cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/pbh7kz5vj9.1
title: ASYPCK, a program for calculating asymptotic solutions of the coupled equations of electron collision theory

description: Title of program: ASYPRO Catalogue Id: AAJB_v1_0 Nature of problem ASYPRO is a preprocessor for the program ASYPCK. Versions of this program held in the CPC repository in Mendeley Data AAJB_v1_0; ASYPRO; 10.1016/0010-4655(80)90070-3 AAJB_v2_0; ASYPRO2; 10.1016/0010-4655(81)90032-1 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)
DOI: 10.17632/wbf33js9y2.1
title: ASYPCK2, an extended version of ASYPCK

description: Title of program: ASYSLIM Catalogue Id: AAJA_v3_0 [AANM] Nature of problem The program ASYPCK2 contains a large variety of facilities, some of which require a reasonable amount of core. In some instances the program ASYPCK2 is run for cases which do not use all of the facilities and hence there can arise a wastage of core. A streamlined version of ASYPCK2 has been developed, removing some of the features and methods of the program, but which can require considerably less high speed storage. This streamlined version of ASYPCK2 is known as ASYSLIM, and is produced from A ... Versions of this program held in the CPC repository in Mendeley Data AAJA_v1_0; ASYPCK; 10.1016/0010-4655(80)90070-3 AAJA_v2_0; ASYPCK2; 10.1016/0010-4655(81)90032-1 AAJA_v3_0; ASYSLIM; 10.1016/0010-4655(81)90032-1 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)
DOI: 10.26302/sshade/experiment_op_20180717_001
title: Cu K edge XAS HERFD (Kalpha1) of CuO at ambient conditions
DOI: 10.7910/dvn/qe2tkh/rjgbll
title: sensitivity_analysis.m

description: :unav
DOI: 10.26302/sshade/experiment_dt_20170706_004
title: Fe K edge XAS transmission of natural hedenbergite CaFeSi2O6 at ambient conditions
DOI: 10.26302/sshade/experiment_gs_20170713_003
title: Ag K edge XAS transmission of Ag2CO3
DOI: 10.5286/isis.e.rb13786-1
title: Empty GEM with candle stick 12Wx34H chop:19302,0off,19393
DOI: 10.5286/isis.e.rb13786-2
title: KCN RT test on candle stick 12Wx34H chop:19302,0off,19393
DOI: 10.5286/isis.e.rb13786-3
title: 8mm Can in CCR 12Wx34H chop:19302,0off,19393
DOI: 10.5281/zenodo.3564445
title: Flesh yield of commercial mussels cultured in the Ria de Ares Betanzos

description: Flesh yield of commercial mussels cultured in the Ría de Ares-Betanzos (A Coruña, NW Spain). The flesh yield is calculated as the percentage of the total weight of 1 kg of live mussels &gt; 50 mm collected in a mussel raft that is meat weight after opening the valves with water vapour. Flesh yield data have been aggregated monthly and the seasonal cycle of each year has been adjusted to the following harmonic function: FY (%) = A1 + A2* cosine (2*Pi*t/12 + A3) where A1 is the seasonal average value of FY, A2 is half the amplitude of the seasonal cycle of FY; and A3 is the month of the year when FY is halfway between the seasonal minimum and the seasonal maximum. The values of A1, A2 and A3 for years 2002 to 2012 are reported for the two mussel cultivation areas of the Ría de Ares-Betanzos (Arnela and Lorbé) These data have been published in X.A. Álvarez-Salgado, U. Labarta, V. Vinseiro and M.J. Fernández Reiriz (2017). Environmental drivers of mussels flesh yield in a coastal upwelling system. Ecological Indicators 79, 323-329.
DOI: 10.5281/zenodo.3564444
title: Flesh yield of commercial mussels cultured in the Ria de Ares Betanzos

