cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6973924
title: An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° ET over the conterminous United States, 1980–2015 (time-merged version)

description: This dataset contains the 1980–2015 monthly evapotranspiration simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration). The file name has four parts: the variable collection, the used parameterization, the time period, the suffix, and the compression format. The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97). The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).
DOI: 10.5281/zenodo.6973923
title: An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° ET over the conterminous United States, 1980–2015 (time-merged version)

description: This dataset contains the 1980–2015 monthly evapotranspiration simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration). The file name has four parts: the variable collection, the used parameterization, the time period, the suffix, and the compression format. The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97). The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).
DOI: 10.25581/spruce.100/1874948
title: SPRUCE Vegetation Phenology in Experimental Plots from Phenocam Imagery, 2015-2021

description: This data set consists of PhenoCam data from the SPRUCE experiment from the beginning of whole ecosystem warming (Hanson et al. 2017) in August 2015 through March 31 of 2022, with start- and end-of-season phenological transition dates derived through the end of autumn 2021. Digital cameras, or phenocams, installed in each SPRUCE enclosure track seasonal variation in vegetation “greenness”, a proxy for vegetation phenology and associated physiological activity. Three separate regions of interest (ROIs) were defined for each camera field of view, corresponding to different vegetation types and demarcating (1) Picea trees (vegetation type EN, for evergreen needleleaf); (2) Larix trees (vegetation type DN, for deciduous needleleaf); and (3) the mixed shrub layer (vegetation type SH). User Note: This dataset supersedes previous versions of SPRUCE Vegetation Phenology in Experimental Plots from Phenocam Imagery. A list of previous versions can be found in the Related Datasets section of the user guide.
DOI: 10.34740/kaggle/dsv/4055915
title: Elon Musk Tweets
DOI: 10.5281/zenodo.6976420
title: Date of Square-root higher-order Weyl semimetals

description: Date of Square-root higher-order Weyl semimetals
DOI: 10.5281/zenodo.6976419
title: Date of Square-root higher-order Weyl semimetals

description: Date of Square-root higher-order Weyl semimetals
DOI: 10.6084/m9.figshare.19137791
title: CODCLAB_Level2

description: Lake-basin scale characteristics assigned to the basins are stored in shapefiles associated with lake-basin polygons, such as supply coefficient of lakes, etc.
DOI: 10.6084/m9.figshare.19137791.v1
title: CODCLAB_Level2

description: Lake-basin scale characteristics assigned to the basins are stored in shapefiles associated with lake-basin polygons, such as supply coefficient of lakes, etc.
DOI: 10.6084/m9.figshare.19137788
title: CODCLAB_Level3

description: All lake-basin attributes are provided in Level 3 tables associated with the lake ID, i.e., ‘Anth_CODCLAB.xlsx’ file, which stores anthropogenic information including lake ID, population density, GDP, etc.
DOI: 10.6084/m9.figshare.19137788.v1
title: CODCLAB_Level3

description: All lake-basin attributes are provided in Level 3 tables associated with the lake ID, i.e., ‘Anth_CODCLAB.xlsx’ file, which stores anthropogenic information including lake ID, population density, GDP, etc.
DOI: 10.6084/m9.figshare.20391066
title: Supporting dataset for genome assembly of the bivalve mollusk Mactra veneriformis

description: Supporting dataset for genome assembly of the bivalve mollusk <em>Mactra veneriformis</em>
DOI: 10.6084/m9.figshare.20391066.v3
title: Supporting dataset for genome assembly of the bivalve mollusk Mactra veneriformis

description: Supporting dataset for genome assembly of the bivalve mollusk <em>Mactra veneriformis</em>
DOI: 10.6084/m9.figshare.20391066.v2
title: Supporting dataset for genome assembly of the bivalve mollusk Mactra veneriformis

description: Supporting dataset for genome assembly of the bivalve mollusk <em>Mactra veneriformis</em>
DOI: 10.5281/zenodo.6973956
title: An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° runoff over the conterminous United States, 1980–2015 (time-merged version)

description: This dataset contains the 1980–2015 monthly evapotranspiration simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include the surface and subsurface runoff. The file name has four parts: the variable collection, the used parameterization, the time period, the suffix, and the compression format. The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97). The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).
DOI: 10.5281/zenodo.6973955
title: An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° runoff over the conterminous United States, 1980–2015 (time-merged version)

