cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/fdcswnnkwz.2
title: Aplicativos móveis do setor público brasileiro a nível federal publicados na Google Play

description: Esta base de dados reúne informações sobre aplicativos móveis oferecidos por órgãos das três esferas de Poder do setor público brasileiro a nível federal. Os dados foram coletados em outubro de 2019 tendo como ponto de partida as páginas de desenvolvedores de cada órgão do setor público. Foram mapeados ao todo 203 aplicativos móveis. O conjunto de dados inclui as seguintes variáveis: 1) Nome do aplicativo 2) Id do Aplicativo na Google Play 3) Descrição do app 4) Página de desenvolvedor em que foi publicado 5) Poder 6) Tipo de administração 7) Tipo de Unidade 8) Data da última atualização no momento da coleta 9) Categoria do app na Google Play 10) Tamanho do app (Em MB) 11) Média da nota na Google Play 12) Média da nota dos comentários 13) Diferença entre nota geral e notas dos comentários 14) Número de avaliações recebidas 15) Número de comentários (1/07/2014 e 30/07/2019) 16) Número de respostas dos desenvolvedores 17) Faixa de Instalações 18) Versão disponível no momento da coleta 19) Versão mínima do Android que o app requer 20) Publica política de privacidade? 21) Link do aplicativo na GooglePlay 22) Contato do desenvolvedor
DOI: 10.17632/hx8xbyjmf5.2
title: raw/primary data on statistical analysis and performance evaluationof backtracking search optimisation algorithm compared with its counterpart algorithms

description: This data is collected from the simulation results of three experiments to fairly compare BSA with particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FF), and differential evolution (DE) on minimising 16 benchmark problem by taking these conditions into account. The conditions are initialising control parameters, the dimension of the problems, their search space, and number of iterations needed to minimize a problem, the performance of the computer used to code the algorithms and their programming style, getting a balance on the effect of randomization, and the use of different type of optimization problem in terms of hardness and their cohort. Hence, this dataset is about the unbiased comparison of BSA with PSO, ABC, FF, and DE on solving 16 benchmark problems with different levels of hardness scores in three tests. The experimental results demonstrate that in solving various cohorts of numerical optimisation problems such as problems with different hardness score levels, problem dimensions, and search spaces, BSA is statistically superior to the aforementioned algorithms.
DOI: 10.5281/zenodo.3565740
title: Information Based Behavioural Intervention Study (Guildford case)

description: iSCAPE Dataset Reference No. = DS_PD_014, DS_PD_019 Following datasets are acquired/generated during the implementation of behavioural intervention study in Guildford: Participant's detailed activity-travel diary Introductory and Followup questionnaire responses
DOI: 10.5281/zenodo.3565741
title: Information Based Behavioural Intervention Study (Guildford case)

description: iSCAPE Dataset Reference No. = DS_PD_014, DS_PD_019 Following datasets are acquired/generated during the implementation of behavioural intervention study in Guildford: Participant's detailed activity-travel diary Introductory and Followup questionnaire responses
DOI: 10.15785/sbgrid/687
title: X-Ray Diffraction data from C. elegans RIG-5 IG1 homodimer, source of 6ON6 structure

description: Native dataset
description: Data processed in XDS with parameters from the header successfully. However, significant differences were observed for some important parameters post-processing: ORGX = 1227.4, ORGY = 1272.9, detector distance = 269.33 mm.
DOI: 10.17632/g8rrmmbp7r.1
title: Creating Value with Model Based Systems Engineering

description: Results from online surveys and semi structured interviews among the employees of the researched contstruction company
DOI: 10.5281/zenodo.3565756
title: (POST) Socio-economic and cultural dataset in relation to Persuasive Strategies to boost Energy Efficiency and in the UK, Spain, Greece and Austria

description: The dataset has been created from obtaining post-pilot answers from 106 participants of four different countries in the EU (the questionnaire can be studied in <strong>GreenSoul_Validation_Questionnaire-POST.pdf</strong>). It is composed by several factors which are explained in<strong> POST-coding.ods </strong>file. All these factors are contained in: "<strong>POST-results-socio-economic-model.ods</strong>" and "<strong>POST-results-treatments-evaluation.ods</strong>" along with their answers by participants. Finally, we provided a cleaned version of the dataset to study how can a researcher is able to forecast the ranking that a user will give to different persuasion strategies according to user profiles: "<strong>POST-results-ranking-model.ods</strong>"
DOI: 10.7910/dvn/dzsdpq/h23hva
title: replication files.zip

description: The zip file contains data, statistical code and preserved folder structure for replication of results
DOI: 10.5281/zenodo.3565749
title: DESED_real

