This data set is a collection of materials used to integrate Research Data Management Planning into the University of Canberra (UC).

This work was completed as part of the outputs for the (ARDC) Institutional Underpinnings (IU) Project in 2022: https://ardc.edu.au/multi_project/australias-research-data-management-framework/

The aim of sharing these materials is to provide awareness and allow the reproduction of the outcomes of this project at other institutions. Below, we briefly describe two components of this project: (1) Integration and (2) Engagement:

(1) Integrating a data management planning (DMP) tool (ReDBox) with UC's research manager (Pure) in accordance with new UC DMP Policy. We wanted a seamless workflow for researchers to create and develop a data management plan for new research projects. That is, an integrated system that is initiated when a project is awarded in Pure, spurring the creation of a ReDBox data management plan with fields pre-filled with information from the Pure project so that researchers do not need to re-fill the same information in different platforms.

(2) Raise awareness of this integration and the benefits of DMP for researchers. We wanted to educate researchers on the aims of DMP (specifically risk-management and data governance) as well as how to engage with that at UC, i.e. using Pure and ReDBox for their research projects and associated data management.

Please refer to the README contained in the Outputs folder for additional information, and the project page: DOI: 10.5281/zenodo.7655390. This project was one of 25 funded in the IU initative.

This dataset has been developed as part of the Australian Research Data Commons Data Discoveries program (https://ardc.edu.au/project/machine-learning-dataset-creation-for-australian-fish-species-from-baited-remote-underwater-videos-bruv/), with the aim to futher advance research into machine learning for the automated detection of fish from video. The dataset was generated from over 3000 videos which were historically analysed with the Event Measure software package and sourced from the Australian Institute of Marine Science (AIMS), University of Western Australia (UWA) and Curtin University of Technology. The dataset is comprised of the following: - ~80k labelled crops of fish extracted from the videos, from over 500 species, 200 genera and 70 families - ~45k bounding box annotations (suitable for YOLO,RetinaNet) of fish/no fish across 1800 frames

The Seamap Australia National Benthic Habitat Layer (NBHL) is a nationally synthesised database of seafloor habitat data, classified according to the Seamap Australia National Benthic Habitat Classification Scheme (https://vocabs.ardc.edu.au/viewById/129). In generating the Seamap Australia NBHL, datasets from data providers around Australia are collated and centrally hosted by IMAS (UTAS). Through time, some datasets become superseded by newer, more accurate data for the same region (improved data collection or processing methodology). This record aggregates all habitat datasets that have been collated as part of the Seamap Australia project, but are no longer considered the most accurate/up to date habitat for a particular region and have been superseded by another product. The parent record for the Seamap Australia NBHL provides an aggregation point for all "current" habitat datasets: https://metadata.imas.utas.edu.au/geonetwork/srv/eng/catalog.search#/metadata/4739e4b0-4dba-4ec5-b658-02c09f27ab9a

