cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25824/redu/xtbvmd
title: 22457A-34616mm - XPS das micropartículas.JPG
DOI: 10.25824/redu/g1snzz
title: Tensão Superficial - CAC1,25 P20_R1.tab
DOI: 10.25824/redu/w6ltrw
title: Antocianinas nas micropartículas - Etapa 1 da pesquisa.tab
DOI: 10.25824/redu/biv09x
title: 22547C_06 -MEV das micropartículas.jpg
DOI: 10.25824/redu/i9i2pq
title: Tensão Superficial - CAC1,25(apenas_Malto)_R2.tab
DOI: 10.25824/redu/zcmg1v
title: 22547C-P1 - XPS das micropartículas.rtf
DOI: 10.25824/redu/hgr9vh
title: 22547C_04 -MEV das micropartículas.jpg
DOI: 10.25824/redu/gkz8ca
title: Tensão superficial MRPH Replicata 1.tab
DOI: 10.25824/redu/rfm3k1
title: 22547B_02 -MEV das micropartículas.jpg
DOI: 10.25824/redu/2p9hyk
title: Tensão superficial LRPH Replicata 1.tab
DOI: 10.25824/redu/p9izqm
title: 22547B_04 -MEV das micropartículas.jpg
DOI: 10.25824/redu/4wqavo
title: Resultados da Análise de Tg das micropartículas.tab
DOI: 10.25824/redu/pxll8e
title: 22547D_02 -MEV das micropartículas.jpg
DOI: 10.25824/redu/vhacm7
title: 22547B_11 -MEV das micropartículas.jpg
DOI: 10.25824/redu/nixtce
title: 22547D_07-MEV das micropartículas.jpg
DOI: 10.25824/redu/ksemkc
title: Informações e legendas da análise de XPS.rtf
DOI: 10.25824/redu/pme4s6
title: Perfil_Físico-Químico_Nutricional_Suco_de_Uva.xlsx
DOI: 10.25824/redu/qtqrqm
title: Tensão Superficial - CAC0,75(apenas_Malto)_R1.tab
DOI: 10.25824/redu/anqz3b
title: 22547D-P2 - XPS das micropartículas.rtf
DOI: 10.25824/redu/dz7jv4
title: Tamanho e Distribuição do tamanho das micropartículas - Etapa 2 da Pesquisa.xlsx
DOI: 10.25824/redu/pkztzz
title: Tensão superficial HRPH Replicata 1.tab
DOI: 10.25824/redu/7ankwa
title: 22547A_01 -MEV das micropartículas.jpg
DOI: 10.25824/redu/1kezfq
title: 22547C_09 -MEV das micropartículas.jpg
DOI: 10.25824/redu/xjdfqr
title: 22457B-34278mm - XPS das micropartículas.JPG
DOI: 10.25824/redu/ghyjph
title: Tensão superficial LRPH Replicata 2.tab
DOI: 10.25824/redu/pjvnhv
title: 22547D_08 -MEV das micropartículas.jpg
DOI: 10.25824/redu/inxvcz
title: 22547C_10 -MEV das micropartículas.jpg
DOI: 10.25824/redu/kv6gmf
title: Relevância das propriedades de encapsulação da proteína do farelo de arroz e seus hidrolisados na retenção de antocianinas em micropartículas de suco de uva obtidas por spray drying

description: Essa base de dados contém imagens de microscopia (MEV) e dados brutos das análises de caracterização de suco de uva, das soluções de alimentação (tensão superficial) e de micropartículas (conteúdo de antocianinas e compostos fenólicos, capacidade antioxidante, XPS, transição vítrea e propriedades físico-químicas).
DOI: 10.25345/c5hd7p37z
title: MassIVE MSV000093140 - Diurnal rhythmicity of fecal microbiota and metabolite profiles in the first year of life
title: Microbiota assembly in the infant gut is influenced by time and duration of dietary exposure to breast-milk, infant formula and solid foods.
DOI: 10.5517/ccdc.csd.cc23hghl
title: CCDC 1951279: Experimental Crystal Structure Determination

description: Related Article: Varma H. Rambaran, S.M. Saumya, Soumyabrata Roy, K.P. Sonu, Muthusamy Eswaramoorthy, Sebastian C. Peter|2020|Inorg.Chim.Acta|504|119448|doi:10.1016/j.ica.2020.119448
description: Related Article: Varma H. Rambaran, Wilson Sue Chee Ming, Nadia Singh, R. J. Nandini, Marisha Tang Kai, Arun Surendran, Mahesh Krishna, Abdul Jaleel|2023|Inorg.Chim.Acta|556|121662|doi:10.1016/j.ica.2023.121662
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.cc2bgd3b
title: CCDC 2158781: Experimental Crystal Structure Determination

