cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25919/cazx-xg51
title: Consumption based emissions intensity of the NEM

description: State level consumption based carbon emissions intensity factors calculated using a power flow-tracing model of the National Electricity Market. Consumption based factors can be used for calculating Scope 1 & 2 carbon emissions from grid consumed electricity.
DOI: 10.26186/133830
title: Potential recharge to groundwater estimation, grid dataset, McBride Basalt Province

description: This grid dataset is an estimation of the relative surface potential for recharge within the McBride Basalt Province. This process combined numerous factors together as to highlight the areas likely to have higher potential for recharge to occur. Soil permeability and surface geology are the primary inputs. Vegetation and slope were excluded from consideration, as these were considered to add too much complexity. Furthermore, this model does not include rainfall intensity – although this is known to vary spatially through average rainfall grids, this model is a depiction of the ground ability for recharge to occur should a significant rainfall event occur in each location. The relative surface potential recharge presented is estimated through a combination of soil and geological factors, weighting regions that are considered likely to have greater potential for recharge (e.g. younger basalts, vent-proximal facies, and highly permeable soils). Near-surface permeability of soil layers has been considered as a quantified input to the ability for water to infiltrate soil strata. It was hypothesised that locations proximal to volcanic vents would be preferential recharge sites, due to deeply penetrative columnar jointing. This suggestion is based on observations in South Iceland, where fully-penetrating columnar joint sets are more prevalent in proximal facies compared to distal facies in South Iceland (Bergh & Sigvaldson 1991). To incorporate this concept, preferential recharge sites are assumed to be within the polygons of vent-proximal facies as derived from detailed geological mapping datasets. Remaining geology has been categorised to provide higher potential recharge through younger lava flows. As such, a ranking between geological units has been used to provide the variation in potential recharge estimates. <b>References</b> Bergh, S. G., & Sigvaldason, G. E. (1991). Pleistocene mass-flow deposits of basaltic hyaloclastite on a shallow submarine shelf, South Iceland. Bulletin of Volcanology, 53(8), 597-611. doi:10.1007/bf00493688
DOI: 10.26186/133835
title: Exploring for the Future—Groundwater contours for the McBride and Nulla basalt provinces: Upper Burdekin region, North Queensland

description: This data release presents regional scale groundwater contours developed for the Upper Burdekin Groundwater Project in North Queensland, conducted as part of Exploring for the Future (EFTF), an Australian Government funded geoscience data and information acquisition program. The four-year (2016-20) program focused on better understanding the potential mineral, energy and groundwater resources in northern Australia. The Upper Burdekin Groundwater Project is a collaborative study between Geoscience Australia and the Queensland Government. It focuses on basalt groundwater resources in two geographically separate areas: the Nulla Basalt Province (NBP) in the south and the McBride Basalt Province (MBP) in the north. This data release includes separate, regional-scale groundwater contour datasets for the Nulla and McBride basalt provinces developed by Geoscience Australia in: Cook, S. B. & Ransley, T. R., 2020. Exploring for the Future—Groundwater level interpretations for the McBride and Nulla basalt provinces: Upper Burdekin region, North Queensland. Geoscience Australia, Canberra, http://pid.geoscience.gov.au/dataset/ga/135439. As detailed in that document, the groundwater contours were drawn by hand based on: - Groundwater levels from monitoring bores measured mostly on 17 February 2019 following extensive rainfall. - Surface topography. - Surface water features (rivers and springs). - Remote sensing data. The inferred groundwater contours were used in various Upper Burdekin Groundwater Project components to frame hydrogeological discussions. It is important to note that they were drawn following a wet period; groundwater contours are temporally variable and those presented in this data release therefore only represent part of the regional groundwater flow system.
DOI: 10.26186/133849
title: Potential recharge to groundwater estimation, grid dataset, Nulla Basalt Province

