cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7820822
title: MS-BioGraphs MSA10

description: https://blogs.qub.ac.uk/DIPSA/MS-BioGraphs/ Name MS-BioGraphs – MSA10 URL https://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MSA10 Graph Explanation Vertices represent proteins and each edge represents the sequence similarity between its two endpoints Edge Weighted Yes Directed Yes Number of Vertices 1,757,323,526 Number of Edges 25,236,632,682 Maximum In-Degree 207,279 Maximum Out-Degree 62,060 Minimum Weight 98 Maximum Weight 634,925 Number of Zero In-Degree Vertices 6,437,984 Number of Zero Out-Degree Vertices 9,926,249 Average In-Degree 14.4 Average Out-Degree 14.4 Size of The Largest Weakly Connected Component 15,576,385,764 Number of Weakly Connected Components 628,505,933 Creation Details MS-BioGraphs: Sequency Similarity Graph Datasets Format WebGraph License CC BY-NC-SA QUB IDF 2223-052 DOI 10.5281/zenodo.7820823 Citation <pre> Koohi Esfahani, Mohsen, Boldi, Paolo, Vandierendonck, Hans, Kilpatrick, Peter, Vigna,Sebastiano. (2023). MS-BioGraphs - MSA10. https://doi.org/10.5281/zenodo.7820823. http://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MSA10.</pre> Bibtex <pre> @misc{MS-BioGraphs-MSA10, year = {2023}, author = {Mohsen Koohi Esfahani and Paolo Boldi and Hans Vandierendonck and Peter Kilpatrick and Sebastiano Vigna}, title = {{MS-BioGraphs - MSA10}}, doi = {10.5281/zenodo.7820823}, url = {http://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MSA10}, howpublished= {\url{http://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MSA10}}}</pre>
DOI: 10.5281/zenodo.7820823
title: MS-BioGraphs MSA10

description: https://blogs.qub.ac.uk/DIPSA/MS-BioGraphs/ Name MS-BioGraphs – MSA10 URL https://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MSA10 Graph Explanation Vertices represent proteins and each edge represents the sequence similarity between its two endpoints Edge Weighted Yes Directed Yes Number of Vertices 1,757,323,526 Number of Edges 25,236,632,682 Maximum In-Degree 207,279 Maximum Out-Degree 62,060 Minimum Weight 98 Maximum Weight 634,925 Number of Zero In-Degree Vertices 6,437,984 Number of Zero Out-Degree Vertices 9,926,249 Average In-Degree 14.4 Average Out-Degree 14.4 Size of The Largest Weakly Connected Component 15,576,385,764 Number of Weakly Connected Components 628,505,933 Creation Details MS-BioGraphs: Sequency Similarity Graph Datasets Format WebGraph License CC BY-NC-SA QUB IDF 2223-052 DOI 10.5281/zenodo.7820823 Citation <pre> Koohi Esfahani, Mohsen, Boldi, Paolo, Vandierendonck, Hans, Kilpatrick, Peter, Vigna,Sebastiano. (2023). MS-BioGraphs - MSA10. https://doi.org/10.5281/zenodo.7820823. http://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MSA10.</pre> Bibtex <pre> @misc{MS-BioGraphs-MSA10, year = {2023}, author = {Mohsen Koohi Esfahani and Paolo Boldi and Hans Vandierendonck and Peter Kilpatrick and Sebastiano Vigna}, title = {{MS-BioGraphs - MSA10}}, doi = {10.5281/zenodo.7820823}, url = {http://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MSA10}, howpublished= {\url{http://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MSA10}}}</pre>
DOI: 10.5281/zenodo.7820807
title: MS-BioGraphs MS

