cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25423/cmcc/medsea_analysisforecast_wav_006_017_medwam4
title: Mediterranean Sea Waves Analysis and Forecast (Copernicus Marine Service MED-Waves, MEDWAΜ4 system)
title: MEDSEA_ANALYSISFORECAST_WAV_006_017

description: The Mediterranean Sea Waves Analysis and Forecast system is composed by hourly wave parameters at 1/24º horizontal resolution covering the Mediterranean Sea and extending up to 18.125W into the Atlantic Ocean. Med-Waves modelling system is based on WAM Cycle 6 wave model and resolves the prognostic part of the wave spectrum with 24 directional and 32 logarithmically distributed frequency bins, while model solutions are corrected by an optimal interpolation data assimilation scheme of along track satellite significant wave height observations. The atmospheric forcing is provided by the operational ECMWF model. The wave model is forced with hourly averaged surface currents and sea level obtained from CMS Med MFC at 1/24° resolution. The wave system includes 2 forecast cycles providing Mediterranean wave analysis and 10 days of wave forecasts.
DOI: 10.5517/ccdc.csd.cc2c73qg
title: CCDC 2181585: Experimental Crystal Structure Determination

description: Related Article: Shuyuan Mo, Zhangyan Huang, Zi Ye, Jie Yin, Sitian Zhang, Jun Yao, Yaxin Zhang, Zhihong Huang, Hanxiao Zeng, Zhengxi Hu, Jianping Wang, Yonghui Zhang|2023|Phytochemistry|212|113722|doi:10.1016/j.phytochem.2023.113722
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.cc2cx97v
title: CCDC 2201937: Experimental Crystal Structure Determination

description: Related Article: Fang Liu, Jin-Yang Li, Cai-Bei Han, Jun-Hao Wang, Si-Yi Tong, Xue-Kun Wang, Yan-Tuan Li, Wen-Jun Sun|2023|Eur.J.Pharm.Sci.|187|106469|doi:10.1016/j.ejps.2023.106469
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.cc2f8l8l
title: CCDC 2242579: Experimental Crystal Structure Determination

description: Related Article: Sajan Pradhan, Fahimeh Mohammadi, Jean Bouffard|2023|J.Am.Chem.Soc.|145|12214|doi:10.1021/jacs.3c02314
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: 10.25423/cmcc/medsea_analysis_forecast_wav_006_017
title: Mediterranean Sea Waves Analysis and Forecast (CMEMS MED-Waves)
title: MEDSEA_ANALYSIS_FORECAST_WAV_006_017

description: MEDSEA_ANALYSIS_FORECAST_WAV_006_017 is the nominal product of the Mediterranean Sea Waves Forecasting system, composed by hourly wave parameters at 1/24º horizontal resolution covering the Mediterranean Sea and extending up to -18.125W into the Atlantic Ocean. The waves forecast component (Med-Waves system) is a wave model based on the upgraded WAM Cycle 4.6.2. In the wave model the continuous wave spectrum is approximated by means of step functions which are constant in a frequency-direction bin. The Med-Waves modelling system resolves the prognostic part of the wave spectrum with 24 directional and 32 logarithmically distributed frequency bins and the model solutions are corrected by an optimal interpolation data assimilation scheme of all available along track satellite significant wave height observations. The Med-Waves set up includes a coarse grid domain with a resolution of 1/6° covering the North Atlantic Ocean from 75° W to 10° E and from 70° N to 10° S and a nested fine grid domain with a resolution of 1/24° covering the Mediterranean Sea from 18.125° W to 36.2917° E and from 30.1875° N to 45.9792° N. The system provides a Mediterranean wave analysis and 10 days Mediterranean wave forecasts updated daily.
DOI: 10.5281/zenodo.8027806
title: VRGestures: Controller and Hand Gesture Datasets for Virtual Reality

description: 15 VR Controller gestures 11 one-handed VR Hand Gestures for each hand 2 two-handed VR Hand Gestures
DOI: 10.5281/zenodo.8027807
title: VRGestures: Controller and Hand Gesture Datasets for Virtual Reality

description: 15 VR Controller gestures 11 one-handed VR Hand Gestures for each hand 2 two-handed VR Hand Gestures
DOI: 10.18709/perscido.2023.06.ds392
title: The optical shape of natural snow computed with a ray-tracing model. Data from Robledano et al.: Unraveling the optical shape of snow, 2023.

description: This dataset contains the simulation results computed with the RSRT model over natural snow samples. 

