cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4678089
title: CKGG: A Chinese Knowledge Graph for High-School Geography Education and Beyond (Reified Version)

description: <strong>List of files</strong> belong_by_admin_reified.nt.gz: "part of lowest administrative divisions" triples converted to triples with "part of" predicate climate_reified.nt.gz: climate type of location, also metadata about climate types (name, description, polygon) cn_code_align_reified.nt.gz: data from National Bureau of Statistics of China (administrative divisions), has property of label, admin code and part of admin division. cn_stat_data_reified.nt.gz: statistical data of province from National Bureau of Statistics of China entity_rank_score_reified.nt.gz: ranking score of entity feature_belong_reified.nt.gz: part of calculated using polygons of places geonames_reified.nt.gz: data loaded from GeoNames, including: timezone, labels, latitude, longitude, belong to administrative divison, belong to country, GeoNames feature code, population. geonames_type_reified.nt.gz: rdf:type of locations globalsolaratlas_reified.nt.gz: annual solar radiation data in kWh/m<sup>2</sup> node_align_reified.nt.gz: alignment to geonames and wikidata ocean_current_reified.nt.gz: influenced by ocean current, also metadata about ocean currents (name, linestring) precip_reified_xxx.nt.gz: monthly and annual precipitation data of entities in meters temperature_reified_xxx.nt.gz: monthly and annual temperature data of entities in celsius degree to_ocean_distance_reified.nt.gz: location distance to ocean in meters wiki_links_reified.nt.gz: wikipedia links of locations wikidata_natural_earth_reified.nt.gz: reified data loaded from wikidata and natural earth (label, coordinate, polygon) <strong>Ontology</strong> ontology.owl: CKGG ontology mapping.owl: CKGG ontology mapping to other ontologies (Clinga, DBpedia, GeoSPARQL, SKOS) <strong>For more details, please refer to http://w3id.org/ckgg</strong>
DOI: 10.5281/zenodo.4678088
title: CKGG: A Chinese Knowledge Graph for High-School Geography Education and Beyond (Reified Version)

description: <strong>List of files</strong> belong_by_admin_reified.nt.gz: "part of lowest administrative divisions" triples converted to triples with "part of" predicate climate_reified.nt.gz: climate type of location, also metadata about climate types (name, description, polygon) cn_code_align_reified.nt.gz: data from National Bureau of Statistics of China (administrative divisions), has property of label, admin code and part of admin division. cn_stat_data_reified.nt.gz: statistical data of province from National Bureau of Statistics of China entity_rank_score_reified.nt.gz: ranking score of entity feature_belong_reified.nt.gz: part of calculated using polygons of places geonames_reified.nt.gz: data loaded from GeoNames, including: timezone, labels, latitude, longitude, belong to administrative divison, belong to country, GeoNames feature code, population. geonames_type_reified.nt.gz: rdf:type of locations globalsolaratlas_reified.nt.gz: annual solar radiation data in kWh/m<sup>2</sup> node_align_reified.nt.gz: alignment to geonames and wikidata ocean_current_reified.nt.gz: influenced by ocean current, also metadata about ocean currents (name, linestring) precip_reified_xxx.nt.gz: monthly and annual precipitation data of entities in meters temperature_reified_xxx.nt.gz: monthly and annual temperature data of entities in celsius degree to_ocean_distance_reified.nt.gz: location distance to ocean in meters wiki_links_reified.nt.gz: wikipedia links of locations wikidata_natural_earth_reified.nt.gz: reified data loaded from wikidata and natural earth (label, coordinate, polygon) <strong>Ontology</strong> ontology.owl: CKGG ontology mapping.owl: CKGG ontology mapping to other ontologies (Clinga, DBpedia, GeoSPARQL, SKOS) <strong>For more details, please refer to http://w3id.org/ckgg</strong>
DOI: 10.5281/zenodo.4668711
title: CKGG: A Chinese Knowledge Graph for High-School Geography Education and Beyond

