cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7607192
title: Impact of climate warming on phenological asynchrony of plankton dynamics across Europe

description: This dataset includes all data as well as the scripts used to produce figures from both the main text and the supporting information from Gronchi et al. (2023) "Impact of climate warming on phenological asynchrony of plankton dynamics across Europe". Detailed description: Data_TDM_Validation.csv: file containing observed versus simulated timings of TDM for 18 lakes of western Europe. result_full_reference.mat and result_full_const_4C_warming.mat contain the 31-years medians of the lake phenologies for the 16 different simulated lake types and for both the reference and the constant +4°C climate scenario. The two scripts Plot_Fig_1_and_3.m and Plot_Supplement.m are responsible for the production, after treatment of the data, of figures 1 and 3 from the main text and most of the supplement respectively. These scripts include a detailed description of the variables used for producing these figures. Figures 2, 4, S1, S2 and S3 were done with R. For each of these figures there is their respective R script and the reorganized dataset used to produce them. Because of the large size of the inputs and outputs files analyzed in this study we only included the scripts (Output_Generation_Example.m and lakelayerdepth_1.m) and the data ( Meteo_Era_52.5_13.5.dat; Z30_ST00_KW06_52.5_13.5.LST Z30_ST00_KW06_52.5_13.5.Temp) necessary for generation of the phenology medians for one specific lake type at the reference scenario as example. The full dataset (~4To) can be given upon request. Ackowledgment: This product includes color specifications and designs developed by Cynthia Brewer (http://colorbrewer.org/)
DOI: 10.5281/zenodo.7622996
title: Data for "Convective Organization and Dry Tropical Cyclones in Direct Numerical Simulations of Idealized Fluid Setups"

description: Supporting data and analysis scripts for work contained in the manuscript Velez-Pardo, Martin & Cronin, Timothy W. (2023) Convective Organization and Dry Tropical Cyclones in Direct Numerical Simulations of Idealized Fluid Setups. README and scripts for running simulations and for data post-processing are found in README_and_scripts.zip. Data obtained using supercomputer Cheyenne (doi:10.5065/D6RX99HX) provided by NCAR's Computational and Information Systems Laboratory (CISL), sponsored by the National Science Foundation.
DOI: 10.5281/zenodo.7622997
title: Data for "Convective Organization and Dry Tropical Cyclones in Direct Numerical Simulations of Idealized Fluid Setups"

description: Supporting data and analysis scripts for work contained in the manuscript Velez-Pardo, Martin & Cronin, Timothy W. (2023) Convective Organization and Dry Tropical Cyclones in Direct Numerical Simulations of Idealized Fluid Setups. README and scripts for running simulations and for data post-processing are found in README_and_scripts.zip. Data obtained using supercomputer Cheyenne (doi:10.5065/D6RX99HX) provided by NCAR's Computational and Information Systems Laboratory (CISL), sponsored by the National Science Foundation.
DOI: 10.5281/zenodo.7542461
title: Impact of climate warming on phenological asynchrony of plankton dynamics across Europe

description: This dataset includes all data as well as the scripts used to produce figures from both the main text and the supporting information from Gronchi et al. (2023) "Impact of climate warming on phenological asynchrony of plankton dynamics across Europe". Detailed description: Data_TDM_Validation.csv: file containing observed versus simulated timings of TDM for 18 lakes of western Europe. result_full_reference.mat and result_full_const_4C_warming.mat contain the 31-years medians of the lake phenologies for the 16 different simulated lake types and for both the reference and the constant +4°C climate scenario. The two scripts Plot_Fig_1_and_3.m and Plot_Supplement.m are responsible for the production, after treatment of the data, of figures 1 and 3 from the main text and most of the supplement respectively. These scripts include a detailed description of the variables used for producing these figures. Figures 2, 4, S1, S2 and S3 were done with R. For each of these figures there is their respective R script and the reorganized dataset used to produce them. Because of the large size of the inputs and outputs files analyzed in this study we only included the scripts (Output_Generation_Example.m and lakelayerdepth_1.m) and the data ( Meteo_Era_52.5_13.5.dat; Z30_ST00_KW06_52.5_13.5.LST Z30_ST00_KW06_52.5_13.5.Temp) necessary for generation of the phenology medians for one specific lake type at the reference scenario as example. The full dataset (~4To) can be given upon request. Ackowledgment: This product includes color specifications and designs developed by Cynthia Brewer (http://colorbrewer.org/)
DOI: 10.5281/zenodo.7627042
title: Impact of climate warming on phenological asynchrony of plankton dynamics across Europe