description: Link to the associated github repository: https://github.com/turpaultn/Desed Link to the paper: <em>https://hal.inria.fr/hal-02160855</em>. Domestic Environment Sound Event Detection (DESED). <strong>Description</strong> This dataset is the real part of the DESED dataset. It is a subpart of Audioset. There is the material to: Download the subset of Audioset used in DCASE 2019 task 4. <strong>Files:</strong> <strong>DESED_real_metadata.tar.gz</strong> : Annotations of subset of Audioset. <strong>DESED_real_source.tar.gz</strong>: src files you can find on github: https://github.com/turpaultn/DESED . Source files to download dcase2019 files. <strong>If you simply want the synthetic dataset used in DCASE 2019 task 4.</strong> <strong>DESED_real_dcase2019_meta.tar.gz</strong> : Metadata with the code to download the audio like in dcase 2019 task 4.
DOI: 10.5281/zenodo.3565759
title: IO Islamic 166. Mirât-aladwâr, General History

description: IO Islamic 166. Mirât-aladwâr, General History
DOI: 10.5281/zenodo.3565760
title: IO Islamic 166. Mirât-aladwâr, General History

description: IO Islamic 166. Mirât-aladwâr, General History
DOI: 10.5281/zenodo.3565761
title: ENwikIR1k

description: The dev dataset obtained with wikIR tool
DOI: 10.25405/data.ncl.10255736.v1
title: Model data and results for simulations of the Royal Botanic Garden Edinburgh.

description: CityCAT simulations of 1 in 100 year event with durations from 15 minutes to 6 hours. For 3 options. 1) no pond, 2) current pond, 3) current pond with rain garden. Includes input and output datasets and the model. Shetran simulation of the Royal Botanic Garden Edinburgh from January 2018 to July 2019. Includes input and output datasets and the model.
DOI: 10.5519/qd.0a81f4lj
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4484127 records)
DOI: 10.5281/zenodo.3560149
title: AmpliconTagger pipeline databases

description: This file repository holds database files to use with the AmpliconTagger pipeline published in GigaScience (DOI:10.1093/gigascience/giz146). Among which are the SILVA R128 RDP classifier training sets at genus and species levels.
DOI: 10.5281/zenodo.3560150
title: AmpliconTagger pipeline databases

description: This file repository holds database files to use with the AmpliconTagger pipeline published in GigaScience (DOI:10.1093/gigascience/giz146). Among which are the SILVA R128 RDP classifier training sets at genus and species levels.
DOI: 10.6084/m9.figshare.10047416.v1
title: Data-files of ecology and evolution

description: Data used by the manuscript "Sap flow characteristics of <i>Amorpha fruticosa</i>: Implications of water use strategy in a semiarid system with secondary salinization in northwestern China"<br>
DOI: 10.5281/zenodo.3565768
title: IO Islamic 836. Ta'rîkh-i-Alfî, General History

description: IO Islamic 836. Ta’rîkh-i-Alfî, General History
DOI: 10.5281/zenodo.3565769
title: IO Islamic 836. Ta'rîkh-i-Alfî, General History

description: IO Islamic 836. Ta’rîkh-i-Alfî, General History
DOI: 10.5281/zenodo.3565518
title: Mouse Knockdown/knockout Phenotypes of Replicated 40 genes

description: Details of mouse knockdown/knockout Phenotypes for 40 replicated genes
DOI: 10.5281/zenodo.3565519
title: Mouse Knockdown/knockout Phenotypes of Replicated 40 genes

description: Details of mouse knockdown/knockout Phenotypes for 40 replicated genes
DOI: 10.5281/zenodo.3565750
title: A route to school informational intervention for air pollution exposure reduction

description: iSCAPE Dataset Reference No. = DS_PD_020 Following datasets are gathered during the implementation of route to school intervention study in Antwerp (Belgium) Introductory Questionnaire Responses Feedback Questionnaire Responses
DOI: 10.5281/zenodo.3565751
title: A route to school informational intervention for air pollution exposure reduction

description: iSCAPE Dataset Reference No. = DS_PD_020 Following datasets are gathered during the implementation of route to school intervention study in Antwerp (Belgium) Introductory Questionnaire Responses Feedback Questionnaire Responses
DOI: 10.21227/zz4p-hd42
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/sabr-6h27
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: 10.7910/dvn/m8wthu/gy6nmm
title: maven.sqlite3.bz2