This is an Australian extract of Speedtest Open data available at Amazon WS (link below - opendata.aws).AWS data licence is "CC BY-NC-SA 4.0", so use of this data must be:- non-commercial (NC)- reuse must be share-alike (SA)(add same licence).This restricts the standard CC-BY Figshare licence.A world speedtest open data was dowloaded (>400Mb, 7M lines of data). An extract of Australia's location (lat, long) revealed 88,000 lines of data (attached as csv).A Jupyter notebook of extract process is attached.See Binder version at Github - https://github.com/areff2000/speedtestAU.+> Install: 173 packages | Downgrade: 1 packages | Total download: 432MBBuild container time: approx - load time 25secs.=> Error: Timesout - BUT UNABLE TO LOAD GLOBAL DATA FILE (6.6M lines).=> Error: Overflows 8GB RAM container provided with global data file (3GB)=> On local JupyterLab M2 MBP; loads in 6 mins.Added Binder from ARDC service: https://binderhub.rc.nectar.org.auDocs: https://ardc.edu.au/resource/fair-for-jupyter-notebooks-a-practical-guide/A link to Twitter thread of outputs provided.A link to Data tutorial provided (GitHub), including Jupyter Notebook to analyse World Speedtest data, selecting one US State.Data Shows: (Q220)- 3.1M speedtests- 762,000 devices- 88,000 grid locations (600m * 600m), summarised as a point- average speed 33.7Mbps (down), 12.4M (up)- Max speed 724Mbps- data is for 600m * 600m grids, showing average speed up/down, number of tests, and number of users (IP). Added centroid, and now lat/long.See tweet of image of centroids also attached.NB: Discrepancy Q2-21, Speedtest Global shows June AU average speedtest at 80Mbps, whereas Q2 mean is 52Mbps (v17; Q1 45Mbps; v14). Dec 20 Speedtest Global has AU at 59Mbps. Could be possible timing difference. Or spatial anonymising masking shaping highest speeds. Else potentially data inconsistent between national average and geospatial detail. Check in upcoming quarters.NextSteps:Histogram - compare Q220, Q121, Q122. per v1.4.ipynb.Versions:v40: Added AUS Q125 (93k lines avg d/l 116.6 Mbps u/l 21.35 Mbps). Imported using v2 Jupyter notebook (MBP 16Gb). Mean tests: 16.9. Mean devices: 5.13. Download, extract and publish: 14 mins.v39: Added AUS Q424 (95k lines avg d/l 110.9 Mbps u/l 21.02 Mbps). Imported using v2 Jupyter notebook (MBP 16Gb). Mean tests: 17.2. Mean devices: 5.24. Download, extract and publish: 14 mins.v38: Added AUS Q324 (92k lines avg d/l 107.0 Mbps u/l 20.79 Mbps). Imported using v2 Jupyter notebook (iMac 32Gb). Mean tests: 17.7. Mean devices: 5.33.Added github speedtest-workflow-importv2vis.ipynb Jupyter added datavis code to colour code national map. (per Binder on Github; link below).v37: Added AUS Q224 (91k lines avg d/l 97.40 Mbps u/l 19.88 Mbps). Imported using speedtest-workflow-importv2 jupyter notebook. Mean tests:18.1. Mean devices: 5.4.v36 Load UK data, Q1-23 and compare to AUS and NZ Q123 data. Add compare image (au-nz-ukQ123.png), calc PlayNZUK.ipynb, data load import-UK.ipynb. UK data bit rough and ready as uses rectangle to mark out UK, but includes some EIRE and FR. Indicative only and to be definitively needs geo-clean to exclude neighbouring countries.v35 Load Melb geo-maps of speed quartiles (0-25, 25-50, 50-75, 75-100, 100-). Avg in 2020; 41Mbps. Avg in 2023; 86Mbps. MelbQ323.png, MelbQ320.png. Calc with Speedtest-incHist.ipynb code. Needed to install conda mapclassify. ax=melb.plot(column=...dict(bins[25,50,75,100]))v34 Added AUS Q124 (93k lines avg d/l 87.00 Mbps u/l 18.86 Mbps). Imported using speedtest-workflow-importv2 jupyter notebook. Mean tests:18.3. Mean devices: 5.5.v33 Added AUS Q423 (92k lines avg d/l 82.62 Mbps). Imported using speedtest-workflow-importv2 jupyter notebook. Mean tests:18.0. Mean devices: 5.6. Added link to Github.v32 Recalc Au vs NZ for upload performance; added image. using PlayNZ Jupyter. NZ approx 40% locations at or above 100Mbps. Aus

The Australian Research Data Commons (ARDC) in 2021 commenced the Institutional Underpinnings (IU) project, a collaborative project between 25 of Australia’s universities to develop a shared approach to university research data management in the form of a nationally-agreed framework. During 2021, participating institutions, including Federation University Australia, prioritised and developed an initial series of eight ‘elements’ of such a framework. Phase Two of the IU project (until August 2022) involved the testing of one of more of these elements by each participating University, with Phase Three (August until November 2022) using data from the testing phase to finalise the national framework.

Federation University Australia nominated a Phase Two project which would test the Culture Change element through undertaking a Scoping Study to identify current University practices, resources and infrastructure related to research data management, together with developing a plan to implement, achieve and monitor sustainable best practice RDM. This project was undertaken on the University’s behalf by Sae Ra Germaine (Manager of Member and Academic Services) and Emily Pyers (Business Analyst (Metadata & Repositories)) from CAVAL Ltd.

