cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7882606
title: MUSES Fractional Vegetation Coverage (FVC) Monthly Global 500m SIN Grid in 2014

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 500 m spatial resolution and monthly 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 500 m 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 2014. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2013</strong></em>, <em>and <strong>click here</strong> to download the MUSES FVC product <strong>in 2015</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2014 Spatial Resolution: 500 m Temporal Resolution: 1 month 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.7882605
title: MUSES Fractional Vegetation Coverage (FVC) Monthly Global 500m SIN Grid in 2014

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 500 m spatial resolution and monthly 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 500 m 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 2014. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2013</strong></em>, <em>and <strong>click here</strong> to download the MUSES FVC product <strong>in 2015</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2014 Spatial Resolution: 500 m Temporal Resolution: 1 month 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.7883583
title: desmognathus_wrighti_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7883582
title: desmognathus_wrighti_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7883591
title: pionus_menstruus_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7883590
title: pionus_menstruus_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7883589
title: maDeepLabCut 3-mouse demo project

description: see https://github.com/DeepLabCut/DeepLabCut/blob/main/examples/COLAB/COLAB_3miceDemo.ipynb
DOI: 10.5281/zenodo.7883588
title: maDeepLabCut 3-mouse demo project

description: see https://github.com/DeepLabCut/DeepLabCut/blob/main/examples/COLAB/COLAB_3miceDemo.ipynb
DOI: 10.5281/zenodo.7883600
title: sardinella_fimbriata_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7883599
title: sardinella_fimbriata_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7811047
title: Dataset and RScript - Effect of insecticide on termite alarm behavior

description: Dataset and RScript of the MS "How to perceive the insecticide? The Neotropical termite <em>Nasutitermes corniger</em> (Termitidae: Nasutitermitinae) triggers alert behavior after exposure to imidacloprid".
DOI: 10.5281/zenodo.7811046
title: Dataset and RScript - Effect of insecticide on termite alarm behavior

description: Dataset and RScript of the MS "How to perceive the insecticide? The Neotropical termite <em>Nasutitermes corniger</em> (Termitidae: Nasutitermitinae) triggers alert behavior after exposure to imidacloprid".
DOI: 10.5281/zenodo.7883608
title: sardinella_gibbosa_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7883607
title: sardinella_gibbosa_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7883313
title: MUSES Leaf Area Index (LAI) Monthly Global 1km SIN Grid in 2012

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 LAI product at 1 km spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2012. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2011</strong></em>, <em>and <strong>click here</strong> to download the MUSES LAI product <strong>in 2013</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2012 Spatial Resolution: 1 km Temporal Resolution: 1 month Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7883312
title: MUSES Leaf Area Index (LAI) Monthly Global 1km SIN Grid in 2012

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 LAI product at 1 km spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2012. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2011</strong></em>, <em>and <strong>click here</strong> to download the MUSES LAI product <strong>in 2013</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2012 Spatial Resolution: 1 km Temporal Resolution: 1 month Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7883610
title: sardinella_hualiensis_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7883609
title: sardinella_hualiensis_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7883621
title: sardinella_lemuru_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7883620
title: sardinella_lemuru_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7883623
title: sardinella_tawilis_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7883622
title: sardinella_tawilis_zanfona_1.fasta.gz

description: .
DOI: 10.6084/m9.figshare.22726340
title: nanflora_current2022_Apr 30, 2023__CLM.accdb

description: botany
DOI: 10.6084/m9.figshare.22726340.v1
title: nanflora_current2022_Apr 30, 2023__CLM.accdb

description: botany
DOI: 10.5281/zenodo.7883633
title: The expression level of RcCKXs.

description: The expression level of RcCKXs.
DOI: 10.5281/zenodo.7883632
title: The expression level of RcCKXs.