description: Flesh yield of commercial mussels cultured in the Ría de Ares-Betanzos (A Coruña, NW Spain). The flesh yield is calculated as the percentage of the total weight of 1 kg of live mussels &gt; 50 mm collected in a mussel raft that is meat weight after opening the valves with water vapour. Flesh yield data have been aggregated monthly and the seasonal cycle of each year has been adjusted to the following harmonic function: FY (%) = A1 + A2* cosine (2*Pi*t/12 + A3) where A1 is the seasonal average value of FY, A2 is half the amplitude of the seasonal cycle of FY; and A3 is the month of the year when FY is halfway between the seasonal minimum and the seasonal maximum. The values of A1, A2 and A3 for years 2002 to 2012 are reported for the two mussel cultivation areas of the Ría de Ares-Betanzos (Arnela and Lorbé) These data have been published in X.A. Álvarez-Salgado, U. Labarta, V. Vinseiro and M.J. Fernández Reiriz (2017). Environmental drivers of mussels flesh yield in a coastal upwelling system. Ecological Indicators 79, 323-329.
DOI: 10.5286/isis.e.rb13786-4
title: KCN in CCR at RT in 8mm V can 12Wx34H chop:19302,0off,19393
DOI: 10.5517/ccdc.csd.cc22dsx6
title: CCDC 1918928: Experimental Crystal Structure Determination

description: Related Article: Nathaniel Owusu Boadi, Selina Ama Saah, Johannes A. M. Awudza|2020|J.Chem.|2020|7|doi:10.1155/2020/8349549
DOI: 10.5286/isis.e.rb13786-5
title: KCN warming CCR stopped 1 12Wx34H chop:19302,0off,19393
DOI: 10.5286/isis.e.rb13786-6
title: KCN cooling in new CCR 1 12Wx34H chop:19302,0off,19393
DOI: 10.26302/sshade/experiment_gs_20170712_002
title: Ag K edge XAS transmission of AgO
DOI: 10.5286/isis.e.rb13786-7
title: MnO in CCR(II) at RT 12Wx34H chop:19302,0off,19393
DOI: 10.26302/sshade/experiment_gs_20170712_003
title: Ag K edge XAS transmission of natural Ag2S
DOI: 10.26302/sshade/experiment_soc_20181115_001
title: Fe K edge XAS HERFD (Kbeta1,3) and XES of synthetic magnetite Fe3O4 at ambient conditions
DOI: 10.21227/52xn-qe80
title: Experimental Data for Metacognitive Skills

description: The increasing student’s enrollment, the cost ofeducation and the need to make learning resources accessibleany time at anywhere are propelling educational institutions todevelop a pedagogical framework to support in-class activitieswith online learning courses. Moreover, the increasing deploymentof skilled-based courses on online learning environmentoccasioned by the advancement of smart and wireless technologyboth in formal and informal paradigm means that there is a needto develop effective ways to support online learning paradigm.The transformation system brought by advancement in smarttechnologies can provide a learning environment that can supportonline learning process. The self-regulated learning process hasbeen identified as one of the effective ways of supportingonline process. The smart learning environment can be designedand developed to support self-regulated learning process. Themetacognitive skills such as goal setting, time management, helpseeking,task strategy and self-evaluation can be developed insmart learning environment to provide skills enhancement tosupport online learning process. Earlier, we developed artificialneural network (ANN)-based learning agent to support the developmentof smart learning environment support online learning.However, to test our approach, there is a lack of dataset toimplement the applicability of our methodology. In this guidingnote, we present the process of generating the dataset, the trainingand testing process how, and the trained weights of the datasetcan be used for predicting students’ learning style that cantrigger recommendations based on the behaviour of the student inonline learning environment. This dataset is randomly generatedsimulating the response of students; there are possibilities thatusing the dataset in similar purposes in future work could resultin different outcomes and this we consider a limitation of the dataset.
DOI: 10.26302/sshade/experiment_ik_20180418_001
title: Ce L3 edge XAS HERFD of CeO2 (nanos) at 10K
DOI: 10.26302/sshade/experiment_gs_20161209_001
title: Ag K edge XAS fluorescence of frozen Ag malate solution
DOI: 10.21227/83qr-8d34
title: Experimental Data for Metacognitive Skills

