cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7579237
title: MUSES Leaf Area Index (LAI) 8-Day Global 1km SIN Grid in 2015

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 1km 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>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2015. <em>Please <strong>click here</strong> to download the MUSES LAI product<strong> in 2014</strong></em>, and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2016</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2015 Spatial Resolution: 1km Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> 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, 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>. (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.7579236
title: MUSES Leaf Area Index (LAI) 8-Day Global 1km SIN Grid in 2015

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 1km 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>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2015. <em>Please <strong>click here</strong> to download the MUSES LAI product<strong> in 2014</strong></em>, and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2016</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2015 Spatial Resolution: 1km Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> 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, 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>. (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: 10.14457/nida.the.2018.98
title: มาตรการในทางกฎหมาย เพื่อป้องกันการขัดกันของตำแหน่งหน้าที่และการขัดกันแห่งผลประโยชน์ในคณะกรรมการรัฐวิสาหกิจ
DOI: 10.14457/nida.the.2017.123
title: A model of website development in creating trustworthiness for medical tourism decision making in Thailand
DOI: 10.14457/nida.the.2018.99
title: ปัญหาการจำกัดสิทธิของบุคคลหรือคณะบุคคลที่กระทำการเป็นปฏิปักษ์ต่อรัฐธรรมนูญแห่งราชอาณาจักรไทย
DOI: 10.14457/nida.the.2017.124
title: Guidelines for developing employee engagement to enhance innovative organization in Thai hotel business
DOI: 10.14457/nida.the.2017.125
title: Analyzing social media content to gain competitive intelligence
DOI: 10.14457/nida.the.2020.64
title: Public policy recommendations for enhancing hotel business order in Phuket Thailand
DOI: 10.14457/nida.the.2020.65
title: The model of yacht tourism management in Phuket
DOI: 10.14457/nida.the.2020.66
title: Policy implication for Patong beach management through multi-stakeholder participatory approach
DOI: 10.14457/nida.the.2020.67
title: Developing destination safety indicators and strategy for promoting tourists' safety confidence
DOI: 10.14457/nida.the.2020.68
title: Human resource management practice model of Suvarnabhumi Airport, Thailand
DOI: 10.14457/nida.the.2021.41
title: Causal factors affecting integrated policy implementation of tourism in Thailand
DOI: 10.14457/nida.the.2020.69
title: Airport technical efficiency and business model innovations: a case of local and regional airports in Thailand
DOI: 10.6084/m9.figshare.21972458
title: Magnetic Needle Steering Control using Lyapunov Redesign

description: Experimental data for Figure 5 published in "Magnetic Needle Steering Control using Lyapunov Redesign" by Richard L. Pratt and Andrew J. Petruska.
DOI: 10.34740/kaggle/dsv/4912399
title: 🇺🇦 Ukraine Conflict Twitter Dataset
DOI: 10.5281/zenodo.7580482
title: Legowo Planting System

description: This is extended data of the assesment of legowo planting system and its counterpart in Southeast Sulawesi. Indonesia
DOI: 10.5281/zenodo.7498887
title: Legowo Planting System

description: This is extended data of the assesment of legowo planting system and its counterpart in Southeast Sulawesi. Indonesia
DOI: 10.6084/m9.figshare.21972482
title: Lilium tsingtauense，the first complete mitochondrial genome of Lilium

description: <em>Lilium</em> is a horticultural flower with significant commercial value that combines ornamental, edible and medicinal values. The <em>Lilium</em> chloroplast genome has been reported; however, the sequence of its mitochondrial genome is still lacking. Since the origin of the endosymbiotic event, mitochondria have diverged during the evolution of the eukaryotic lineage. Unlike other eukaryotes, the plant mitogenome exhibits a striking diversity of differences in many features, especially the discovery of its multichromosomal structure, making it one of the genomes of greatest interest to molecular biologists. The absence of resources for mitogenomics has limited the study of the evolution of multichromosomal mitochondrial genomes. We sequenced and assembled annotated the mitogenome of<em> Lilium tsingtauense </em>based on BGI short reads and Nanopore long reads, characterized the first complete mitogenome of <em>Lilium</em>, and analyzed it in comparison with those of other angiosperms.
DOI: 10.6084/m9.figshare.21972518
title: A ZTF-7RPS-2 complex mediates the cold-warm response in C. elegans.xlsx