description: The expression level of RcCKXs.
DOI: 10.13020/ncdt-j687
title: Data for Relative abundance of Zebra Mussels (Dreissena polymorpha) in various sediment types in White Bear Lake, Minnesota, June 2021
DOI: 10.5281/zenodo.7878290
title: Adapting camera-trap placement based on animal behaviour for rapid detection: a focus on the Endangered, white-bellied pangolin (Phataginus tricuspis)

description: Table containing detection data of species using two camera trap placement strategies (log vs non-log)
DOI: 10.5281/zenodo.7878289
title: Adapting camera-trap placement based on animal behaviour for rapid detection: a focus on the Endangered, white-bellied pangolin (Phataginus tricuspis)

description: Table containing detection data of species using two camera trap placement strategies (log vs non-log)
DOI: 10.22008/fk2/7ckyd6/ak2rkb
title: G100_Mellemfjord_69V1_ N_400dpi.tif
DOI: 10.22008/fk2/7ckyd6
title: Geologisk kort over Grønland, 1:100 000, Mellemfjord 69 V.1 Nord

description: Geologisk kort over Grønland / Geological map of Greenland - 1:100 000, central West Greenland, Qeqertarsuaq / Disko
DOI: 10.22008/fk2/7ckyd6/6i3v6v
title: G100_Mellemfjord_69V1_ N_400dpi.pdf
DOI: 10.6084/m9.figshare.22726100
title: 情效投稿数据信息

description: (1) Import the original data into spss format, see SPSS file "(1) Row data". (2) Complete the scoring conversion of data according to the scoring method of each variable. For example, the original 1-5 scoring data in the questionnaire star is converted to 0-4 according to the scale scoring requirements. See "(2) Complete data scoring conversion " in the SPSS file. (3) According to the scoring method of each variable , the corresponding topic is divided, and the mean value of each dimension and the total mean value are calculated. See SPSS file "(3) Complete the reverse order and calculate the mean value". (4) To eliminate the data of subjects with short answering time and regular answering data, see the SPSS file "(4) eliminate regular Questionnaire". (5) After data processing is completed, the corresponding reliability test, descriptive analysis, correlation analysis and mediation effect analysis are carried out according to the final data file (5). See SPSS file "(5) Data of final analysis ".
DOI: 10.6084/m9.figshare.22726100.v1
title: 情效投稿数据信息

description: (1) Import the original data into spss format, see SPSS file "(1) Row data". (2) Complete the scoring conversion of data according to the scoring method of each variable. For example, the original 1-5 scoring data in the questionnaire star is converted to 0-4 according to the scale scoring requirements. See "(2) Complete data scoring conversion " in the SPSS file. (3) According to the scoring method of each variable , the corresponding topic is divided, and the mean value of each dimension and the total mean value are calculated. See SPSS file "(3) Complete the reverse order and calculate the mean value". (4) To eliminate the data of subjects with short answering time and regular answering data, see the SPSS file "(4) eliminate regular Questionnaire". (5) After data processing is completed, the corresponding reliability test, descriptive analysis, correlation analysis and mediation effect analysis are carried out according to the final data file (5). See SPSS file "(5) Data of final analysis ".
DOI: 10.22008/fk2/ke1gcb
title: Geological map of the Nuussuaq Basin in southern Nuussuaq, 1:100 000, special map Paatuut, with detailed sections

description: Geologisk kort over Grønland / Geological map of Greenland - 1:100 000, central West Greenland, Nuussuaq
DOI: 10.22008/fk2/ke1gcb/e94nji
title: G100_Nuussuaq_Basin_in_southern_Nuussuaq_with_detailed_sections_Paatuut_400dpi.tif
DOI: 10.22008/fk2/ke1gcb/rysfak
title: G100_Nuussuaq_Basin_in_southern_Nuussuaq_with_detailed_sections_Paatuut_400dpi.pdf
DOI: 10.22008/fk2/9av6kb/dzsjai
title: G100_Pingu_69V2_ N_400dpi.tif
DOI: 10.22008/fk2/9av6kb
title: Geological map of Greenland, 1:100 000, Pingu 69 V.2 Nord