description: This dataset includes all data as well as the scripts used to produce figures from both the main text and the supporting information from Gronchi et al. (2023) "Impact of climate warming on phenological asynchrony of plankton dynamics across Europe". Detailed description: Data_TDM_Validation.csv: file containing observed versus simulated timings of TDM for 18 lakes of western Europe. result_full_reference.mat and result_full_const_4C_warming.mat contain the 31-years medians of the lake phenologies for the 16 different simulated lake types and for both the reference and the constant +4°C climate scenario. The two scripts Plot_Fig_1_and_3.m and Plot_Supplement.m are responsible for the production, after treatment of the data, of figures 1 and 3 from the main text and most of the supplement respectively. These scripts include a detailed description of the variables used for producing these figures. Figures 2, 4, S1, S2 and S3 were done with R. For each of these figures there is their respective R script and the reorganized dataset used to produce them. Because of the large size of the inputs and outputs files analyzed in this study we only included the scripts (Output_Generation_Example.m and lakelayerdepth_1.m) and the data ( Meteo_Era_52.5_13.5.dat; Z30_ST00_KW06_52.5_13.5.LST Z30_ST00_KW06_52.5_13.5.Temp) necessary for generation of the phenology medians for one specific lake type at the reference scenario as example. The full dataset (~4To) can be given upon request. Ackowledgment: This product includes color specifications and designs developed by Cynthia Brewer (http://colorbrewer.org/)
DOI: 10.6084/m9.figshare.22064786.v1
title: Appendix 2.pdf

description: Lameness
DOI: 10.5281/zenodo.7625812
title: Data Availability Statement (DAS)

description: Data Availability Statement (DAS)
DOI: 10.5281/zenodo.7625811
title: Data Availability Statement (DAS)

description: Data Availability Statement (DAS)
DOI: 10.5281/zenodo.7626728
title: Data Availability Statement (DAS)

description: Data Availability Statement (DAS)
DOI: 10.6084/m9.figshare.22064786
title: Appendix 2.pdf

description: Lameness
DOI: 10.6084/m9.figshare.22064786.v2
title: Appendix 2.pdf

description: Lameness
DOI: 10.6084/m9.figshare.22064801
title: Appendix 3.pdf

description: Lameness
DOI: 10.6084/m9.figshare.22064801.v1
title: Appendix 3.pdf

description: Lameness
DOI: 10.5281/zenodo.7623482
title: ESM Atlas v0 representative random sample of predicted protein structures

description: A representative random sample of the ESM Atlas v0 dataset introduced in "Evolutionary-scale prediction of atomic level protein structure with a language model.".<br> All predictions can be accessed in the ESM Metagenomic Atlas (https://esmatlas.com) open science resource, released on 2022-11-01.<br> Sample size: 997,405.
DOI: 10.5281/zenodo.7623481
title: ESM Atlas v0 representative random sample of predicted protein structures

description: A representative random sample of the ESM Atlas v0 dataset introduced in "Evolutionary-scale prediction of atomic level protein structure with a language model.".<br> All predictions can be accessed in the ESM Metagenomic Atlas (https://esmatlas.com) open science resource, released on 2022-11-01.<br> Sample size: 997,405.
DOI: 10.5281/zenodo.7623627
title: ESM Atlas v0 random sample of high confidence predicted protein structures