description: This grid dataset is an estimation of the relative surface potential for recharge within the Nulla Basalt Province. This process combined numerous factors together as to highlight the areas likely to have higher potential for recharge to occur. Soil permeability and surface geology are the primary inputs. Vegetation and slope were excluded from consideration, as these were considered to add too much complexity. Furthermore, this model does not include rainfall intensity – although this is known to vary spatially through average rainfall grids, this model is a depiction of the ground ability for recharge to occur should a significant rainfall event occur in each location. The relative surface potential recharge presented is estimated through a combination of soil and geological factors, weighting regions that are considered likely to have greater potential for recharge (e.g. younger basalts, vent-proximal facies, and highly permeable soils). Near-surface permeability of soil layers has been considered as a quantified input to the ability for water to infiltrate soil strata. It was hypothesised that locations proximal to volcanic vents would be preferential recharge sites, due to deeply penetrative columnar jointing. This suggestion is based on observations in South Iceland, where fully-penetrating columnar joint sets are more prevalent in proximal facies compared to distal facies in South Iceland (Bergh & Sigvaldson 1991). To incorporate this concept, preferential recharge sites are assumed to be within the polygons of vent-proximal facies as derived from detailed geological mapping datasets. Remaining geology has been categorised to provide higher potential recharge through younger lava flows. As such, a ranking between geological units has been used to provide the variation in potential recharge estimates. <b>Reference</b> Bergh, S. G., & Sigvaldason, G. E. (1991). Pleistocene mass-flow deposits of basaltic hyaloclastite on a shallow submarine shelf, South Iceland. Bulletin of Volcanology, 53(8), 597-611. doi:10.1007/bf00493688
DOI: 10.26186/134101
title: East Kimberley vegetation structure

description: The Vegetation Structure classes dataset was derived from Vegetation Height Model (VHM) and Fractional Cover Model (FCM) LiDAR products. The National Vegetation Information System framework was used to classify vegetation height and canopy/cover density into (sub-)stratum, growth forms, and structural formation classes. The classifications contain descriptions and spatial extents of the vegetation types for the East Kimberley LiDAR survey area. The displayed classifications include 19 dominant structural formation classes, and 43 dominant sub-structural formation classes for lower-, mid-, and upper stratum. High resolution LiDAR imagery, including Digital Elevation Model (DEM), Canopy Height Model (CHM), Vegetation Height Model (VHM), Vegetation Cover Model (VCM) and Fractional Cover Model (FCM) surfaces were acquired for the East Kimberley area in June 2017. All the data were released in 2019 (Geoscience Australia, 2019). For the purposes of vegetation structure mapping, the two input datasets were resampled, classified and combined to produce a vegetation structure map for the East Kimberley area. The methods are described by Lawrie et al. (2012), with the following differences: • resampling used Focal Statistic Min in ArcGIS as it more accurately represented vegetation extent • VHM was used instead of CHM as CHM did not include low vegetation (i.e ground cover). • VHM and FCM were classified into height and foliage cover classes using the Australian Vegetation Attribute Manual (NVIS Technical Working Group, 2017). Authors acknowledge the tremendous work of the Geoscience Australia Elevation team who carried out post processing, classification, production, quality assurance and delivery of all released LiDAR data products (see Geoscience Australia, 2019). In particular, the authors thank Graham Hammond, Kevin Kennedy, Jonathan Weales, Grahaem Chiles, Robert Kay, Shane Crossman, and Simon Costelloe. Geoscience Australia, 2019. Kimberley East - LiDAR data. Geoscience Australia, Canberra. C7FDA017-80B2-4F98-8147-4D3E4DF595A2 http://pid.geoscience.gov.au/dataset/ga/129985 Lawrie, K.C., Brodie, R.S., Tan, K.P., Gibson, D., Magee, J., Clarke, J.D.A., Halas, L., Gow, L., Somerville, P., Apps, H.E., Christensen, N.B., Brodie, R.C., Abraham, J., Smith, M., Page, D., Dillon, P., Vanderzalm, J., Miotlinski, K., Hostetler, S., Davis, A., Ley-Cooper, A.Y., Schoning, G., Barry, K. and Levett, K. 2012. BHMAR Project: Data Acquisition, processing, analysis and interpretation methods. Geoscience Australia Record 2012/11. 826p. NVIS Technical Working Group. 2017 Chapter 4.0 NVIS attributes listed and described in detail. In: Australian Vegetation Attribute Manual: National. Vegetation Information System, Version 7.0. Department of the Environment and Energy, Canberra. Prep by Bolton, M.P., deLacey, C. and Bossard, K.B. (Eds).
DOI: 10.26186/134108
title: East Kimberley remotely sensed datasets

