cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.34820/fk2/cxsxfs
title: DETERMINANT VARIABLES OF ENTERPRISE RISK MANAGEMENT (ERM), AUDIT OPINIONS AND COMPANY VALUE ON INSURANCE EMITENTS LISTED IN INDONESIA STOCK EXCHANGE

description: Risk is inherent uncertainty and must be faced in working life both individually and in an organization. Risks in the form of uncertainty occur due to lack or unavailability of sufficient information about what will happen in the company in the future. This study aims to test and obtain empirical results about determinants of Enterprise Risk Management (ERM) disclosure, audit opinion, and company value by using independent commissioner variables, company size, Leverage, and Risk Management Committee (RMC). The population in this study were all insurance issuers listed on the Indonesia Stock Exchange in 2013-2017 the amount of 15 companies. The sampling technique was carried out using the Purposive Sampling method which produced 12 samples during from 2013-2017. The data used is secondary data with documentation techniques consisting of annual reports of insurance issuers in 2013-2017. The tool used to test hypotheses using path analysis with SPSS Version 22. The results show that company size, leverage, and RMC have a significant effect on ERM disclosure. However, independent commissioners have no significant effect on ERM. For further researchers can use objects such as mining companies that have a higher potential risk.
DOI: 10.13020/7tpp-c606
title: Survey of anglers who had a fishing license in the 2018-2019 fishing season
DOI: 10.5281/zenodo.6390432
title: YinXin2021/WRF-LES-DustDepositionScheme: v1.0.0

description: Particle deposition simulation using LES: Simulation results obtained in the LES study of dust deposition on the desert surface, available at: https://github.com/YinXin2021/WRF-LES-DustDepositionScheme/commits/v1.0.0
DOI: 10.5281/zenodo.6390431
title: YinXin2021/WRF-LES-DustDepositionScheme: v1.0.0

description: Particle deposition simulation using LES: Simulation results obtained in the LES study of dust deposition on the desert surface, available at: https://github.com/YinXin2021/WRF-LES-DustDepositionScheme/commits/v1.0.0
DOI: 10.6084/m9.figshare.17860655
title: Exploring <i>Klebsiella pneumoniae</i> capsule polysaccharide proteins to design multiepitope subunit vaccine to fight against pneumonia

description: <i>Klebsiella pneumoniae</i> is an emerging human pathogen causing neonatal lung disease, catheter-associated infections, and nosocomial outbreaks with high fatality rates. Capsular polysaccharide (CPS) protein plays a major determinant in virulence and is considered as a promising target for vaccine development. In this study, we used immunoinformatic approaches to design a multi-peptide vaccine against K. pneumonia. The epitopes were selected through several immune filters, such as antigenicity, conservancy, nontoxicity, non-allergenicity, binding affinity to HLA alleles, overlapping epitopes, and peptides having common epitopes. Finally, a construct comprising 2 B-Cell, 8 CTL, 2 HTL epitopes, along with adjuvant, linkers was designed. Peptide-HLA interaction analysis showed strong binding of these epitopes with several common HLA molecules. The in silico immune simulation and population coverage analysis of the vaccine showed its potential to evoke strong immune responses.. Further, the interaction between vaccine and immune was evaluated by docking and simulation, revealing high affinity and complex stability. Codon adaptation and in silico cloning revealed higher expression of vaccine in E. coli K12 expression system. Conclusively, the findings of the present study suggest that the designed novel multi-epitopic vaccine holds potential for further experimental validation against the pathogen.
DOI: 10.6084/m9.figshare.17860655.v1
title: Exploring <i>Klebsiella pneumoniae</i> capsule polysaccharide proteins to design multiepitope subunit vaccine to fight against pneumonia

description: <i>Klebsiella pneumoniae</i> is an emerging human pathogen causing neonatal lung disease, catheter-associated infections, and nosocomial outbreaks with high fatality rates. Capsular polysaccharide (CPS) protein plays a major determinant in virulence and is considered as a promising target for vaccine development. In this study, we used immunoinformatic approaches to design a multi-peptide vaccine against K. pneumonia. The epitopes were selected through several immune filters, such as antigenicity, conservancy, nontoxicity, non-allergenicity, binding affinity to HLA alleles, overlapping epitopes, and peptides having common epitopes. Finally, a construct comprising 2 B-Cell, 8 CTL, 2 HTL epitopes, along with adjuvant, linkers was designed. Peptide-HLA interaction analysis showed strong binding of these epitopes with several common HLA molecules. The in silico immune simulation and population coverage analysis of the vaccine showed its potential to evoke strong immune responses.. Further, the interaction between vaccine and immune was evaluated by docking and simulation, revealing high affinity and complex stability. Codon adaptation and in silico cloning revealed higher expression of vaccine in E. coli K12 expression system. Conclusively, the findings of the present study suggest that the designed novel multi-epitopic vaccine holds potential for further experimental validation against the pathogen.
DOI: 10.5281/zenodo.6390615
title: INNOSETA database 2021: industry solutions