One component of this Phase Two Project was a survey of University researchers, with plans for follow up interviews with interested respondents to the Survey.

The survey questions above, as well as the proposed interview questions, have been made available for reuse under a CC BY 4.0 licence.

The Seamap Australia National Benthic Habitat Layer (NBHL) is a compilation of benthic habitat datasets obtained from various sectors including research, government, industry and community sources, across Australia. Disparate datasets are integrated into a single national-scale benthic habitat database, and classified uniformly under a national classification scheme implemented as a controlled vocabulary (https://vocabs.ardc.edu.au/viewById/129). For acceptance into the 'formal' (validated_ Seamap Australia NBHL (see https://metadata.imas.utas.edu.au/geonetwork/srv/eng/catalog.search#/metadata/4739e4b0-4dba-4ec5-b658-02c09f27ab9a), source habitat datasets must meet a set of Acceptance Criteria (documented in https://seamapaustralia.org/wp-content/uploads/2023/01/SeamapAustraliaDataAcceptanceGuidelines.pdf). Broadly speaking, for inclusion in the Seamap Australia NBHL, datasets must: (1) be well-described by metadata or associated documentation; (2) employ a single, consistent classification scheme which avoids non-deterministic or ambiguous terms; (3) bequality-controlled by the provider prior to contribution; (4) beacquired using an established and community-endorsed form of data collection (eg satellite, aerial or acoustic remote sensing); and (5) have documented evidence of ground-truthing validation at the time of data collection (e.g. drop camera, towed video, benthic grabs). This record describes habitat datasets that meet Acceptance Criteria 1-4, but have not been validated/ground-truthed and are therefore ineligible for inclusion in the formal Seamap Australia NBHL. They have been synthesised and uniformly classified using an identical methodology to the NBHL, but represent an intermediate collection of habitat datasets that would benefit from field ground-truthing in order to validate the habitat classifications. The Seamap Australia synthesis of unvalidated habitat datasets can be viewed, analysed and downloaded from the Seamap Australia data portal (https://seamapaustralia.org/map). This dataset should be considered a “live” asset and will continue to develop as more unvalidated habitat datasets are collected or made available. The most current (2024) version of the data is available from the following endpoints: WMS: https://geoserver.imas.utas.edu.au/geoserver/seamap/wms WFS: https://geoserver.imas.utas.edu.au/geoserver/seamap/wfs Layer name: SeamapAus_NBHL_unvalidated Various download options are supplied in the “Online resources” section of this record.

The Austraits Plant Dictionary (APD) offers detailed descriptions for more than 500 plant trait concepts.

APD includes trait focused on plant morphology, plant nutrient concentrations, plant physiology, plant life history, and plant fire response. The definitions will be useful to researchers from diverse disciplines, including plant functional ecology, plant taxonomy, and conservation biology. All trait concepts are supported by comprehensive metadata including trait descriptions, allowable trait values, allowable ranges, preferred units, keywords, references, and links to matches in a selection of trait databases. The traits describe here also fully support the AusTraits plant trait database, doi.org/10.5281/zenodo.3568417.

The APD can be viewed online at:

• https://w3id.org/APD
• https://vocabs.ardc.edu.au/viewById/649 (Research Vocabularies Australia)

The project GitHub repository is at:

• https://github.com/traitecoevo/APD

Mingfang Wu and Jo Dalvean of ARDC discuss linking research and industry to power up innovation.

The Australian Research Data Commons (ARDC) in 2021 commenced the Institutional Underpinnings (IU) project, a collaborative project between 25 of Australia’s universities to develop a shared approach to university research data management in the form of a nationally-agreed framework. During 2021, participating institutions, including Federation University Australia, prioritised and developed an initial series of eight ‘elements’ of such a framework. Phase Two of the IU project (until August 2022) involved the testing of one of more of these elements by each participating University , with Phase Three (August until November 2022) using data from the testing phase to finalise the national framework.

Federation University Australia nominated a Phase Two project which would test the Culture Change element through undertaking a Scoping Study to identify current University practices, resources and infrastructure related to research data management, together with developing a plan to implement, achieve and monitor sustainable best practice RDM. This project was undertaken on the University’s behalf by Sae Ra Germaine (Manager of Member and Academic Services) and Emily Pyers (Business Analyst (Metadata & Repositories)) from CAVAL Ltd.

