cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24270154.v3
title: <b>Segmentation Living Image</b> data

description: This study introduces the concept of "structural beauty" as an objective computational approach for evaluating the aesthetic appeal of images. Through the utilization of the Segment anything model (SAM), we propose a method that leverages recursive segmentation to extract finer-grained substructures. Additionally, by reconstructing the hierarchical structure, we obtain a more accurate representation of substructure quantity and hierarchy. This approach reproduces and extends our previous research, allowing for the simultaneous assessment of Livingness in full-color images without the need for grayscale conversion or separate computations for foreground and background Livingness. Furthermore, the application of our method to the Scenic or Not dataset, a repository of subjective scenic ratings, demonstrates a high degree of consistency with subjective ratings in the 0-6 score range. This underscores that structural beauty is not solely a subjective perception, but a quantifiable attribute accessible through objective computation. Through our case studies, we have arrived at three significant conclusions. 1) our method demonstrates the capability to accurately segment meaningful objects, including trees, buildings, and windows, as well as abstract substructures within paintings. 2) we observed that the clarity of an image impacts our computational results; clearer images tend to yield higher Livingness scores. However, for equally blurry images, Livingness does not exhibit a significant reduction, aligning with human visual perception. 3) our approach fundamentally differs from methods employing Convolutional Neural Networks (CNNs) for predicting image scores. Our method not only provides computational results but also offers transparency and interpretability, positioning it as a novel avenue in the realm of Explainable AI (XAI).
DOI: 10.6084/m9.figshare.24270154.v2
title: <b>Segmentation Living Image</b> data

description: This study introduces the concept of "structural beauty" as an objective computational approach for evaluating the aesthetic appeal of images. Through the utilization of the Segment anything model (SAM), we propose a method that leverages recursive segmentation to extract finer-grained substructures. Additionally, by reconstructing the hierarchical structure, we obtain a more accurate representation of substructure quantity and hierarchy. This approach reproduces and extends our previous research, allowing for the simultaneous assessment of Livingness in full-color images without the need for grayscale conversion or separate computations for foreground and background Livingness. Furthermore, the application of our method to the Scenic or Not dataset, a repository of subjective scenic ratings, demonstrates a high degree of consistency with subjective ratings in the 0-6 score range. This underscores that structural beauty is not solely a subjective perception, but a quantifiable attribute accessible through objective computation. Through our case studies, we have arrived at three significant conclusions. 1) our method demonstrates the capability to accurately segment meaningful objects, including trees, buildings, and windows, as well as abstract substructures within paintings. 2) we observed that the clarity of an image impacts our computational results; clearer images tend to yield higher Livingness scores. However, for equally blurry images, Livingness does not exhibit a significant reduction, aligning with human visual perception. 3) our approach fundamentally differs from methods employing Convolutional Neural Networks (CNNs) for predicting image scores. Our method not only provides computational results but also offers transparency and interpretability, positioning it as a novel avenue in the realm of Explainable AI (XAI).
DOI: 10.57841/casdc.0002639
title: 原料预处理研究论文数据
DOI: 10.57841/casdc.0002640
title: 混合原料高负荷协同厌氧发酵数据
DOI: 10.57841/casdc.0002641
title: 厌氧产物处理综合利用论文数据集
DOI: 10.6084/m9.figshare.24270424
title: Scientists in Multilateral Diplomacy. The case of the Members of the IPCC Bureau

description: Scientific experts have become structuring actors in global environmental governance.They have put environmental problems on the agenda, facilitated the establishmentof multilateral agreements, and continue to provide the information to supportinternational policymaking. This paper, drawing on the literature on transnationalprofessionals, introduces the notion of ‘transnational diplomat-scientists’ to describea group of influential scientific experts that has risen to considerable power andincreasingly acts as diplomatic actors in environmental agreement-making processes.Through qualitative methods and visual network analysis, we explore the case of theipcc Bureau members and discuss the professional profiles and multipositionality ofits members for the sixth assessment cycle (2015–2023). Strategically positioned at thescience-policy interface, these actors connect different disciplines and stakeholders atdifferent levels. Through such a positioning and their capacity to manage expectationsand facilitate compromises, diplomat-scientists play a crucial role in upholding theauthority of science in multilateral negotiations.
DOI: 10.6084/m9.figshare.24270424.v1
title: Scientists in Multilateral Diplomacy. The case of the Members of the IPCC Bureau