description: INNOSETA database 2021: industry solutions
DOI: 10.5281/zenodo.6390614
title: INNOSETA database 2021: industry solutions

description: INNOSETA database 2021: industry solutions
DOI: 10.5281/zenodo.6390598
title: PB2007 French acoustic-articulatory speech database

description: <strong>PB2007 acoustic-articulatory speech dataset</strong> Badin, P.,Bailly G., Ben Youssef A., Elisei F., Savariaux C., Hueber T. <br> Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab, 38000 Grenoble, France<br> <br> LICENSE:<br> ========<br> This dataset is made available under the Creative Commons Attribution Share-Alike (CC-BY-SA) license <br> CREDITS - ATTRIBUTION:<br> ======================<br> If using this dataset, please cite one of the following studies (all of them exploit this dataset) <br> - Ben Youssef, A., Badin, P., Bailly, G. & Heracleous, P. (2009). Acoustic-to-articulatory inversion using speech recognition and trajectory formation based on phoneme hidden Markov models. In Interspeech 2009, vol., pp. 2255-2258. Brighton, UK.<br> - Ben Youssef, A., Badin, P. & Bailly, G. (2010). Can tongue be recovered from face? The answer of data-driven statistical models. In Interspeech 2010 (11th Annual Conference of the International Speech Communication Association) (T. Kobayashi, K. Hirose & S. Nakamura, editors), vol., pp. 2002-2005. Makuhari, Japan.<br> - Hueber T., Bailly G., Badin P., Elisei F., "Speaker Adaptation of an Acoustic-Articulatory Inversion Model<br> using Cascaded Gaussian Mixture Regressions", Proceedings of Interspeech, Lyon, France, 2013, pp. 2753-2757. DATA FILES DESCRIPTION:<br> =======================<br> /_seq/: <br> Electro-magnetic Articulography data, recorded at 100Hz<br> Sensors :<br> PAR01 : LT_x (lower incisor, x coordinate)<br> PAR02 : tip_x (tongue tip, x coordinate)<br> PAR03 : mid_x (tongue dorsum, x coordinate)<br> PAR04 : bck_x (tongue back, x coordinate)<br> PAR05 : LL_vis_x (lower lips, x coordinate)<br> PAR06 : UL_vis_x (upper lips, x coordinate)<br> PAR07 : LT_z (lower incisor, z coordinate)<br> PAR08 : tip_z (tongue tip, z coordinate)<br> PAR09 : mid_z (tongue dorsum, z coordinate)<br> PAR10 : bck_z (tongue back, z coordinate)<br> PAR11 : LL_vis_z (lower lips, z coordinate)<br> PAR12 : UL_vis_z (upper lips, z coordinate) /_wav16: <br> subject audio signal, synchronized with the EMA data<br> Format: PCA wav, 16kHz, 16bits /_lab: phonetic segmentation using the following set<br> __ (long pause), _ (short pause), a, e^ (as in "lait"), e (as in "blé"), i, y (as in "voiture"), u (as in "loup"), o^ (as in "pomme"),x (as in "pneu"), x^ (as in "coeur"), a~ (as in "flan"), e~ (as in "in"), x~ (as in "un"), o~ (as in "mon"), p, t, k, f, s, s^ (as in "CHat"), b, d, g, v, z, z^ (as in "les Gens"), m, n, r, l, w, h, j, o, q (schwa)<br>
DOI: 10.18728/igb-fred-623.0
title: Demnitz_Isotope_Hydrological_Data

description: Meteorological data were measured at Hasenfelde - reported here as hourly data - Wind speed, air temperature, air pressure, humidity, precipitation, radiation Precipitation isotopes - Daily samples at Hasenfelde - Weekly samples at various locations (stream locations) in the catchment Throughfall and throughfall isotopes - Taken at soil moisture measurement sites - weekly samples Discharge - Daily measurements reported at Demnitz Mill (and rating curve) and Berkenbruck Groundwater Level at 5 locations - Elevation of surface, below ground, bottom of well, and time-series of water level (translated to m a.s.l.) Groundwater isotopes at 5 locations - Roughly monthly samples at various locations in the catchment, spread through the center and south reaches of catchment Soil Moisture - Measured at 2 locations (Forest A and Grass A) at 3 depths 20,60, and 100cm with soil temperature at each depth Soil Isotopes - Measured through bulk soil sampling at locations (Forest A and Grass A) at multiple depths - Depth ranges indicate bulk soil samples taken between specified depths Sapflow - Measured average sapflow of forest stand at Forest A
DOI: 10.5281/zenodo.6390597
title: PB2007 French acoustic-articulatory speech database