description: A random sample out of the 225M high confidence predictions in the ESM Atlas v0 dataset introduced in "Evolutionary-scale prediction of atomic level protein structure with a language model.".<br> All predictions can be accessed in the ESM Metagenomic Atlas (https://esmatlas.com) open science resource, released on 2022-11-01.<br> High confidence is defined as mean pLDDT &gt; 0.7 and pTM &gt; 0.7 and corresponds to ∼36% of the total 617M proteins folded.<br> This is the random sample used for analysis in the paper as well as visualization on the esmatlas.com Explore page.<br> Sample size: 999,520 based on 999,996 unique randomly sampled IDs and 0.05% missing data in the processing pipeline.
DOI: 10.5281/zenodo.7623626
title: ESM Atlas v0 random sample of high confidence predicted protein structures

description: A random sample out of the 225M high confidence predictions in the ESM Atlas v0 dataset introduced in "Evolutionary-scale prediction of atomic level protein structure with a language model.".<br> All predictions can be accessed in the ESM Metagenomic Atlas (https://esmatlas.com) open science resource, released on 2022-11-01.<br> High confidence is defined as mean pLDDT &gt; 0.7 and pTM &gt; 0.7 and corresponds to ∼36% of the total 617M proteins folded.<br> This is the random sample used for analysis in the paper as well as visualization on the esmatlas.com Explore page.<br> Sample size: 999,520 based on 999,996 unique randomly sampled IDs and 0.05% missing data in the processing pipeline.
DOI: 10.18112/openneuro.ds004478.v1.0.1
title: Neural activity induced by sensory stimulation can drive large-scale cerebrospinal fluid flow during wakefulness in humans
DOI: 10.5517/ccdc.csd.cc1k9m6p
title: CCDC 1439212: Experimental Crystal Structure Determination

description: Related Article: Dongmei Liu, Gaoyun Dong, Xiong Wang, Fengmei Nie, Xia Li|2020|CrystEngComm|22|7877|doi:10.1039/D0CE01256D
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.cc29zlpl
title: CCDC 2144570: Experimental Crystal Structure Determination

description: Related Article: Badri Z. Momeni, S. Farzad Hosseini, Jan Janczak|2023|J.Mol.Struct.|1279|135038|doi:10.1016/j.molstruc.2023.135038
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.cc2bsqc7
title: CCDC 2168709: Experimental Crystal Structure Determination

description: Related Article: Yi Zhang, Yuhang Liu, Pran Gopal Karmaker, Lilei Zhang, Kaijing Yang, Lianfang Chen, Xiupei Yang|2023|ACS Applied Materials and Interfaces|15|6177|doi:10.1021/acsami.2c19766
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: 10.5281/zenodo.7627119
title: Supporting data for Macrophage phagocytosis of SARS-CoV-2 infected cells mediates potent plasmacytoid dendritic cell activation

description: Supporting data for the presented data of the study entitled "Macrophage phagocytosis of SARS-CoV-2 infected cells mediates potent plasmacytoid dendritic cell activation"
DOI: 10.5281/zenodo.7627118
title: Supporting data for Macrophage phagocytosis of SARS-CoV-2 infected cells mediates potent plasmacytoid dendritic cell activation

description: Supporting data for the presented data of the study entitled "Macrophage phagocytosis of SARS-CoV-2 infected cells mediates potent plasmacytoid dendritic cell activation"
DOI: 10.5281/zenodo.4300911
title: Tumor MHC Class I Expression Associates with Interleukin-2 Response in Melanoma

description: The processed multiplexed IF data and sample ID corresponding to the raw data in record (https://zenodo.org/record/4300912#.Y-WGBuzMKY-). All code used to produce the results of this study are available at https://github.com/cBio-MSKCC/Halo_Melanoma_IL2.
description: Processed data. See original upload for raw files: https://zenodo.org/record/4300912#.Y-WGBuzMKY-
DOI: 10.5281/zenodo.7627120
title: Tumor MHC Class I Expression Associates with Interleukin-2 Response in Melanoma

description: The processed multiplexed IF data and sample ID corresponding to the raw data in record (https://zenodo.org/record/4300912#.Y-WGBuzMKY-). All code used to produce the results of this study are available at https://github.com/cBio-MSKCC/Halo_Melanoma_IL2.
description: Processed data. See original upload for raw files: https://zenodo.org/record/4300912#.Y-WGBuzMKY-
DOI: 10.5281/zenodo.4300912
title: Tumor MHC Class I Expression Associates with Interleukin-2 Response in Melanoma