description: Scientific experts have become structuring actors in global environmental governance.They have put environmental problems on the agenda, facilitated the establishmentof multilateral agreements, and continue to provide the information to supportinternational policymaking. This paper, drawing on the literature on transnationalprofessionals, introduces the notion of ‘transnational diplomat-scientists’ to describea group of influential scientific experts that has risen to considerable power andincreasingly acts as diplomatic actors in environmental agreement-making processes.Through qualitative methods and visual network analysis, we explore the case of theipcc Bureau members and discuss the professional profiles and multipositionality ofits members for the sixth assessment cycle (2015–2023). Strategically positioned at thescience-policy interface, these actors connect different disciplines and stakeholders atdifferent levels. Through such a positioning and their capacity to manage expectationsand facilitate compromises, diplomat-scientists play a crucial role in upholding theauthority of science in multilateral negotiations.
DOI: 10.5281/zenodo.8279739
title: The coenzyme A precursor pantethine restrains sarcoma growth through promotion of type 1 immunity - Run 1 cDNA 2

description: <strong>Dataset from article</strong> : The coenzyme A precursor pantethine restrains sarcoma growth through promotion of type 1 immunity 10X Single-cell RNA-sequencing dataset (2/5).<br> RUN 1(/2), cDNA 2(/2). This repository contains results and necessary files for reproducing the analyses in Miallot et al. (2023). The instructions for reproducing the analysis are given in github (https://github.com/CIML-bioinformatic/PNlab_Sarcoma) and extra files are available in GEO/SRA (GSE221164).
DOI: 10.5281/zenodo.8279738
title: The coenzyme A precursor pantethine restrains sarcoma growth through promotion of type 1 immunity - Run 1 cDNA 2

description: <strong>Dataset from article</strong> : The coenzyme A precursor pantethine restrains sarcoma growth through promotion of type 1 immunity 10X Single-cell RNA-sequencing dataset (2/5).<br> RUN 1(/2), cDNA 2(/2). This repository contains results and necessary files for reproducing the analyses in Miallot et al. (2023). The instructions for reproducing the analysis are given in github (https://github.com/CIML-bioinformatic/PNlab_Sarcoma) and extra files are available in GEO/SRA (GSE221164).
DOI: 10.5281/zenodo.8279752
title: The coenzyme A precursor pantethine restrains sarcoma growth through promotion of type 1 immunity - Run 2 cDNA 1

description: <strong>Dataset from article</strong> : The coenzyme A precursor pantethine restrains sarcoma growth through promotion of type 1 immunity 10X Single-cell RNA-sequencing dataset (3/5).<br> RUN 2(/2), cDNA 1(/2). This repository contains results and necessary files for reproducing the analyses in Miallot et al. (2023). The instructions for reproducing the analysis are given in github (https://github.com/CIML-bioinformatic/PNlab_Sarcoma) and extra files are available in GEO/SRA (GSE221164).
DOI: 10.5281/zenodo.8279751
title: The coenzyme A precursor pantethine restrains sarcoma growth through promotion of type 1 immunity - Run 2 cDNA 1

description: <strong>Dataset from article</strong> : The coenzyme A precursor pantethine restrains sarcoma growth through promotion of type 1 immunity 10X Single-cell RNA-sequencing dataset (3/5).<br> RUN 2(/2), cDNA 1(/2). This repository contains results and necessary files for reproducing the analyses in Miallot et al. (2023). The instructions for reproducing the analysis are given in github (https://github.com/CIML-bioinformatic/PNlab_Sarcoma) and extra files are available in GEO/SRA (GSE221164).
DOI: 10.5281/zenodo.8279754
title: The coenzyme A precursor pantethine restrains sarcoma growth through promotion of type 1 immunity - Run 2 cDNA 2

description: <strong>Dataset from article</strong> : The coenzyme A precursor pantethine restrains sarcoma growth through promotion of type 1 immunity 10X Single-cell RNA-sequencing dataset (4/5).<br> RUN 2(/2), cDNA 2(/2). This repository contains results and necessary files for reproducing the analyses in Miallot et al. (2023). The instructions for reproducing the analysis are given in github (https://github.com/CIML-bioinformatic/PNlab_Sarcoma) and extra files are available in GEO/SRA (GSE221164).
DOI: 10.5281/zenodo.8279753
title: The coenzyme A precursor pantethine restrains sarcoma growth through promotion of type 1 immunity - Run 2 cDNA 2

description: <strong>Dataset from article</strong> : The coenzyme A precursor pantethine restrains sarcoma growth through promotion of type 1 immunity 10X Single-cell RNA-sequencing dataset (4/5).<br> RUN 2(/2), cDNA 2(/2). This repository contains results and necessary files for reproducing the analyses in Miallot et al. (2023). The instructions for reproducing the analysis are given in github (https://github.com/CIML-bioinformatic/PNlab_Sarcoma) and extra files are available in GEO/SRA (GSE221164).
DOI: 10.5281/zenodo.8279759
title: The coenzyme A precursor pantethine restrains sarcoma growth through promotion of type 1 immunity - Merged datasets

