cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25909/28943720
title: Resilient Housing Guideline for Older People: How to get ready for future climates

description: The project aims to advance knowledge about housing design and indoor environment to improve the wellbeing of older people with low socio-economic status in South Australia, including those with culturally and linguistically diverse backgrounds. It will gather information about indoor living environment and relationships with wellbeing of the occupants, household energy use and operational costs, to explore affordable improvement strategies. The project is significant to address the problems faced by one-third of the population who are unable to afford proper housing and fuel-poor. Improved living conditions will lead to better quality of life and reduce public health costs while providing environmental benefits through reduced energy use.
DOI: 10.25909/28943720.v1
title: Resilient Housing Guideline for Older People: How to get ready for future climates

description: The project aims to advance knowledge about housing design and indoor environment to improve the wellbeing of older people with low socio-economic status in South Australia, including those with culturally and linguistically diverse backgrounds. It will gather information about indoor living environment and relationships with wellbeing of the occupants, household energy use and operational costs, to explore affordable improvement strategies. The project is significant to address the problems faced by one-third of the population who are unable to afford proper housing and fuel-poor. Improved living conditions will lead to better quality of life and reduce public health costs while providing environmental benefits through reduced energy use.
DOI: 10.25909/29068553
title: CANdid

description: A collection of CAN bus data from multiple vehicles, along with GPS data and detailed labels of actions performed, with video of the driver and vehicle instrument cluster.
DOI: 10.25909/29068553.v2
title: CANdid

description: A collection of CAN bus data from multiple vehicles, along with GPS data and detailed labels of actions performed, with video of the driver and vehicle instrument cluster.
DOI: 10.25909/29095643
title: Deidentified individual data file

description: Deidentified individual data file and data key for <b>Influence of gamma transcranial alternating current stimulation frequency and intensity on motor cortex excitability in young and older adults</b>.
DOI: 10.25909/29095643.v1
title: Deidentified individual data file

description: Deidentified individual data file and data key for <b>Influence of gamma transcranial alternating current stimulation frequency and intensity on motor cortex excitability in young and older adults</b>.
DOI: 10.25909/29097107
title: Data for phenotyping of industrial hemp (<i>Cannabis sativa</i> L.) experiments

description: Environmental and phenotyping data from two industrial hemp experiments conducted using The Plant Accelerator (Australian Plant Phenomics Network) Smarthouse (#0541) and DroughtSpotter (#0546) platforms during a Postgraduate Internship Award. The experiment compared the growth and response to water deficit over time in three hemp genotypes (Felina 32, Ferimon 12, Han NE). data_code.zip contains the code used for analysis, as well as all data for the Smarthouse (#0541) and DroughtSpotter (#0546) experiments.See related material for DOIs for additional R codes used and the image datasets from the phenotyping studies.
DOI: 10.25909/29097107.v2
title: Data for phenotyping of industrial hemp (<i>Cannabis sativa</i> L.) experiments

description: Environmental and phenotyping data from two industrial hemp experiments conducted using The Plant Accelerator (Australian Plant Phenomics Network) Smarthouse (#0541) and DroughtSpotter (#0546) platforms during a Postgraduate Internship Award. The experiment compared the growth and response to water deficit over time in three hemp genotypes (Felina 32, Ferimon 12, Han NE). data_code.zip contains the code used for analysis, as well as all data for the Smarthouse (#0541) and DroughtSpotter (#0546) experiments.See related material for DOIs for additional R codes used and the image datasets from the phenotyping studies.
DOI: 10.25909/29146499
title: Participant Information Sheet: Allied Health Clinician Perspectives on Game-based Interventions for Autistic Children

description: This is an Honours student research project Participant Information Sheet for participant recruitment.
DOI: 10.25909/29146499.v1
title: Participant Information Sheet: Allied Health Clinician Perspectives on Game-based Interventions for Autistic Children

description: This is an Honours student research project Participant Information Sheet for participant recruitment.
DOI: 10.25909/29176364
title: MCCN Case Study 2 - Spatial projection via modelled data