description: <strong>PB2007 acoustic-articulatory speech dataset</strong> Badin, P.,Bailly G., Ben Youssef A., Elisei F., Savariaux C., Hueber T. <br> Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab, 38000 Grenoble, France<br> <br> LICENSE:<br> ========<br> This dataset is made available under the Creative Commons Attribution Share-Alike (CC-BY-SA) license <br> CREDITS - ATTRIBUTION:<br> ======================<br> If using this dataset, please cite one of the following studies (all of them exploit this dataset) <br> - Ben Youssef, A., Badin, P., Bailly, G. & Heracleous, P. (2009). Acoustic-to-articulatory inversion using speech recognition and trajectory formation based on phoneme hidden Markov models. In Interspeech 2009, vol., pp. 2255-2258. Brighton, UK.<br> - Ben Youssef, A., Badin, P. & Bailly, G. (2010). Can tongue be recovered from face? The answer of data-driven statistical models. In Interspeech 2010 (11th Annual Conference of the International Speech Communication Association) (T. Kobayashi, K. Hirose & S. Nakamura, editors), vol., pp. 2002-2005. Makuhari, Japan.<br> - Hueber T., Bailly G., Badin P., Elisei F., "Speaker Adaptation of an Acoustic-Articulatory Inversion Model<br> using Cascaded Gaussian Mixture Regressions", Proceedings of Interspeech, Lyon, France, 2013, pp. 2753-2757. DATA FILES DESCRIPTION:<br> =======================<br> /_seq/: <br> Electro-magnetic Articulography data, recorded at 100Hz<br> Sensors :<br> PAR01 : LT_x (lower incisor, x coordinate)<br> PAR02 : tip_x (tongue tip, x coordinate)<br> PAR03 : mid_x (tongue dorsum, x coordinate)<br> PAR04 : bck_x (tongue back, x coordinate)<br> PAR05 : LL_vis_x (lower lips, x coordinate)<br> PAR06 : UL_vis_x (upper lips, x coordinate)<br> PAR07 : LT_z (lower incisor, z coordinate)<br> PAR08 : tip_z (tongue tip, z coordinate)<br> PAR09 : mid_z (tongue dorsum, z coordinate)<br> PAR10 : bck_z (tongue back, z coordinate)<br> PAR11 : LL_vis_z (lower lips, z coordinate)<br> PAR12 : UL_vis_z (upper lips, z coordinate) /_wav16: <br> subject audio signal, synchronized with the EMA data<br> Format: PCA wav, 16kHz, 16bits /_lab: phonetic segmentation using the following set<br> __ (long pause), _ (short pause), a, e^ (as in "lait"), e (as in "blé"), i, y (as in "voiture"), u (as in "loup"), o^ (as in "pomme"),x (as in "pneu"), x^ (as in "coeur"), a~ (as in "flan"), e~ (as in "in"), x~ (as in "un"), o~ (as in "mon"), p, t, k, f, s, s^ (as in "CHat"), b, d, g, v, z, z^ (as in "les Gens"), m, n, r, l, w, h, j, o, q (schwa)<br>
DOI: 10.5281/zenodo.4957145
title: Alternating Reading Task in L2 English

description: Audio from Italian-native speakers (d0102 to d2122) and French-native (d3031 to d5051) speakers doing an Alternating Reading Task in English. Participants first talk on their own for us to get a baseline of their speech manners. Then, participants read a story together in alternating speaking turns for four runs. At last, we asked our participants to imitate their interlocutor. Demographics of the participants can be found in the CSV files.
DOI: 10.5287/bodleian:vy6oebymr
title: DNA-PAINT microscope data of a DNA nanostructure printer
DOI: 10.5519/qd.8b2d7alz
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5281/zenodo.6390646
title: INNOSETA database 2021: projects

description: INNOSETA database 2021: projects
DOI: 10.5281/zenodo.6390645
title: INNOSETA database 2021: projects