description: <strong>Dataset from article</strong> : The coenzyme A precursor pantethine restrains sarcoma growth through promotion of type 1 immunity 10X Single-cell RNA-sequencing dataset (5/5).<br> Merged datasets: Run 1 cDNA 1 + Run 1 cDNA 2 + Run 2 cDNA 1 + Run 2 cDNA 2 This repository contains results and necessary files for reproducing the analyses in Miallot et al. (2023). The instructions for reproducing the analysis are given in github (https://github.com/CIML-bioinformatic/PNlab_Sarcoma) and extra files are available in GEO/SRA (GSE221164).
DOI: 10.5281/zenodo.8279758
title: The coenzyme A precursor pantethine restrains sarcoma growth through promotion of type 1 immunity - Merged datasets

description: <strong>Dataset from article</strong> : The coenzyme A precursor pantethine restrains sarcoma growth through promotion of type 1 immunity 10X Single-cell RNA-sequencing dataset (5/5).<br> Merged datasets: Run 1 cDNA 1 + Run 1 cDNA 2 + Run 2 cDNA 1 + Run 2 cDNA 2 This repository contains results and necessary files for reproducing the analyses in Miallot et al. (2023). The instructions for reproducing the analysis are given in github (https://github.com/CIML-bioinformatic/PNlab_Sarcoma) and extra files are available in GEO/SRA (GSE221164).
DOI: 10.5281/zenodo.8277073
title: The coenzyme A precursor pantethine restrains sarcoma growth through promotion of type 1 immunity - Run 1 cDNA 1

description: <strong>Dataset from article</strong> : The coenzyme A precursor pantethine restrains sarcoma growth through promotion of type 1 immunity 10X Single-cell RNA-sequencing dataset (1/5).<br> RUN 1(/2), cDNA 1(/2). This repository contains results and necessary files for reproducing the analyses in Miallot et al. (2023). The instructions for reproducing the analysis are given in github (https://github.com/CIML-bioinformatic/PNlab_Sarcoma) and extra files are available in GEO/SRA (GSE221164).
DOI: 10.5281/zenodo.8277072
title: The coenzyme A precursor pantethine restrains sarcoma growth through promotion of type 1 immunity - Run 1 cDNA 1

description: <strong>Dataset from article</strong> : The coenzyme A precursor pantethine restrains sarcoma growth through promotion of type 1 immunity 10X Single-cell RNA-sequencing dataset (1/5).<br> RUN 1(/2), cDNA 1(/2). This repository contains results and necessary files for reproducing the analyses in Miallot et al. (2023). The instructions for reproducing the analysis are given in github (https://github.com/CIML-bioinformatic/PNlab_Sarcoma) and extra files are available in GEO/SRA (GSE221164).
DOI: 10.57745/9yehcb
title: pdb2spn
DOI: 10.57745/4didpk
title: libxdrfile.a
DOI: 10.57745/c6k6bj
title: biospring
DOI: 10.57745/cvqzeo
title: libhdf5_hl.200.dylib
DOI: 10.57745/lpys2r
title: libhdf5.200.dylib
DOI: 10.57745/soeay5
title: libbiospring-core.a
DOI: 10.57745/wvzwwq
title: libnetcdf-cxx4.1.dylib
DOI: 10.57745/3qy2we
title: libsz.2.dylib
DOI: 10.57745/mbluah
title: libzstd.1.dylib
DOI: 10.57745/t4qacn
title: libnetcdf.19.dylib
DOI: 10.57745/xcggyd
title: libomp.dylib
DOI: 10.57745/yjdifo
title: biospring
DOI: 10.57745/eb76ug
title: LICENSE.txt
DOI: 10.57745/gjkgj4
title: UnityMol_BioSpring_MacOS.zip
DOI: 10.57745/probq0
title: pdb2spn
DOI: 10.57745/themuf
title: .DS_Store
DOI: 10.57745/uholym
title: UnityMol_BioSpring_Windows.zip
DOI: 10.57745/yhxx6l
title: LICENSE.md
DOI: 10.57745/6ripyf
title: UMolState.py
DOI: 10.57745/jh9lqe
title: model.msp
DOI: 10.57745/mewbyi
title: model.pdb
DOI: 10.57745/q8ew7b
title: model.nc
DOI: 10.57745/wjaxvh
title: UnityMol_BioSpring_Linux.zip
DOI: 10.57745/0icngz
title: model.pdb
DOI: 10.57745/adw1gl
title: test_dynPlot.py
DOI: 10.57745/d7qwpg
title: model.msp
DOI: 10.57745/kxa21n
title: UMolState.py
DOI: 10.57745/yxmxye
title: model.nc
DOI: 10.57745/bdamgq
title: pos4ar1.dat
DOI: 10.57745/c9hvoy
title: UMolState.py
DOI: 10.57745/cevqha
title: model.msp
DOI: 10.57745/evt6b5
title: README.md
DOI: 10.57745/n9sirx
title: model.nc
DOI: 10.57745/yz1pu8
title: model.pdb
DOI: 10.57745/fkjpup
title: 00_README.md