description: The MCCN project is to deliver tools to assist the agricultural sector to understand crop-environment relationships, specifically by facilitating generation of data cubes for spatiotemporal data. This repository contains Jupyter notebooks to demonstrate the functionality of the MCCN data cube components.The dataset contains input files for the case study (source_data), RO-Crate metadata (ro-crate-metadata.json), results from the case study (results), and Jupyter Notebook (MCCN-CASE 2.ipynb)<b>Research Activity Identifier (RAiD)</b>RAiD: https://doi.org/10.26292/8679d473<b>Case Studies</b>This repository contains code and sample data for the following case studies. Note that the analyses here are to demonstrate the software and result should not be considered scientifically or statistically meaningful. No effort has been made to address bias in samples, and sample data may not be available at sufficient density to warrant analysis. All case studies end with generation of an RO-Crate data package including the source data, the notebook and generated outputs, including netcdf exports of the datacubes themselves.<b>Case Study 2 - Spatial projection via modelled data</b><b>Description</b>Estimate soil pH and electrical conductivity at 45 cm depth across a farm based on values collected from soil samples. This study demonstrates: 1) Description of spatial assets using STAC, 2) Loading heterogeneous data sources into a cube, 3) Spatial projection in xarray using different algorithms offered by the pykrige and rioxarray packages.<b>Data sources</b><b>BradGinns_SOIL2004_SoilData.csv</b> - Soil measurements from the University of Sydney Llara Campey farm site from 2004, corresponding to sites L1, L3 and L4 describing mid-depth, soil apparent electrical conductivity (ECa), GammaK, Clay, Silt, Sand, pH and soil electrical conductivity (EC)<b>Llara_Campey_field_boundaries_poly.shp</b> - Field boundary shapes for the University of Sydney Llara Campey farm site<b>Dependencies</b>This notebook requires Python 3.10 or higherInstall relevant Python libraries with: <b>pip install mccn-engine rocrate rioxarray pykrige</b>Installing mccn-engine will install other dependencies<b>Overview</b>Select soil sample measurements for pH or EC at 45 cm depthSplit sample measurements into 80% subset to model interpolated layers and 20% to test interpolated layersGenerate STAC metadata for layersLoad data cubeInterpolate pH and EC across site using the 80% subset and three different 2D interpolation methods from rioxarray (nearest, linear and cubic) and one from pykrige (linear)Calculate the error between each layer of interpolated values and measured values for the 20% setaside for testingCompare the mean and standard deviation of the errors for each interpolation methodClean up and package results as RO-Crate<b>Notes</b>The granularity of variability in soil data significantly compromises all methodsDepending on the 80/20 split, different methods may appear more reliable, but the pykrige linear method is most often best<br>
DOI: 10.25909/29176364.v1
title: MCCN Case Study 2 - Spatial projection via modelled data