description: INNOSETA database 2021: projects
DOI: 10.5281/zenodo.6385129
title: Exploratory pilot study on resource allocation along the dementia continuum under constrained and unconstrained budget scenarios

description: Supporting quantitative data for a pilot longitudinal balance of care study.
DOI: 10.5281/zenodo.6385128
title: Exploratory pilot study on resource allocation along the dementia continuum under constrained and unconstrained budget scenarios

description: Supporting quantitative data for a pilot longitudinal balance of care study.
DOI: 10.5281/zenodo.6390705
title: INNOSETA database: training

description: INNOSETA database: training
DOI: 10.5281/zenodo.6390704
title: INNOSETA database: training

description: INNOSETA database: training
DOI: 10.6084/m9.figshare.19434515
title: ESWC 2022 -- Query Validation -- Experimental Data RDF

description: This is an experimental data for our ESWC 2022 paper presented as a knowledge graph in RDF format.
DOI: 10.6084/m9.figshare.19434515.v1
title: ESWC 2022 -- Query Validation -- Experimental Data RDF

description: This is an experimental data for our ESWC 2022 paper presented as a knowledge graph in RDF format.
DOI: 10.5281/zenodo.6390714
title: pCRM9 aeroelastic aircraft wing model for NASTRAN without RBE2

description: <strong>pCRM9 aeroelastic aircraft wing model for NASTRAN without RBE2</strong> Based on the model published by Paul LANCELOT from Delft University of Technology, Faculty of Aerospace Engineering, Department of Aerospace Structures and Materials Lancelot, Paul (2021): pCRM9 aeroelastic aircraft wing model for NASTRAN. 4TU.ResearchData. Dataset. DOI: 10.4121/16834387.v1 Modified by: Saullo G. P. Castro removing RBE2 elements and the input related to the aeroelastic analysis. Delft University of Technology, Faculty of Aerospace Engineering, Department of Aerospace Structures and Materials Contact Information: s.g.p.castro@tudelft.nl Delft University of Technology - Faculty of Aerospace Engineering Kluyverweg 1, 2629HS Delft, The Netherlands <strong>General Introduction</strong> <br> This is the pCRM9 aeroelastic finite element model. It is based on the University of Michigan undeflected CRM geometry: Brooks, Timothy; Kenway, Gaetan G.W.; Martins, Joaquim (2019), “uCRM: undeflected Common Research Model”, Mendeley Data, V1, DOI: 10.17632/gpk4zn73xn.1 This FEM model is tailored for optimisation and load analysis using NASTRAN. <strong>Content</strong> pCRM9_103_MAIN_FILE.bdf is the main file to run the analysis with NASTRAN SOL103, natural frequency solution.<br> pCRM9_CONM2_MTOW.dat contains the weigth and balance informations of the model.<br> pCRM9_mat.dat contains the material informtions of the model (in this case aluminium).<br> pCRM9_model_2.dat contains the finite elements informations of the model (GRID, CQUAD, RBE cards etc.).<br> pCRM9_PSHELL.dat contains the mechanical propeties for each elements of the model.<br> pCRM9_ribs_fem.dat contains the finite elements informations relative to the ribs of the wingbox.<br> pCRM9_FEM_model.PNG is an illustration of the structural model.
DOI: 10.5281/zenodo.6384059
title: pCRM9 aeroelastic aircraft wing model for NASTRAN without RBE2

description: <strong>pCRM9 aeroelastic aircraft wing model for NASTRAN without RBE2</strong> Based on the model published by Paul LANCELOT from Delft University of Technology, Faculty of Aerospace Engineering, Department of Aerospace Structures and Materials Lancelot, Paul (2021): pCRM9 aeroelastic aircraft wing model for NASTRAN. 4TU.ResearchData. Dataset. DOI: 10.4121/16834387.v1 Modified by: Saullo G. P. Castro removing RBE2 elements and the input related to the aeroelastic analysis. Delft University of Technology, Faculty of Aerospace Engineering, Department of Aerospace Structures and Materials Contact Information: s.g.p.castro@tudelft.nl Delft University of Technology - Faculty of Aerospace Engineering Kluyverweg 1, 2629HS Delft, The Netherlands <strong>General Introduction</strong> <br> This is the pCRM9 aeroelastic finite element model. It is based on the University of Michigan undeflected CRM geometry: Brooks, Timothy; Kenway, Gaetan G.W.; Martins, Joaquim (2019), “uCRM: undeflected Common Research Model”, Mendeley Data, V1, DOI: 10.17632/gpk4zn73xn.1 This FEM model is tailored for optimisation and load analysis using NASTRAN. <strong>Content</strong> pCRM9_103_MAIN_FILE.bdf is the main file to run the analysis with NASTRAN SOL103, natural frequency solution.<br> pCRM9_CONM2_MTOW.dat contains the weigth and balance informations of the model.<br> pCRM9_mat.dat contains the material informtions of the model (in this case aluminium).<br> pCRM9_model_2.dat contains the finite elements informations of the model (GRID, CQUAD, RBE cards etc.).<br> pCRM9_PSHELL.dat contains the mechanical propeties for each elements of the model.<br> pCRM9_ribs_fem.dat contains the finite elements informations relative to the ribs of the wingbox.<br> pCRM9_FEM_model.PNG is an illustration of the structural model.
DOI: 10.6084/m9.figshare.19434668
title: Data from 'Do genetically informed distribution models improve range predictions in past climates? A case study with balsam poplar.'