description: This dataset contains the 1980–2015 monthly evapotranspiration simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include the surface and subsurface runoff. The file name has four parts: the variable collection, the used parameterization, the time period, the suffix, and the compression format. The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97). The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).
DOI: 10.6084/m9.figshare.19145360
title: CODCLAB_sub-basins

description: CODCLAB of sub-basins for five large lakes (ID39, ID110,ID131,ID330 and ID533).
DOI: 10.6084/m9.figshare.19145360.v1
title: CODCLAB_sub-basins

description: CODCLAB of sub-basins for five large lakes (ID39, ID110,ID131,ID330 and ID533).
DOI: 10.6084/m9.figshare.19181606.v1
title: CODCLAB_1 km

description: CODCLAB_1 km is a unified spatial-temporal resolution dataset (with the lowest temporal resolution and 1 km spatial resolution ) resampled from CODCLAB_Level 1
DOI: 10.6084/m9.figshare.19181606
title: CODCLAB_1 km

description: CODCLAB_1 km is a unified spatial-temporal resolution dataset (with the lowest temporal resolution and 1 km spatial resolution ) resampled from CODCLAB_Level 1
DOI: 10.6084/m9.figshare.20469629
title: Additional file 1 of Identification of influential probe types in epigenetic predictions of human traits: implications for microarray design

description: Additional file 1: Table S1. Demographic and summary data for Set 1 and Set 2 - Generation Scotland. Table S2. REML analyses - primary probe sets (non-mQTL probes). Table S3. REML analyses—permuted probe sets. Table S4. DNAm-based prediction analyses—primary probe sets (non-mQTL probes). Table S5. R2 estimates from linear DNAm-based predictors in the literature. Table S6. DNAm-based prediction analyses—permuted sets. Table S7. REML for chronological age. Table S8. DNAm-based prediction of chronological age. Table S9. Enrichment of genomic features in highly variable sites. Table S10. Description of probe sets compared in variance component and prediction analyses. Table S11. REML—mQTL probes vs. non-mQTL probes. Table S12. REML—EWAS Catalog probes vs. non-mQTL probes. Table S13. REML—hypo- and hypermethylated probes vs. non-mQTL probes. Table S14. REML for chronological age—sensitivity analyses. Table S15. DNAm-based prediction—mQTL probes vs. non-mQTL probes. Table S16. DNAm-based prediction—EWAS Catalog probes vs. non-mQTL probes. Table S17. DNAm-based prediction—hypo- and hypermethylated probes vs. non-mQTL probes. Table S18. DNAm-based prediction of chronological age—sensitivity analyses.
DOI: 10.6084/m9.figshare.20469629.v1
title: Additional file 1 of Identification of influential probe types in epigenetic predictions of human traits: implications for microarray design

description: Additional file 1: Table S1. Demographic and summary data for Set 1 and Set 2 - Generation Scotland. Table S2. REML analyses - primary probe sets (non-mQTL probes). Table S3. REML analyses—permuted probe sets. Table S4. DNAm-based prediction analyses—primary probe sets (non-mQTL probes). Table S5. R2 estimates from linear DNAm-based predictors in the literature. Table S6. DNAm-based prediction analyses—permuted sets. Table S7. REML for chronological age. Table S8. DNAm-based prediction of chronological age. Table S9. Enrichment of genomic features in highly variable sites. Table S10. Description of probe sets compared in variance component and prediction analyses. Table S11. REML—mQTL probes vs. non-mQTL probes. Table S12. REML—EWAS Catalog probes vs. non-mQTL probes. Table S13. REML—hypo- and hypermethylated probes vs. non-mQTL probes. Table S14. REML for chronological age—sensitivity analyses. Table S15. DNAm-based prediction—mQTL probes vs. non-mQTL probes. Table S16. DNAm-based prediction—EWAS Catalog probes vs. non-mQTL probes. Table S17. DNAm-based prediction—hypo- and hypermethylated probes vs. non-mQTL probes. Table S18. DNAm-based prediction of chronological age—sensitivity analyses.
DOI: 10.5517/ccdc.csd.cc2b5brm
title: CCDC 2150090: Experimental Crystal Structure Determination

description: Related Article: Sikabwe Noki, Edikarlos Brasil, Haixiang Zhang, Thomas Bruckdorfer, Beatriz G. de la Torre, Fernando Albericio|2022|J.Org.Chem.|87|9443|doi:10.1021/acs.joc.2c01056
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2b5bsn
title: CCDC 2150091: Experimental Crystal Structure Determination