description: https://blogs.qub.ac.uk/DIPSA/MS-BioGraphs/ Name MS-BioGraphs – MS URL https://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MS Graph Explanation Vertices represent proteins and each edge represents the sequence similarity between its two endpoints Edge Weighted Yes Directed No Number of Vertices 1,757,323,526 Number of Edges 2,488,069,027,875 Maximum Degree 814,957 Minimum Weight 98 Maximum Weight 634,925 Number of Zero-Degree Vertices 6,437,984 Average Degree 1,415.8 Size of The Largest WCC 2,486,890,448,664 Number of WCC 148,861,367 Creation Details MS-BioGraphs: Sequency Similarity Graph Datasets Format WebGraph License CC BY-NC-SA QUB IDF 2223-052 DOI 10.5281/zenodo.7820808 Citation <pre> Koohi Esfahani, Mohsen, Boldi, Paolo, Vandierendonck, Hans, Kilpatrick, Peter, Vigna,Sebastiano. (2023). MS-BioGraphs - MS. https://doi.org/10.5281/zenodo.7820808. http://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MS.</pre> Bibtex <pre> @misc{MS-BioGraphs-MS, year = {2023}, author = {Mohsen Koohi Esfahani and Paolo Boldi and Hans Vandierendonck and Peter Kilpatrick and Sebastiano Vigna}, title = {{MS-BioGraphs - MS}}, doi = {10.5281/zenodo.7820808}, url = {http://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MS}, howpublished= {\url{http://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MS}}}</pre>
DOI: 10.5281/zenodo.7820808
title: MS-BioGraphs MS

description: https://blogs.qub.ac.uk/DIPSA/MS-BioGraphs/ Name MS-BioGraphs – MS URL https://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MS Graph Explanation Vertices represent proteins and each edge represents the sequence similarity between its two endpoints Edge Weighted Yes Directed No Number of Vertices 1,757,323,526 Number of Edges 2,488,069,027,875 Maximum Degree 814,957 Minimum Weight 98 Maximum Weight 634,925 Number of Zero-Degree Vertices 6,437,984 Average Degree 1,415.8 Size of The Largest WCC 2,486,890,448,664 Number of WCC 148,861,367 Creation Details MS-BioGraphs: Sequency Similarity Graph Datasets Format WebGraph License CC BY-NC-SA QUB IDF 2223-052 DOI 10.5281/zenodo.7820808 Citation <pre> Koohi Esfahani, Mohsen, Boldi, Paolo, Vandierendonck, Hans, Kilpatrick, Peter, Vigna,Sebastiano. (2023). MS-BioGraphs - MS. https://doi.org/10.5281/zenodo.7820808. http://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MS.</pre> Bibtex <pre> @misc{MS-BioGraphs-MS, year = {2023}, author = {Mohsen Koohi Esfahani and Paolo Boldi and Hans Vandierendonck and Peter Kilpatrick and Sebastiano Vigna}, title = {{MS-BioGraphs - MS}}, doi = {10.5281/zenodo.7820808}, url = {http://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MS}, howpublished= {\url{http://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MS}}}</pre>
DOI: 10.5281/zenodo.7820810
title: MS-BioGraphs MSA500

description: https://blogs.qub.ac.uk/DIPSA/MS-BioGraphs/ Name MS-BioGraphs – MSA500 URL https://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MSA500 Graph Explanation Vertices represent proteins and each edge represents the sequence similarity between its two endpoints Edge Weighted Yes Directed Yes Number of Vertices 1,757,323,526 Number of Edges 1,244,904,754,157 Maximum In-Degree 229,442 Maximum Out-Degree 814,461 Minimum Weight 98 Maximum Weight 634,925 Number of Zero In-Degree Vertices 6,437,984 Number of Zero Out-Degree Vertices 16,843,087 Average In-Degree 711.0 Average Out-Degree 715.3 Size of The Largest Weakly Connected Component 1,244,203,865,823 Number of Weakly Connected Components 148,861,367 Creation Details MS-BioGraphs: Sequency Similarity Graph Datasets Format WebGraph License CC BY-NC-SA QUB IDF 2223-052 DOI 10.5281/zenodo.7820810 Citation <pre> Koohi Esfahani, Mohsen, Boldi, Paolo, Vandierendonck, Hans, Kilpatrick, Peter, Vigna,Sebastiano. (2023). MS-BioGraphs - MSA500. https://doi.org/10.5281/zenodo.7820810. http://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MSA500.</pre> Bibtex <pre> @misc{MS-BioGraphs-MSA500, year = {2023}, author = {Mohsen Koohi Esfahani and Paolo Boldi and Hans Vandierendonck and Peter Kilpatrick and Sebastiano Vigna}, title = {{MS-BioGraphs - MSA500}}, doi = {10.5281/zenodo.7820810}, url = {http://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MSA500}, howpublished= {\url{http://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MSA500}}}</pre>
DOI: 10.5281/zenodo.7820809
title: MS-BioGraphs MSA500