description: <strong>List of files</strong> admin.nt.gz: part of administrative divisions (country, admin1, admin2, admin3, admin4) admin_code.nt.gz: administrative division code altitude.nt.gz: altitude in meters belong_by_admin.nt.gz: "part of lowest administrative divisions" triples converted to triples with "part of" predicate climate.nt.gz: climate type of location, also metadata about climate types (name, description, polygon) cn_stat_data.nt.gz: statistical data of province from National Bureau of Statistics of China coord.nt.gz: coordinate (latitude and longitude) entity_rank_score.nt.gz: ranking score of entity feature_belong.nt.gz: part of calculated using polygons of places geonames_code.nt.gz: geonames feature code gis.nt.gz: polygons of place globalsolaraltas.nt.gz: annual solar radiation data in kWh/m<sup>2</sup> label.nt.gz: labels node_align.nt.gz: alignment to geonames and wikidata ocean_current.nt.gz: influenced by ocean current, also metadata about ocean currents (name, linestring) pop.nt.gz: population precip_xxx.nt.gz: monthly and annual precipitation data of entities in meters temperature_xxx.nt.gz: monthly and annual temperature data of entities in celsius degree to_ocean_distance.nt.gz: location distance to ocean in meters types.nt.gz: rdf:type of locations tz.nt.gz: metadata of timezones (label, offset to gmt) tz_loc.nt.gz: timezone of locations wikilinks.nt.gz: wikipedia links of locations <strong>Ontology</strong> ontology.owl: CKGG ontology mapping.owl: CKGG ontology mapping to other ontologies (Clinga, DBpedia, GeoSPARQL, SKOS) <strong>For more details, please refer to http://w3id.org/ckgg</strong>
DOI: 10.5281/zenodo.4668710
title: CKGG: A Chinese Knowledge Graph for High-School Geography Education and Beyond

description: <strong>List of files</strong> admin.nt.gz: part of administrative divisions (country, admin1, admin2, admin3, admin4) admin_code.nt.gz: administrative division code altitude.nt.gz: altitude in meters belong_by_admin.nt.gz: "part of lowest administrative divisions" triples converted to triples with "part of" predicate climate.nt.gz: climate type of location, also metadata about climate types (name, description, polygon) cn_stat_data.nt.gz: statistical data of province from National Bureau of Statistics of China coord.nt.gz: coordinate (latitude and longitude) entity_rank_score.nt.gz: ranking score of entity feature_belong.nt.gz: part of calculated using polygons of places geonames_code.nt.gz: geonames feature code gis.nt.gz: polygons of place globalsolaraltas.nt.gz: annual solar radiation data in kWh/m<sup>2</sup> label.nt.gz: labels node_align.nt.gz: alignment to geonames and wikidata ocean_current.nt.gz: influenced by ocean current, also metadata about ocean currents (name, linestring) pop.nt.gz: population precip_xxx.nt.gz: monthly and annual precipitation data of entities in meters temperature_xxx.nt.gz: monthly and annual temperature data of entities in celsius degree to_ocean_distance.nt.gz: location distance to ocean in meters types.nt.gz: rdf:type of locations tz.nt.gz: metadata of timezones (label, offset to gmt) tz_loc.nt.gz: timezone of locations wikilinks.nt.gz: wikipedia links of locations <strong>Ontology</strong> ontology.owl: CKGG ontology mapping.owl: CKGG ontology mapping to other ontologies (Clinga, DBpedia, GeoSPARQL, SKOS) <strong>For more details, please refer to http://w3id.org/ckgg</strong>
DOI: 10.6084/m9.figshare.14413751
title: Picozoa: getOrganelle contigs

description: Contigs identified by getOrganelle as potentially encoding a plastid genome<br>
DOI: 10.6084/m9.figshare.14413685
title: Picozoa: plastid genes trees

description: gene alignments and trees for core plastid genes often retained when photosynthesis and the plastid genomes are lost as well as trees including genes from Rhodelphis predicted to be targeted to the plastid.
DOI: 10.6084/m9.figshare.14413751.v1
title: Picozoa: getOrganelle contigs

description: Contigs identified by getOrganelle as potentially encoding a plastid genome<br>
DOI: 10.6084/m9.figshare.14413646
title: Picozoa: LGT trees and selections

description: inferred trees for clusters that are candidates for LGT and selections of putative LGTs for a selection of 33 eukaryotic species/groups
DOI: 10.6084/m9.figshare.14413646.v1
title: Picozoa: LGT trees and selections

