There has been a continued upward trend in the population share of active social media users in Australia. As of February 2022, approximately **** percent of the Australian population were active users compared to just ** percent in 2015. Preferred social media brands and most popular activities Facebook was the most popular social media brand in Australia in 2019, with ** percent saying they used the platform the most often. Elsewhere, ** percent said they used Instagram and six percent used Snapchat. Social media is used by Australians for a variety of activities. The most popular use is as a means of communication, with over **** of users regularly sending private messages and ** percent commenting on posts. Active users also post pictures and videos, with ** percent of users saying they have posted visual content. When do Australians use social media? In 2018, most social media use took place during Australians free time; ** percent said they used social media platforms in the evening, ** percent were first thing in the morning users, and ** percent said they logged on during breaks. Interestingly, when it comes to users being banned from social media, just over **** said in 2019 that they somewhat agree that bans are ineffective.

Orkut social network and ground-truth communities

https://snap.stanford.edu/data/com-Orkut.html

Dataset information

Orkut (http://www.orkut.com/) is a free on-line social network where users form friendship each other. Orkut also allows users form a group which
other members can then join. We consider such user-defined groups as
ground-truth communities. We provide the Orkut friendship social network
and ground-truth communities. This data is provided by Alan Mislove et al. (http://socialnetworks.mpi-sws.org/data-imc2007.html)

We regard each connected component in a group as a separate ground-truth
community. We remove the ground-truth communities which have less than 3
nodes. We also provide the top 5,000 communities with highest quality
which are described in our paper (http://arxiv.org/abs/1205.6233). As for
the network, we provide the largest connected component.

Dataset statistics
Nodes 3,072,441
Edges 117,185,083
Nodes in largest WCC 3072441 (1.000)
Edges in largest WCC 117185083 (1.000)
Nodes in largest SCC 3072441 (1.000)
Edges in largest SCC 117185083 (1.000)
Average clustering coefficient 0.1666
Number of triangles 627584181
Fraction of closed triangles 0.01414
Diameter (longest shortest path) 9
90-percentile effective diameter 4.8

Source (citation)
J. Yang and J. Leskovec. Defining and Evaluating Network Communities based on Ground-truth. ICDM, 2012. http://arxiv.org/abs/1205.6233

Files
File Description
com-orkut.ungraph.txt.gz Undirected Orkut network
com-orkut.all.cmty.txt.gz Orkut communities
com-orkut.top5000.cmty.txt.gz Orkut communities (Top 5,000)

Notes on inclusion into the SuiteSparse Matrix Collection, July 2018:

The graph in the SNAP data set is 1-based, with nodes numbered 1 to
3,072,626.

In the SuiteSparse Matrix Collection, Problem.A is the undirected
Orkut network, a matrix of size n-by-n with n=3,072,441, which is
the number of unique user id's appearing in any edge.

Problem.aux.nodeid is a list of the node id's that appear in the SNAP data set. A(i,j)=1 if person nodeid(i) is friends with person nodeid(j). The
node id's are the same as the SNAP data set (1-based).

C = Problem.aux.Communities_all is a sparse matrix of size n by 15,301,901 which represents the same number communities in the com-orkut.all.cmty.txt file. The kth line in that file defines the kth community, and is the
column C(:,k), where where C(i,k)=1 if person nodeid(i) is in the kth
community. Row C(i,:) and row/column i of the A matrix thus refer to the
same person, nodeid(i).

Ctop = Problem.aux.Communities_to...

This anonymized data set consists of one month's (October 2018) web tracking data of 2,148 German users. For each user, the data contains the anonymized URL of the webpage the user visited, the domain of the webpage, category of the domain, which provides 41 distinct categories. In total, these 2,148 users made 9,151,243 URL visits, spanning 49,918 unique domains. For each user in our data set, we have self-reported information (collected via a survey) about their gender and age.

We acknowledge the support of Respondi AG, which provided the web tracking and survey data free of charge for research purposes, with special thanks to François Erner and Luc Kalaora at Respondi for their insights and help with data extraction.

The data set is analyzed in the following paper:

• Kulshrestha, J., Oliveira, M., Karacalik, O., Bonnay, D., Wagner, C. "Web Routineness and Limits of Predictability: Investigating Demographic and Behavioral Differences Using Web Tracking Data." Proceedings of the International AAAI Conference on Web and Social Media. 2021. https://arxiv.org/abs/2012.15112.

The code used to analyze the data is also available at https://github.com/gesiscss/web_tracking.

If you use data or code from this repository, please cite the paper above and the Zenodo link.