description: :unav
DOI: 10.57841/casdc.0002642
title: 高效合成氨催化剂实验数据
DOI: 10.18712/nsd-nsd2940-v3
title: Norwegian Citizen Panel Round 19, 2020

description: The Norwegian Citizen Panel is a platform for internet surveys of public opinion in important areas of society and politics in Norway. Participants are randomly recruited from the Norwegian population register, and they are encouraged to participate over time. The panel was fielded for the first time the fall of 2013 and as of 2017 the survey is carried out three times a year. The University of Bergen owns and is responsible for the Citizen panel. The company Ideas2Evidence recruits respondents, produces the survey, and provides documentation of the data. Data is stored and shared by Sikt - Norwegian Agency for Shared Services in Education and Research. For access to time series data, or text data, please contact DIGSSCORE. The Norwegian Citizen Panel welcomes research proposals for survey content. More information about calls and other updates is available at www.medborger.uib.no http://www.medborger.uib.no

The full answer choices for all questions can be found in the codebook, which is located further down the page.
DOI: 10.57841/casdc.0002643
title: 高效氨分解催化剂实验数据
DOI: 10.57841/casdc.0002644
title: 氨分解-钯膜分离制氢技术小型系统实验数据
DOI: 10.57841/casdc.0002645
title: 工业余热-相变储热建筑清洁供暖示范
DOI: 10.23728/b2share.66c289b7af8f4b96bafdc906e17ce9eb
title: Changes in the metabolic signature and biomarkers associated with the intake of whole-grain wheat and whole-grain rye in a dose-response dietary intervention study

description: Epidemiological studies have shown associations between whole-grain intake and lowered disease risk. A sufficient level of whole-grain intake to reach the health benefits has not been established, and there is limited knowledge about the impact of whole-grain intake on metabolite levels. In this clinical intervention study, we aimed to identify plasma and urine metabolites associated with two different intake levels of whole-grain wheat and rye and to correlate them with clinical plasma biomarkers. Healthy volunteers (N = 68) were divided into two groups receiving either whole-grain wheat or whole-grain rye in two four-week interventions with 48 and 96 g/d of whole grains consumed. The metabolomics of the plasma samples was performed with UPLC–QTOF-MS. Plasma alkylresorcinols were quantified with GC-MS and plasma and urinary mammalian lignans with HPLC-ECD. The high-dose intervention impacted the metabolite profile, including microbial metabolites, more in the rye-enriched diet compared with wheat. Among the increased metabolites were alkylresorcinol glucuronides, sinapyl alcohol, and pipecolic acid betaine, while the decreased metabolites included acylcarnitines and ether lipids. Plasma alkylresorcinols, urinary enterolactone, and total mammalian lignans reflected the study diets in a dose-dependent manner. Several key metabolites linked with whole-grain consumption and gut microbial metabolism increased in a linear manner between the two interventions. The results reveal that an increase in whole-grain intake, particularly rye, is strongly reflected in the metabolite profile, is correlated with clinical variables, and suggests that a diet rich in whole grains promotes the growth and/or metabolism of microbes producing potentially beneficial microbial metabolites.
description: The raw data file includes the untargeted LC-MS data as a peak area table with drift-corrected signal abundances, identifications, and statistical results.
DOI: 10.5281/zenodo.7006719
title: 671.part1

description: 671.part1
DOI: 10.5281/zenodo.7006718
title: 671.part1

description: 671.part1
DOI: 10.18728/igb-fred-841.0
title: Multiple stressor effects on leaf litter decomposition in freshwater ecosystems: a meta-analysis

description: Data used to determine overall interaction effect sizes, including effect sizes for each treatment (stressors) and their interactions.
DOI: 10.15151/esrf-es-1312810200
title: REE recovery exploiting zeolite cation exchange

