Dataset from: Transformation from NHx to NOy deposition aggravated China’s forest soil acidification

description: Dataset from: Transformation from NHx to NOy deposition aggravated China’s forest soil acidification <br> This dataset includes 1. the fluxes of NHx and NOy deposition fluxes from 1980 to 2019, 2. net proton production by NHx and NOy deposition and the contribution ratio of NHx to NOy to soil acidification from 1980 to 2019 3. the average net proton production by NHx and NOy deposition and the contribution ratio of NHx to NOy to soil acidification in different province in China <br> Brief methed: The nitrogen deposition data is obtained from the data set produced by our group by combining measured data, data integration and remote sensing data The net proton production by different nitrogen components was caculated by leached amount of NO3-. More detailed methed can be found in the manuscript.
DOI: 10.6084/m9.figshare.22783469.v2
title: Dataset from: Transformation from NHx to NOy deposition aggravated China’s forest soil acidification

description: Dataset from: Transformation from NHx to NOy deposition aggravated China’s forest soil acidification <br> This dataset includes 1. the fluxes of NHx and NOy deposition fluxes from 1980 to 2019, 2. net proton production by NHx and NOy deposition and the contribution ratio of NHx to NOy to soil acidification from 1980 to 2019 3. the average net proton production by NHx and NOy deposition and the contribution ratio of NHx to NOy to soil acidification in different province in China <br> Brief methed: The nitrogen deposition data is obtained from the data set produced by our group by combining measured data, data integration and remote sensing data The net proton production by different nitrogen components was caculated by leached amount of NO3-. More detailed methed can be found in the manuscript.
DOI: 10.6084/m9.figshare.22783469.v1
title: Datasets.xlsxTransformation from NHx to NOy deposition aggravated China’s forest soil acidification

description: Dataset from: Transformation from NHx to NOy deposition aggravated China’s forest soil acidification <br> This dataset includes 1. the fluxes of NHx and NOy deposition fluxes from 1980 to 2019, 2. net proton production by NHx and NOy deposition and the contribution ratio of NHx to NOy to soil acidification from 1980 to 2019 3. the average net proton production by NHx and NOy deposition and the contribution ratio of NHx to NOy to soil acidification in different province in China <br> Brief methed: The nitrogen deposition data is obtained from the data set produced by our group by combining measured data, data integration and remote sensing data The net proton production by different nitrogen components was caculated by leached amount of NO3-. More detailed methed can be found in the manuscript.
DOI: 10.6084/m9.figshare.22783538
title: GL4U amplicon 2023 dry-run notebooks

description: Notebooks used for the GL4U 2023 amplicon internal dry-run
DOI: 10.6084/m9.figshare.22783538.v1
title: GL4U amplicon 2023 dry-run notebooks

description: Notebooks used for the GL4U 2023 amplicon internal dry-run
DOI: 10.5281/zenodo.7911657
title: BCdatabaser - coi.arthropoda.none.2023-05-08.zip

description: Parameters: --marker-search-string COI OR CO1 OR 'Cytochrome oxidase 1' OR 'Cytochrome oxidase I' --taxonomic-range Arthropoda --sequence-length-filter 100:2000 --sequences-per-taxon 9
description: This dataset was automatically created with data from NCBI using the BCdatabaser tool
description: {"references": ["Keller et al. (2019) BCdatabaser: on-the-fly reference database creation for (meta-)barcoding. EcoEvoRxiv (2019); https://doi.org/10.32942/osf.io/cmfu2 - https://github.com/molbiodiv/bcdatabaser", "eutils: Sayers E. E-utilities Quick Start. 2008 Dec 12 [Updated 2018 Oct 24]. In: Entrez Programming Utilities Help [Internet]. Bethesda (MD): National Center for Biotechnology Information (US); 2010-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK25500/", "NCBI::Taxonomy: F\u00f6rster et al. https://github.com/greatfireball/NCBI-Taxonomy - F\u00f6rster F. greatfireball/NCBI-Taxonomy v0.90. Zenodo. 2018 Oct 15; https://doi.org/10.5281/zenodo.1462861", "SeqFilter: Hackl et al. https://github.com/BioInf-Wuerzburg/SeqFilter", "dispr: Cofield et al. https://github.com/douglasgscofield/dispr", "Krona: Ondov BD, Bergman NH, and Phillippy AM. Interactive metagenomic visualization in a Web browser. BMC Bioinformatics. 2011 Sep 30; 12(1):385."]}
DOI: 10.5281/zenodo.7911656
title: BCdatabaser - coi.arthropoda.none.2023-05-08.zip