description: The MCCN project is to deliver tools to assist the agricultural sector to understand crop-environment relationships, specifically by facilitating generation of data cubes for spatiotemporal data. This repository contains Jupyter notebooks to demonstrate the functionality of the MCCN data cube components.The dataset contains input files for the case study (source_data), RO-Crate metadata (ro-crate-metadata.json), results from the case study (results), and Jupyter Notebook (MCCN-CASE 2.ipynb)<b>Research Activity Identifier (RAiD)</b>RAiD: https://doi.org/10.26292/8679d473<b>Case Studies</b>This repository contains code and sample data for the following case studies. Note that the analyses here are to demonstrate the software and result should not be considered scientifically or statistically meaningful. No effort has been made to address bias in samples, and sample data may not be available at sufficient density to warrant analysis. All case studies end with generation of an RO-Crate data package including the source data, the notebook and generated outputs, including netcdf exports of the datacubes themselves.<b>Case Study 2 - Spatial projection via modelled data</b><b>Description</b>Estimate soil pH and electrical conductivity at 45 cm depth across a farm based on values collected from soil samples. This study demonstrates: 1) Description of spatial assets using STAC, 2) Loading heterogeneous data sources into a cube, 3) Spatial projection in xarray using different algorithms offered by the pykrige and rioxarray packages.<b>Data sources</b><b>BradGinns_SOIL2004_SoilData.csv</b> - Soil measurements from the University of Sydney Llara Campey farm site from 2004, corresponding to sites L1, L3 and L4 describing mid-depth, soil apparent electrical conductivity (ECa), GammaK, Clay, Silt, Sand, pH and soil electrical conductivity (EC)<b>Llara_Campey_field_boundaries_poly.shp</b> - Field boundary shapes for the University of Sydney Llara Campey farm site<b>Dependencies</b>This notebook requires Python 3.10 or higherInstall relevant Python libraries with: <b>pip install mccn-engine rocrate rioxarray pykrige</b>Installing mccn-engine will install other dependencies<b>Overview</b>Select soil sample measurements for pH or EC at 45 cm depthSplit sample measurements into 80% subset to model interpolated layers and 20% to test interpolated layersGenerate STAC metadata for layersLoad data cubeInterpolate pH and EC across site using the 80% subset and three different 2D interpolation methods from rioxarray (nearest, linear and cubic) and one from pykrige (linear)Calculate the error between each layer of interpolated values and measured values for the 20% setaside for testingCompare the mean and standard deviation of the errors for each interpolation methodClean up and package results as RO-Crate<b>Notes</b>The granularity of variability in soil data significantly compromises all methodsDepending on the 80/20 split, different methods may appear more reliable, but the pykrige linear method is most often best<br>
DOI: 10.25909/29176640
title: MCCN Case Study 5 - Produce farm zone map

description: The MCCN project is to deliver tools to assist the agricultural sector to understand crop-environment relationships, specifically by facilitating generation of data cubes for spatiotemporal data. This repository contains Jupyter notebooks to demonstrate the functionality of the MCCN data cube components.The dataset contains input files for the case study (data), RO-Crate metadata (ro-crate-metadata.json), results from the case study (result), and Jupyter Notebook (MCCN-CASE 5.ipynb)<b>Research Activity Identifier (RAiD)</b>RAiD: https://doi.org/10.26292/8679d473<b>Case Studies</b>This repository contains code and sample data for the following case studies. Note that the analyses here are to demonstrate the software and result should not be considered scientifically or statistically meaningful. No effort has been made to address bias in samples, and sample data may not be available at sufficient density to warrant analysis. All case studies end with generation of an RO-Crate data package including the source data, the notebook and generated outputs, including netcdf exports of the datacubes themselves.<b>Case Study 5 - Produce farm zone map</b><b>Description</b>Use soil sample data and crop yield data to develop a zone map for a farm. This study demonstrates: 1) Loading heterogeneous data sources into a cube, and 2) Analysis and visualisation using pykrige and KMeans.<b>Data Sources</b>Use Llara-Campey data including yield values and soil maps to develop classification of farm area into contiguous zones of relatively self-similar productivity. Variables should include the minimum zone area and the maximum number of zone classes to return.This notebook can be delivered as a tool into which the user can load their own data in the form of spreadsheets containing points and associated values for the variables to take into account in the analysis. The requirement is either for comprehensive (raster) coverage for the area or of a set of point-based measurements for each variable (in which case a simple kriging or mesh interpolation will be applied).<b>Dependencies</b>This notebook requires Python 3.10 or higherInstall relevant Python libraries with: <b>pip install mccn-engine rocrate pykrige scikit-learn</b>Installing mccn-engine will install other dependencies<br>
DOI: 10.25909/29176640.v1
title: MCCN Case Study 5 - Produce farm zone map

