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DatasetDear data users, This repository contains 3 sets of files corresponding to the data analysis (R Script) and dataset (xlsx or csv files) for 3 sections of this paper. Attached are the following pairs of R script and dataset for the section of: <> ----> R script: R_official_oysterrecruitment_percentage.R ---->dataset: merged_data_oct2021_forEA.csv The variable names and description of each variable contained in this data are as follows. *Please refer to the PhD thesis or Leong et al 2022 Restoration Ecology and thesis for specific description and methods of calculation for each variable. estuary * - categorical variables of estuary -either Port Hacking, Crookhaven River or Hunter River tile_id - unique replicate of each tile where the response variable (oyster counts and percentages) where collected from reef_id - unique replicate of each tile where the response variable (oyster counts and percentages) where collected from area* - reef area dist.to.edge * - distance of each tile to the nearest patch edge in meters elevation* - surface elevation of the base of each tile relative to mean sea level prox.index* - proximity index of each reef in relation to other reefs on a landscape scale iso.index* - proximity index of each reef in relation to other reefs on a landscape scale circle* - circularity of each reef frac* - two-dimensional fractional dimensionality of each reef liveoys_oct2020 - live counts of oysters on each tile at the end of the experiment deadoys_oct2020 - dead counts of oysters on each tile at the end of the experiment totoys_oct2020 - total (live + dead) counts of oysters on each tile at the end of the experiment percent_live_oct2020 – live / (live + dead) *100% oysters on each tile at the end of the experiment <> ----> R script: R_official_abiotic_biotic_comparison.R ---->dataset: comparing abiotic and biotic_factors_noattributes_6jan2022.xlsx (31.82 KB) estuary * - categorical variables of estuary -either Port Hacking, Crookhaven River or Hunter River tile_id - unique replicate of each tile where the response variable (oyster counts and percentages) where collected from reef_id - unique replicate of each tile where the response variable (oyster counts and percentages) where collected from liveoys_aug2020 - live counts of oysters on each tile when the abiotic variables where measured totoys_aug2020 percent_live_aug2020 - live counts of oysters on each tile when the abiotic variables where measured deadoys_aug2020 – dead counts of oysters on each tile when the abiotic variables where measured cover_area – total cover area (in mm2) oysters on each tile when the abiotic variables where measured sed.rate – sedimentation rate (grams/ day) of sediment traps deployed next to tile replicates. cov.temp_logger – Covariation of temperature (no unit) based upon on temperature measured by temperature logger attached to selected tile replicates for a specific deployment period q5_logger – 5th quartile of daily temperature in Celsius measured by temperature logger attached to selected tile replicates for a specific deployment period q95_logger – 95th quartile of daily temperature in Celsius measured by temperature logger attached to selected tile replicates for a specific deployment period <> ----> R script: regional recruitment_R script.R --->dataset: regional recruitment_for R.csv The variable names and description of each variable contained in this dataset are as the following. For detailed information on how variables collected in this dataset, please refer to PhD thesis and Leong et al 2023/2024 Ecological Applications publication and thesis. estuary - categorical variables of estuary -either Port Hacking, Crookhaven River, Hunter River, Shell Point (Georges River), Bermagui and Hawkesbury River est_type - categorical variables of estuary type as either “non-sed” (not-sedimented heavy estuary) or “sed” (sedimented heavy estuary) tile_id - unique replicate of each tile where the response variable (oyster counts and percentages) where collected from elevation - surface elevation of the base of each tile relative to mean sea level totrec_all - counts of total oyster recruits on the all surfaces per tile (not used for the analysis in thesis and Leong et al 2023 Ecological Applications) totrec_top - counts total oyster recruits on the top surface per tile (not used for the analysis in thesis and Leong et al 2023 Ecological Applications) totrec_deadonlytop – counts of dead oyster recruits on the top surface per tile (not used for the analysis in thesis and Leong et al 2023 Ecological Applications) totrec_liveonly - live counts of dead oyster recruits on the top surface per tile (not used for the analysis in thesis and Leong et al 2023 Ecological Applications) totrec_per – percentage live counts (live/total *100%) of oyster recruits on all surface of each tile sediment_shell – dry weight of sediment collected in sediment trap next to each tile For any queries regarding the contents of the file above, please email first co-author, Dr. Rick Leong at rick.leong@unsw.edu.au For dataset pertaining to <>, please email corresponding author and/or co-author Dr. Mitchell Gibbs for permission and data analysis files.
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DatasetThis NGIDS dataset was generated at the next generation cyber range infrastructure of the Australian Centre OF Cyber Security (ACCS) in the University of New South Wale (UNSW)@ Australian Defence Force Academy(ADFA), Canberra. It is the part of the ongoing projects in the ADFA related to the cyber security and can be considered as a realsitic Intrusion detection Systems(IDS) dataset. Generally NGIDS-DS is the collection of normal and abnormal host and network activities which are performed during the simulation. This dataset was generated as major part of the PhD thesis "Developing Reliable Anomaly Detection System for Critical Hosts: A Proactive Defense Paradigm".
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DatasetResults of an eddy-rich ocean, sea-ice, carbon cycle model, with a nominal resolution of 1/10 degree, simulation covering the period 1980-2021 and focusing on changes in total, natural and anthropogenic CO2 fluxes in the Southern Ocean. The data includes: - natural and total CO2 fluxes averaged over years 1982-2021 - natural & total CO2 fluxes, and surface natural DIC for a composite of positive phases of the SAM and negative phases of the SAM - natural and total DIC distributions averaged over years 1980-1982 and 2017-2021
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DatasetThe associated project for this dataset looks at understanding the aggregation of nanoparticles through computational simulations in magnetorheological dampers and other nano-scale applications.
The dataset itself contains a mixture of data input files for ANSYS-Fluent in the form of WPBJ files (Workbench project files for ANSYS-Fluent, CAS files (Case files for ANSYS-Fluent) and C files (User Defined Function files for ANSYS-Fluent). Data output files in the form of DAT files contain values such as X, Y, Z positions, velocity, temperature, density, viscosity, specific heat of constant pressure.
The dataset covers the period January 2017 to December 2018.
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DatasetThe associated project aims to understand how acoustic loads are generated in supersonic combustion ramjets (scramjets) to control sonic fatigue in reusable hypersonic vehicles.
This knowledge promises to revolutionise flight by making space access more affordable than it is today. In this project, three aspects of flow phenomena commonly encountered in scramjet engines are being studied – shock/turbulence interaction, fuel injection in a cold supersonic cross-stream and shock/boundary layer interaction for both rigid and compliant surfaces. The intended outcomes include a better understanding of the processes above and construction of an experimental database to validate predictive simulation models that will enable the design of flight-weight reusable hypersonic vehicles. Benefits include reduced cost for access to space and the ability to design and develop advanced aerospace technology.
This dataset contains
- (1) raw high-speed video and image files (.mcf)
- (2) raw pressure transducer data files (.bin from PCB sensors)