description: Parameters: --marker-search-string COI OR CO1 OR 'Cytochrome oxidase 1' OR 'Cytochrome oxidase I' --taxonomic-range Arthropoda --sequence-length-filter 100:2000 --sequences-per-taxon 9
description: This dataset was automatically created with data from NCBI using the BCdatabaser tool
description: {"references": ["Keller et al. (2019) BCdatabaser: on-the-fly reference database creation for (meta-)barcoding. EcoEvoRxiv (2019); https://doi.org/10.32942/osf.io/cmfu2 - https://github.com/molbiodiv/bcdatabaser", "eutils: Sayers E. E-utilities Quick Start. 2008 Dec 12 [Updated 2018 Oct 24]. In: Entrez Programming Utilities Help [Internet]. Bethesda (MD): National Center for Biotechnology Information (US); 2010-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK25500/", "NCBI::Taxonomy: F\u00f6rster et al. https://github.com/greatfireball/NCBI-Taxonomy - F\u00f6rster F. greatfireball/NCBI-Taxonomy v0.90. Zenodo. 2018 Oct 15; https://doi.org/10.5281/zenodo.1462861", "SeqFilter: Hackl et al. https://github.com/BioInf-Wuerzburg/SeqFilter", "dispr: Cofield et al. https://github.com/douglasgscofield/dispr", "Krona: Ondov BD, Bergman NH, and Phillippy AM. Interactive metagenomic visualization in a Web browser. BMC Bioinformatics. 2011 Sep 30; 12(1):385."]}
DOI: 10.5281/zenodo.7774291
title: CoCon: A Data Set on Combined Contextualized Research Artifact Use

description: CoCon is a large graph data set reflecting the combined use of research artifacts, contextualized in academic publications’ full-text. It comprises 35 k artifacts (data sets, methods, models, and tasks) and 340 k publications. The data set is generated from Papers With Code and unarXive. You can find a Python package for loading the data as a NetworkX or Pytorch Geometric graph in this GitHub repository<br>
DOI: 10.5281/zenodo.7774292
title: CoCon: A Data Set on Combined Contextualized Research Artifact Use

description: CoCon is a large graph data set reflecting the combined use of research artifacts, contextualized in academic publications’ full-text. It comprises 35 k artifacts (data sets, methods, models, and tasks) and 340 k publications. The data set is generated from Papers With Code and unarXive. You can find a Python package for loading the data as a NetworkX or Pytorch Geometric graph in this GitHub repository<br>
DOI: 10.3886/e190741v1-138420
title: The Social Metacognition of moral judgment: How others’ judgments influence our own

description: This project investigates the impact of others’ moral judgments on one’s judgments regarding trolley-like dilemmas. To achieve this aim, two complementary studies were conducted. In Study 1 (329 participants), we aimed to measure one’s initial moral inclination and manipulate the perceived moral competence of the “other” and the moral orientation (deontological or utilitarian) of others’ responses. To shed light on how the participant's judgment process unfolds when their initial moral intuitions conflict or accord with the moral judgments of the social source, a second Study 2 with 101 participants was designed. In this study, we use a modified version of the “two-response” paradigm that begins by assessing participants’ intuitive moral judgments accompanied by their feelings of rightness. However, before inviting participants to give the deliberated response, they were exposed to the moral judgments of two other (fictional) participants.
description: Response Rates: Study 1: A total of 337 participants were recruited via the web-based platform Prolific. Data from one participant who did not comply with the instructions were discarded, leaving a total of 329 participants (Mean age=23.44, SD=7.49; 189 [57%] women). Study 2: A total of 104 participants were initially recruited via the web-based platform Prolific. Data from 3 participants who did not comply with the instructions were discarded, leaving a total of 101 participants (Mean age = 26.1, SD=10.6; 71 [70%] women).
description: Presence of Common Scales: Study 1: participants’ moral inclination was established using a 9-point rating scale; participants' perception about their own moral sophistication when compared to the expert and naïve source was established using a 9-point rating scale; participants' agreement with the expert or naïve source preference concerning trolley-type moral dilemmas was accessed using a 9-point rating scale; finally, participants' answered the problem by judging whether the agent should pursue the utilitarian course of action also using a 9-point rating scale. <br>Study 2: participant's acceptability of the utilitarian course of action for the trolley-type problem was accessed using a 9-point rating scale. The Feelings of Rightness of their judgments and Confidence in the final Judgment were accessed using a 9-point rating scale.<br><br>
description: Study 1: 329 participants were recruited via the web-based platform Prolific.<br>Study 2: 101 participants were recruited via the web-based platform Prolific.
description: Persons aged 18 and over living in Lisbon, Portugal.<br>.
description: web-based survey;
DOI: 10.5281/zenodo.7845374
title: Bibliographic Information on PNAS Direct and Contributed Submissions