description: This compilation data release is a selection of remotely sensed imagery used in the Exploring for the Future (EFTF) East Kimberley Groundwater Project. Datasets include: • Mosaic 5 m digital elevation model (DEM) with shaded relief • Normalised Difference Vegetation Index (NDVI) percentiles • Tasselled Cap exceedance summaries • Normalised Difference Moisture Index (NDMI) • Normalised Difference Wetness Index (NDWI) The 5m spatial resolution digital elevation model with associated shaded relief image were derived from the East Kimberley 2017 LiDAR survey (Geoscience Australia, 2019b). The Normalised Difference Vegetation Index (NDVI) percentiles include 20th, 50th, and 80th for dry seasons (April to October) 1987 to 2018 and were derived from the Landsat 5,7 and 8 data stored in Digital Earth Australia (see Geoscience Australia, 2019a). Tasselled Cap Exceedance Summary include brightness, greenness and wetness as a composite image and were also derived from the Landsat data. These surface reflectance products can be used to highlight vegetation characteristics such as wetness and greenness, and land cover. The Normalised Difference Moisture Index (NDMI) and Normalised Difference Water Index (NDWI) were derived from the Sentinel-2 satellite imagery. These datasets have been classified and visually enhanced to detect vegetation moisture stress or water-logging and show distribution of moisture. For example, positive NDWI values indicate waterlogged areas while waterbodies typically correspond with values greater than 0.2. Waterlogged areas also correspond to NDMI values of 0.2 to 0.4. Geoscience Australia, 2019a. Earth Observation Archive. Geoscience Australia, Canberra. http://dx.doi.org/10.4225/25/57D9DCA3910CD Geoscience Australia, 2019b. Kimberley East - LiDAR data. Geoscience Australia, Canberra. C7FDA017-80B2-4F98-8147-4D3E4DF595A2 http://pid.geoscience.gov.au/dataset/ga/129985
DOI: 10.26181/21588276
title: cellCounts

description: This page includes the data and code necessary to reproduce the results of the following paper: Yang Liao, Dinesh Raghu, Bhupinder Pal, Lisa Mielke and Wei Shi. cellCounts: fast and accurate quantification of 10x Chromium single-cell RNA sequencing data. Under review. A Linux computer running an operating system of CentOS 7 (or later) or Ubuntu 20.04 (or later) is recommended for running this analysis. The computer should have &gt;2 TB of disk space and &gt;64 GB of RAM. The following software packages need to be installed before running the analysis. Software executables generated after installation should be included in the $PATH environment variable. R (v4.0.0 or newer) https://www.r-project.org/ Rsubread (v2.12.2 or newer) http://bioconductor.org/packages/3.16/bioc/html/Rsubread.html CellRanger (v6.0.1) https://support.10xgenomics.com/single-cell-gene-expression/software/overview/welcome STARsolo (v2.7.10a) https://github.com/alexdobin/STAR sra-tools (v2.10.0 or newer) https://github.com/ncbi/sra-tools Seurat (v3.0.0 or newer) https://satijalab.org/seurat/ edgeR (v3.30.0 or newer) https://bioconductor.org/packages/edgeR/ limma (v3.44.0 or newer) https://bioconductor.org/packages/limma/ mltools (v0.3.5 or newer) https://cran.r-project.org/web/packages/mltools/index.html Reference packages generated by 10x Genomics are also required for this analysis and they can be downloaded from the following link (2020-A version for individual human and mouse reference packages should be selected): https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest After all these are done, you can simply run the shell script ‘<strong>test-all-new.bash</strong>’ to perform all the analyses carried out in the paper. This script will automatically download the mixture scRNA-seq data from the SRA database, and it will output a text file called ‘<strong>test-all.log</strong>’ that contains all the screen outputs and speed/accuracy results of CellRanger, STARsolo and cellCounts.
DOI: 10.26181/21588276.v3
title: cellCounts