ELTEX-Blockchain: A Domain-Specific Dataset for Cybersecurity 🔐 12k Synthetic Social Media Messages for Early Cyberattack Detection on Blockchain

Dataset Statistics | Category | Samples | Description | |--------------|-------------|-----------------| | Cyberattack | 6,941 | Early warning signals and indicators of cyberattacks | | General | 4,507 | Regular blockchain discussions (non-security related) |

Dataset Structure Each entry in the dataset contains: - message_id: Unique identifier for each message - content: The text content of the social media message - topic: Classification label ("cyberattack" or "general")

Performance Gemma-2b-it fine-tuned on this dataset: - Achieves a Brier score of 0.16 using only synthetic data in our social media threat detection task - Shows competitive performance on this specific task when compared to general-purpose models known for capabilities in cybersecurity tasks, like granite-3.2-2b-instruct, and cybersecurity-focused LLMs trained on Primus - Demonstrates promising results for smaller models on this specific task, with our best hybrid model achieving an F1 score of 0.81 on our blockchain threat detection test set, though GPT-4o maintains superior overall accuracy (0.84) and calibration (Brier 0.10)

Attack Type Distribution | Attack Vectors | Seed Examples | |-------------------|------------------------| | Social Engineering & Phishing | Credential theft, wallet phishing | | Smart Contract Exploits | Token claim vulnerabilities, flash loans | | Exchange Security Breaches | Hot wallet compromises, key theft | | DeFi Protocol Attacks | Liquidity pool manipulation, bridge exploits |

For more about Cyberattack Vectors, read Attack Vectors Wiki

Citation bibtex @misc{razmyslovich2025eltexframeworkdomaindrivensynthetic, title={ELTEX: A Framework for Domain-Driven Synthetic Data Generation}, author={Arina Razmyslovich and Kseniia Murasheva and Sofia Sedlova and Julien Capitaine and Eugene Dmitriev}, year={2025}, eprint={2503.15055}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2503.15055}, }

Please cite this paper when using this dataset: N. Thakur, “Mpox narrative on Instagram: A labeled multilingual dataset of Instagram posts on mpox for sentiment, hate speech, and anxiety analysis,” arXiv [cs.LG], 2024, URL: https://arxiv.org/abs/2409.05292Abstract: The world is currently experiencing an outbreak of mpox, which has been declared a Public Health Emergency of International Concern by WHO. During recent virus outbreaks, social media platforms have played a crucial role in keeping the global population informed and updated regarding various aspects of the outbreaks. As a result, in the last few years, researchers from different disciplines have focused on the development of social media datasets focusing on different virus outbreaks. No prior work in this field has focused on the development of a dataset of Instagram posts about the mpox outbreak. The work presented in this paper (stated above) aims to address this research gap. It presents this multilingual dataset of 60,127 Instagram posts about mpox, published between July 23, 2022, and September 5, 2024. This dataset contains Instagram posts about mpox in 52 languages.For each of these posts, the Post ID, Post Description, Date of publication, language, and translated version of the post (translation to English was performed using the Google Translate API) are presented as separate attributes in the dataset. After developing this dataset, sentiment analysis, hate speech detection, and anxiety or stress detection were also performed. This process included classifying each post intoone of the fine-grain sentiment classes, i.e., fear, surprise, joy, sadness, anger, disgust, or neutralhate or not hateanxiety/stress detected or no anxiety/stress detected.These results are presented as separate attributes in the dataset for the training and testing of machine learning algorithms for sentiment, hate speech, and anxiety or stress detection, as well as for other applications.The 52 distinct languages in which Instagram posts are present in the dataset are English, Portuguese, Indonesian, Spanish, Korean, French, Hindi, Finnish, Turkish, Italian, German, Tamil, Urdu, Thai, Arabic, Persian, Tagalog, Dutch, Catalan, Bengali, Marathi, Malayalam, Swahili, Afrikaans, Panjabi, Gujarati, Somali, Lithuanian, Norwegian, Estonian, Swedish, Telugu, Russian, Danish, Slovak, Japanese, Kannada, Polish, Vietnamese, Hebrew, Romanian, Nepali, Czech, Modern Greek, Albanian, Croatian, Slovenian, Bulgarian, Ukrainian, Welsh, Hungarian, and Latvian.The following is a description of the attributes present in this dataset:Post ID: Unique ID of each Instagram postPost Description: Complete description of each post in the language in which it was originally publishedDate: Date of publication in MM/DD/YYYY formatLanguage: Language of the post as detected using the Google Translate APITranslated Post Description: Translated version of the post description. All posts which were not in English were translated into English using the Google Translate API. No language translation was performed for English posts.Sentiment: Results of sentiment analysis (using the preprocessed version of the translated Post Description) where each post was classified into one of the sentiment classes: fear, surprise, joy, sadness, anger, disgust, and neutralHate: Results of hate speech detection (using the preprocessed version of the translated Post Description) where each post was classified as hate or not hateAnxiety or Stress: Results of anxiety or stress detection (using the preprocessed version of the translated Post Description) where each post was classified as stress/anxiety detected or no stress/anxiety detected.All the Instagram posts that were collected during this data mining process to develop this dataset were publicly available on Instagram and did not require a user to log in to Instagram to view the same (at the time of writing this paper).