description: The increasing student’s enrollment, the cost ofeducation and the need to make learning resources accessibleany time at anywhere are propelling educational institutions todevelop a pedagogical framework to support in-class activitieswith online learning courses. Moreover, the increasing deploymentof skilled-based courses on online learning environmentoccasioned by the advancement of smart and wireless technologyboth in formal and informal paradigm means that there is a needto develop effective ways to support online learning paradigm.The transformation system brought by advancement in smarttechnologies can provide a learning environment that can supportonline learning process. The self-regulated learning process hasbeen identified as one of the effective ways of supportingonline process. The smart learning environment can be designedand developed to support self-regulated learning process. Themetacognitive skills such as goal setting, time management, helpseeking,task strategy and self-evaluation can be developed insmart learning environment to provide skills enhancement tosupport online learning process. Earlier, we developed artificialneural network (ANN)-based learning agent to support the developmentof smart learning environment support online learning.However, to test our approach, there is a lack of dataset toimplement the applicability of our methodology. In this guidingnote, we present the process of generating the dataset, the trainingand testing process how, and the trained weights of the datasetcan be used for predicting students’ learning style that cantrigger recommendations based on the behaviour of the student inonline learning environment. This dataset is randomly generatedsimulating the response of students; there are possibilities thatusing the dataset in similar purposes in future work could resultin different outcomes and this we consider a limitation of the dataset.
DOI: 10.26302/sshade/experiment_gs_20170713_001
title: Ag K edge XAS transmission of Ag3PO4
DOI: 10.21227/h4np-3126
title: Experimental Data for Metacognitive Skills

description: The increasing student’s enrollment, the cost ofeducation and the need to make learning resources accessibleany time at anywhere are propelling educational institutions todevelop a pedagogical framework to support in-class activitieswith online learning courses. Moreover, the increasing deploymentof skilled-based courses on online learning environmentoccasioned by the advancement of smart and wireless technologyboth in formal and informal paradigm means that there is a needto develop effective ways to support online learning paradigm.The transformation system brought by advancement in smarttechnologies can provide a learning environment that can supportonline learning process. The self-regulated learning process hasbeen identified as one of the effective ways of supportingonline process. The smart learning environment can be designedand developed to support self-regulated learning process. Themetacognitive skills such as goal setting, time management, helpseeking,task strategy and self-evaluation can be developed insmart learning environment to provide skills enhancement tosupport online learning process. Earlier, we developed artificialneural network (ANN)-based learning agent to support the developmentof smart learning environment support online learning.However, to test our approach, there is a lack of dataset toimplement the applicability of our methodology. In this guidingnote, we present the process of generating the dataset, the trainingand testing process how, and the trained weights of the datasetcan be used for predicting students’ learning style that cantrigger recommendations based on the behaviour of the student inonline learning environment. This dataset is randomly generatedsimulating the response of students; there are possibilities thatusing the dataset in similar purposes in future work could resultin different outcomes and this we consider a limitation of the dataset.
DOI: 10.21227/0500-b407
title: Experimental Data for Metacognitive Skills

description: The increasing student’s enrollment, the cost ofeducation and the need to make learning resources accessibleany time at anywhere are propelling educational institutions todevelop a pedagogical framework to support in-class activitieswith online learning courses. Moreover, the increasing deploymentof skilled-based courses on online learning environmentoccasioned by the advancement of smart and wireless technologyboth in formal and informal paradigm means that there is a needto develop effective ways to support online learning paradigm.The transformation system brought by advancement in smarttechnologies can provide a learning environment that can supportonline learning process. The self-regulated learning process hasbeen identified as one of the effective ways of supportingonline process. The smart learning environment can be designedand developed to support self-regulated learning process. Themetacognitive skills such as goal setting, time management, helpseeking,task strategy and self-evaluation can be developed insmart learning environment to provide skills enhancement tosupport online learning process. Earlier, we developed artificialneural network (ANN)-based learning agent to support the developmentof smart learning environment support online learning.However, to test our approach, there is a lack of dataset toimplement the applicability of our methodology. In this guidingnote, we present the process of generating the dataset, the trainingand testing process how, and the trained weights of the datasetcan be used for predicting students’ learning style that cantrigger recommendations based on the behaviour of the student inonline learning environment. This dataset is randomly generatedsimulating the response of students; there are possibilities thatusing the dataset in similar purposes in future work could resultin different outcomes and this we consider a limitation of the dataset.
DOI: 10.26302/sshade/experiment_op_20180115_001
title: Br K edge XAS transmission and HERFD of lithium bromide anydrous salt at ambient conditions
DOI: 10.26302/sshade/experiment_soc_20181115_002
title: Fe K edge XAS HERFD (Kbeta1,3) and XES of synthetic cementite Fe3C at ambient conditions
DOI: 10.26302/sshade/experiment_soc_20181115_005
title: Fe K edge XAS HERFD (Kbeta1,3) and XES of synthetic maghemite gamma-Fe2O3 at ambient conditions
DOI: 10.17632/5j4n53zmck.1
title: Large theropod teeth database and multivariate analyses corrected