It contains all the (B, gG) optical shape parameters results for 33 snow samples over the 400 - 1400 nm wavelength range, as well as other relevant results and the codes needed to reproduce the figures in the associated paper:  "Robledano, A., Picard, G., Dumont, M., Flin, F., Arnaud, L.,  and Libois, Q.: Unraveling the optical shape of snow, Nat. Comm., 2023".

It contains as well the generated geometric shapes (spheres, cubes and a convex shape) used in the associated paper.
DOI: 10.5281/zenodo.7963641
title: Fungicide-free management of Alternaria leaf blotch and fruit spot on apple indicates Alternaria spp. as secondary colonizer

description: Raw data of the article: <strong>"Fungicide-free management of Alternaria leaf blotch and fruit spot on apple indicates <em>Alternaria spp</em>. as secondary colonizer"</strong>, DOI: 10.1038/s41598-023-35448-2 --&gt;see Read Me file
DOI: 10.5281/zenodo.7963642
title: Fungicide-free management of Alternaria leaf blotch and fruit spot on apple indicates Alternaria spp. as secondary colonizer

description: Raw data of the article: <strong>"Fungicide-free management of Alternaria leaf blotch and fruit spot on apple indicates <em>Alternaria spp</em>. as secondary colonizer"</strong>, DOI: 10.1038/s41598-023-35448-2 Leave_blotch_Fruit_spot_with_S_mix: data of the field experiment with the effect of sulfate-mix on leaf blotch and fruit spots ("mean_area" refers to % necrotic area of the leaves, "mean_spot" refers to the mean number of fruit spots)
DOI: 10.5281/zenodo.8037057
title: Fungicide-free management of Alternaria leaf blotch and fruit spot on apple indicates Alternaria spp. as secondary colonizer

description: Raw data of the article: <strong>"Fungicide-free management of Alternaria leaf blotch and fruit spot on apple indicates <em>Alternaria spp</em>. as secondary colonizer"</strong>, DOI: 10.1038/s41598-023-35448-2 --&gt;see Read Me file
DOI: 10.15129/c0f35a36-4f34-4548-b710-30ed929614a6
title: Data for: "Filler-induced heterogeneous nucleation of polymer crystals investigated by molecular dynamics simulations"

description: All of the simulations for this publication were performed with LAMMPS version lammps/intel-2020.4/29Sep2021 This dataset contains the input files for the publication "Filler-induced heterogeneous nucleation of polymer crystals investigated by molecular dynamics simulations" The folder structure splits into - two_wall_systems (contains surfaces (wall) potentials top and bottom of the simulation cell) and - bulk_systems (fully periodic without surfaces). Within each of these, there are "cooling" and "heating" subfolders, and these are further subdivided into cooling/heating rates. The subfolders include one or more of the following: 0.25Gamma0, 0.5Gamma0, Gamma0, 2Gamma0, 10Gamma0 which correspond to the rates in the paper, labelled similarly. The bulk_systems were only performed at the Gamma0 cooling and heating rate. In the bulk_systems, the folders are further subdivided indicating different chain stiffnesses, including - k1 (chain stiffness of ktheta=1) - k2 (chain stiffness of ktheta=2) - k2.4 (chain stiffness of ktheta=2.4) - k3 (chain stiffness of ktheta=3) - k4 (chain stiffness of ktheta=4) In the two_wall_systems, these folders are further subdivided indicating the chain stiffness and the wall interaction strength, for example: - k2.4_wall090 (chain stiffness of ktheta=2.4, wall strength epsilon_w=0.9) - k2.4_wall180 (chain stiffness of ktheta=2.4, wall strength epsilon_w=1.8) - k2.4_wall270 (chain stiffness of ktheta=2.4, wall strength epsilon_w=2.7) Note that for the surface simulations only systems with a chain stiffness of ktheta-2.4 were simulated. All simulations only require two files to run: 1) input file, named in.* 2) molecule template file, named *.txt or a data file named data.* The input file details simulation box dimensions, geometry and forcefield potentials as well as which ensemble to perform the molecular dynamics simulations in. The molecule template file details the coordinates of all beads that make up a single polymer chain. A data file will be generated at the end of the simulation. A lammps trajectory file will also be produced using unwrapped coordinates. For example, to find the files corresponding to a bulk system with a chain stiffness of k=3, cooling down at a rate of Gamma0, the directory path is: bulk_systems &gt; cooling &gt; Gamma0 &gt; k3 &gt; {chainfinaltest.txt, in.cool} where in.cool is the input file and chainfinaltest.txt is the molecule template file. As a second example, to find the files corresponding to a surface system with a chain stiffness of k=2.4 and a wall strength of 1.8, heating at a rate of 2Gamma0, the directory path is: two_wall_systems &gt; heating &gt; 2Gamma0 &gt; k2.4_wall180 &gt; {data.quench_npt, in.heat} where in.heat is the input file and data.quench_npt is the data file.
DOI: 10.5281/zenodo.7950984
title: Universal Dynamic Fitting of Magnetic Resonance Spectroscopy - Data