description: inferred trees for clusters that are candidates for LGT and selections of putative LGTs for a selection of 33 eukaryotic species/groups
DOI: 10.6084/m9.figshare.14413712
title: Picozoa: cleaned single genes, initial supermatrix tree

description: cleaned alignments and fasta files for 317 phylogenetic marker genes. Supermatrix alignments and reconstructed tree<br>
DOI: 10.6084/m9.figshare.14413598
title: Picozoa: initial SAGs

description: genome assemblies and protein predictions for SAGs that were used for Co-assemblies<br>
DOI: 10.6084/m9.figshare.14413649
title: Picozoa: EGT trees and selections

description: inferred trees for clusters that are candidates for EGT and selections of putative EGTs for a selection of 33 eukaryotic species/groups<br>
DOI: 10.6084/m9.figshare.14413649.v1
title: Picozoa: EGT trees and selections

description: inferred trees for clusters that are candidates for EGT and selections of putative EGTs for a selection of 33 eukaryotic species/groups<br>
DOI: 10.6084/m9.figshare.14413502
title: Picozoa: final SAGs and COSAGs

description: final SAGs and COSAGs used for all futher analyses. Included are genome assemblies, protein predictions and gff files.<br>
DOI: 10.6084/m9.figshare.14413595
title: Picozoa: mitochondrial contigs and genomes

description: mitochondrial contigs predicted in SAGs and reconstructed (nearly) complete mitochondrial genomes<br>
DOI: 10.6084/m9.figshare.14413595.v1
title: Picozoa: mitochondrial contigs and genomes

description: mitochondrial contigs predicted in SAGs and reconstructed (nearly) complete mitochondrial genomes<br>
DOI: 10.21227/3qdz-r547
title: Thermal Modeling of Heat Transfer within Copper Heated Roller of Laminated Object Manufacturing Apparatus: Accumulated Heat, 2D Axisymmetric Transient and Stationary Heat Conduction Models

description: Laminated Object Manufacturing (LOM) is an efficient rapid prototyping technology (RPT), a suitable technique with many applications. However, its application for wood product prototyping (WPP), especially in micro-thin wood objects, namely different types of wood veneers, the thermal heat effect emanating from LOM heat sources, challenge the quality of the final 3D wood products. Therefore, regarding the parts and processes, which thermally affect the workpiece during object lamination (part-building phase), namely laser cutting and laminate bonding or hot pressing, this present report is a part of the understanding of the heat transfer (heat accumulation, transient and stationary heat conduction) within the LOM heat roller used for thin laminates bonding or hot-pressing.
DOI: 10.21227/r1j8-tc84
title: ANAZMRJNM2021

description: $$$$$$$$$$$$$$$$$$$$$$$$$$$$$To set the network and system paramter, enter the number of the users, services, functions, nodes,and service life time, please check the follwoing parameters:number_function=5number_node=10number_service=5number_user=5 # average number of the arrival user of each time slotEpisode=2000 # Episode for solvingTs=1000 # duration of a period of time that we evaluate the user arrivial and depurture serviceL=1000mu=240 ### average duration of service lifetimebatch_size = 8number_VM=6Moreover to change the coefficient of the cost of each action, please change the parameter $alpha$in line 376.
DOI: 10.6084/m9.figshare.14420297
title: Supplementary materials to IRME 2004

description: The supplementary materials comprise the experimental item set, the teacher questionnaire, the script and the collected and analyzed dataset used in the study.
DOI: 10.6084/m9.figshare.14420297.v1
title: Supplementary materials to IRME 2004

description: The supplementary materials comprise the experimental item set, the teacher questionnaire, the script and the collected and analyzed dataset used in the study.
DOI: 10.11583/dtu.14198831
title: Resource data from the tall BAO mast

description: This resource dataset consists of 1-minute statistics of wind speed and wind direction measurements from a tall met mast at BOA, CO, US. The period includes 3 year of measurements, which starts in 2002.Further site documentation is available here.<br><b>Data files:</b>1) BAO_all.nc includes all periods2) BAO_concurrent.nc only includes concurrent periods for all channels.<br>
DOI: 10.11583/dtu.14198831.v1
title: Resource data from the tall BAO mast