One component of this Phase Two Project was a survey of University researchers, with plans for follow up interviews with interested respondents to the Survey.

The proposed interview questions above, as well as the survey questions, have been made available for reuse under a CC BY 4.0 licence.

This dataset is a filtered product from “The Historical Bush Fire Boundaries” dataset which is available here.

Below filters are applied to “The Historical Bush fire Boundaries” dataset to prepare this visualisation:

• Records within New South Wales state boundaries.
• Records with known attributes of "extinguish_date" and "ignition_date".
• Duration is a calculated attribute called "duration"; derived from "extinguish_date" and "ignition_date" attributes.
• "duration" in days = "extinguish_date" – "ignition_date" + 1

This dataset is a filtered product from “The Historical Bush fire Boundaries” dataset which is available here.

Below filters are applied to “The Historical Bush fire Boundaries” dataset to prepare this visualisation:

• Records within New South Wales state boundaries.
• Records with known attributes of "extinguish_date" and "ignition_date".
• Duration is a calculated attribute called "duration"; derived from "extinguish_date" and "ignition_date" attributes.
• "duration" in days = "extinguish_date" – "ignition_date" + 1

Slides for presentation at the ARDC Bushfire Data Commons Forum, held on 19 July 2022 (online, Australia).

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

ARDC Stock: A Bright Investment or a Bubble Waiting to Burst?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The Seamap Australia National Benthic Habitat Layer (NBHL) is a compilation of benthic habitat datasets obtained from various sectors including research, government, industry and community sources, across Australia. These disparate datasets have been integrated into a single national-scale benthic habitat database, and classified uniformly under a national classification scheme implemented as a controlled vocabulary (https://vocabs.ardc.edu.au/viewById/129). Creation of this classification scheme complements work undertaken by the National Environmental Science Program (NESP) Marine Biodiversity Hub (Theme D).

For acceptance into the Seamap Australia NBHL, source habitat datasets must meet a set of Acceptance Criteria (documented in https://seamapaustralia.org/wp-content/uploads/2023/01/SeamapAustraliaDataAcceptanceGuidelines.pdf).

Broadly speaking, for inclusion in the Seamap Australia NBHL, datasets must:
(1) be well-described by metadata or associated documentation;
(2) employ a single, consistent classification scheme which avoids non-deterministic or ambiguous terms;
(3) bequality-controlled by the provider prior to contribution;
(4) beacquired using an established and community-endorsed form of data collection (eg satellite, aerial or acoustic remote sensing); and
(5) have documented evidence of ground-truthing validation at the time of data collection (e.g. drop camera, towed video, benthic grabs).

The Seamap Australia NBHL can be viewed, analysed and downloaded from the Seamap Australia data portal (https://seamapaustralia.org/map) – a national repository of seafloor habitat data and a decision support tool for marine managers. All habitat datasets in the Seamap Australia data portal, including the NBHL and all local- to regional-scale contributing datasets, are available for download.

The Seamap Australia NBHL is a data collection of national importance and highlights the diversity of benthic habitats across Australia’s marine estate. This is the first Australian habitat dataset that seamlessly consolidates data from each of Australia’s state and territory providers. This dataset should be considered a “live” asset and will continue to develop as more suitable validated habitat data becomes available for inclusion, and improvements in data collection and analysis techniques enhance its resolution and currency.

The most current (2025) version of the data is available from the following endpoints:
WMS: https://geoserver.imas.utas.edu.au/geoserver/seamap/wms
WFS: https://geoserver.imas.utas.edu.au/geoserver/seamap/wfs
Layer name: SeamapAus_National_Benthic_Habitat_Layer

A download link for the full dataset is supplied in the 'Download and Links' section of this record, along with download links to older versions of the dataset. Note that data is now only available in Geodatabase (.gdb) format as it exceeds Shapefile size limits.

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AI-powered price forecasts for ARDC stock across different timeframes including weekly, monthly, yearly, and multi-year predictions.

Explore historical ownership and registration records by performing a reverse Whois lookup for the email address postmaster@ardc.net..