Please cite the following paper when using this dataset:

N. Thakur, “Five Years of COVID-19 Discourse on Instagram: A Labeled Instagram Dataset of Over Half a Million Posts for Multilingual Sentiment Analysis”, Proceedings of the 7th International Conference on Machine Learning and Natural Language Processing (MLNLP 2024), Chengdu, China, October 18-20, 2024 (Paper accepted for publication, Preprint available at: https://arxiv.org/abs/2410.03293)

Abstract

The outbreak of COVID-19 served as a catalyst for content creation and dissemination on social media platforms, as such platforms serve as virtual communities where people can connect and communicate with one another seamlessly. While there have been several works related to the mining and analysis of COVID-19-related posts on social media platforms such as Twitter (or X), YouTube, Facebook, and TikTok, there is still limited research that focuses on the public discourse on Instagram in this context. Furthermore, the prior works in this field have only focused on the development and analysis of datasets of Instagram posts published during the first few months of the outbreak. The work presented in this paper aims to address this research gap and presents a novel multilingual dataset of 500,153 Instagram posts about COVID-19 published between January 2020 and September 2024. This dataset contains Instagram posts in 161 different languages. After the development of this dataset, multilingual sentiment analysis was performed using VADER and twitter-xlm-roberta-base-sentiment. This process involved classifying each post as positive, negative, or neutral. The results of sentiment analysis are presented as a separate attribute in this dataset.

For each of these posts, the Post ID, Post Description, Date of publication, language code, full version of the language, and sentiment label are presented as separate attributes in the dataset.

The Instagram posts in this dataset are present in 161 different languages out of which the top 10 languages in terms of frequency are English (343041 posts), Spanish (30220 posts), Hindi (15832 posts), Portuguese (15779 posts), Indonesian (11491 posts), Tamil (9592 posts), Arabic (9416 posts), German (7822 posts), Italian (5162 posts), Turkish (4632 posts)

There are 535,021 distinct hashtags in this dataset with the top 10 hashtags in terms of frequency being #covid19 (169865 posts), #covid (132485 posts), #coronavirus (117518 posts), #covid_19 (104069 posts), #covidtesting (95095 posts), #coronavirusupdates (75439 posts), #corona (39416 posts), #healthcare (38975 posts), #staysafe (36740 posts), #coronavirusoutbreak (34567 posts)

The following is a description of the attributes present in this dataset

Post ID: Unique ID of each Instagram post

Post Description: Complete description of each post in the language in which it was originally published

Date: Date of publication in MM/DD/YYYY format

Language code: Language code (for example: “en”) that represents the language of the post as detected using the Google Translate API

Full Language: Full form of the language (for example: “English”) that represents the language of the post as detected using the Google Translate API

Sentiment: Results of sentiment analysis (using the preprocessed version of each post) where each post was classified as positive, negative, or neutral

Open Research Questions

This dataset is expected to be helpful for the investigation of the following research questions and even beyond:

How does sentiment toward COVID-19 vary across different languages?

How has public sentiment toward COVID-19 evolved from 2020 to the present?

How do cultural differences affect social media discourse about COVID-19 across various languages?

How has COVID-19 impacted mental health, as reflected in social media posts across different languages?

How effective were public health campaigns in shifting public sentiment in different languages?

What patterns of vaccine hesitancy or support are present in different languages?

How did geopolitical events influence public sentiment about COVID-19 in multilingual social media discourse?

What role does social media discourse play in shaping public behavior toward COVID-19 in different linguistic communities?

How does the sentiment of minority or underrepresented languages compare to that of major world languages regarding COVID-19?

What insights can be gained by comparing the sentiment of COVID-19 posts in widely spoken languages (e.g., English, Spanish) to those in less common languages?

All the Instagram posts that were collected during this data mining process to develop this dataset were publicly available on Instagram and did not require a user to log in to Instagram to view the same (at the time of writing this paper).