description: These are numerical datas that underlie graphs or summary statistics.
DOI: 10.6084/m9.figshare.21972518.v1
title: A ZTF-7RPS-2 complex mediates the cold-warm response in C. elegans.xlsx

description: These are numerical datas that underlie graphs or summary statistics.
DOI: 10.25919/w7z4-wn64
title: Parkes observations for project P737 semester 2022OCTS_07

description: The aim of this proposal is to carry out tests of the new backend instruments which cannot be carried out during standard maintenance periods. The scheduled time is used to measure beam shapes, determine polarisation calibration solutions and commission the National Facility observing modes for the new wide-band receiver.
DOI: 10.5066/p9flr79f
title: Datasets Used to Create Maps of Pierre Shale Elevation and Surficial Deposit Thickness within and near Ellsworth Air Force Base, South Dakota, 2021

description: This data release contains borehole geophysical data collected within and near Ellsworth Air Force Base, South Dakota, from September 2020 to August 2021. The data accompanies a USGS scientific investigations map from Medler and others (2022). The U.S. Geological Survey, in cooperation with the U.S. Air Force Civil Engineer Center, collected borehole geophysical data and performed simple aquifer tests to estimate the thickness and hydraulic properties of surficial deposits. The purpose of data collection was to create generalized contour maps of Pierre Shale elevation and surficial deposit thickness within and near Ellsworth Air Force Base (study area). Natural gamma and electromagnetic induction (EMI) data were collected to refine or determine surficial deposit thickness at selected wells. Additionally, data from previous geophysical studies and drillers logs were compiled and combined with results from natural gamma and EMI data to delineate the Cretaceous Pierre Shale in the study area. Borehole nuclear magnetic (bNMR) resonance data were collected to estimate hydraulic conductivity and water content of surficial deposits overlying Pierre Shale. Simple aquifer tests using water slugs (slug tests) were performed to estimate hydraulic conductivity of surficial deposits and results were compared to hydraulic conductivity estimates from bNMR data. Also included in this data release are colloidal borescope flowmeter (CBFM) data collected from November to December 2020 at 24 wells. CBFM data were analyzed using Python programming language to obtain the mean horizontal flow direction and mean horizontal flow velocity of groundwater through the wellbore.
DOI: 10.5066/p9h5uux2
title: Microbial Community Composition Data from Blacktail Creek near Williston, North Dakota

description: A large spill of wastewater from oil and gas operations was discovered adjacent to Blacktail Creek near Williston, North Dakota in January 2015. To determine the effects of this spill on streambed microbial communities over time, bed sediment samples were taken from Blacktail Creek upstream, adjacent to, and at several locations downstream from the spill site. Blacktail Creek is a tributary of the Little Muddy River, and additional samples were taken upstream and downstream from the confluence of Blacktail Creek and the Little Muddy River. Samples were collected in February 2015, June 2015, June 2016, and June 2017. DNA was extracted from these sediments, and sequencing of the 16S ribosomal RNA gene was performed to enable analysis of the microbial community structure. Raw sequence data was processed, and taxonomy was assigned based on the Silva 132 database (Yilmaz et al, 2014) using the MOTHUR software package (Schloss et al, 2009). Raw sequence data are available from GenBank at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA666160.
DOI: 10.5066/p9z47ewl
title: Model performance and output variables for phenological events across land cover types in the Northwestern Plains, 1989-2014

description: Many aspects of recurring plant developmental events – vegetation phenology – are measured by remote sensing. By consistently measuring the timing and magnitude of the growing season, it is possible to study the complex relationships among drivers of the seasonal cycle of vegetation, including legacy conditions. We studied the role of current and legacy climate, and contextual factors on the land surface phenology of the U.S. Northern Great Plains. Specifically, we used annual and seasonal climate variables (e.g., temperature, precipitation, growing degree days, and vapor pressure deficit) covering the current year and the past four years derived from the PRISM climate dataset. We also included soils, disturbance, and within pixel land cover heterogeneity as additional independent variables that may also influence phenology. We assessed three phenological measures from the AVHRR satellite sensor (utilizing the years from 1989 to 2014), start of season, peak productivity, and season-long productivity, using variable selection techniques utilizing nested random forest models (VSURF). We identified the top variables for the phenological measures for pixels with predominantly grassland, shrubland, and evergreen forest land cover classes. Datasets in this release include tables from the associated manuscript and supplementary information found in the Larger Work section. Specifically, included tables cover the modeling parameters, final factors derived from the random forest variable selection process, and model performance measures.
DOI: 10.5281/zenodo.7580098
title: MUSES Leaf Area Index (LAI) 8-Day Global 1km SIN Grid in 2014