description: Geologisk kort over Grønland / Geological map of Greenland - 1:100 000, central West Greenland, Qeqertarsuaq/Disko
DOI: 10.22008/fk2/9av6kb/qe9zgn
title: G100_Pingu_69V2_ N_400dpi.pdf
DOI: 10.22008/fk2/peqrzv/nobhck
title: G20_Sikillingi_Western_Nuussuaq_400dpi.tif
DOI: 10.22008/fk2/peqrzv
title: Geological map of the area around Sikillingi, western Nuussuaq, central West Greenland, 1:20 000

description: Geologisk kort over Grønland / Geological map of Greenland - 1:20 000, central West Greenland, Nuussuaq
DOI: 10.22008/fk2/peqrzv/zjoj60
title: G20_Sikillingi_Western_Nuussuaq_400dpi.pdf
DOI: 10.5281/zenodo.7883004
title: Survey questionnaires supporting the journal article with doi:10.30924/mjcmi.27.2.6

description: These questionnaires support the journal article "A fuzzy logic model for evaluating the motivation for high-quality publications: Evidence from a Bulgarian university" with doi:10.30924/mjcmi.27.2.6. They are developed for exploring the motivation for high-quality publications based on fuzzy logic and Vroom's motivation theory. The questionnaires address the internal stakeholders of the university, including the researcher, the department head and the top university management.
DOI: 10.5281/zenodo.7883005
title: Survey questionnaires supporting the journal article with doi:10.30924/mjcmi.27.2.6

description: These questionnaires support the journal article "A fuzzy logic model for evaluating the motivation for high-quality publications: Evidence from a Bulgarian university" with doi:10.30924/mjcmi.27.2.6. They are developed for exploring the motivation for high-quality publications based on fuzzy logic and Vroom's motivation theory. The questionnaires address the internal stakeholders of the university, including the researcher, the department head and the top university management.
DOI: 10.6084/m9.figshare.22713661
title: <strong>Field Survey of 2021 Typhoon Rai –Odette- in the Philippines</strong>

description: Typhoon Rai struck the Philippines on the 16th December 2021, damaging and inundating many coastal areas along the Visayas region of the country due to the high winds, storm surges and wind driven waves it generated. In order to understand the various damage mechanisms, the authors conducted a field survey to measure the storm surge heights at several locations in the provinces of Cebu and Bohol. As part of the survey, local residents were interviewed to understand the phenomena and survey the height reached by the storm surge. The maximum storm surge level measured were 2.54 m, 4.24 m and 4.06 m along the provinces of Cebu and Bohol. Finally, some interesting characteristics of the storm surge are summarised, and the lessons learnt in terms of disaster risk management are discussed
DOI: 10.6084/m9.figshare.22713661.v2
title: <strong>Field Survey of 2021 Typhoon Rai –Odette- in the Philippines</strong>

description: Typhoon Rai struck the Philippines on the 16th December 2021, damaging and inundating many coastal areas along the Visayas region of the country due to the high winds, storm surges and wind driven waves it generated. In order to understand the various damage mechanisms, the authors conducted a field survey to measure the storm surge heights at several locations in the provinces of Cebu and Bohol. As part of the survey, local residents were interviewed to understand the phenomena and survey the height reached by the storm surge. The maximum storm surge level measured were 2.54 m, 4.24 m and 4.06 m along the provinces of Cebu and Bohol. Finally, some interesting characteristics of the storm surge are summarised, and the lessons learnt in terms of disaster risk management are discussed
DOI: 10.5281/zenodo.7883694
title: WP2: Synthetic XUV spectra

description: Set of XUV (0.01 - 10 keV) spectra, computed from parameterized plasma Emission Measure Distributions vs. temperature, for stars with different activity levels and plasma metallicities. Read the Introduction for more details. These spectra are intended as input for models of photochemistry and photoevaporation of exoplanetary atmospheres.
DOI: 10.5281/zenodo.7883693
title: WP2: Synthetic XUV spectra