The project analysed the performance of community detection algorithms on the Twitter social network operating on a graph compressed using minhash signatures. The data supplied gives minhash signatures of roughly 16,000 Twitter users who have been classified into 16 categories. It is described in https://arxiv.org/abs/1601.03958 and together with code at https://github.com/melifluos/LSH-community-detection allows the results within to be replicated. Date Accepted: 2017-11-14

We provide a large-scale dataset of the messages exchanged publicly by the streamers and viewers during the live broadcasts of users identified as adult content producers from the LiveMe platform, a major Social Live Streaming Service (SLSS). The dataset comprises 39,382,838 chat messages exchanged by 1,428,284 users, in the context of 293,271 live broadcasts during a period of approximately two years, from July 2016 to June 2018. The analysis of this dataset can be found in our paper "Large-scale analysis of grooming in modern social networks" (arXiv:2004.08205 [cs.SI]).

The share of the Australian population who were active internet users has remained steady since 2015 and in 2022, 91 percent were active users. An increase in accessibility resulted in the number of internet subscribers increasing year on year over the last eight years, with more than 22 million subscribers in 2022.

Consumer access frequency and use

Over half of Australians access the internet mainly via a broadband connection, however, a growing number use a mobile connection via their mobile phone or tablet as their primary means of accessing the internet. Australians' internet usage covered a wide variety of functions, with the majority of consumers using the internet for online shopping and social media, aside from accessing information via a search engine.

Market share of consumer broadband services

Telstra, Australia’s largest telecommunications provider, had the majority retail market share of internet access services in 2021, followed by Optus. Despite being the most prevalent provider of broadband services, customer satisfaction with Telstra is comparatively low compared to it's major competitors, Aussie Broadband and Vodafone.

The two datasets are Weibo data which are used in paper named "Easier contagion and weaker ties make anger spread faster than joy in social media". (https://arxiv.org/abs/1608.03656)Any issues please feel free to contact Jichang Zhao through jichang@buaa.edu.cn.The first dataset, named "users_data", includes 11,753,609 tweets posted by 92,176 users from Sep., 2014 to Mar., 2015. "tweets.txt" and "graph.txt" are the tweets with emotion label and the follow relationship of all users, respectively. In "graph.txt", every line is a following relation which contain two columns. The first item is the follower and the second is the followee. In "tweets.txt", each tweet is represented by three items which respectively are user's id, created time and emotion. If there is a "==>" in the line, it means that this is a retweet and the tweet after "==>" is the original one.The second dataset, which is in the "event_tweets" directory, includes 40,005,242 tweets of 616 different events. Each file in the directory contains tweets that related to the the specific event. And tweets are represented by the created time and the emotion label.

This dataset, released July 2018, contains Estimated number of people aged 18 years and over who often have a difficulty or cannot get to places neededwith transport, including housebound (modelled estimates), 2014; Estimated number of people aged 18 years and over who experienced a barrier to accessing healthcare whenneeded it in the last 12 months, with main reason being cost of service (modelled estimates), 2014. The data is by Population Health Area (PHA) 2016 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). Population Health Areas, developed by PHIDU, are comprised of a combination of whole SA2s and multiple (aggregates of) SA2s, where the SA2 is an area in the ABS structure. For more information please see the data source notes on the data. Source: Modelled by PHIDU based on the ABS General Social Survey, 2014. AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

This dataset, released December 2017, contains family statistics relating to Single parent families with children aged less than 15 years, 2016; Jobless families with children aged less than 15 years, 2016; Children aged less than 15 years in jobless families, 2016; Children in families where the mother has low educational attainment, 2016. The data is by Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS).

There are 31 PHNs set up by the Australian Government. Each network is controlled by a board of medical professionals and advised by a clinical council and community advisory committee. The boundaries of the PHNs closely align with the Local Hospital Networks where possible.

For more information please see the data source notes on the data.

Source: Compiled by PHIDU based on the ABS Census of Population and Housing, August 2016; the ABS Census of Population and Housing, August 2016 (unpublished) data.

AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

This dataset, released February 2021, contains housing and transport statistics relating to the Households in dwellings receiving rent assistance from the Australian Government, June 2020; Aboriginal households in dwellings receiving rent assistance from the Australian Government, June 2016; Persons living in rented social housing dwellings, 2016; Social housing (rented) dwellings, 2016; Persons living in privately rented dwellings, 2016; Privately rented dwellings, 2016; Low income households with mortgage stress, 2016; Low income households with rental stress, 2016; Low income households under financial stress from mortgage or rent, 2016; Low income households, 2016; Housing suitability, 2016; Private dwellings with no motor vehicle, 2016; Persons living in crowded dwellings, 2016; Persons living in severely crowded dwellings, 2016; Aboriginal persons living in crowded dwellings, 2016; Aboriginal persons living in severely crowded dwellings, 2016.