description: https://blogs.qub.ac.uk/DIPSA/MS-BioGraphs/ Name MS-BioGraphs – MSA500 URL https://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MSA500 Graph Explanation Vertices represent proteins and each edge represents the sequence similarity between its two endpoints Edge Weighted Yes Directed Yes Number of Vertices 1,757,323,526 Number of Edges 1,244,904,754,157 Maximum In-Degree 229,442 Maximum Out-Degree 814,461 Minimum Weight 98 Maximum Weight 634,925 Number of Zero In-Degree Vertices 6,437,984 Number of Zero Out-Degree Vertices 16,843,087 Average In-Degree 711.0 Average Out-Degree 715.3 Size of The Largest Weakly Connected Component 1,244,203,865,823 Number of Weakly Connected Components 148,861,367 Creation Details MS-BioGraphs: Sequency Similarity Graph Datasets Format WebGraph License CC BY-NC-SA QUB IDF 2223-052 DOI 10.5281/zenodo.7820810 Citation <pre> Koohi Esfahani, Mohsen, Boldi, Paolo, Vandierendonck, Hans, Kilpatrick, Peter, Vigna,Sebastiano. (2023). MS-BioGraphs - MSA500. https://doi.org/10.5281/zenodo.7820810. http://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MSA500.</pre> Bibtex <pre> @misc{MS-BioGraphs-MSA500, year = {2023}, author = {Mohsen Koohi Esfahani and Paolo Boldi and Hans Vandierendonck and Peter Kilpatrick and Sebastiano Vigna}, title = {{MS-BioGraphs - MSA500}}, doi = {10.5281/zenodo.7820810}, url = {http://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MSA500}, howpublished= {\url{http://blogs.qub.ac.uk/DIPSA/MS-BioGraphs-MSA500}}}</pre>
DOI: 10.5281/zenodo.8299865
title: Data for "Using Convolutional Neural Network to Emulate Seasonal Tropical Cyclone Activity"

description: The trained 600-member ensemble convolutional neural networks (CNNs) for seasonal tropical cyclone (TC) activity to allow future studies. Please refer Fu et al. (2023; <em>Using Convolutional Neural Network to Emulate Seasonal Tropical Cyclone Activity</em>) for more details.
DOI: 10.25581/spruce.107/1996808
title: SPRUCE Ground Observations of Phenology in Experimental Plots, 2022

description: This data set consists of one comma separated (*.csv) file containing phenological transition dates, as derived from direct observations of vegetative and reproductive phenology recorded by a human observer, from the SPRUCE experiment during 2022 (2022-03-28 to 2021-11-17), the seventh full year of whole-ecosystem warming (Hanson et al. 2017). Both spring and autumn phenological events are included. Since April 2016, human observers have been directly tracking the phenology of both woody and herbaceous species on a weekly schedule within the SPRUCE experimental chambers, these data are reported in annual ground observations data sets (see Related Data Sets). The observed date reported here is the first survey date in 2022 on which an event/phenophase was definitively observed. This data set also contains a companion file in HTML (*.html) containing figures showing the relationship between the day of year and temperature treatment for different phenological phases by species for 2022.
DOI: 10.25581/spruce.108/1996813
title: SPRUCE Vegetation Phenology in Experimental Plots from Phenocam Imagery, 2015-2022

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 2023, with start- and end-of-season phenological transition dates derived through the end of autumn 2022. 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). This data set consists of three sets of data files: 3-day summary product files: One file for each camera and each ROI (i.e. vegetation type), characterizing vegetation color at a 3-day time step Contains 35 files in *.csv format inside a compressed (*.zip) file. Transition date file: Estimates “greenness rising” (spring) and “greenness falling” (autumn) transition dates derived from the smoothed daily green chromatic coordinate (GCC) values, for each camera and each ROI (i.e. vegetation type) Contains one file in *.csv format Snow flag files: Indicate days with snow on trees or snow on ground for each experimental enclosure Contains two files in *.csv format, one for snow on trees and one for snow on ground This data set consists of two sets of companion files: Accompanying HTML files show the 90th quantiles of the mean GCC plotted together with transition dates for each vegetation type and plot. Contains three files in HTML format, one for each vegetation type One additional file in HTML format with the transition dates plotted for each vegetation type, by year R files for processing Phenocam files and flags. Contains five files in R file (*.R) format in one compressed (*.zip) file User Note: All imagery is posted in near-real time to the PhenoCam Project web page (http://phenocam.sr.unh.edu/), where it is publicly available. Scroll to “spruce” in the Gallery or link directly to the 29 SPRUCE cameras at https://tinyurl.com/y7z5mau7. The data reported here are based on the complete camera record from SPRUCE and supersedes the previously released phenocam datasets (see Related Data Sets). The estimated transition dates for previously released datasets may differ slightly (in most cases, by ±3 days or less), because following standard PhenoCam processing protocols (Richardson et al. 2018, Scientific Data), smoothing and interpolation, outlier removal, and transition date estimation are always conducted using the full data record.
DOI: 10.5439/1996906
title: noaaaos (b0)