description: This dataset contains observations of 46,391 articles published as "Direct" or "Contributed" submissions in PNAS. Only includes publications in the Biological, Physical and Social Sciences sections. Variables: extract_id: incremental ID topic: main section sub_label: labelled submission type pub_year: publication year review_time: number of days from the manuscript was received until it was accepted for publication ncs_full: The field normalised citation score, full counting -
DOI: 10.5281/zenodo.7845375
title: Bibliographic Information on PNAS Direct and Contributed Submissions

description: This dataset contains observations of 46,391 articles published as "Direct" or "Contributed" submissions in PNAS. Only includes publications in the Biological, Physical and Social Sciences sections. Variables: extract_id: incremental ID topic: main section sub_label: labelled submission type pub_year: publication year review_time: number of days from the manuscript was received until it was accepted for publication ncs_full: The field normalised citation score, full counting -
DOI: 10.5281/zenodo.7852887
title: Análisis cienciométrico de la producción y colaboración científica y tecnológica de dos universidades peruanas

description: El rol de las universidades como instituciones formadoras, en la actualidad, gira en torno a la promoción de la actividad científica y la investigación, fuentes de innovación tecnológica, etc. El objetivo principal de este estudio fue describir el comportamiento de la producción y colaboración científica y tecnológica de dos universidades peruanas (UNMSM y PUCP) entre 2011-2020. Esta investigación se rigió bajo un método cienciométrico, el cual permitió alcanzar los objetivos señalados anteriormente. Para realizar el análisis de datos, se utilizaron el software Publish or Perish, VOSviewer y MS Excel. Dentro los principales hallazgos obtenidos, se identificaron un total de 2,934 artículos y 42 patentes para la UNMSM y 2,112 artículos y 60 patentes para la PUCP. Respecto a la producción científica, en 2011 la diferencia era de 99 artículos y en 2020, esta diferencia se amplió a 188 artículos. Asimismo, las tendencias de la producción científica de ambas entidades tienden a ser positivas, sin embargo; la UNMSM se encuentra mejor posicionada que la PUCP. Respecto a la producción tecnológica, se evidenció que el año 2016, la PUCP alcanzó su pico máximo de publicación de patentes con 14 documentos. Por su parte, la UNMSM alcanza dicho pico en el año 2019, con 9 patentes. Se concluye que los indicadores referidos a la producción científico-tecnológica de ambas universidades son positivos. Estos resultados deben ser tomados con cautela, ya que el presente estudio se limita a cuantificar la producción científico-tecnológica, pero no aborda aspectos vinculados a la calidad y pertenencia de estas.
DOI: 10.5281/zenodo.7852886
title: Análisis cienciométrico de la producción y colaboración científica y tecnológica de dos universidades peruanas

description: El rol de las universidades como instituciones formadoras, en la actualidad, gira en torno a la promoción de la actividad científica y la investigación, fuentes de innovación tecnológica, etc. El objetivo principal de este estudio fue describir el comportamiento de la producción y colaboración científica y tecnológica de dos universidades peruanas (UNMSM y PUCP) entre 2011-2020. Esta investigación se rigió bajo un método cienciométrico, el cual permitió alcanzar los objetivos señalados anteriormente. Para realizar el análisis de datos, se utilizaron el software Publish or Perish, VOSviewer y MS Excel. Dentro los principales hallazgos obtenidos, se identificaron un total de 2,934 artículos y 42 patentes para la UNMSM y 2,112 artículos y 60 patentes para la PUCP. Respecto a la producción científica, en 2011 la diferencia era de 99 artículos y en 2020, esta diferencia se amplió a 188 artículos. Asimismo, las tendencias de la producción científica de ambas entidades tienden a ser positivas, sin embargo; la UNMSM se encuentra mejor posicionada que la PUCP. Respecto a la producción tecnológica, se evidenció que el año 2016, la PUCP alcanzó su pico máximo de publicación de patentes con 14 documentos. Por su parte, la UNMSM alcanza dicho pico en el año 2019, con 9 patentes. Se concluye que los indicadores referidos a la producción científico-tecnológica de ambas universidades son positivos. Estos resultados deben ser tomados con cautela, ya que el presente estudio se limita a cuantificar la producción científico-tecnológica, pero no aborda aspectos vinculados a la calidad y pertenencia de estas.
DOI: 10.6084/m9.figshare.22784678
title: Original data of heterospecific pollen in three Silene species by Kai HAO et al

