cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.22345150.v1
title: Legoland evaluation.pdf

description: Preliminary evaluations were conducted online within two weeks upon release of Phase 1 <em>Metabolism Metro </em>to collect feedback from students. The online questionnaire consisted of 10 questions by which we evaluated if the courseware had been positively affecting students’ motivation and learning outcomes. The 10 questions are charted below. Students would respond by rating on a scale from strongly agree, agree, neutral, disagree to strongly disagree. (1) Have you ever used the courseware? (2) How often did you use the courseware during the course? (3) The courseware motivated me to learn by arousing my interest and curiosity about the subject. (4) The courseware helped to improve my understanding of difficult concepts. (5) The courseware allowed me to learn at my own pace. (6) The courseware helped me understand the course content better. (7) The courseware can highlight important concepts. (8) The courseware is easy to use. (9) The courseware is attractive. (10) In general, I am satisfied with the courseware.
DOI: 10.6084/m9.figshare.22345150
title: Legoland evaluation.pdf

description: Preliminary evaluations were conducted online within two weeks upon release of Phase 1 <em>Metabolism Metro </em>to collect feedback from students. The online questionnaire consisted of 10 questions by which we evaluated if the courseware had been positively affecting students’ motivation and learning outcomes. The 10 questions are charted below. Students would respond by rating on a scale from strongly agree, agree, neutral, disagree to strongly disagree. (1) Have you ever used the courseware? (2) How often did you use the courseware during the course? (3) The courseware motivated me to learn by arousing my interest and curiosity about the subject. (4) The courseware helped to improve my understanding of difficult concepts. (5) The courseware allowed me to learn at my own pace. (6) The courseware helped me understand the course content better. (7) The courseware can highlight important concepts. (8) The courseware is easy to use. (9) The courseware is attractive. (10) In general, I am satisfied with the courseware.
DOI: 10.25946/20410707
title: Primary school teachers’ understanding of the concept of global competence in Punjab, Pakistan: DATASET

description: This research aimed to examine primary school teachers’ understanding of the concept of global competence and the way they perceive they are implementing global competence in their classrooms in Punjab, Pakistan. A qualitative study has been designed to achieve this aim. The proposed research collected data using a point in time survey with purposeful sampling from 121 teachers of primary schools of South Punjab, Central Punjab, and North Punjab. Analysis of the survey questionnaire responses conducted through a thematic analysis to establish the teachers’ understandings and methods of teaching of global concepts. The findings of this research may be used to inform policy design and decisions about how to plan and implement professional development for teachers of Punjab, Pakistan to meet national strategic goals and to contribute in the achievement of Sustainable Development Goals 2030.
DOI: 10.25946/20410707.v1
title: Primary school teachers’ understanding of the concept of global competence in Punjab, Pakistan: DATASET

description: This research aimed to examine primary school teachers’ understanding of the concept of global competence and the way they perceive they are implementing global competence in their classrooms in Punjab, Pakistan. A qualitative study has been designed to achieve this aim. The proposed research collected data using a point in time survey with purposeful sampling from 121 teachers of primary schools of South Punjab, Central Punjab, and North Punjab. Analysis of the survey questionnaire responses conducted through a thematic analysis to establish the teachers’ understandings and methods of teaching of global concepts. The findings of this research may be used to inform policy design and decisions about how to plan and implement professional development for teachers of Punjab, Pakistan to meet national strategic goals and to contribute in the achievement of Sustainable Development Goals 2030.
DOI: 10.5281/zenodo.7485062
title: Dataset and source code for the paper "Finding Near-Optimal Configurations in Colossal Product Spaces with Statistical Confidence"

description: Dataset and source code for the paper "Finding Near-Optimal Configurations in Colossal Product Spaces with Statistical Confidence"
DOI: 10.5281/zenodo.7776627
title: Dataset and source code for the paper "Finding Near-Optimal Configurations in Colossal Product Spaces with Statistical Confidence"

description: Dataset and source code for the paper "Finding Near-Optimal Configurations in Colossal Product Spaces with Statistical Confidence"
DOI: 10.6084/m9.figshare.22345117
title: A New Calculation Scheme for the Vertical Distribution of Salinity in the Ocean Model