description: Database for large theropod teeth modified from Hendrickx et al. (2015) by removing spinosaurids and tyrannosauroids, and adding Torvosaurus teeth by Malafaia et al. (2017) and Tyrannotitan teeth by myself. Also I provide PCA and DA results.
DOI: 10.21227/bw1d-6g88
title: Experimental Data for Metacognitive Skills

description: The increasing student’s enrollment, the cost ofeducation and the need to make learning resources accessibleany time at anywhere are propelling educational institutions todevelop a pedagogical framework to support in-class activitieswith online learning courses. Moreover, the increasing deploymentof skilled-based courses on online learning environmentoccasioned by the advancement of smart and wireless technologyboth in formal and informal paradigm means that there is a needto develop effective ways to support online learning paradigm.The transformation system brought by advancement in smarttechnologies can provide a learning environment that can supportonline learning process. The self-regulated learning process hasbeen identified as one of the effective ways of supportingonline process. The smart learning environment can be designedand developed to support self-regulated learning process. Themetacognitive skills such as goal setting, time management, helpseeking,task strategy and self-evaluation can be developed insmart learning environment to provide skills enhancement tosupport online learning process. Earlier, we developed artificialneural network (ANN)-based learning agent to support the developmentof smart learning environment support online learning.However, to test our approach, there is a lack of dataset toimplement the applicability of our methodology. In this guidingnote, we present the process of generating the dataset, the trainingand testing process how, and the trained weights of the datasetcan be used for predicting students’ learning style that cantrigger recommendations based on the behaviour of the student inonline learning environment. This dataset is randomly generatedsimulating the response of students; there are possibilities thatusing the dataset in similar purposes in future work could resultin different outcomes and this we consider a limitation of the dataset.
DOI: 10.17632/8vp43836t5.1
title: Theropod teeth matrix for phylogenetic analysis

description: Matrix taken from Young et al. (2019), adding codings for Uruguayan and Tanzanian taxa.
DOI: 10.17632/9x62992sw6.2
title: Brazilian Forest Dataset

description: Dataset created to monitor the Brazilian vegetation combining 4 different systems: (i) an inventory of Brazilian seed plants created to map the country biodiversity; (ii) the Fraction of Absorbed Photosynthetically Active Radiation; (iii) the NASA Power database to include meteorological data; and (iv) the DATASUS system which makes available geographical information from Brazil. The final dataset comprises a large number of attributes including meteorological and vegetational features, which correspond to a total of 8 and 8471 attributes, respectively. Moreover, the dataset contains 20 labels and 865 geographical positions (latitude and longitude) used during the vegetation monitoring. This project makes available raw and preprocessed data, and Machine Learning models (including source codes) adjusted to : i) predict the occurrence of specific type of vegetation in different region without requiring a constant monitoring task; ii) monitor whether or not the prediction accuracy is changing after collecting new data, which provides an important tool to detect how the environment is evolving over time; and iii) study this dataset as an extra data source to better understand and simulate meteorological influences on predicted vegetation types. More information about the original datasets: (i) http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2175-78602015000401085 (ii) https://fapar.jrc.ec.europa.eu/Home.php (iii) https://power.larc.nasa.gov/ (iv) http://datasus.saude.gov.br/
DOI: 10.17632/dfm22pn63b.1
title: Supporting Information to data in brief article "Metagenomic dataset on lichen Dirinaria sp. from the Great Rann of Kutch and tropical moist deciduous Dang forest of Gujarat"

description: Supporting annotation tables of DG18 and KK01 with COG, SEED and Kegg classification
DOI: 10.17632/4s4f744t24.3
title: Supplement to Le Houedec et al., Suborbital hydrological variability inferred from coupled benthic and planktic foraminiferal-based proxies in the southeastern Mediterranean during the last 19 ka

description: Table S1- total picked out benthic foraminifera from PS009PC core Table S2 - Carbon and oxygen stable isotopes measurements from PS009PC core
DOI: 10.17632/dfm22pn63b.2
title: Supporting Information to data in brief article "Metagenomic dataset on lichen Dirinaria sp. from the Great Rann of Kutch and tropical moist deciduous Dang forest of Gujarat"