description: Original data for the in vivo experiments included in the article Universal Dynamic Fitting of Magnetic Resonance Spectroscopy by William T Clarke, Clémence Ligneul, Michiel Cottaar, Betina Ip and Saad Jbabdi (Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK). This data accompanies the online code repository. Note that this repository contains preprocessed versions of this data already. Two files are included: fmrs_invivo_unprocessed_data.zip - NIfTI-MRS formatted data relating to the fMRS in vivo example in this work. Data is originally from 10.1016/j.neuroimage.2017.04.030. dwmrs_invivo_original_data.zip = matlab .mat data relating tot he dwMRS in vivo example in this work. Data is originally from 10.1016/j.neuroimage.2019.02.046. If you use this data in your work please cite this work (Universal Dynamic Fitting of Magnetic Resonance Spectroscopy) once available and the originating publications.
DOI: 10.5281/zenodo.7950983
title: Universal Dynamic Fitting of Magnetic Resonance Spectroscopy - Data

description: Original data for the in vivo experiments included in the article Universal Dynamic Fitting of Magnetic Resonance Spectroscopy by William T Clarke, Clémence Ligneul, Michiel Cottaar, Betina Ip and Saad Jbabdi (Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK). This data accompanies the online code repository. Note that this repository contains preprocessed versions of this data already. Two files are included: fmrs_invivo_unprocessed_data.zip - NIfTI-MRS formatted data relating to the fMRS in vivo example in this work. Data is originally from 10.1016/j.neuroimage.2017.04.030. dwmrs_invivo_original_data.zip = matlab .mat data relating tot he dwMRS in vivo example in this work. Data is originally from 10.1016/j.neuroimage.2019.02.046. If you use this data in your work please cite this work (Universal Dynamic Fitting of Magnetic Resonance Spectroscopy) once available and the originating publications.
DOI: 10.5281/zenodo.8020620
title: Raw data for the article "Peptide-Hypervalent Iodine Reagent Chimeras: Enabling Peptide Functionalization and Macrocyclization"

description: Raw NMR, HPLC and MS data for the article "Peptide-Hypervalent Iodine Reagent Chimeras: Enabling Peptide Functionalization and Macrocyclization" published in Angewandte Chemie, DOI: https://doi.org/10.1002/anie.202306036 The number of the folders either correspond to compounds numbers in the article or the name of the folder is self-describing. All details concerning conditions and equipment for measurements can be found in the supporting information of the article.
DOI: 10.5281/zenodo.8020619
title: Raw data for the article "Peptide-Hypervalent Iodine Reagent Chimeras: Enabling Peptide Functionalization and Macrocyclization"

description: Raw NMR, HPLC and MS data for the article "Peptide-Hypervalent Iodine Reagent Chimeras: Enabling Peptide Functionalization and Macrocyclization" published in Angewandte Chemie, DOI: https://doi.org/10.1002/anie.202306036 The number of the folders either correspond to compounds numbers in the article or the name of the folder is self-describing. All details concerning conditions and equipment for measurements can be found in the supporting information of the article.
DOI: 10.5281/zenodo.8021086
title: Raw data for the article "Synthesis of Trifluoromethylated Alkenes: Hypervalent Iodine Meets High-Valent Copper"