description: Data include:<br>admix: Admixture proportions for 85 balsam poplar (<i>Populus balsamifera</i>)<br>modernOccurrences: occurrences used to train SDMs<br>pollenSites: location of pollen sites, extracted from the Neotoma Paleoecology database
DOI: 10.6084/m9.figshare.19434668.v1
title: Data from 'Do genetically informed distribution models improve range predictions in past climates? A case study with balsam poplar.'

description: Data include:<br>admix: Admixture proportions for 85 balsam poplar (<i>Populus balsamifera</i>)<br>modernOccurrences: occurrences used to train SDMs<br>pollenSites: location of pollen sites, extracted from the Neotoma Paleoecology database
DOI: 10.5281/zenodo.6390718
title: INNOSETA database

description: INNOSETA database
DOI: 10.5281/zenodo.6390717
title: INNOSETA database

description: INNOSETA database
DOI: 10.14457/cu.the.2014.1606
title: Degumming of thai silk by low-pressure plasma treatment
DOI: 10.14457/cu.the.2014.1607
title: Methane dehydrogenation and coupling to ethylene using Ni/HZSM-5 catalysts
DOI: 10.14457/cu.the.2014.1608
title: The Optimization-based synthesis and design of water/wastewater network for water management in petroleum refinery effluent treatment plant
DOI: 10.14457/cu.the.2014.1609
title: Catalytic dehydration of Bio-ethanol to hydrocarbons over SnOx-and SbOx-doped SAPO-34
DOI: 10.6084/m9.figshare.12130980.v136
title: MOOD Maps of Google community mobility change during the COVID-19 outbreak

description: The MOOD project (MOnitoring Outbreak events for Disease surveillance in a data science context. H2020) has geo-referenced the data Google has published as a series of PDF files presenting reports on national and subnational human mobility levels relative to a baseline data of late January 2020. The details and the PDF files can be found at https://www.google.com/covid19/mobility/.<br>More detail on these files can be found at https://www.moodspatialdata.com/humanmobilityforcovid19 <br>The first set of data were released on April 2 2020 and have been revised weekly since then. The maps now utilise the CSV data released by Google. Please note that the maps figures use a mean of the previous three days, while the Google PDFs use a single days data so there will be differences between values in our maps when compare to the Google PDFs.<br>The authors have extracted the majority of these data into a series of excel spreadsheets. Each worksheet provides the data for % change in numbers of records at various types of location categories illustrated by: retail and recreation, grocery and pharmacy, parks and beaches, transit stations, workplaces and residential (columns f to K). A second set of columns calculates the difference of each value from the mean values for each category (columns L to P) Columns A to E contain geographical details. Column Q contains the names used to link to a mapping file.There are separate worksheets for the date of the data from each dated release (e.g. 2903, 0504 etc.) and separate worksheets calculating the changes between specific dates.<br>A second spreadsheet has been added calculating the 3 day moving mean of each day from the 15th of February. Each day is referenced by the Gregorian calendar day count. So day 48 = Feb 17th.<br>The maps (for EU & Global) display these data. We provide 600 dpi jpegs of the Global (“WD”) and European (“EU”) mapped values at the latest date available, for each of the mobility categories: retail and recreation (“retrec”) , grocery and pharmacy (“grocphar”) , parks (“parks”) , transit stations (“transit”), residential (“resid”) and workplaces (“work”). We also provide maps of the changes from the previous week (“ch”).<br>All data extracting and subsequent processing have been carried out by ERGO (Environmental Research Group Oxford, c/o Dept Zoology, University of Oxford) on behalf of the MOOD H2020 project. Data will be periodically updated. Additional maps can be obtained on request to the authors.