cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.26183/hypg-7591
title: Mining impact assessment and residential sale impact dataset

description: 	
The study adds to the knowledge of NSW mining impacts and their association with residential property sale values, although the impact measured at Broken Hill is less than that at other major mine sites in Australia; Mt Isa and Kalgoorlie (6.2 - 6.9%/km). If omitted variables were added to this study's regression analysis, up to 8.153% per km is possible. The study is the first to use a combined WMA/HPA approach in mine and property impact assessment. Metal market spot prices do not appear to trigger price variation in the residential market, and lead and zinc ore production from all NSW mines exhibits a long-term decline. Three Data formats are utilised, and all are stored digitally; Mine impact data, Sales data and Multiple Regression of Sales Data.
1. Mine Impact Assessment: Excel spreadsheet with values at the 33 sites where mining impacts were subjectively determined. Sites were at the centre of mining activity (Broken Hill Miners Memorial) 0 meters, 200m, 500m, 1000m, 1500m, 2000m, 3000m, 4000m and 5000m away in north, east, south and westerly directions. Each site records a range of biophysical, economic, social and other impacts of mining at each location, assessed by the investigator and weighted according to documented methodology. An overall impacts rating (positive or negative) is generated to reflect the impact and distance from mining activity.
2. Sales Data: Excel spreadsheet Broken Hill sales records of all residential properties sold during calendar years 2000, 2005, 2010, 2015 and 2019, comprising property address, characteristics of each residence, area of land on which residence is located, distance to a range of services, and the centre of mining activity.
3. Multiple Regression Calculations: Robust Multiple Regression, Correlation and other statistical calculations undertaken from Broken Hill sales data records.
Excel spreadsheets, Multiple Regression output files
Please direct all enquires to Dr Ian Lavering ORCID 0000-0002-2094-4653
DOI: 10.6084/m9.figshare.22756883
title: translanguaging forum and English only forum reflective diaries

description: the reflective diaries of translanguaging forum and English-only forum comparison
DOI: 10.6084/m9.figshare.22756883.v1
title: translanguaging forum and English only forum reflective diaries

description: the reflective diaries of translanguaging forum and English-only forum comparison
DOI: 10.5281/zenodo.7894135
title: Data in experimental stand-alone microgrid: solar production, domestic loads, battery storage and meteorological series.

description: At the centre of Reunion Island, a region denoted « Mafate » is non-connected to the main electrical grid. Presented data come from an experimental local microgrid between 3 homes at the place called « Roche Plate », where electrical production is obtained by photovoltaic panels and storage by batteries.
DOI: 10.5281/zenodo.7894134
title: Data in experimental stand-alone microgrid: solar production, domestic loads, battery storage and meteorological series.

description: At the centre of Reunion Island, a region denoted « Mafate » is non-connected to the main electrical grid. Presented data come from an experimental local microgrid between 3 homes at the place called « Roche Plate », where electrical production is obtained by photovoltaic panels and storage by batteries.
DOI: 10.5281/zenodo.7894147
title: Data in experimental stand-alone microgrid: solar production, domestic loads, battery storage and meteorological series.

description: At the centre of Reunion Island, a region denoted « Mafate » is non-connected to the main electrical grid. Presented data come from an experimental local microgrid between 3 homes at the place called « Roche Plate », where electrical production is obtained by photovoltaic panels and storage by batteries.
DOI: 10.25345/c5cf9jh3m
title: MassIVE MSV000091850 - Soybean 24h seed germination exudate proteome in response to LCO and Th17
title: This study was conducted to profile the proteins that the seeds exude during germination in response to biotstimulants lipo-chitooligosaccharide and thuricin17. This early proteome dictates in part the establishment of the seedling and garnering a conducive microbiome in the rhizosphere.
DOI: 10.25901/515t-kw80
title: Adelaide River Flux Data Release 2023_v1
DOI: 10.6084/m9.figshare.22756811
title: Characteristic remanent magnetization component