description: Raw data (multiplexed IF and NanoString) supporting the findings of the publication "Tumor MHC Class I Expression Associates with Intralesional IL2 Response in Melanoma." The processed multiplexed IF data and sample ID key are available at (https://zenodo.org/record/7627120). All code used to produce the results of this study are available at https://github.com/cBio-MSKCC/Halo_Melanoma_IL2.
DOI: 10.6084/m9.figshare.22064777
title: regions.txt

description: Geometries of Tel Aviv, Haifa and Jerusalem
DOI: 10.18112/openneuro.ds004493.v1.0.0
title: Neural activity induced by sensory stimulation can drive large-scale cerebrospinal fluid flow during wakefulness in humans
DOI: 10.18112/openneuro.ds004493.v1.0.1
title: Neural activity induced by sensory stimulation can drive large-scale cerebrospinal fluid flow during wakefulness in humans
DOI: 10.6084/m9.figshare.22064735
title: HegeL.csv

description: HeGeL dataset - Place descriptions in Hebrew
DOI: 10.6084/m9.figshare.22064735.v2
title: HegeL.csv

description: HeGeL dataset - Place descriptions in Hebrew
DOI: 10.6084/m9.figshare.22064735.v1
title: HegeL.csv

description: HeGeL dataset - Place descriptions in Hebrew
DOI: 10.6084/m9.figshare.21743984
title: Peucedanum praeruptorum genome assembly and gene annotations

description: The whole genome sequencing of Peucedanum praeruptorum
DOI: 10.5281/zenodo.7620773
title: MUSES Leaf Area Index (LAI) 8-Day 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 LAI product at 500m spatial resolution and 8-day 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 2014. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2013</strong></em><strong>, </strong>and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2015</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2014 Spatial Resolution: 500m Temporal Resolution: 8 days 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.7620772
title: MUSES Leaf Area Index (LAI) 8-Day 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 LAI product at 500m spatial resolution and 8-day 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 2014. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2013</strong></em><strong>, </strong>and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2015</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2014 Spatial Resolution: 500m Temporal Resolution: 8 days 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: 10.5517/ccdc.csd.cc24n8dh
title: CCDC 1985686: Experimental Crystal Structure Determination

description: Related Article: Sampath Thavaselvan, Kanniyappan Parthasarathy|2020|Org.Lett.|22|3810|doi:10.1021/acs.orglett.0c01055
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.cc24bzj0
title: CCDC 1976762: Experimental Crystal Structure Determination

description: Related Article: Qi Xu, Chu Wang, Xuebo Chen, Ying Wang, Zhenyao Shen, Hua Jiang|2022|Org.Chem.Front.|9|4981|doi:10.1039/D2QO01096H
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.cc2c7wvc
title: CCDC 2182333: Experimental Crystal Structure Determination

description: Related Article: Theodore Gerard, Yang Wei, Erwin Weerawardhana, Adriana Lugosan, Matthias Zeller, Diane A. Dickie, Pengfei Li, Wei-Tsung Lee|2023|ACS Omega|8|3835|doi:10.1021/acsomega.2c06058
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.cc2c7wwd
title: CCDC 2182334: Experimental Crystal Structure Determination

description: Related Article: Theodore Gerard, Yang Wei, Erwin Weerawardhana, Adriana Lugosan, Matthias Zeller, Diane A. Dickie, Pengfei Li, Wei-Tsung Lee|2023|ACS Omega|8|3835|doi:10.1021/acsomega.2c06058
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.cc2c7wxf
title: CCDC 2182335: Experimental Crystal Structure Determination

description: Related Article: Theodore Gerard, Yang Wei, Erwin Weerawardhana, Adriana Lugosan, Matthias Zeller, Diane A. Dickie, Pengfei Li, Wei-Tsung Lee|2023|ACS Omega|8|3835|doi:10.1021/acsomega.2c06058
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.