description: The MCCN project is to deliver tools to assist the agricultural sector to understand crop-environment relationships, specifically by facilitating generation of data cubes for spatiotemporal data. This repository contains Jupyter notebooks to demonstrate the functionality of the MCCN data cube components.The dataset contains input files for the case study (data), RO-Crate metadata (ro-crate-metadata.json), results from the case study (result), and Jupyter Notebook (MCCN-CASE 5.ipynb)<b>Research Activity Identifier (RAiD)</b>RAiD: https://doi.org/10.26292/8679d473<b>Case Studies</b>This repository contains code and sample data for the following case studies. Note that the analyses here are to demonstrate the software and result should not be considered scientifically or statistically meaningful. No effort has been made to address bias in samples, and sample data may not be available at sufficient density to warrant analysis. All case studies end with generation of an RO-Crate data package including the source data, the notebook and generated outputs, including netcdf exports of the datacubes themselves.<b>Case Study 5 - Produce farm zone map</b><b>Description</b>Use soil sample data and crop yield data to develop a zone map for a farm. This study demonstrates: 1) Loading heterogeneous data sources into a cube, and 2) Analysis and visualisation using pykrige and KMeans.<b>Data Sources</b>Use Llara-Campey data including yield values and soil maps to develop classification of farm area into contiguous zones of relatively self-similar productivity. Variables should include the minimum zone area and the maximum number of zone classes to return.This notebook can be delivered as a tool into which the user can load their own data in the form of spreadsheets containing points and associated values for the variables to take into account in the analysis. The requirement is either for comprehensive (raster) coverage for the area or of a set of point-based measurements for each variable (in which case a simple kriging or mesh interpolation will be applied).<b>Dependencies</b>This notebook requires Python 3.10 or higherInstall relevant Python libraries with: <b>pip install mccn-engine rocrate pykrige scikit-learn</b>Installing mccn-engine will install other dependencies<br>
DOI: 10.25909/29253974
title: Coexistence, extinction and survival—The evolutionary history of Bison species in Western Eurasia (Supplementary Table S1)

description: European bison (<i>Bison bonasus</i>) are one of the few European megafaunal species to survive the Late Pleistocene mass megafaunal extinction. Current conservation management efforts have proceeded in the absence of information about their evolutionary history, which has been obscured by recent severe population bottlenecks. We characterised mitochondrial genomes from 135 ancient bison samples spanning &gt;50 thousand years (&gt;50 ka) across the Eurasian continent and detected three distinct phylogenetic groups: two distinct clades of European bison and the extinct steppe bison (<i>Bison priscus</i>). The geographical distributions and dietary/ecological signatures of the three groups overlapped during the Late Pleistocene climate cycles and track environmental changes including vegetation cover and human impacts. The abundance of European bison specimens responded negatively to the extent of forest cover, including Holocene cycles of abrupt atmospheric-ocean circulation changes originating in the North Atlantic. European bison remain preferentially adapted to an open environment, but with today’s anthropogenic landscapes, are now largely restricted to forest habitats with negative implications under scenarios of future environmental change.
DOI: 10.25909/29253974.v1
title: Coexistence, extinction and survival—The evolutionary history of Bison species in Western Eurasia (Supplementary Table S1)

description: European bison (<i>Bison bonasus</i>) are one of the few European megafaunal species to survive the Late Pleistocene mass megafaunal extinction. Current conservation management efforts have proceeded in the absence of information about their evolutionary history, which has been obscured by recent severe population bottlenecks. We characterised mitochondrial genomes from 135 ancient bison samples spanning &gt;50 thousand years (&gt;50 ka) across the Eurasian continent and detected three distinct phylogenetic groups: two distinct clades of European bison and the extinct steppe bison (<i>Bison priscus</i>). The geographical distributions and dietary/ecological signatures of the three groups overlapped during the Late Pleistocene climate cycles and track environmental changes including vegetation cover and human impacts. The abundance of European bison specimens responded negatively to the extent of forest cover, including Holocene cycles of abrupt atmospheric-ocean circulation changes originating in the North Atlantic. European bison remain preferentially adapted to an open environment, but with today’s anthropogenic landscapes, are now largely restricted to forest habitats with negative implications under scenarios of future environmental change.
DOI: 10.25909/29265767
title: Dataset for "Emerging threats to Antarctic conservation"

description: This dataset contains anonymised responses collected as part of a global horizon scan aimed at identifying emerging conservation challenges in the Antarctic region. The horizon scan involved a modified Delphi process with 131 expert participants from 42 countries. Participants submitted up to fifteen emerging challenges each, which were iteratively refined over multiple survey rounds to develop a final list of ten priority threats. The dataset includes cleaned responses along with categorisation used for thematic analysis. These data underpin the findings reported in a peer-reviewed article in <i>Nature Ecology & Evolution</i>.
DOI: 10.25909/29265767.v1
title: Dataset for "Emerging threats to Antarctic conservation"