description: This resource dataset consists of 1-minute statistics of wind speed and wind direction measurements from a tall met mast at BOA, CO, US. The period includes 3 year of measurements, which starts in 2002.Further site documentation is available here.<br><b>Data files:</b>1) BAO_all.nc includes all periods2) BAO_concurrent.nc only includes concurrent periods for all channels.<br>
DOI: 10.5281/zenodo.4692428
title: Towards an open-source landscape for 3D CSEM modelling

description: Accompanying data to journal article submitted to Geophysical Journal International (GJI-20-1062). GitHub repo: https://github.com/swung-research/3d-csem-open-source-landscape arXiv.org: https://arxiv.org/abs/2010.12926 The Marlim R3D model can be found at: Original, fine resistivity model: https://doi.org/10.5281/zenodo.400233 Upscaled computational model: https://doi.org/10.5281/zenodo.3748491 CSEM data set: https://doi.org/10.5281/zenodo.1256786 Noise-free CSEM data set: https://doi.org/10.5281/zenodo.1807134
DOI: 10.18419/darus-1401/1
title: Figure 7c,d - SAM on ZnO surface.003

description: AFM raw data of SAM functionalized ZnO (1010) substrate
DOI: 10.18419/darus-1401/2
title: Figure 7a,b - bare ZnO (1010).002

description: AFM raw data of bare ZnO (1010) surface
DOI: 10.18419/darus-1401/3
title: Figure S2 - thioacetate.SPA

description: FTIR raw data of thioacetate
DOI: 10.18419/darus-1401/4
title: Figure S2 - thiol.SPA

description: FTIR raw data of thiol
DOI: 10.18419/darus-1401/5
title: Figure S1 - 1H-NMR_HS(CH2)3N3.rar

description: Proton NMR raw data of thiol
DOI: 10.18419/darus-1401/6
title: Figure S1 - 1H-NMR_CH3COS(CH2)3N3.rar

description: Proton NMR raw data of thioacetate
DOI: 10.18419/darus-1401/7
title: Figure 8 - XPS data RT- extended eV range for S peaks- Raw Data-C1s.tab

description: :unav
DOI: 10.18419/darus-1401/8
title: Figure 8 - XPS data RT- Raw Data.xls

description: :unav
DOI: 10.18419/darus-1401/9
title: Figure 9 - XPS data - RT - Raw Data.xls

description: :unav
DOI: 10.18419/darus-1401/10
title: Figure 9 - XPS data - 100°C - Raw data.xls

description: :unav
DOI: 10.18419/darus-1401/11
title: Figure 9 - XPS data - 300°C - Raw data.xls

description: :unav
DOI: 10.18419/darus-1401/12
title: Figure 8 - XPS data RT- extended eV range for S peaks- Raw Data.xls

description: :unav
DOI: 10.18419/darus-1401/13
title: Figure 8 - XPS data RT- extended eV range for S peaks- Raw Data-S2p.tab

description: :unav
DOI: 10.18419/darus-1401/14
title: Figure 8 - XPS data RT- Raw Data-S2p.tab

description: :unav
DOI: 10.18419/darus-1401/15
title: Figure 8 - XPS data RT- Raw Data-O1s.tab

description: :unav
DOI: 10.18419/darus-1401/16
title: Figure 8 - XPS data RT- Raw Data-Zn2p.tab

description: :unav
DOI: 10.18419/darus-1401/17
title: Figure 8 - XPS data RT- Raw Data-N1s.tab

description: :unav
DOI: 10.18419/darus-1401/18
title: Figure 8 - XPS data RT- Raw Data-ppAt.tab

description: :unav
DOI: 10.18419/darus-1401/19
title: Figure 8 - XPS data RT- Raw Data-C1s.tab

description: :unav
DOI: 10.18419/darus-1401/20
title: Figure 8 - XPS data RT- Raw Data-Sur_ar.tab

description: :unav
DOI: 10.18419/darus-1401/21
title: Figure 9 - XPS data - RT - Raw Data-C1s.tab