description: Related Article: Shishir Ghosh, Subas Rajbanshi, Md. Assaduzzaman Tipu, Tasneem A Siddiquee|2022|J.Bangladesh Chem.Soc.|34|35|
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: 10.57841/casdc.0002910
title: 新一代甲醇制烯烃技术中试数据集
DOI: 10.6084/m9.figshare.24347032
title: all-automatically-generated-alignments.rar

description: all automatically generated protein alignments used in the phylogenetic pipeline
DOI: 10.6084/m9.figshare.24347032.v1
title: all-automatically-generated-alignments.rar

description: all automatically generated protein alignments used in the phylogenetic pipeline
DOI: 10.57841/casdc.0002911
title: PMMA风能叶片热塑性树脂的制备技术相关实验数据数据集
DOI: 10.57841/casdc.0002912
title: 还原态Ni-Nb-O复合物加氢催化剂的制备用于游离脂肪酸高选择性转化制备正构烷烃
DOI: 10.25345/c5cr5np64
title: MassIVE MSV000093141 - Proteomics analysis of Embryo growth media
title: Bottom up proteomics analysis of a compendium of growth media used in growth of Embryos for in vitro fertilization and their additives.
DOI: 10.21227/henx-kg41
title: Heart disease

description: Acute myocardial infarction (AMI) is the main cause of death in developed and developing countries. AMI is a serious medical problem that necessitates hospitalization and sometimes results in death. Patients hospitalized in the emergency department (ED) should therefore receive an immediate diagnosis and treatment. Many studies have been conducted on the prognosis of AMI with hemogram parameters. However, no study has investigated potential hemogram parameters for the diagnosis of AMI using an interpretable artificial intelligence-based clinical approach. The purpose of this research is to implement the principles of explainable artificial intelligence (XAI) in the analysis of hematological predictors for AMI. In this retrospective analysis, 477 (48.6%) patients with AMI and 504 (51.4%) healthy individuals were enrolled and assessed in predicting AMI. Of the patients with AMI, 182 (38%) had an ST-segment elevation MI (STEMI), and 295 (62%) had a non-ST-segment elevation MI (NSTEMI). Demographic and hematological information of the patients was analyzed to determine AMI. The XAI approach combined with machine learning approaches (Extreme Gradient Boosting, XGB; Adaptive Boosting, AB; Light Gradient Boosting Machine, LGBM) was applied for the estimation of AMI and distinguishing subgroups of AMI (STEMI and NSTEMI). The SHAP approach was used to explain the predictions intuitively. After selecting the 10 most important hematological parameters for AMI, the LGBM model achieved 83% and 74% accuracy for prediction of AMI, and distinguishing subgroups of AMI (STEMI and NSTEMI), respectively. SHAP results showed that neutrophil (NEU), white blood cell (WBC), platelet width of distribution (PDW), and basophil (BA) were the most important for AMI prediction. Mean corpuscular volume (MCV), BA, monocytes (MO), and lymphocytes (LY) were the most important hematological parameters that distinguish STEMI from NSTEMI. The proposed model serves as a valuable tool for physicians, facilitating the diagnosis, treatment, and follow-up of patients with AMI and distinguishing subgroups of AMI (STEMI and NSTEMI). Analyzing readily accessible hemogram parameters empowers medical professionals to make informed decisions and provide enhanced care to a wide range of individuals.
DOI: 10.5281/zenodo.10018220
title: Input files accompanying the paper "Instrumenting Stellar Oscillation Codes with Parfait Models"

description: A zip archive containing input files accompanying the paper "Instrumenting Stellar Oscillation Codes with Parfait Models" by R. H. D. Townsend and R. V. Kuenzi.
DOI: 10.5281/zenodo.10018219
title: Input files accompanying the paper "Instrumenting Stellar Oscillation Codes with Parfait Models"

description: A zip archive containing input files accompanying the paper "Instrumenting Stellar Oscillation Codes with Parfait Models" by R. H. D. Townsend and R. V. Kuenzi.
DOI: 10.57745/z7aull
title: Raman_Oils_raw.csv

description: Raman Spectra of EVOO and Sunflower Oil mixtures
DOI: 10.57745/6pmscn
title: Raman_Spectra_ReadMe.pdf

description: :unav
DOI: 10.5281/zenodo.6617194
title: Semi-automatic and manual shallow landslide inventories of two extreme rainfall events.