description: The aerosol observing system (AOS) is the primary platform for in situ aerosol measurements at Earth’s surface. Each AOS has a different complement of instruments, which are selected to ensure the best measurements at each deployment site. The AOS measures aerosol optical properties to better understand how particles interact with solar radiation and influence the Earth's radiation balance. These measurements are useful for calculating parameters used in radiative forcing calculations, such as the aerosol single scattering albedo, asymmetry parameter, mass scattering efficiency, and hygroscopic growth. Measurements made with the AOS form a long-term record at the ARM fixed sites. Shorter measurement records are available from mobile deployments in a wide variety of geographical regions. These measurements are valuable for: identifying long-term changes in aerosol properties identifying atmospheric processes controlling aerosol life cycle and their influence on clouds providing experimental data for developing and testing atmospheric climate models.
DOI: 10.5439/1996907
title: qmeaeriprof (c1)

description: qmeaeriprof
DOI: 10.5439/1996908
title: aeriprof (c1)

description: aeriprof
DOI: 10.5439/1996909
title: rl (a0)

description: The Raman lidar (RL) is an active, ground-based, laser remote-sensing instrument that provides height- and time-resolved measurements of water-vapor mixing ratio, temperature, aerosol, and cloud optical properties. The RL operates in the ultraviolet (UV) and is sensitive to both molecular and aerosol backscatter. The RL works by transmitting short pulses of UV laser light into the atmosphere. As the light propagates, a small fraction of the light energy is scattered back to the lidar transceiver where it is collected and recorded as a time-resolved signal. From the delay between the outgoing pulse and the backscattered signal, the instrument infers the distance to the scattering volume. As the name implies, the RL makes use of the Raman effect in which light is inelastically scattered by atmospheric N2, O2, and H2O molecules. The ARM RL uses a number of narrow-band detection channels specifically tuned to sense the Raman backscatter from these molecules. Several other detection channels are configured to measure elastically backscattered light from atmospheric aerosol. The raw signals from these detection channels are combined and processed to yield measurements of water vapor mixing ratio, temperature, aerosol backscatter coefficient, extinction, and depolarization ratio.
DOI: 10.5439/1996910
title: mpl (a1)

description: The micropulse lidar (MPL) is a ground-based, optical, remote-sensing system designed primarily to determine the altitude of clouds; however, it is also used for detection of atmospheric aerosols. The physical principle is the same as for radar. Pulses of energy are transmitted into the atmosphere; the energy scattered back to the transceiver is collected and measured as a time-resolved signal, thereby detecting clouds and aerosols in real time. From the time delay between each outgoing pulse and the backscattered signal, the distance to the scatterer is inferred. Post-processing of the lidar return characterizes the extent and properties of aerosols or other particles in a region.
DOI: 10.5439/1996911
title: mplnor1camp (c1)

description: mplnor1camp
DOI: 10.5439/1996912
title: irt10m (b1)

description: The infrared thermometer (IRT) is a ground-based radiation pyrometer that measures the equivalent blackbody brightness temperature of the scene in its field of view. The downwelling version has a narrow field of view for measuring sky temperature and detecting clouds. The upwelling version has a wide field of view for measuring the narrowband radiating temperature of the ground surface.
DOI: 10.5439/1996913
title: qmeaerimeansss (c1)

description: qmeaerimeansss
DOI: 10.5439/1996914
title: qmeaerilblss (c1)