The data is by Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS).

There are 31 PHNs set up by the Australian Government. Each network is controlled by a board of medical professionals and advised by a clinical council and community advisory committee. The boundaries of the PHNs closely align with the Local Hospital Networks where possible.

For more information please see the data source notes on the data.

Source: Compiled by PHIDU based on data from the Department of Social Services, June 2020; and the ABS Census: Dwellings, 2016; Compiled by PHIDU based on data from the Department of Social Services, June 2016; and the ABS Census: Dwellings, 2016; Compiled by PHIDU based on the ABS Census of Population and Housing, August 2016; Compiled by PHIDU based on the ABS Census of Population and Housing, August 2016 (unpublished) data;

AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

This dataset, released August 2020, contains statistics on the population's access to the internet based on internet connection types (Dial-up, Broadband and 'Other'). The data is by Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS).

There are 31 PHNs set up by the Australian Government. Each network is controlled by a board of medical professionals and advised by a clinical council and community advisory committee. The boundaries of the PHNs closely align with the Local Hospital Networks where possible.

For more information please see the data source notes on the data.

Source: Compiled by PHIDU based on the ABS Census of Population and Housing, August 2016.

AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

This dataset, released July 2018, contains the Index of Relative Socio-economic Disadvantage (IRSD), 2016. The data is by Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS).

There are 31 PHNs set up by the Australian Government. Each network is controlled by a board of medical professionals and advised by a clinical council and community advisory committee. The boundaries of the PHNs closely align with the Local Hospital Networks where possible.

For more information please see the data source notes on the data.

Source: Compiled by PHIDU based on ABS Socio-economic Indexes for Areas (SEIFA), 2016 data. The LGA data were re-produced from the ABS originals. Data for other geographic levels were constructed using population weighted averages, based on the published ABS SA2 data

AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

This dataset, released February 2020, contains statistics about the labour force relating to Unemployment, March 2019; Labour force participation, March 2019; Female labour force participation, 2016.

The data is by Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS).

There are 31 PHNs set up by the Australian Government. Each network is controlled by a board of medical professionals and advised by a clinical council and community advisory committee. The boundaries of the PHNs closely align with the Local Hospital Networks where possible.

For more information please see the data source notes on the data.

Source: Compiled by PHIDU based on the Small Area Labour Markets - Australia, Department of Employment, Skills, Small and Family Business, March Quarter 2019; Compiled by PHIDU based on the Small Area Labour Markets - Australia, Department of Employment, Skills, Small and Family Business, March Quarter 2019; and the ABS Estimated Resident Population, 30 June 2018; Compiled by PHIDU based on the ABS Census of Population and Housing, August 2016.

AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

This dataset, released November 2018, contains housing and transport statistics relating to the Households in dwellings receiving rent assistance from the Australian Government, June 2017; Dwellings rented from the government housing authority, 2016; Dwellings rented by households from a housing co-operative, community or church group, June 2016; Low income households with mortgage stress, 2016; Low income households with rental stress, 2016; Low income households under financial stress from mortgage or rent, 2016; Low income households, 2016; Housing suitability, 2016; Private dwellings with no motor vehicle, 2016. The data is by Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS).

There are 31 PHNs set up by the Australian Government. Each network is controlled by a board of medical professionals and advised by a clinical council and community advisory committee. The boundaries of the PHNs closely align with the Local Hospital Networks where possible.

For more information please see the data source notes on the data.

Source: Compiled by PHIDU based on data from the Department of Social Services, June 2017; ABS Census of Population and Housing, August 2016; and the ABS Census: Dwellings, 2016.

AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

This dataset, released September 2020, contains male estimated resident population by 5 year age groups: 0-4 years to 85+ years, 2019. The data is by Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS).

There are 31 PHNs set up by the Australian Government. Each network is controlled by a board of medical professionals and advised by a clinical council and community advisory committee. The boundaries of the PHNs closely align with the Local Hospital Networks where possible.

For more information please see the data source notes on the data.

Source: Compiled by PHIDU based on ABS 3235.0 Population by Age and Sex, Regions of Australia, 30 June 2019.

AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

This dataset, released in October 2019, contains the statistics of the Skill stream, 2016.

The data is by Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS).

There are 31 PHNs set up by the Australian Government. Each network is controlled by a board of medical professionals and advised by a clinical council and community advisory committee. The boundaries of the PHNs closely align with the Local Hospital Networks where possible.

For more information please see the data source notes on the data.

Source: Compiled by PHIDU based on the ABS Census and Migrants Integrated Dataset, August 2016.

AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.