description: :unav
DOI: 10.7910/dvn/m8wthu/ikdyap
title: weld.sqlite3.bz2

description: :unav
DOI: 10.5281/zenodo.3565368
title: ATTA Biogeochemistry

description: Datasets collected as part of the collaborative projects studying the impact of leaf cutter ants (Atta cephalotes) on biogeochemical cycling in tropical rainforest soils.
description: {"references": ["Allen, MF (2016). If you want to understand nature, stick your head in the sand! Interdisciplinary Journal of Landscape Architecture", "Aronson, EL, Dierick, D, Botthoff, JK, Oberbauer, S, Zelikova, TJ, Harmon, TC, Rundel, P, Johnson, RF, Swanson, AC, Pinto-Tomas, AA, Artavia-Leon, A, Matarrita-Carranza, B, Allen, MF (2019). ENSO-influenced drought drives methane flux dynamics in a tropical wet forest soil. Journal of Geophysical Research: Biogeosciences. https://doi.org/10.1029/2018JG004832", "Fernandez-Bou, AS, Dierick, D, Swanson, AC, Allen, MF, Alvarado, AGF, Artavia-Le\u00f3n, A, Carrasquillo-Quintana, O, Lachman, DA, Oberbauer, S, Pinto-Tom\u00e1s, AA, Rodr\u00edquez-Reyes, Y, Rundel, P, Schwendenmann, L, Zelikova, TJ, Harmon, TC (2019). The role of the ecosystem engineer, the leaf-cutter ant Atta cephalotes, on soil CO2 dynamics in a wet tropical rainforest. Journal of Geophysical Research: Biogeosciences. https://doi.org/10.1029/2018JG004723", "Harmon, TC, Dierick, D, Trahan, N, Allen, MF, Rundel, PW, Oberbauer, SF, Schwendenmann, L, Zelikova TJ (2015). Low-cost soil CO2 efflux and point concentration sensing systems for terrestrial ecology applications. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210X.12426", "Swanson, AC, Schwendenmann, L, Allen, MF, Aronson EL, Artavia-Le\u00f3n, A, Dierick, D, Fernandez-Bou, AS, Harmon, TC, Murillo-Cruz, C, Oberbauer, SF, Pinto-Tom\u00e1s, AA, Rundel, PW, Zelikova, TJ (2019). Welcome to the Atta world. A framework for understanding the effects of lead-cutter ants on ecosystem functions. Functional Ecology. https://doi.org/10.1111/1365-2435.13319"]}
DOI: 10.7910/dvn/m8wthu/64i5nd
title: drools.sqlite3.bz2

description: :unav
DOI: 10.7910/dvn/m8wthu/ehn72g
title: resteasy.sqlite3.bz2

description: :unav
DOI: 10.7910/dvn/m8wthu/htbrc4
title: Readme.md

description: :unav
DOI: 10.7910/dvn/m8wthu/ib38kc
title: infinispan.sqlite3.bz2

description: :unav
DOI: 10.7910/dvn/m8wthu/ijsznd
title: derby.sqlite3.bz2

description: :unav
DOI: 10.7910/dvn/m8wthu/iubgha
title: seam2.sqlite3.bz2

description: :unav
DOI: 10.7910/dvn/m8wthu/lb6lxx
title: wildfly.sqlite3.bz2

description: :unav
DOI: 10.7910/dvn/m8wthu/mojry8
title: bots.tab

description: :unav
DOI: 10.7910/dvn/m8wthu/xnbhdb
title: groovy.sqlite3.bz2

description: :unav
DOI: 10.7910/dvn/m8wthu/9qtpuh
title: log4j2.sqlite3.bz2

description: :unav
DOI: 10.7910/dvn/m8wthu/hdyaxp
title: pig.sqlite3.bz2

description: :unav
DOI: 10.7910/dvn/m8wthu/mutlkf
title: teiid.sqlite3.bz2

description: :unav
DOI: 10.7910/dvn/m8wthu/ootwwp
title: collaboration_per_issue_type.tab

description: :unav
DOI: 10.21227/y5jg-at52
title: Experimental Data for Metacognitive Skills

description: The increasing student’s enrollment, the cost of education and the need to make learning resources accessible any time at anywhere are propelling educational institutions to develop a pedagogical framework to support in-class activities with online learning courses. Moreover, the increasing deployment of skilled-based courses on online learning environment occasioned by the advancement of smart and wireless technology both in formal and informal paradigm means that there is a need to develop effective ways to support online learning paradigm.The transformation system brought by advancement in smart technologies can provide a learning environment that can support online learning process. The self-regulated learning process has been identified as one of the effective ways of supporting online process. The smart learning environment can be designed and developed to support self-regulated learning process. Themetacognitive skills such as goal setting, time management, helpseeking, task strategy and self-evaluation can be developed in smart learning environment to provide skills enhancement tosupport online learning process. Earlier, we developed artificial neural network (ANN)-based learning agent to support the development of smart learning environment support online learning.However, to test our approach, there is a lack of dataset to implement the applicability of our methodology. In this guiding note, we present the process of generating the dataset, the trainingand testing process how, and the trained weights of the dataset can be used for predicting students’ learning style that can trigger recommendations based on the behaviour of the student inonline learning environment. This dataset is randomly generated simulating the response of students; there are possibilities that using the dataset in similar purposes in future work could resultin different outcomes and this we consider a limitation of the dataset.
DOI: 10.7910/dvn/vx6st9/aj3yho
title: Memo.pdf