description: In this project we will evaluate the oxidation state of Ce in zeolites exploited as cation exchangers for the recovery of REEs from leaching liquors. The knowledge of Ce oxidation state is a key factor in the determination of the cation exchange capacity of the selected zeolite. The evaluation of this parameters is also fundamental for a correct interpretation of the cation exchange dynamic.
DOI: 10.6084/m9.figshare.24270583
title: MGWNB data and code

description: MGWNB data and code
DOI: 10.5281/zenodo.8420242
title: CHANGES IN THE SENSITIZATION RATES FOR VARIOUS RESPIRATORY AND FOOD ALLERGENS BY AGE

description: Numerical information about sensitization rate for all the allergens.
DOI: 10.5281/zenodo.8420241
title: CHANGES IN THE SENSITIZATION RATES FOR VARIOUS RESPIRATORY AND FOOD ALLERGENS BY AGE

description: Numerical information about sensitization rate for all the allergens.
DOI: 10.5517/ccdc.csd.cc2c2891
title: CCDC 2176922: Experimental Crystal Structure Determination

description: Related Article: Parag Roy, Soumalya Chakraborty, Noopur Pandey, Nimmy Kumari, Sourav Chougule, Amrita Chatterjee, Kaberi Chatterjee, Pallab Mandal, Bapi Gorain, Ananta V. Dhotre, Arvind Kumar Bansal, and Animesh Ghosh|2023|Mol.Pharmaceutics|20|5226|doi:10.1021/acs.molpharmaceut.3c00646
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/6644278
title: OnlineFree
DOI: 10.34740/kaggle/dsv/6644277
title: AutoXGB
DOI: 10.34740/kaggle/dsv/6644268
title: LUFlow Network Intrusion Detection Data Set
DOI: 10.5519/qd.n0ojpivz
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (2 records)
DOI: 10.6084/m9.figshare.23960964
title: Sub-MeV electron precipitation driven by EMIC waves through nonlinear sub-cyclotron resonances: Processed test-particle data

description: This dataset contains results from test-particle simulations produced with NumPy (Python) that were used in the GRL manuscript "Sub-MeV electron precipitation driven by EMIC waves through nonlinear sub-cyclotron resonances: Processed test-particle data". A sufficiently recent version of Numpy for Python 3 must be installed to open the files.Files with the prefix "trajs" represent selected trajectories to demonstrate the nonlinear and nonresonant effects of EMIC waves on electrons. "trajs_hs" are the field-aligned distances, "trajs_lats" are the corresponding magnetic latitudes, and "trajs_pitchangs_eq" are the equatorial pitch angles along trajectories.File "Daas.npy" contains pitch-angle diffusion coefficients in energy--pitch-angle space obtained for selected sets of EMIC wave parameters (see the linked manuscript).File "Daas_line.npy" contains diffusion coefficients for all wave parameters used in the manuscript, but for only two initial equatorial pitch angles, 2.5 deg and 14.5 deg. The data are averaged with a 3-point rolling average over energies. The averaged energy bins are in the file "energs0_avg.npy".Files "precip_line.npy" and "energs0_avg_line" are similar to those from the previous paragraph but contain electron PSD precipitation ratios instead of diffusion coefficients.
DOI: 10.6084/m9.figshare.23960964.v1
title: Sub-MeV electron precipitation driven by EMIC waves through nonlinear sub-cyclotron resonances: Processed test-particle data

description: This dataset contains results from test-particle simulations produced with NumPy (Python) that were used in the GRL manuscript "Sub-MeV electron precipitation driven by EMIC waves through nonlinear sub-cyclotron resonances: Processed test-particle data". A sufficiently recent version of Numpy for Python 3 must be installed to open the files.Files with the prefix "trajs" represent selected trajectories to demonstrate the nonlinear and nonresonant effects of EMIC waves on electrons. "trajs_hs" are the field-aligned distances, "trajs_lats" are the corresponding magnetic latitudes, and "trajs_pitchangs_eq" are the equatorial pitch angles along trajectories.File "Daas.npy" contains pitch-angle diffusion coefficients in energy--pitch-angle space obtained for selected sets of EMIC wave parameters (see the linked manuscript).File "Daas_line.npy" contains diffusion coefficients for all wave parameters used in the manuscript, but for only two initial equatorial pitch angles, 2.5 deg and 14.5 deg. The data are averaged with a 3-point rolling average over energies. The averaged energy bins are in the file "energs0_avg.npy".Files "precip_line.npy" and "energs0_avg_line" are similar to those from the previous paragraph but contain electron PSD precipitation ratios instead of diffusion coefficients.
DOI: 10.5519/qd.oaeco89s
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (2 records)