description: The data needed for graphing in the article
DOI: 10.6084/m9.figshare.22345117.v2
title: A New Calculation Scheme for the Vertical Distribution of Salinity in the Ocean Model

description: The data needed for graphing in the article
DOI: 10.6084/m9.figshare.22345117.v1
title: A New Calculation Scheme for the Vertical Distribution of Salinity in the Ocean Model

description: The data needed for graphing in the article
DOI: 10.25901/xarp-6r86
title: Collie Flux Data Release 2022_v2
DOI: 10.6084/m9.figshare.22274479
title: Microbiomes of Arctic glacier foreland ecosystems and their metabolic potentials

description: These MAGs were reconstructed using a total of 27 shotgun metagenomic datasets obtained from three distinct Glacier forelands, including Midtre Lovénbreen glacier (Svalbard), Russell glacier (Greenland), and Storglaciaren (Sweden). The MAGs were categorized into high (n=229), medium (n= 447), and low quality (n= 231) and were affiliated with 21 bacterial and 2 archaeal phyla. The taxonomic classification showed that about 3.5%, 22%, and 98% of MAGs remained unclassified at family, genera, and species levels, respectively, and thus potentially belonging to novel taxa. The compendium of MAGs comprised autotrophic, phototrophic, and chemolithotrophic groups including sulfide-oxidizing, sulfate-reducing bacteria, denitrifiers, methanogens, and nitrogen-fixing groups.
DOI: 10.25901/zppq-y387
title: Riggs Creek Flux Data Release 2022_v2
DOI: 10.25901/p9bd-y623
title: Robson Creek Flux Data Release 2022_v2
DOI: 10.25901/4wc2-zc34
title: Samford Ecological Research Facility Flux Data Release 2022_v2
DOI: 10.25901/px80-ad14
title: Silver Plains Flux Data Release 2022_v2
DOI: 10.25901/dc2s-2r22
title: Sturt Plains Flux Data Release 2022_v2
DOI: 10.25901/zjms-tm60
title: Ti Tree East Flux Data Release 2022_v2
DOI: 10.25901/70az-np75
title: Adelaide River Flux Data Release 2022_v2
DOI: 10.6084/m9.figshare.22345192
title: ARRIVE checklist

description: ARRIVE guidelines checklist (author)
DOI: 10.6084/m9.figshare.22345192.v1
title: ARRIVE checklist

description: ARRIVE guidelines checklist (author)
DOI: 10.6084/m9.figshare.22329757
title: Supplementary tables data for “Experimental determination of tin partitioning between titanite, ilmenite and granitic melts using improved capsule designs”

description: This is a excel file of Supplementary tables for “Experimental determination of tin partitioning between titanite, ilmenite and granitic melts using improved capsule designs”
DOI: 10.6084/m9.figshare.22329757.v1
title: Supplementary tables data for “Experimental determination of tin partitioning between titanite, ilmenite and granitic melts using improved capsule designs”

description: This is a excel file of Supplementary tables for “Experimental determination of tin partitioning between titanite, ilmenite and granitic melts using improved capsule designs”
DOI: 10.6084/m9.figshare.22329757.v2
title: Supplementary tables data for “Experimental determination of tin partitioning between titanite, ilmenite and granitic melts using improved capsule designs”

description: This is a excel file of Supplementary tables for “Experimental determination of tin partitioning between titanite, ilmenite and granitic melts using improved capsule designs”
DOI: 10.5281/zenodo.7772737
title: MUSES Leaf Area Index (LAI) 8-Day Global 250m SIN Grid in 2010

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global LAI product at 250m spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2022; Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). This dataset is the MUSES LAI product in 2010. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2009</strong></em> and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2011</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2010 Spatial Resolution: 250m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.1 Valid Range: 0 – 100 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product from Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7772736
title: MUSES Leaf Area Index (LAI) 8-Day Global 250m SIN Grid in 2010

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global LAI product at 250m spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2022; Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). This dataset is the MUSES LAI product in 2010. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2009</strong></em> and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2011</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2010 Spatial Resolution: 250m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.1 Valid Range: 0 – 100 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product from Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7771124
title: MUSES Leaf Area Index (LAI) 8-Day Global 250m SIN Grid in 2011