description: This dataset contains the polygons of automatic and manually based shallow landslides related to two extreme rainfall events. The dataset, in KML format, can be visualized on a GIS or the satellite image layer of Google Earth. With more details, it is possible to find: AOI_2016: The study area of the extreme rainfall of November 2016, Tanerello and Arroscia Valleys NW Italy. The 2016_PL: The inventory of potential shallow landslides semi-automatically mapped on the base of Sentinel-2 images related to extreme rainfall events that hit NW Italy in November 2016 The 2016_ML: The inventory of shallow landslides manually mapped on high-resolution images of Google Earth, related to extreme rainfall events that hit NW Italy in November 2016 AOI_2019_large: The study area of the extreme rainfall of October 2019 NW Italy. AOI_2019: The testing area of the extreme rainfall of October 2019, Gavi Area NW Italy. The 2019_PL_all: The inventory of potential shallow landslides semi-automatically mapped on the base of Sentinel-2 images related to extreme rainfall events that hit NW Italy in October 2019 (whole Study area) The 2019_PL: The inventory of potential shallow landslides semi-automatically mapped on the base of Sentinel-2 images related to extreme rainfall events that hit NW Italy in October 2019 (Gavi test area) The 2019_ML: The inventory of shallow landslides manually mapped on high-resolution images of Google Earth, related to extreme rainfall events that hit NW Italy in October 2019 GEE_Script: List of codes used in Google Earth Engine to produce NDVI time series or averaged NDVI on some sample studied areas With more details, the Google Earth Engine scripts used are: NDVI Time series of some sampled areas to select the best pair of images for the PL creation (Tanarello and Arroscia Valleys and GAVI AOIs https://code.earthengine.google.com/8f4d08db4d34099bb1a4cd1fd15bf05b?noload=true Sampled NDVI Time series from different intersection cases for Tanarello and Arroscia Valleys study area https://code.earthengine.google.com/47e5883e0fe7beae589df4c42421dd3b?noload=true Sampled NDVI Time series from some test areas over the Gavi study area https://code.earthengine.google.com/e97fd46102cca3ee25b441a8ebff8400?noload=true Multi-temporal averaged pre- and post-event NDVI (NDVI<sub>var</sub> ) for the whole Gavi study area https://code.earthengine.google.com/f982f831c74afcca3d1a95a9dd18424b?noload=true Multi-temporal averaged pre- and post-event NDVI (NDVI<sub>var</sub> ) for the whole Arroscia-Tanarello study area https://code.earthengine.google.com/a3390b262cef1b5f42837c88d8791b5b?noload=true
description: The methodology and the data used for the production of this dataset will be deeply described in a paper that is going to be submitted to the NHESS journal.
DOI: 10.57992/nmrxiv.p18
title: The extremely short-lived peptide-polyene antimicrobial epifadin enables nasal commensal to eliminate Staphylococcus aureus
DOI: 10.57992/nmrxiv.p18.s86
title: synthetic peptide amide LDLD
DOI: 10.57992/nmrxiv.p18.s86.d509
title: 12
DOI: 10.57992/nmrxiv.p18.s86.d510
title: 13
DOI: 10.57992/nmrxiv.p18.s86.d511
title: 10
DOI: 10.57992/nmrxiv.p18.s86.d512
title: 11
DOI: 10.57992/nmrxiv.p18.s87
title: Epifadin at 303 K in DMSO batch 2
DOI: 10.57992/nmrxiv.p18.s87.d513
title: 13
DOI: 10.57992/nmrxiv.p18.s87.d514
title: 10
DOI: 10.57992/nmrxiv.p18.s88
title: Epifadin at 303 K in DMSO batch 1
DOI: 10.57992/nmrxiv.p18.s88.d515
title: 10
DOI: 10.57992/nmrxiv.p18.s88.d516
title: 18
DOI: 10.57992/nmrxiv.p18.s88.d517
title: 12
DOI: 10.57992/nmrxiv.p18.s88.d518
title: 20
DOI: 10.57992/nmrxiv.p18.s89
title: Epifadin at 308 K in DMSO
DOI: 10.57992/nmrxiv.p18.s89.d519
title: 19
DOI: 10.57992/nmrxiv.p18.s89.d520
title: 18
DOI: 10.35090/gatech/70296
title: A Human Lower-Limb Biomechanics and Wearable Sensors Dataset During Cyclic and Non-Cyclic Activities
DOI: 10.23729/f07ebf11-cde0-4ceb-a2b6-7868db940b02
title: High clay soils preview