description: :unav
DOI: 10.18419/darus-1401/22
title: Figure 9 - XPS data - RT - Raw Data-S2p.tab

description: :unav
DOI: 10.18419/darus-1401/23
title: Figure 9 - XPS data - RT - Raw Data-Zn2p-AR.tab

description: :unav
DOI: 10.18419/darus-1401/24
title: Figure 9 - XPS data - RT - Raw Data-O1s.tab

description: :unav
DOI: 10.18419/darus-1401/25
title: Figure 9 - XPS data - RT - Raw Data-Survey-1.tab

description: :unav
DOI: 10.18419/darus-1401/26
title: Figure 9 - XPS data - RT - Raw Data-N1s.tab

description: :unav
DOI: 10.18419/darus-1401/27
title: Figure 9 - XPS data - RT - Raw Data-C1s AR.tab

description: :unav
DOI: 10.18419/darus-1401/28
title: Figure 9 - XPS data - RT - Raw Data-Zn2p.tab

description: :unav
DOI: 10.18419/darus-1401/29
title: Figure 9 - XPS data - RT - Raw Data-Si2p.tab

description: :unav
DOI: 10.18419/darus-1401/30
title: Figure 9 - XPS data - 100°C - Raw data-C1s_AR.tab

description: :unav
DOI: 10.18419/darus-1401/31
title: Figure 9 - XPS data - 100°C - Raw data-Survey.tab

description: :unav
DOI: 10.18419/darus-1401/32
title: Figure 9 - XPS data - 100°C - Raw data-Zn2p3_AR.tab

description: :unav
DOI: 10.18419/darus-1401/33
title: Figure 9 - XPS data - 100°C - Raw data-Zn2p3.tab

description: :unav
DOI: 10.18419/darus-1401/34
title: Figure 9 - XPS data - 100°C - Raw data-Si2p.tab

description: :unav
DOI: 10.18419/darus-1401/35
title: Figure 9 - XPS data - 100°C - Raw data-N1s.tab

description: :unav
DOI: 10.18419/darus-1401/36
title: Figure 9 - XPS data - 100°C - Raw data-ppAt.tab

description: :unav
DOI: 10.18419/darus-1401/37
title: Figure 9 - XPS data - 100°C - Raw data-O1s.tab

description: :unav
DOI: 10.18419/darus-1401/38
title: Figure 9 - XPS data - 100°C - Raw data-S2p.tab

description: :unav
DOI: 10.18419/darus-1401/39
title: Figure 9 - XPS data - 100°C - Raw data-C1s.tab

description: :unav
DOI: 10.18419/darus-1401/40
title: Figure 8 - XPS data RT- Raw Data-ppAt.tab

description: :unav
DOI: 10.18419/darus-1401/41
title: Figure 9 - XPS data - 300°C - Raw data-S2p.tab

description: :unav
DOI: 10.18419/darus-1401/42
title: Figure 9 - XPS data - 300°C - Raw data-Zn2p3_AR8.tab

description: :unav
DOI: 10.18419/darus-1401/43
title: Figure 9 - XPS data - 300°C - Raw data-Survey.tab

description: :unav
DOI: 10.18419/darus-1401/44
title: Figure 9 - XPS data - 300°C - Raw data-Zn2p3.tab

description: :unav
DOI: 10.18419/darus-1401/45
title: Figure 9 - XPS data - 300°C - Raw data-C1s_AR8.tab

description: :unav
DOI: 10.18419/darus-1401/46
title: Figure 9 - XPS data - 300°C - Raw data-ppAt.tab

description: :unav
DOI: 10.18419/darus-1401/47
title: Figure 9 - XPS data - 300°C - Raw data-N1s.tab

description: :unav
DOI: 10.18419/darus-1401/48
title: Figure 9 - XPS data - 300°C - Raw data-O1s.tab

description: :unav
DOI: 10.18419/darus-1401/49
title: Figure 9 - XPS data - 300°C - Raw data-C1s.tab

description: :unav
DOI: 10.18419/darus-1401/50
title: Figure 9 - XPS data - 300°C - Raw data-Si2p.tab

description: :unav
DOI: 10.18419/darus-1401
title: Data from Bill group for: Adsorption of azide-functionalized thiol linkers on zinc oxide surfaces