description: qmeaerilblss
DOI: 10.5439/1996915
title: aerilbldiffss (c1)

description: aerilbldiffss
DOI: 10.15473/1997004
title: Virtual Flow Solver - Geophysics: A 3D Incompressible Navier-Stokes Solver

description: Virtual Flow Solver - Geophysics (VFS-Geophysics) is a three-dimensional (3D) incompressible Navier-Stokes solver based on the Curvilinear Immersed Boundary (CURVIB) method. The CURVIB is a sharp interface type of immersed boundary (IB) method that enables the simulation of fluid flows in the presence of geometrically complex moving bodies. The CURVIB method can be applied to wind/MHK turbine simulations and energy applications. VFS-Geophysics is the result of many years of research work by several graduate students, post-docs, and research associates that have been involved in the Computational Hydrodynamics and Biofluids Laboratory directed by Professor Fotis Sotiropoulos. The preparation of the present manual has been supported by the U.S. Department of Energy (DE-EE 0005482).
DOI: 10.57841/casdc.0001995
title: 微动频率对钢丝拉扭复合微动腐蚀疲劳行为影响研究
DOI: 10.57841/casdc.0001996
title: 双尖槽干气密封特性对比研究
DOI: 10.5517/ccdc.csd.cc2ckb8k
title: CCDC 2191398: Experimental Crystal Structure Determination

description: Related Article: Jesse L. Peltier, Melinda R. Serrato, Valentin Thery, Jacques Pecaut, Eder Tomás-Mendivil, Guy Bertrand, Rodolphe Jazzar, David Martin|2022|Chem.Commun.|59|595|doi:10.1039/D2CC05404C
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.cc2gxnrt
title: CCDC 2291667: Experimental Crystal Structure Determination

description: Related Article: Mihai Răducă|2023|CSD Communication|||
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: 10.14457/nida.the.1997.13
title: สถานภาพทางด้านเศรษฐกิจของแรงงานไทยหลังกลับจากการทำงานต่างประเทศ : ศึกษาเปรียบเทียบตามประสบการณ์ประเทศที่ไป /
DOI: 10.57841/casdc.0001997
title: 钢-聚四氟乙烯摩擦界面的摩擦起电行为
DOI: 10.5281/zenodo.8299933
title: SBB-2023-Data

description: Supplementary data for research article "Divergent edaphic-climatic controls on soil carbon decomposition rate and its transformed formats"
DOI: 10.5281/zenodo.8299932
title: SBB-2023-Data

description: Supplementary data for research article "Divergent edaphic-climatic controls on soil carbon decomposition rate and its transformed formats"
DOI: 10.6084/m9.figshare.24024948
title: Time to experience Biofield Sensations Between Hands - A Randomised Control Trial

description: Duration and time taken to first experience biofield sensations between hands is recorded.
DOI: 10.57841/casdc.0001998
title: 表面织构与离子液体润滑组合体系的摩擦学性能研究
DOI: 10.5281/zenodo.8226681
title: ELITE emissivity: global land 1km 8-day BBE at SIN Grid (2010)

description: The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (https://elite.bnu.edu.cn). This dataset is the ELITE global land BBE product at 1km spatial resolution and 8-day temporal resolution. It is provided on a Sinusoidal grid and ranges from 2000 to 2015 (continuously updated). This dataset was generated from the Moderate Resolution Imaging Spectrometer (MODIS) data using the algorithms developed by Cheng et al. (2014; 2016). This is the ELITE BBE product in 2010. Please <strong><em>click here</em></strong> to download the ELITE BBE product in 2009 and <strong><em>click here</em></strong> to download the ELITE LST product in 2011. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2010 Spatial Resolution: 1 km Temporal Resolution: 8-day Data Format: HDF Scale: 0.0001 <strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong> Cheng, J., & Liang, S. (2014). Estimating the broadband longwave emissivity of global bare soil from the MODIS shortwave albedo product.<em> Journal of Geophysical Research Atmospheres</em>, 119(2), 614-634. Cheng, J., Liang, S., Verhoef, W., Shi, S., & Liu, Q. (2016). Estimating the Hemispherical Broadband Longwave Emissivity of Global Vegetated Surfaces Using a Radiative Transfer Model. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54(2), 905-917. If you have any questions, please contact Prof. Jie Cheng (eliteqrs@126.com).