description: Supporting annotation tables of DG18 and KK01 with COG, SEED and Kegg classification
DOI: 10.17632/fpj7tjh9j9.1
title: Dataset of "Significant predictors of the construction of new nuclear power plants"
DOI: 10.17632/prm2nwp8xd.1
title: Supporting data PRRSV paper 2019

description: Mean voltage changes for each sample and how they relate to the S/B ratio (Sample/Blank ratio) calculated for each sample and PRRS IDEXX X3 ratios and results. The mean S/B ratios for PRRSV IDEXX X3 positive and negative samples are calculated and shown graphically (Figure 10, in text)
DOI: 10.17632/43b3nx6d7f.1
title: Dataset for "The energy and Gross Domestic Product causality nexus in Latin America 1900-2010"

description: This dataset contains the annual modern energy consumption (the sum of coal, oil, gas, and primary electricity) of 20 Latin American countries from 1856 to year 2010. It has been built from pre-existing compilations by energy historians which have been linked to internationally available statistics after 1970. Data are expressed in terajules (TJ). This energy database is the one used by Levia and Rubio-Varas (2020)(https://doi.org/10.32479/ijeep.8670).
DOI: 10.17632/6s3r2894f6.1
title: Tweets da Família Bolsonaro entre 2017 e 2018

description: Linha do tempo dos perfis oficiais no Twitter de Jair Messias Bolsonaro, Eduardo Bolsonaro, Flávio Bolsonaro e Carlos Bolsonaro entre 2017 e 2018.
DOI: 10.17632/cpgw6yb2wy.1
title: Chemical analyses of the Keivy peralkaline granites

description: Silicate chemical analyses of the Early Precambrian Keivy peralkaline granites from the northeastern Fennoscandian shield (Kola peninsula, Russia) are given in two tables. Data on both individual samples of peralkaline granites and averages for different petrographical varieties of these rocks are available. References on original data sources are given in notes to tables (translation from Russian into English).
DOI: 10.17632/cvw8djvtgx.1
title: Constraining Interseismic Deformation of the Cascadia Subduction Zone: New Insights from Estimates of Vertical Land Motion over Different Timescales

description: urate_glacier.grd: The present-day vertical land motion rates (mm/yr) due to the 20th-century glacier mass loss on a 10-arc minute resolution grid along the Pacific coast of North America urate_gwd.grd: The present-day vertical land motion rates (mm/yr) due to the ground water extraction on a 10-arc minute resolution grid along the Pacific coast of North America
DOI: 10.17632/s7cmcz3wj9.2
title: Sweet pepers maturity data

description: Two crops for each cultivar, red sweet peppers (cultivar: Banji; seed company: Efal) and yellow sweet peppers (cultivar: Liri Seed; company: Hazera), were harvested in a commercial greenhouse in Camehin located in southwest Israel. The first crop (denoted as ‘Set1’) was harvested from the 12th fruit setting on November 2017 and the second crop (denoted as ‘Set2’) from the 16th fruit setting on January 2019. The peppers were selected from four maturity classes. The peppers were initially classified into immature (classes 1 and 2, defined as ‘not to be harvested’) and mature (classes 3 and 4, defined as ‘to be harvested’) peppers by professional human harvesters in the greenhouse. The mature peppers were manually harvested in the morning of the experiment by the harvesters, and then were randomly chosen for the experiment by two expert observers and classified into classes 3 and 4 according to surface color (95–100% colored were defined as class 4; all others were defined as class 3). Classes 1 and 2 were manually harvested by the observers according to surface color (0–5% colored were defined as class 1; all others were defined as class 2). For set 1 the third letter in each file name indicates the maturity level of the pepper. (A-class 1, B-class2, C-class 3, D-class 4) For set 2 the L_ on the file name indicates the maturity level of the pepper (for example R_L2_P10_S1 is a pepper from class 2)
DOI: 10.17632/k37rrshy77.4
title: Raw Data: Intra- and Inter-generational changes in the cortical DNA methylome in response to therapeutic intermittent hypoxia in mice

description: Hyper-and hypo-methylated regions (500 bp) identified by methylated DNA immunoprecipitation and sequencing (MeDIP-seq) and hypo- and hyper-methylated gene promoters (DMGPs) in the prefrontal cortex of male mice treated directly with repetitive hypoxic conditioning (F0-RHC), and in the prefrontal cortex of their untreated male F1-generation offspring (F1-*RHC), relative to age-matched control mice.