description: High clay content soil samples imaged to investigate their structure properties. Results featured in Soinne. H., Keskinen. R., Tähtikarhu, M., Kuva, J., Hyväluoma. J., 2023. Effects of organic carbon and clay contents on structure-related properties of arable soils with high clay content. European Journal of Soil Science e13424 DOI: 10.1111/ejss.13424. Preview of PAS-dataset https://doi.org/10.23729/39806138-60cf-4e97-9695-7a294ad5432a
DOI: 10.25345/c5804xw29
title: MassIVE MSV000093142 - Streptomyces MK37H Metabolomics
title: Antimicrobial peptides has specific area of drug discovery
DOI: 10.5517/ccdc.csd.cc29xtpr
title: CCDC 2142865: Experimental Crystal Structure Determination

description: Related Article: Qingbing Yuan, Zeming Huang, Weikang Wu, Dongjing Hong, Shan Zhu, Jieding Wei, Shuangliu Zhou, Shaowu Wang|2022|Dalton Trans.|51|17795|doi:10.1039/D2DT02654F
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: 10.4121/44873411-37de-4f14-83c7-6939bed488d2
title: Restaurant activity scheduling data

description: The dataset contains the input file for PyNOMAD. These input files can be used and were used to simulate different restaurant settings. The results from these simulations are used to quantify the amount of close contacts (COVID-19) between customers and staff in a restaurant and how this depends on the geometry and service setup of a restaurant.
DOI: 10.4121/44873411-37de-4f14-83c7-6939bed488d2.v1
title: Restaurant activity scheduling data

description: The dataset contains the input file for PyNOMAD. These input files can be used and were used to simulate different restaurant settings. The results from these simulations are used to quantify the amount of close contacts (COVID-19) between customers and staff in a restaurant and how this depends on the geometry and service setup of a restaurant.
DOI: 10.5517/ccdc.csd.cc2d4mx3
title: CCDC 2208995: Experimental Crystal Structure Determination

description: Related Article: Avantika Hasija, Amy J. Thompson, Lakhvir Singh, S. N. Megha, Kiran S. R. N. Mangalampalli, John. C. McMurtrie, Mitradip Bhattacharjee, Jack K. Clegg, Deepak Chopra|2023|Small|19|2206169|doi:10.1002/smll.202206169
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.cc2d79qp
title: CCDC 2211562: Experimental Crystal Structure Determination

description: Related Article: R. Thrilokraj, Rajeev V. Hegde, Arnab Ghosh, Akshay S. Limaye, Haridas B. Rode, Balasubramanian Sridhar, Ramesh B. Dateer|2023|New J.Chem.|47|8268|doi:10.1039/D3NJ00988B
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.cc2dph5s
title: CCDC 2225185: Experimental Crystal Structure Determination

description: Related Article: Rajkumar S. Birajdar, Rajesh G. Gonnade, Harshawardhan V. Pol, Prabhu M. Basava, Dhammaraj Rokade, Sheetal Nandimath, Samir H. Chikkali|2023|Polym.Chem.|14|3239|doi:10.1039/D3PY00311F
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.cc2f720s
title: CCDC 2241114: Experimental Crystal Structure Determination

description: Related Article: G. Premkumar, Toka Swu, Richa Gupta, R. Kothandaraman|2023|New J.Chem.|47|15677|doi:10.1039/D3NJ01927F
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.cc2fnjyl
title: CCDC 2254070: Experimental Crystal Structure Determination

description: Related Article: Sachin Kumar, Bajrang Lal, Ram Kumar Tittal, Gurleen Singh, Jandeep Singh, Vikas D. Ghule, Renu Sharma, Jagjivan K. Sabane|2023|Sens. Diagn.|2|1267|doi:10.1039/D3SD00109A
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/6730512
title: COVID Vaccination in World (updated daily)
DOI: 10.6084/m9.figshare.24135188
title: Model Mixing Using Bayesian Additive Regression Trees