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global LAI product at 250m spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2022; Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). This dataset is the MUSES LAI product in 2011. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2010</strong></em> and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2012</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2011 Spatial Resolution: 250m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.1 Valid Range: 0 – 100 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product from Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7771123
title: MUSES Leaf Area Index (LAI) 8-Day Global 250m SIN Grid in 2011

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global LAI product at 250m spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2022; Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). This dataset is the MUSES LAI product in 2011. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2010</strong></em> and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2012</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2011 Spatial Resolution: 250m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.1 Valid Range: 0 – 100 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product from Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.6084/m9.figshare.22345114
title: CROATIA_TILE_POPULATION

description: Data for Good Meta. High resolution population estimates for Croatia Includes total population, men, women, women of reproductive age, elderly, youth, and children subgroups. Creative Commons Attribute International License. To facilitate population data retrieval across scale, we segment spatial coverage into 14 equal sized tiles. GPU enabled spatial join via RapidsAI was employed to assign population information with each vector tile. <br> <strong>Reference</strong>: Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 26 March 2023.
DOI: 10.6084/m9.figshare.22345114.v2
title: CROATIA_TILE_POPULATION

description: Data for Good Meta. High resolution population estimates for Croatia Includes total population, men, women, women of reproductive age, elderly, youth, and children subgroups. Creative Commons Attribute International License. To facilitate population data retrieval across scale, we segment spatial coverage into 14 equal sized tiles. GPU enabled spatial join via RapidsAI was employed to assign population information with each vector tile. <br> <strong>Reference</strong>: Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 26 March 2023.
DOI: 10.6084/m9.figshare.22345114.v1
title: CROATIA_TILE_POPULATION

description: Data for Good Meta. High resolution population estimates for Croatia Includes total population, men, women, women of reproductive age, elderly, youth, and children subgroups. Creative Commons Attribute International License. To facilitate population data retrieval across scale, we segment spatial coverage into 14 equal sized tiles. GPU enabled spatial join via RapidsAI was employed to assign population information with each vector tile. <br> <strong>Reference</strong>: Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 26 March 2023.
DOI: 10.34740/kaggle/dsv/5252612
title: Michelin Guide Restaurants
DOI: 10.25901/1rd9-9k32
title: Cow Bay Flux Data Release 2022_v2
DOI: 10.6084/m9.figshare.22346206
title: ChatGPT conversation: Chrome Extension: Get Text.

description: A conversation using OpenAI chat: 03/27/2023 21:52:06
DOI: 10.6084/m9.figshare.22346206.v1
title: ChatGPT conversation: Chrome Extension: Get Text.

description: A conversation using OpenAI chat: 03/27/2023 21:52:06
DOI: 10.34740/kaggle/dsv/5252640
title: Michelin Guide Restaurants
DOI: 10.34740/kaggle/dsv/5252654
title: Michelin Guide Restaurants
DOI: 10.34740/kaggle/dsv/5252656
title: Michelin Guide Restaurants
DOI: 10.6084/m9.figshare.22317658
title: Dataset : A systematic investigation of detectors for low signal-to-noise ratio EMG signals

description: The simulated surface EMG data using the three models(Gaussian, Laplacian and Biophysical model) that was used for evaluating the performance of different EMG detecion algorithm is provided here. Both the validation dataset and Training dataset is provided here.
DOI: 10.6084/m9.figshare.22346194
title: Raw data.zip

description: The raw data had been collected in the file that includes CSV files that can be opened with OMNIC or Origin software (base line corrected), an Excel spreadsheet (raw data) and the peak area graphs used in the calculations. The calibration curve, specific calculation procedures and significance analysis are also provided in the file.
DOI: 10.6084/m9.figshare.22346194.v1
title: Raw data.zip

description: The raw data had been collected in the file that includes CSV files that can be opened with OMNIC or Origin software (base line corrected), an Excel spreadsheet (raw data) and the peak area graphs used in the calculations. The calibration curve, specific calculation procedures and significance analysis are also provided in the file.