description: Related Article: Sikabwe Noki, Edikarlos Brasil, Haixiang Zhang, Thomas Bruckdorfer, Beatriz G. de la Torre, Fernando Albericio|2022|J.Org.Chem.|87|9443|doi:10.1021/acs.joc.2c01056
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2brxhj
title: CCDC 2167938: Experimental Crystal Structure Determination

description: Related Article: Min Li, Jingjing Sun, Wenjie Kuang, Lina Zhou, Dandan Han, Junbo Gong|2022|Cryst.Growth Des.|22|5027|doi:10.1021/acs.cgd.2c00533
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2brxjk
title: CCDC 2167939: Experimental Crystal Structure Determination

description: Related Article: Min Li, Jingjing Sun, Wenjie Kuang, Lina Zhou, Dandan Han, Junbo Gong|2022|Cryst.Growth Des.|22|5027|doi:10.1021/acs.cgd.2c00533
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.20469702
title: mouse_CellChatDB

description: Manually curated database for mouse LRI. Jin, S., Guerrero-Juarez, C.F., Zhang, L. et al. Inference and analysis of cell-cell communication using CellChat. Nat Commun 12, 1088 (2021). https://doi.org/10.1038/s41467-021-21246-9.
DOI: 10.6084/m9.figshare.20469720
title: human_CellChatDB

description: Manually curated database for human LRI. Jin, S., Guerrero-Juarez, C.F., Zhang, L. et al. Inference and analysis of cell-cell communication using CellChat. Nat Commun 12, 1088 (2021). https://doi.org/10.1038/s41467-021-21246-9.
DOI: 10.6084/m9.figshare.20469717
title: stables.zip

description: The link between nutrition and chronic non-communicable diseases (NCDs) has received considerable attention. According to the Lancet journal, dietary risk contributed to 11 million adult deaths in 2017
DOI: 10.6084/m9.figshare.20469717.v1
title: stables.zip

description: The link between nutrition and chronic non-communicable diseases (NCDs) has received considerable attention. According to the Lancet journal, dietary risk contributed to 11 million adult deaths in 2017
DOI: 10.34740/kaggle/dsv/4056100
title: BART Ridership
DOI: 10.21227/4wss-4w39
title: young and old voice of Trump

description: We collected several videos about Trump's interviews when he was young and old and the video of the president's swearing in speech on the Internet.
DOI: 10.6084/m9.figshare.20469726
title: Curated Metadat for tfdA HMM

description: Metadata for sequences generated from publicly available datasets in Genbank (accessed August 2019).
DOI: 10.6084/m9.figshare.20469726.v1
title: Curated Metadat for tfdA HMM

description: Metadata for sequences generated from publicly available datasets in Genbank (accessed August 2019).
DOI: 10.34740/kaggle/dsv/4056153
title: Russian Invasion of Ukraine
DOI: 10.6084/m9.figshare.20469777
title: Dataset Behavior of İnnovative Teaching with Technology during teaching: Primary School Teachers

description: This dataset aimed to explore factors predicting behavior of innovative teaching with technology (BITT) among Indonesian primary school teachers during teaching. A set of instruments was adapted and validated through content validity and measurement model assessment in the partial least square structural equation modeling (PLS-SEM).
DOI: 10.6084/m9.figshare.19841758.v1
title: Effect of habitual reading direction on saccadic eye movements: A pilot study

description: The raw data investigated the effect of habitual reading direction on saccadic eye movements (i.e. prosaccade, antisaccade and self-paced saccade).
DOI: 10.6084/m9.figshare.20470158
title: data.xlsx

description: This data set includes the resource parameters and zonification of earthquake in Pakistan.
DOI: 10.6084/m9.figshare.20470158.v1
title: data.xlsx

description: This data set includes the resource parameters and zonification of earthquake in Pakistan.
DOI: 10.6084/m9.figshare.20471181
title: Kimchi

description: Data set contains biblioetric details of the articles published in Scopus.
DOI: 10.34740/kaggle/dsv/4056398
title: WallStreetBets 2022
DOI: 10.6084/m9.figshare.20471874
title: 额尔古纳气象.xlsx

description: percipetation
DOI: 10.6084/m9.figshare.20471874.v1
title: 额尔古纳气象.xlsx

description: percipetation
DOI: 10.5281/zenodo.5509634
title: Data sets for "Suppression of mantle convection in Uranus by superionic water stiffer than liquid"