description: "style_length.txt" is an original data set of style length under hand-pollination and isolate-pollination treatments among three Silene species; <br> "hand_pollination_HP_percent.txt" is an original data set of seed number under hand-pollination treatments with different heterospecific pollen percent among three Silene species. <br> "hand_pollination_HP_number.txt"is an original data set of seed number under hand-pollination treatments with different heterospecific pollen number among three Silene species. <br> "natural_pollen_loads.txt"is an original data set of pollen loads on natural stigmas among three Silene species.
DOI: 10.6084/m9.figshare.22784939
title: Application of quantitative structure-activity relationships in the prediction of new compounds with anti-leukemic activity

description: Application of quantitative structure-activity relationships in the prediction of new compounds with anti-leukemic activity
DOI: 10.6084/m9.figshare.22784939.v1
title: Application of quantitative structure-activity relationships in the prediction of new compounds with anti-leukemic activity

description: Application of quantitative structure-activity relationships in the prediction of new compounds with anti-leukemic activity
DOI: 10.5281/zenodo.7903590
title: HiTIC-Monthly: A Monthly High Spatial Resolution (1 km) Human Thermal Index Collection over China during 2003–2020 [GeoTIFF]

description: The monthly <strong>Hi</strong>gh spatial resolution human <strong>T</strong>hermal <strong>I</strong>ndex <strong>C</strong>ollection (<strong>HiTIC-Monthly</strong>) includes <strong><em>near-surface air temperature</em> </strong>(SAT) and 11 commonly used<strong> <em>human-perceived temperature</em> </strong>indices: indoor Apparent Temperature (AT<sub>in</sub>), outdoor shaded Apparent Temperature (AT<sub>out</sub>), Discomfort Index (DI), Effective Temperature (ET), Heat Index (HI), Humidex (HMI), Modified Discomfort Index (MDI), Net Effective Temperature (NET), simplified Wet Bulb Globe Temperature (sWBGT), Wet-Bulb Temperature (WBT), and Wind Chill Temperature (WCT). This dataset has a high spatial resolution of 1 km × 1 km and covers mainland China from January 2003 to December 2020. The overall R-square, root mean square error, and mean absolute error of the 12 thermal indices in the HiTIC dataset are 0.996, 0.693°C, and 0.512°C, respectively. It is compressed by year, and each zip package is composed of 12 monthly images in the<strong> GeoTIFF format</strong>. The unit of the dataset is 0.01 degree Celsius (°C), and the values are stored in an integer type (Int16) for saving storage space, and need to be divided by 100 to get the values in degree Celcius when in use. The projection coordinate system of the dataset is Albers Equal Area Conic Projection. The naming rule and other detailed information can be found in “README.pdf”. If you have any questions when using the HiTIC-Monthly dataset, please feel free to contact Miss Hui Zhang via zhangh573@mail2.sysu.edu.cn, Dr. Ming Luo via luom38@mail.sysu.edu.cn, or Dr. Yongquan Zhao via zhaoyq66@mail.sysu.edu.cn. More details on the procedure of producing the HiTIC-Monthly dataset and its accuracy assessment can be found in: Zhang, H., Luo, M., Zhao, Y., Lin, L., Ge, E., Yang, Y., Ning, G., Cong, J., Zeng, Z., Gui, K., Li, J., Chan, T. O., Li, X., Wu, S., Wang, P., and Wang, X.: HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020, Earth Syst. Sci. Data, 15, 359–381, https://doi.org/10.5194/essd-15-359-2023, 2023.
description: For HiTIC-Monthly dataset in NetCDF format, please refer to https://zenodo.org/record/6895533.