description: This dataset contains anonymised responses collected as part of a global horizon scan aimed at identifying emerging conservation challenges in the Antarctic region. The horizon scan involved a modified Delphi process with 131 expert participants from 42 countries. Participants submitted up to fifteen emerging challenges each, which were iteratively refined over multiple survey rounds to develop a final list of ten priority threats. The dataset includes cleaned responses along with categorisation used for thematic analysis. These data underpin the findings reported in a peer-reviewed article in <i>Nature Ecology & Evolution</i>.
DOI: 10.25909/29312042
title: X-ray Velocimetry, CT, Flexivent and Histology dataset of mice with lung tumours.

description: Data supporting the paper 'Mapping Lung Cancer Ventilation Dynamics: A Pilot Mouse Study Using Functional Imaging and Lung Mechanics' <br>Abstract:In vivo models that replicate and reproduce human lung cancer and its response to therapy are necessary for the development of new therapeutic strategies and understanding drug resistance. Imaging lung tumors in live animals to monitor tumor growth and response to therapy is challenging due to the location of the lungs and their constant movement during breathing. Additionally, methods such as computed tomography (CT) only provide structural information and not functional information about how well the lungs are working.X-ray velocimetry (XV) is a novel functional lung imaging technique that generates 3D maps of regional lung expansion during breathing. In other lung diseases it has been shown to provide spatial information on where ventilation changes occur. The aim of this pilot study was to use XV and flexiVent lung mechanics assessments to determine the effect of tumor growth on lung function in mice at 2- or 3-weeks post tumor induction, and to evaluate the efficacy of these two tools.Histological analysis showed that tumour growth was not uniform between animals. At 3-weeks post tumor induction, some XV ventilation and flexiVent lung mechanics parameters were significantly different from baseline metrics. In addition, the forced expiratory volume, small-scale ventilation heterogeneity, and the average CT gray value correlated with the tumour counts from the histology. In some mice XV revealed localised regions with altered expansion rates.This pilot study demonstrated that changes in lung function can be identified following tumor induction, and that the model and techniques could be used in the future to determine response to anti-tumor drugs.
DOI: 10.25909/29312042.v1
title: X-ray Velocimetry, CT, Flexivent and Histology dataset of mice with lung tumours.

description: Data supporting the paper 'Mapping Lung Cancer Ventilation Dynamics: A Pilot Mouse Study Using Functional Imaging and Lung Mechanics' <br>Abstract:In vivo models that replicate and reproduce human lung cancer and its response to therapy are necessary for the development of new therapeutic strategies and understanding drug resistance. Imaging lung tumors in live animals to monitor tumor growth and response to therapy is challenging due to the location of the lungs and their constant movement during breathing. Additionally, methods such as computed tomography (CT) only provide structural information and not functional information about how well the lungs are working.X-ray velocimetry (XV) is a novel functional lung imaging technique that generates 3D maps of regional lung expansion during breathing. In other lung diseases it has been shown to provide spatial information on where ventilation changes occur. The aim of this pilot study was to use XV and flexiVent lung mechanics assessments to determine the effect of tumor growth on lung function in mice at 2- or 3-weeks post tumor induction, and to evaluate the efficacy of these two tools.Histological analysis showed that tumour growth was not uniform between animals. At 3-weeks post tumor induction, some XV ventilation and flexiVent lung mechanics parameters were significantly different from baseline metrics. In addition, the forced expiratory volume, small-scale ventilation heterogeneity, and the average CT gray value correlated with the tumour counts from the histology. In some mice XV revealed localised regions with altered expansion rates.This pilot study demonstrated that changes in lung function can be identified following tumor induction, and that the model and techniques could be used in the future to determine response to anti-tumor drugs.
DOI: 10.25909/29323673
title: Results

description: This repository contain results on "Planning Battery Electric Bus Systems: No One-Size-Fits-All Solution for Cost and Emissions Across Climates and Chemistries".