description: This is the source data shown in Figs. 1a, 3a, and Supplementary Figs. 2, 3, 5a, and 7 for the article "Suppression of mantle convection in Uranus by superionic water stiffer than liquid" by Kimura and Murakami.
DOI: 10.5281/zenodo.6667963
title: Data sets for "Suppression of mantle convection in Uranus by superionic water stiffer than liquid"

description: This is the source data shown in Figs. 1a, 3a, and Supplementary Figs. 2, 3, 4, 5, 6, 8, and 13 for the article "Suppression of mantle convection in Uranus by superionic water stiffer than liquid" by Kimura and Murakami.
DOI: 10.5281/zenodo.5509633
title: Data sets for "Superionic water with stiffness between molecular ice and liquid"

description: This is the source data shown in Figs. 1, 3A, and Figs. S2, S3, S4, S5, S6, S7, and S12 for the article "Superionic water with stiffness between molecular ice and liquid" by Kimura and Murakami.
DOI: 10.5281/zenodo.6982494
title: Data sets for "Superionic water with stiffness between molecular ice and liquid"

description: This is the source data shown in Figs. 1, 3A, and Figs. S2, S3, S4, S5, S6, S7, and S12 for the article "Superionic water with stiffness between molecular ice and liquid" by Kimura and Murakami.
DOI: 10.5281/zenodo.6331380
title: Geopotential-based Multivariate MJO Index (GMM Index)

description: This repository contains the data of the Geopotential-based Multivariate MJO Index (GMM Index).<br> <strong>About the GMM Index</strong> The GMM index is an RMM-like index, derived from outgoing longwave radiation (OLR), 200hPa and 850hPa zonal wind, that can be extended to the pre-satellite era where satellite-based OLR data is unavailable. The short record of OLR observation limits the data length of the RMM index (Wheeler and Hendon, 2004), hindering the research of long-term variability of the Madden-Julian Oscillation (MJO) in the past century. And, the GMM index, which can be extended to the early 20th century (and even to the 19th century), is proposed to solve the problem. The construction method of the GMM index is the same as the RMM index, except that: the OLR input is derived from upper-tropospheric geopotential data, based on the intrinsic relationship between MJO convection and upper-level geopotential (see Leung and Qian, 2017); the OLR, 200hPa and 850hPa zonal wind input of the GMM index are bandpass-filtered anomalies. Please refer to Leung et al. (2022) for more detail about the calculation procedure and the theory behind it. <strong>Files</strong> GMM index derived based on the ERA-Interim reanalysis Location: data/gmm_index_erai.csv Temporal coverage: 1979–2013 GMM index derived based on the ERA-20C reanalysis Location: data/gmm_index_era20c.csv Temporal coverage: 1900–2010 GMM index derived based on the NOAA-20CRv3 reanalysis Link: (to be uploaded) Temporal coverage: 1836–2015 <strong>Citation</strong> If you use the GMM index in a publication or for any other purposes, please cite Leung, J.CH., Qian, W., Zhang, P. et al. Geopotential-based Multivariate MJO Index: extending RMM-like indices to pre-satellite era. Clim Dyn (2022). https://doi.org/10.1007/s00382-022-06142-2 Zenodo archive: https://doi.org/10.5281/zenodo.6331379 <strong>References</strong> Leung, J.CH., Qian, W., Zhang, P. et al. Geopotential-based Multivariate MJO Index: extending RMM-like indices to pre-satellite era. Clim Dyn (2022). https://doi.org/10.1007/s00382-022-06142-2 Leung, J.CH., Qian, W. Monitoring the Madden–Julian oscillation with geopotential height. Clim Dyn 49, 1981–2006 (2017). https://doi.org/10.1007/s00382-016-3431-x Wheeler, M.C., Hendon, H.H. An All-Season Real-Time Multivariate MJO Index: Development of an Index for Monitoring and Prediction. Mon Weather Rev 132:1917–1932 (2004). https://journals.ametsoc.org/view/journals/mwre/132/8/1520-0493_2004_132_1917_aarmmi_2.0.co_2.xml <strong>Contact</strong> If you have any questions about the data, feel free to contact Dr. Jeremy Leung (chleung@pku.edu.cn or liangzx@gd121.cn).
description: {"references": ["Leung, J.CH. et al. Geopotential-based Multivariate MJO Index: extending RMM-like indices to pre-satellite era. Clim Dyn (2022). https://doi.org/10.1007/s00382-022-06142-2"]}