description: :unav
DOI: 10.7910/dvn/vx6st9/cugwhj
title: RP_Syntax.do

description: :unav
DOI: 10.7910/dvn/vx6st9/cydtl2
title: Log.txt

description: :unav
DOI: 10.5281/zenodo.3565553
title: Raw microarray gene expression datasets included in the eQTL Catalogue

description: Raw microarray intensity values for five datasets: CEDAR Fairfax_2012 Fairfax_2014 Naranbhai_2015 Kasela_2017
DOI: 10.5281/zenodo.3565554
title: Raw microarray gene expression datasets included in the eQTL Catalogue

description: Raw microarray intensity values for five datasets: CEDAR Fairfax_2012 Fairfax_2014 Naranbhai_2015 Kasela_2017
DOI: 10.5281/zenodo.3565780
title: LCI data for materials and processes comparing energy and water use of aqueous and gas-based metalworking fluids

description: Datasets containing life cycle inventories for materials and processes, and results of the analysis in the article titled, "Comparing energy and water use of aqueous and gas-based metalworking fluids" published in the <em>Journal of Industrial Ecology.</em>
DOI: 10.5281/zenodo.3565781
title: LCI data for materials and processes comparing energy and water use of aqueous and gas-based metalworking fluids

description: Datasets containing life cycle inventories for materials and processes, and results of the analysis in the article titled, "Comparing energy and water use of aqueous and gas-based metalworking fluids" published in the <em>Journal of Industrial Ecology.</em>
DOI: 10.7910/dvn/vx6st9/cxzilr
title: RP_Dataset.tab

description: :unav
DOI: 10.21227/bfed-xz32
title: Experimental Data for Metacognitive Skills

description: The increasing student’s enrollment, the cost of education and the need to make learning resources accessible any time at anywhere are propelling educational institutions to develop a pedagogical framework to support in-class activities with online learning courses. Moreover, the increasing deployment of skilled-based courses on online learning environment occasioned by the advancement of smart and wireless technology both in formal and informal paradigm means that there is a need to develop effective ways to support online learning paradigm.The transformation system brought by advancement in smart technologies can provide a learning environment that can support online learning process. The self-regulated learning process has been identified as one of the effective ways of supporting online process. The smart learning environment can be designed and developed to support self-regulated learning process. Themetacognitive skills such as goal setting, time management, helpseeking, task strategy and self-evaluation can be developed in smart learning environment to provide skills enhancement tosupport online learning process. Earlier, we developed artificial neural network (ANN)-based learning agent to support the development of smart learning environment support online learning.However, to test our approach, there is a lack of dataset to implement the applicability of our methodology. In this guiding note, we present the process of generating the dataset, the trainingand testing process how, and the trained weights of the dataset can be used for predicting students’ learning style that can trigger recommendations based on the behaviour of the student inonline learning environment. This dataset is randomly generated simulating the response of students; there are possibilities that using the dataset in similar purposes in future work could result in different outcomes and this we consider a limitation of the dataset.
DOI: 10.21227/2dck-6y78
title: Experimental Data for Metacognitive Skills

description: The increasing student’s enrollment, the cost of education and the need to make learning resources accessible any time at anywhere are propelling educational institutions to develop a pedagogical framework to support in-class activities with online learning courses. Moreover, the increasing deployment of skilled-based courses on online learning environment occasioned by the advancement of smart and wireless technology both in formal and informal paradigm means that there is a need to develop effective ways to support online learning paradigm.The transformation system brought by advancement in smart technologies can provide a learning environment that can support online learning process. The self-regulated learning process has been identified as one of the effective ways of supporting online process. The smart learning environment can be designed and developed to support self-regulated learning process. The metacognitive skills such as goal setting, time management, helpseeking, task strategy and self-evaluation can be developed in smart learning environment to provide skills enhancement tosupport online learning process. Earlier, we developed artificial neural network (ANN)-based learning agent to support the development of smart learning environment support online learning.However, to test our approach, there is a lack of dataset to implement the applicability of our methodology. In this guiding note, we present the process of generating the dataset, the trainingand testing process how, and the trained weights of the dataset can be used for predicting students’ learning style that can trigger recommendations based on the behaviour of the student inonline learning environment. This dataset is randomly generated simulating the response of students; there are possibilities that using the dataset in similar purposes in future work could resultin different outcomes and this we consider a limitation of the dataset.