description: This page includes the data and code necessary to reproduce the results of the following paper: Yang Liao, Dinesh Raghu, Bhupinder Pal, Lisa Mielke and Wei Shi. cellCounts: fast and accurate quantification of 10x Chromium single-cell RNA sequencing data. Under review. A Linux computer running an operating system of CentOS 7 (or later) or Ubuntu 20.04 (or later) is recommended for running this analysis. The computer should have &gt;2 TB of disk space and &gt;64 GB of RAM. The following software packages need to be installed before running the analysis. Software executables generated after installation should be included in the $PATH environment variable. R (v4.0.0 or newer) https://www.r-project.org/ Rsubread (v2.12.2 or newer) http://bioconductor.org/packages/3.16/bioc/html/Rsubread.html CellRanger (v6.0.1) https://support.10xgenomics.com/single-cell-gene-expression/software/overview/welcome STARsolo (v2.7.10a) https://github.com/alexdobin/STAR sra-tools (v2.10.0 or newer) https://github.com/ncbi/sra-tools Seurat (v3.0.0 or newer) https://satijalab.org/seurat/ edgeR (v3.30.0 or newer) https://bioconductor.org/packages/edgeR/ limma (v3.44.0 or newer) https://bioconductor.org/packages/limma/ mltools (v0.3.5 or newer) https://cran.r-project.org/web/packages/mltools/index.html Reference packages generated by 10x Genomics are also required for this analysis and they can be downloaded from the following link (2020-A version for individual human and mouse reference packages should be selected): https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest After all these are done, you can simply run the shell script ‘<strong>test-all-new.bash</strong>’ to perform all the analyses carried out in the paper. This script will automatically download the mixture scRNA-seq data from the SRA database, and it will output a text file called ‘<strong>test-all.log</strong>’ that contains all the screen outputs and speed/accuracy results of CellRanger, STARsolo and cellCounts.
DOI: 10.26181/21588276.v2
title: cellCounts

description: This page includes the data and code necessary to reproduce the results of the following paper: Yang Liao, Dinesh Raghu, Bhupinder Pal, Lisa Mielke and Wei Shi. cellCounts: fast and accurate quantification of 10x Chromium single-cell RNA sequencing data. Under review. A Linux computer running an operating system of CentOS 7 (or later) or Ubuntu 20.04 (or later) is recommended for running this analysis. The computer should have &gt;2 TB of disk space and &gt;64 GB of RAM. The following software packages need to be installed before running the analysis. Software executables generated after installation should be included in the $PATH environment variable. R (v4.0.0 or newer) https://www.r-project.org/ Rsubread (v2.12.2 or newer) http://bioconductor.org/packages/3.16/bioc/html/Rsubread.html CellRanger (v6.0.1) https://support.10xgenomics.com/single-cell-gene-expression/software/overview/welcome STARsolo (v2.7.10a) https://github.com/alexdobin/STAR sra-tools (v2.10.0 or newer) https://github.com/ncbi/sra-tools Seurat (v3.0.0 or newer) https://satijalab.org/seurat/ edgeR (v3.30.0 or newer) https://bioconductor.org/packages/edgeR/ limma (v3.44.0 or newer) https://bioconductor.org/packages/limma/ mltools (v0.3.5 or newer) https://cran.r-project.org/web/packages/mltools/index.html Reference packages generated by 10x Genomics are also required for this analysis and they can be downloaded from the following link (2020-A version for individual human and mouse reference packages should be selected): https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest After all these are done, you can simply run the shell script ‘<strong>test-all.bash</strong>’ to perform all the analyses carried out in the paper. This script will automatically download the mixture scRNA-seq data from the SRA database, and it will output a text file called ‘<strong>test-all.log</strong>’ that contains all the screen outputs and speed/accuracy results of CellRanger, STARsolo and cellCounts.
DOI: 10.26186/135277
title: Solid Geology of the North Australian Craton, 1:1 000 000 scale, 1st edition (2020)