description: Raw NMR, IR and MS data for the article "Synthesis of Trifluoromethylated Alkenes: Hypervalent Iodine Meets High-Valent Copper" published in Angewandte Chemie, DOI: https://doi.org/10.1002/anie.202306128 The number of the folders either correspond to compounds numbers in the article or the name of the folder is self-describing. All details concerning conditions and equipment for measurements can be found in the supporting information of the article.
DOI: 10.5281/zenodo.8021085
title: Raw data for the article "Synthesis of Trifluoromethylated Alkenes: Hypervalent Iodine Meets High-Valent Copper"

description: Raw NMR, IR and MS data for the article "Synthesis of Trifluoromethylated Alkenes: Hypervalent Iodine Meets High-Valent Copper" published in Angewandte Chemie, DOI: https://doi.org/10.1002/anie.202306128 The number of the folders either correspond to compounds numbers in the article or the name of the folder is self-describing. All details concerning conditions and equipment for measurements can be found in the supporting information of the article.
DOI: 10.34740/kaggle/dsv/5928649
title: Spatiotemporal Wildlife Dataset
DOI: 10.18712/nsd-nsd0512-v4
title: Consumer Expenditure Survey 1987

description: "The Consumer Expenditure Survey 1987" is conducted by Statistics Norway. The data is organized in several sections and contains background information on the household (collected through an initial interview that maps the household composition), detailed consumption in terms of value and quantity (recorded by diary keeping, respectively for household and individuals), the households purchases of durable consumer goods (tangible capital goods, collected by a comprehensive exit interview), consumption of own products and income and taxes. In this survey the log part is especially well documented. This leads to an extensive amount of information on houshold cosumption patterns. The information presented is also used as the basis for calculating the CPI. Through the data collection the households are treated as independent respondents. For this purpose, a household was defined as persons living permanently in common housing with shared costs.
This study is the second of the panel period between 1986-1988.
DOI: 10.6084/m9.figshare.22194535
title: FEEDS database

description: MEROPS database and DIAMOND builded file to use in the FEEDS tools.
DOI: 10.6084/m9.figshare.22194535.v6
title: FEEDS database

description: MEROPS database and DIAMOND builded file to use in the FEEDS tools.
DOI: 10.57967/hf/0767
title: hg
DOI: 10.57967/hf/0765
title: hg
DOI: 10.6084/m9.figshare.23514741
title: <strong>The Nonlinear Relationship Between Financial Stress, Inflation, and Economic Activity: An Empirical Study in an Emerging Economy. Dataset</strong>

description: The dataset represents the joint dynamics of Financial Stress Index (FSI), Consumer Price Index (CPI) calculated and provided by the National Bank of Ukraine (NBU) and Gross Domestic Product (GDP) provided by SSSU for Ukraine. <br> The monthly dataset range is Feb 2004-Feb 2022, the effective balanced range is Jan 2011-Dec 2021. <br> The daily FSI data is aggregated into monthly series as a period average. The CPI series are monthly. The quarterly GDP data is seasonally adjusted and interpolated into monthly data with the use of ARIMA model and cubic spline method accordingly, converted into year-over-year series (dGDP).
DOI: 10.6084/m9.figshare.23514741.v1
title: <strong>The Nonlinear Relationship Between Financial Stress, Inflation, and Economic Activity: An Empirical Study in an Emerging Economy. Dataset</strong>

description: The dataset represents the joint dynamics of Financial Stress Index (FSI), Consumer Price Index (CPI) calculated and provided by the National Bank of Ukraine (NBU) and Gross Domestic Product (GDP) provided by SSSU for Ukraine. <br> The monthly dataset range is Feb 2004-Feb 2022, the effective balanced range is Jan 2011-Dec 2021. <br> The daily FSI data is aggregated into monthly series as a period average. The CPI series are monthly. The quarterly GDP data is seasonally adjusted and interpolated into monthly data with the use of ARIMA model and cubic spline method accordingly, converted into year-over-year series (dGDP).
DOI: 10.6084/m9.figshare.23514777
title: Pre/Post test data.xlsx

description: Pre-/Post- test data of junior primary immersion and non-immersion classes in listening, reading & writing, and speaking & vocabulary.
DOI: 10.6084/m9.figshare.23514051
title: Activation Extending Based on Long-range Dependencies for Weakly Supervised Semantic Segmentation