description: Primary and processed data files obtained by the Bill group in relation to the figures appearing in the journal article and supporting information are included. Specifically, Atomic force microscopy (AFM) images of bare ZnO(101̄0) surface and ZnO surface immobilized with 3-azidopropyl thiol, X-ray photoelectron spectroscopy (XPS), proton nuclear magnetic resonance (¹H - NMR) and Fourier transform infrared spectroscopy - attenuated total reflectance (FTIR-ATR) data are provided. The labelling used is the same as that of the publication.
DOI: 10.6084/m9.figshare.14406434
title: Faculty readiness for online teaching at Imam Abdulrahman Bin Faisal University during the COVID-19 crisis: a cross-sectional study

description: <b>Background:</b> The outbreak of the COVID-19 pandemic has affected the education sector around the world. In order to control the spread of the virus, eLearning practice has been introduced in Saudi higher education. Such online communication has become an important tool to narrow the teaching practice gap. This study assessed the characteristics of eLearning and distance learning and the inclination of Imam Abdulrahman BinFaisal University (IAU) faculty members in terms of skills, and managing classes and tests using online learning tools. <b>Methods:</b> A QuestionPro questionnaire with 22 questions on eLearning experience, training experience, and skills and knowledge in the educational process of IAU teaching faculty was conducted through the online university e-mail domain. The questionnaire’s reliability was studied using Cronbach’s a coefficient. The criterion value was statistically studied using the KMO (Kaiser-Meyer-Olkin) and Bartlett’s test. The variables associated with the present survey model were analysed using Structural Equation Modelling (SEM). <b>Results: </b>The study showed positive responses and readiness (skills and abilities) and the effectiveness of IAU’s faculty members to perform e-learning activities during COVID-19. IAU faculty received a strong positive response, and the respondents were also impressed with and agreed on trainer knowledge, session management, communication and expertise on training topics. <b>Conclusions: </b>The positive response indicates the readiness of IAU to provide the necessary support (tools, information and updates) required for a successful online educational process.
DOI: 10.6084/m9.figshare.14406434.v1
title: Faculty readiness for online teaching at Imam Abdulrahman Bin Faisal University during the COVID-19 crisis: a cross-sectional study

description: <b>Background:</b> The outbreak of the COVID-19 pandemic has affected the education sector around the world. In order to control the spread of the virus, eLearning practice has been introduced in Saudi higher education. Such online communication has become an important tool to narrow the teaching practice gap. This study assessed the characteristics of eLearning and distance learning and the inclination of Imam Abdulrahman BinFaisal University (IAU) faculty members in terms of skills, and managing classes and tests using online learning tools. <b>Methods:</b> A QuestionPro questionnaire with 22 questions on eLearning experience, training experience, and skills and knowledge in the educational process of IAU teaching faculty was conducted through the online university e-mail domain. The questionnaire’s reliability was studied using Cronbach’s a coefficient. The criterion value was statistically studied using the KMO (Kaiser-Meyer-Olkin) and Bartlett’s test. The variables associated with the present survey model were analysed using Structural Equation Modelling (SEM). <b>Results: </b>The study showed positive responses and readiness (skills and abilities) and the effectiveness of IAU’s faculty members to perform e-learning activities during COVID-19. IAU faculty received a strong positive response, and the respondents were also impressed with and agreed on trainer knowledge, session management, communication and expertise on training topics. <b>Conclusions: </b>The positive response indicates the readiness of IAU to provide the necessary support (tools, information and updates) required for a successful online educational process.
DOI: 10.5257/unido/ind4/2020
title: UNIDO INDSTAT 4 (2020 Edition)

description: The INDSTAT4 database contains time series data from 1990 onwards. Data are available for country, year and ISIC at the 3- and 4-digit level of ISIC (Revision 3), which comprises more than 150 manufacturing sectors and sub-sectors. All value data are originally stored in national currency at current prices. The system allows for data conversion from national currency into current US dollars using the average period exchange rates as given in the International Monetary Fund International Financial Statistics (IFS). In support of the statistical data, relevant metadata information is collected from national data suppliers and OECD, which is available to users in a highly flexible form.