description: In modern computer experiment applications, one often encounters the situation where various models of a physical system are considered, each implemented as a simulator on a computer. An important question in such a setting is determining the best simulator, or the best combination of simulators, to use for prediction and inference. Bayesian model averaging (BMA) and stacking are two statistical approaches used to account for model uncertainty by aggregating a set of predictions through a simple linear combination or weighted average. Bayesian model mixing (BMM) extends these ideas to capture the localized behavior of each simulator by defining input-dependent weights. One possibility is to define the relationship between inputs and the weight functions using a flexible nonparametric model that learns the local strengths and weaknesses of each simulator. This article proposes a BMM model based on Bayesian Additive Regression Trees (BART). The proposed methodology is applied to combine predictions from Effective Field Theories (EFTs) associated with a motivating nuclear physics application. Supplementary materials for this article are available online. Source code is available at https://github.com/jcyannotty/OpenBT.
DOI: 10.6084/m9.figshare.24135188.v1
title: Model Mixing Using Bayesian Additive Regression Trees

description: In modern computer experiment applications, one often encounters the situation where various models of a physical system are considered, each implemented as a simulator on a computer. An important question in such a setting is determining the best simulator, or the best combination of simulators, to use for prediction and inference. Bayesian model averaging (BMA) and stacking are two statistical approaches used to account for model uncertainty by aggregating a set of predictions through a simple linear combination or weighted average. Bayesian model mixing (BMM) extends these ideas to capture the localized behavior of each simulator by defining input-dependent weights. One possibility is to define the relationship between inputs and the weight functions using a flexible nonparametric model that learns the local strengths and weaknesses of each simulator. This article proposes a BMM model based on Bayesian Additive Regression Trees (BART). The proposed methodology is applied to combine predictions from Effective Field Theories (EFTs) associated with a motivating nuclear physics application. Supplementary materials for this article are available online. Source code is available at https://github.com/jcyannotty/OpenBT.
DOI: 10.6084/m9.figshare.24346786
title: Regression_L9.csv

description: Regression table
DOI: 10.6084/m9.figshare.24346786.v1
title: Regression_L9.csv

description: Regression table
DOI: 10.25403/upresearchdata.24271168
title: Agroinfiltrated Cercospora candidate effector's disease scores in Nicotiana benthamiana and Nicotiana tabacum

description: In this dataset candidate effectors in <em>Cercospora zeina</em> and <em>Cercospora zeae-maydis </em>were identified. The genes were studied using bioinformatics analysis and further studied in planta by Agroinfiltrations into two tobacco species, <em>Nicotiana benthamiana</em> and <em>Nicotiana tabacum</em>. After each infiltration the response of the genes in tobacco was monitored over a specific time period and scores from 0-4 were given for each infiltrated area. The scores can be found in the excel spreadsheet and the scores were used to perform a t-test on to determine statistical significance.
DOI: 10.25403/upresearchdata.24271168.v1
title: Agroinfiltrated Cercospora candidate effector's disease scores in Nicotiana benthamiana and Nicotiana tabacum

description: In this dataset candidate effectors in <em>Cercospora zeina</em> and <em>Cercospora zeae-maydis </em>were identified. The genes were studied using bioinformatics analysis and further studied in planta by Agroinfiltrations into two tobacco species, <em>Nicotiana benthamiana</em> and <em>Nicotiana tabacum</em>. After each infiltration the response of the genes in tobacco was monitored over a specific time period and scores from 0-4 were given for each infiltrated area. The scores can be found in the excel spreadsheet and the scores were used to perform a t-test on to determine statistical significance.
DOI: 10.5517/ccdc.csd.cc29h7dg
title: CCDC 2129805: Experimental Crystal Structure Determination

description: Related Article: Zhi Wang, Rakesh Kumar Gupta, Fahri Alkan, Bao-Liang Han, Lei Feng, Xian-Qiang Huang, Zhi-Yong Gao, Chen-Ho Tung, Di Sun|2023|J.Am.Chem.Soc.|145|19523|doi:10.1021/jacs.3c01119
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.cc29h7gj
title: CCDC 2129807: Experimental Crystal Structure Determination

description: Related Article: Zhi Wang, Rakesh Kumar Gupta, Fahri Alkan, Bao-Liang Han, Lei Feng, Xian-Qiang Huang, Zhi-Yong Gao, Chen-Ho Tung, Di Sun|2023|J.Am.Chem.Soc.|145|19523|doi:10.1021/jacs.3c01119
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.