description: The Solid Geology of the North Australian Craton 1:1M scale dataset 1st edition (2020) is a seamless chronostratigraphic solid geology dataset of the North Australian Craton that covers north of Western Australia, Northern Territory and north-west Queensland. The data maps stratigraphic units concealed under cover by effectively removing the overlying cover (Liu et al., 2015). This dataset comprises five chronostratigraphic time slices, namely: Cenozoic, Mesozoic, Paleozoic, Neoproterozoic, and Pre-Neoproterozoic. As an example, the Mesozoic time slice (or layer) shows Mesozoic age geology that would be present if all Cenozoic units were removed. The Pre-Neoproterozoic time slice shows what would be visible if all Neoproterozoic, Paleozoic, Mesozoic, and Cenozoic units were removed. Geological units are represented as polygon and line geometries and, are attributed with information regarding stratigraphic nomenclature and hierarchy, age, lithology, and primary data source. The datasets also contains geological contacts, structural features, such as faults and shears, and miscellaneous supporting lines like crater impacts or structural grain within stratigraphic units. This is the second staged release of Geoscience Australia's national time based solid geology mapping program commenced under the Federal Government’s Exploring for the Future program. The Cenozoic time slice layer was extracted from Raymond, O.L., Liu, S., Gallagher, R., Highet, L.M., Zhang, W., 2012. Surface Geology of Australia, 1:1 000 000 scale, 2012 edition [Digital Dataset]. Geoscience Australia, Commonwealth of Australia, Canberra. http://www.ga.gov.au and retains the data schema of that dataset. For this layer’s metadata, refer to http://pid.geoscience.gov.au/dataset/ga/74619 NOTE: Specialised Geographic Information System (GIS) software is required to view this data.
DOI: 10.26186/135655
title: NDVI combined classification for the McBride and Nulla basalt provinces

description: Normalised Difference Vegetation Index (NDVI) was used to map vegetation with potential access to groundwater in the basalt provinces in the Upper Burdekin. NDVI is widely used to infer vegetation density and/or vigour. Several studies (e.g. Barron et al., 2014; Gou et al., 2015; Lv et al., 2013) have used NDVI to identify groundwater-dependent vegetation (GDV) based on the hypothesis that during dry seasons or extended dry periods, soil moisture progressively becomes depleted. Under these conditions, GDV are expected to exhibit minimal or no reduction in condition relative to vegetation subject to the same conditions that do not have access to groundwater.<br> <b>References: </b><br> Barron OV, Emelyanova I, Van Niel TG, Pollock D and Hodgson G (2014) Mapping groundwater-dependent ecosystems using remote sensing measures of vegetation and moisture dynamics. Hydrological processes 28(2), 372-385. Doi: 10.1002/hyp.9609; Gou S, Gonzales S and Miller GR (2015) Mapping Potential Groundwater-Dependent Ecosystems for Sustainable Management. Ground Water 53(1), 99-110. Doi: 10.1111/gwat.12169; Lv J, Wang X-S, Zhou Y, Qian K, Wan L, Eamus D and Tao Z (2013) Groundwater-dependent distribution of vegetation in Hailiutu River catchment, a semi-arid region in China. Ecohydrology 6(1), 142-149. Doi: 10.1002/eco.1254.
DOI: 10.26186/135872
title: Borehole induction conductivity and natural gamma data release - EFTF Program

description: Borehole induction conductivity (IC) and gamma logging are geophysical techniques that provide bulk electrical conductivity and natural gamma trends of geological formations. The measured unit of IC is millisiemens per metre, whereas natural gamma is either counts per second or American Petroleum Index (API). The data were acquired as part of the Exploring for the Future program at field sites within the East Kimberley area in Western Australia, and the northern and southern Stuart Corridor projects in the Northern Territory. Data may be downloaded as Log ASCII Standard (LAS) format files or viewed through the Geoscience Australia Portal, or accessed via Geoscience Australia’s WMS and WFS web services.
DOI: 10.34740/kaggle/dsv/4741866
title: BBC News