description: Weakly supervised semantic segmentation (WSSS) principally obtains pseudo-labels based on the class activation maps (CAM) to handle expensive annotation resources. However, CAM easily involves false and local activation due to the the lack of annotation information. This paper suggests weakly supervised learning as semantic information mining to extend object mask. We proposes a novel architecture to mining semantic information by modeling through long-range dependencies from in-sample and inter-sample. Considering the confusion caused by the long-range dependencies, the images are divided into blocks and carried out self-attention operation on the premise of fewer classes to obtain long-range dependencies, to reduce false predictions. Moreover, we perform global to local weighted self-supervised contrastive learning among image blocks, and the local activation of CAM is transferred to different foreground area. Experiments verified that superior semantic details and more reliable pseudo-labels are captured through these suggested modules. Experiments on PASCAL VOC 2012 demonstrated the proposed model achieves 65.2$\%$, 64.9$\%$, and 65.8$\%$ mIoU in train, val, and test sets, which is superior to the comparison baselines.
DOI: 10.6084/m9.figshare.23514051.v2
title: Activation Extending Based on Long-range Dependencies for Weakly Supervised Semantic Segmentation

description: Weakly supervised semantic segmentation (WSSS) principally obtains pseudo-labels based on the class activation maps (CAM) to handle expensive annotation resources. However, CAM easily involves false and local activation due to the the lack of annotation information. This paper suggests weakly supervised learning as semantic information mining to extend object mask. We proposes a novel architecture to mining semantic information by modeling through long-range dependencies from in-sample and inter-sample. Considering the confusion caused by the long-range dependencies, the images are divided into blocks and carried out self-attention operation on the premise of fewer classes to obtain long-range dependencies, to reduce false predictions. Moreover, we perform global to local weighted self-supervised contrastive learning among image blocks, and the local activation of CAM is transferred to different foreground area. Experiments verified that superior semantic details and more reliable pseudo-labels are captured through these suggested modules. Experiments on PASCAL VOC 2012 demonstrated the proposed model achieves 65.2$\%$, 64.9$\%$, and 65.8$\%$ mIoU in train, val, and test sets, which is superior to the comparison baselines.
DOI: 10.6084/m9.figshare.23514051.v1
title: Activation Extending Based on Long-range Dependencies for Weakly Supervised Semantic Segmentation

description: Weakly supervised semantic segmentation (WSSS) principally obtains pseudo-labels based on the class activation maps (CAM) to handle expensive annotation resources. However, CAM easily involves false and local activation due to the the lack of annotation information. This paper suggests weakly supervised learning as semantic information mining to extend object mask. We proposes a novel architecture to mining semantic information by modeling through long-range dependencies from in-sample and inter-sample. Considering the confusion caused by the long-range dependencies, the images are divided into blocks and carried out self-attention operation on the premise of fewer classes to obtain long-range dependencies, to reduce false predictions. Moreover, we perform global to local weighted self-supervised contrastive learning among image blocks, and the local activation of CAM is transferred to different foreground area. Experiments verified that superior semantic details and more reliable pseudo-labels are captured through these suggested modules. Experiments on PASCAL VOC 2012 demonstrated the proposed model achieves 65.2$\%$, 64.9$\%$, and 65.8$\%$ mIoU in train, val, and test sets, which is superior to the comparison baselines.
DOI: 10.5281/zenodo.8037403
title: SciExpeM - 10.5281/zenodo.8037403

description: Balaya, Palani - Energy & Environmental Science, 2008, (1), 645
DOI: 10.5281/zenodo.8037402
title: SciExpeM - 10.5281/zenodo.8037403

description: Balaya, Palani - Energy & Environmental Science, 2008, (1), 645
DOI: 10.5517/ccdc.csd.cc24ls3n
title: CCDC 1984251: Experimental Crystal Structure Determination

description: Related Article: Ahmed H. Tantawy, Xiang-Gao Meng, Hong Jiang, Man-Qun Wang|2020|CSD Communication|||
description: Related Article: Ahmed H. Tantawy, Xiang-Gao Meng, Adel A. Marzouk, Ali Fouad, Ahmed H. Abdelazeem, Bahaa G. M. Youssif, Hong Jiang, Man-Qun Wang|2021|RSC Advances|11|25738|doi:10.1039/D1RA04061H
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: 10.34740/kaggle/dsv/5929089
title: Global Genomic Neurofibromatosis Neoplasia