description: Set of XUV (0.01 - 10 keV) spectra, computed from parameterized plasma Emission Measure Distributions vs. temperature, for stars with different activity levels and plasma metallicities. Read the Introduction for more details. These spectra are intended as input for models of photochemistry and photoevaporation of exoplanetary atmospheres.
DOI: 10.5281/zenodo.7883637
title: Set of Data from "Selective targeting of striatal parvalbumin-expressing interneurons for transgene delivery" https://doi.org/10.1016/j.jneumeth.2021.109105

description: PV<sup>Cre</sup> (+/+) mice were injected in the right striatum with AAV2/1-hsyn-FLEX-eGFP AAV2/9-CBA-FLEX-eGFP or AAV2/9-hsyn-FLEX-eGFP vectors. Two weeks after, cells were manually counted by a blind observer on 2D maximal intensity projections of confocal images transformed into the tiff format using the Multi-point tool in ImageJ. The GraphPad .pzfx files show the quantitative anaylsis of the proportion of GFP+ cells which were PV+ (specificty) , the proportion of PV+ cells which were GFP+ (efficiency), the proportion of PV+ cells in the injected striatum relative to to the injected striatum of PV<sup>Cre</sup> (+/+) mice (absence of vector toxicity) and the proportion of PV+ cells in the striatum of PV<sup>Cre</sup> (+/+) mice versus wt mice.
DOI: 10.5281/zenodo.7883636
title: Set of Data from "Selective targeting of striatal parvalbumin-expressing interneurons for transgene delivery" https://doi.org/10.1016/j.jneumeth.2021.109105

description: PV<sup>Cre</sup> (+/+) mice were injected in the right striatum with AAV2/1-hsyn-FLEX-eGFP AAV2/9-CBA-FLEX-eGFP or AAV2/9-hsyn-FLEX-eGFP vectors. Two weeks after, cells were manually counted by a blind observer on 2D maximal intensity projections of confocal images transformed into the tiff format using the Multi-point tool in ImageJ. The GraphPad .pzfx files show the quantitative anaylsis of the proportion of GFP+ cells which were PV+ (specificty) , the proportion of PV+ cells which were GFP+ (efficiency), the proportion of PV+ cells in the injected striatum relative to to the injected striatum of PV<sup>Cre</sup> (+/+) mice (absence of vector toxicity) and the proportion of PV+ cells in the striatum of PV<sup>Cre</sup> (+/+) mice versus wt mice.
DOI: 10.6084/m9.figshare.22634482
title: Chromatograms and Protein/Peptide Quant

description: Corresponds to forthcoming publication.
DOI: 10.6084/m9.figshare.22634482.v1
title: Chromatograms and Protein/Peptide Quant

description: Corresponds to forthcoming publication.
DOI: 10.6084/m9.figshare.22634482.v2
title: Chromatograms and Protein/Peptide Quant

description: Corresponds to forthcoming publication.
DOI: 10.5281/zenodo.7814090
title: A global review of methane policies reveals only 13% of emissions are covered with unclear effectiveness

description: Achieving the Paris Agreement 1.5°C target requires a reversal of the growing atmospheric concentrations of methane, which is about 80 times more potent than CO<sub>2</sub> on a 20-year timescale. The IPCC 6<sup>th</sup> Assessment Report stated that methane is underregulated but little is known about the effectiveness of existing methane policies. In this Review, we systematically examine existing methane policies across the energy, waste and agriculture sectors. We find that currently only about 13% of methane emissions is covered by methane mitigation policies. Moreover, the effectiveness of these policies is far from clear mainly because methane emissions are largely calculated using potentially unrepresentative estimates instead of direct measurements. Coverage and stringency are two major blind spots in global methane policies. These findings suggest that significant and underexplored mitigation opportunities exist but unlocking them requires policymakers to identify a consistent approach for accurate quantification of methane emissions sources alongside greater policy stringency.