description: High-temperature characteristic remanent magnetization component and low-temperature characteristic remanent magnetization component obtained within the Simao basin
DOI: 10.6084/m9.figshare.22756811.v1
title: Characteristic remanent magnetization component

description: High-temperature characteristic remanent magnetization component and low-temperature characteristic remanent magnetization component obtained within the Simao basin
DOI: 10.5517/ccdc.csd.cc2f353v
title: CCDC 2237366: Experimental Crystal Structure Determination

description: Related Article: James Kaduk|2023|CSD Communication|||
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.cc2f354w
title: CCDC 2237367: Experimental Crystal Structure Determination

description: Related Article: James Kaduk|2023|CSD Communication|||
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.cc2f357z
title: CCDC 2237370: Experimental Crystal Structure Determination

description: Related Article: James Kaduk|2023|CSD Communication|||
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.cc2f3580
title: CCDC 2237371: Experimental Crystal Structure Determination

description: Related Article: James Kaduk|2023|CSD Communication|||
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.cc2f3591
title: CCDC 2237372: Experimental Crystal Structure Determination

description: Related Article: James Kaduk|2023|CSD Communication|||
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.cc2f3ctr
title: CCDC 2237574: Experimental Crystal Structure Determination

description: Related Article: Mikko Hänninen, Reijo Sillanpää|2023|CSD Communication|||
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.cc2f61l9
title: CCDC 2240140: Experimental Crystal Structure Determination

description: Related Article: Raman Kavitha Preethi, Sathananthan Kannadasan, Ponnusamy Shanmugam|2023|Tetrahedron Lett.|120|154448|doi:10.1016/j.tetlet.2023.154448
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: 10.5281/zenodo.7893933
title: MDColabTecNM/UAMor/UNAM

description: MD Data for 5UIG & 7LD4
DOI: 10.5281/zenodo.7893932
title: MDColabTecNM/UAMor/UNAM

description: MD Data for 5UIG & 7LD4
DOI: 10.6084/m9.figshare.22756919
title: Aerosol size distribution and CCN number concentration data Seoul (2018-2020) and Yellow Sea (2017, 2021)

description: This is the data used in the paper " Park, M., Yum, S. S., Seo, P., Ahn, C., Kim, N., Anderson, B. E., & Thornhill, K. L., A new CCN number concentration prediction method based on multiple linear regression and non-negative factorization. Part 2. Application to secure CCN spectra in and around the Korean Peninsula.<em> Journal of Geophysical Research: Atmospheres.</em>" <br> Please consult Minsu Park (ms_park@yonsei.ac.kr) or Seong Soo Yum (ssyum@yonsei.ac.kr) with any questions on usage.
DOI: 10.6084/m9.figshare.22756919.v1
title: Aerosol size distribution and CCN number concentration data Seoul (2018-2020) and Yellow Sea (2017, 2021)

description: This is the data used in the paper " Park, M., Yum, S. S., Seo, P., Ahn, C., Kim, N., Anderson, B. E., & Thornhill, K. L., A new CCN number concentration prediction method based on multiple linear regression and non-negative factorization. Part 2. Application to secure CCN spectra in and around the Korean Peninsula.<em> Journal of Geophysical Research: Atmospheres.</em>" <br> Please consult Minsu Park (ms_park@yonsei.ac.kr) or Seong Soo Yum (ssyum@yonsei.ac.kr) with any questions on usage.
DOI: 10.6084/m9.figshare.22756943
title: Atmospheric modes fiddling the simulated ENSO impact on tropical cyclone genesis over the Northwest Pacific

description: The data used in this study.
DOI: 10.6084/m9.figshare.22732451.v4
title: Summary.xlsx