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 1km 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>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2014. <em>Please <strong>click here</strong> to download the MUSES LAI product<strong> in 2013</strong></em>, and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2015</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2014 Spatial Resolution: 1km Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> 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, 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>. (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.7580097
title: MUSES Leaf Area Index (LAI) 8-Day Global 1km SIN Grid in 2014

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 1km 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>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2014. <em>Please <strong>click here</strong> to download the MUSES LAI product<strong> in 2013</strong></em>, and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2015</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2014 Spatial Resolution: 1km Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> 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, 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>. (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.21936120.v1
title: Life Cycle Inventory for EPS and Mycelium Packaging Box Insert

description: Life Cycle Inventory for EPS and Mycelium Packaging Box Insert
DOI: 10.5519/qd.20vwjw0a
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (3633 records)
DOI: 10.5519/qd.awqxvjlp
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.irqkt4za
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.34740/kaggle/dsv/4912867
title: Aksara Jawa / Hanacaraka
DOI: 10.5281/zenodo.6637059
title: A Collection of Event Logs of Blockchain-based Applications

description: A set of event logs of 101 blockchain-based applications (DApps). For each DApp, there are two event log files. The first one is a raw version where data is encoded by blockchain. The second file is a decoded version where data is decoded into a human-readable format. If a DApp has multiple versions on different blockchain networks, then there are two event log files (encoded and decoded) for each version. In addition, the event registry file includes a comprehensive list of event names and their corresponding signatures obtained from contract ABIs of the 101 DApps.
DOI: 10.5281/zenodo.6637058
title: A Collection of Event Logs of Blockchain-based Applications

description: A set of event logs of 101 blockchain-based applications (DApps). For each DApp, there are two event log files. The first one is a raw version where data is encoded by blockchain. The second file is a decoded version where data is decoded into a human-readable format. If a DApp has multiple versions on different blockchain networks, then there are two event log files (encoded and decoded) for each version. In addition, the event registry file includes a comprehensive list of event names and their corresponding signatures obtained from contract ABIs of the 101 DApps.
DOI: 10.6084/m9.figshare.21972503.v1
title: Untitled Item

description: the raw data for a study
DOI: 10.5519/qd.oqxupp9b
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5281/zenodo.7580683
title: Tabula sapiens filtered data

description: This folder contains pre-filtered files of Tabula sapiens per tissue used to generate scvi models stored in scvi-hub. Due to inconsistencies in the cell-type resolution across donors data was filtered. Please refer to pre-processed files as adata object for the trained scvi models which contains gene filtered and minified data for the models. Data is preprocessed data downloaded from https://cellxgene.cziscience.com/collections/e5f58829-1a66-40b5-a624-9046778e74f5. Please refer to their data usage guide before reusing the data.
DOI: 10.5517/ccdc.csd.cc2763p8
title: CCDC 2061459: Experimental Crystal Structure Determination

description: Related Article: Edgar Marín-Carrillo, Hugo Valdés, Simón Hernández-Ortega, David Morales-Morales|2023|Inorg.Chim.Acta|548|121365|doi:10.1016/j.ica.2022.121365
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2763q9
title: CCDC 2061460: Experimental Crystal Structure Determination

description: Related Article: Edgar Marín-Carrillo, Hugo Valdés, Simón Hernández-Ortega, David Morales-Morales|2023|Inorg.Chim.Acta|548|121365|doi:10.1016/j.ica.2022.121365
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc29c0rg
title: CCDC 2125755: Experimental Crystal Structure Determination

description: Related Article: Tianyu Lu, Boyi Wang, Weixing Chang, Lingyan Liu, Jing Li|2022|J.Org.Chem.|88|818|doi:10.1021/acs.joc.2c01974
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.