description: This study was performed in the frame of a more extensive study dedicated to the integrated analysis of the single-cell transcriptome and chromatin accessibility datasets of peripheral blood mononuclear cells (PBMCs) with a large-scale GWAS of 45 complex traits in Chinese Holstein cattle. Lipopolysaccharide (LPS) is a crucial mediator of chronic inflammation to modulate immune responses. PBMCs include primary T and B cells, natural killer (NK) cells, monocytes (Mono), and dendritic cells (DC). How LPS stimulates PBMCs at the single-cell level in dairy cattle remains largely unknown. We sequenced 30,756 single cells with approximately 62,254 reads per cell for four whole-blood treatments (no, 2 h, 4 h, and 8 h LPS) by single-cell RNA sequencing (scRNA-seq) and single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq). Finally, 7,107 (no), 9,174 (2 h), 6,741 (4 h), and 3,119 (8 h) cells were generated with ~ 15,000 total genes in the whole population. Therefore, the single-cell transcriptome and chromatin accessibility datasets in this study enable a further understanding of the cell types and functions of PBMCs and their responses to LPS stimulation <em>in vitro</em>.
DOI: 10.6084/m9.figshare.22732451.v5
title: Summary.xlsx

description: This study was performed in the frame of a more extensive study dedicated to the integrated analysis of the single-cell transcriptome and chromatin accessibility datasets of peripheral blood mononuclear cells (PBMCs) with a large-scale GWAS of 45 complex traits in Chinese Holstein cattle. Lipopolysaccharide (LPS) is a crucial mediator of chronic inflammation to modulate immune responses. PBMCs include primary T and B cells, natural killer (NK) cells, monocytes (Mono), and dendritic cells (DC). How LPS stimulates PBMCs at the single-cell level in dairy cattle remains largely unknown. We sequenced 30,756 single cells with approximately 62,254 reads per cell for four whole-blood treatments (no, 2 h, 4 h, and 8 h LPS) by single-cell RNA sequencing (scRNA-seq) and single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq). Finally, 7,107 (no), 9,174 (2 h), 6,741 (4 h), and 3,119 (8 h) cells were generated with ~ 15,000 total genes in the whole population. Therefore, the single-cell transcriptome and chromatin accessibility datasets in this study enable a further understanding of the cell types and functions of PBMCs and their responses to LPS stimulation <em>in vitro</em>.
DOI: 10.21994/loar8561
title: Newspapers from 1882

description: Newspapers from 1882. OCR text in csv file.
DOI: 10.5281/zenodo.7894213
title: Bleaching and Gaussian fitting (Inverse Photobleach Flow)

description: Bleaching and Gaussian fitting (Inverse Photobleach Flow)
DOI: 10.5281/zenodo.7894212
title: Bleaching and Gaussian fitting (Inverse Photobleach Flow)

description: Bleaching and Gaussian fitting (Inverse Photobleach Flow)
DOI: 10.5281/zenodo.7894202
title: Optimal transport and model (Membrane-cortex-tension)

description: Optimal Transport and model (Membrane Cortex Tension)
DOI: 10.5281/zenodo.7894203
title: Optimal transport and model (Membrane-cortex-tension)

description: Optimal Transport and model (Membrane Cortex Tension)
DOI: 10.5281/zenodo.7892130
title: MUSES Leaf Area Index (LAI) Monthly Global 1km SIN Grid in 2000

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 1 km spatial resolution and monthly 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 2000. <em>Please</em><em> <strong>click here</strong> to download the MUSES LAI product <strong>in 2001</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2000 Spatial Resolution: 1 km Temporal Resolution: 1 month 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.7892129
title: MUSES Leaf Area Index (LAI) Monthly Global 1km SIN Grid in 2000

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 1 km spatial resolution and monthly 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 2000. <em>Please</em><em> <strong>click here</strong> to download the MUSES LAI product <strong>in 2001</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2000 Spatial Resolution: 1 km Temporal Resolution: 1 month 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.7890996
title: MUSES Leaf Area Index (LAI) Monthly Global 1km SIN Grid in 2002

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 1 km spatial resolution and monthly 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 2002. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2001</strong></em>, <em>and <strong>click here</strong> to download the MUSES LAI product <strong>in 2003</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2002 Spatial Resolution: 1 km Temporal Resolution: 1 month 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).