All schools (public & private) in the state are listed. Contact details such as postal, street address and telephone numbers are also available. For further information or changes contact (08) 9264 4562. Show full description

The master dataset contains comprehensive information for all government schools in NSW. Data items include school locations, latitude and longitude coordinates, school type, student enrolment numbers, electorate information, contact details and more.

This dataset is publicly available through the Data NSW website, and is used to support the School Finder tool.

Data Notes:

• Data relating to healthy canteen is no longer up to date as it is no longer updated by the Department, this data can be sourced through NSW health.

• Student enrolment numbers are based on the census of government school students undertaken on the first Friday of August; and LBOTE numbers are based on data collected in March.

• School information, such as addresses and contact details, are updated regularly as required, and are the most current source of information.

• Data is suppressed for indigenous and LBOTE percentages where student numbers are equal to, or less than five indicated by "np".

• NSSC out of scope schools will not have an enrolment figure.

• NSSC and LBOTE figures are updated annually in December.

• ICSEA values are updated every February with the previous year's ICSEA values. Small schools, SSPs and Senior Secondary schools do not have their ICSEA values published by ACARA.

• Family Occupation and Educational Index (FOEI) is a school-level index of educational disadvantage. Data is extracted in May and values are updated annually in December.

• Following the introduction of part-time study in secondary schools in 1993, student enrolments are generally reported in full-time equivalent units (FTE). The FTE for students studying less than 10 units, the minimum workload, is determined by the formula: 0.1 x the number of units studied and represented as a proportion of the full-time enrolment of 1.0 FTE.

Data Source:

• Education Statistics and Measurement. Centre for Education Statistics and Evaluation.

The file set is a freely downloadable aggregation of information about Australian schools. The individual files represent a series of tables which, when considered together, form a relational database. The records cover the years 2008-2014 and include information on approximately 9500 primary and secondary school main-campuses and around 500 subcampuses. The records all relate to school-level data; no data about individuals is included. All the information has previously been published and is publicly available but it has not previously been released as a documented, useful aggregation. The information includes:
(a) the names of schools
(b) staffing levels, including full-time and part-time teaching and non-teaching staff
(c) student enrolments, including the number of boys and girls
(d) school financial information, including Commonwealth government, state government, and private funding
(e) test data, potentially for school years 3, 5, 7 and 9, relating to an Australian national testing programme know by the trademark 'NAPLAN'

Documentation of this Edition 2016.1 is incomplete but the organization of the data should be readily understandable to most people. If you are a researcher, the simplest way to study the data is to make use of the SQLite3 database called 'school-data-2016-1.db'. If you are unsure how to use an SQLite database, ask a guru.

The database was constructed directly from the other included files by running the following command at a command-line prompt:
sqlite3 school-data-2016-1.db < school-data-2016-1.sql
Note that a few, non-consequential, errors will be reported if you run this command yourself. The reason for the errors is that the SQLite database is created by importing a series of '.csv' files. Each of the .csv files contains a header line with the names of the variable relevant to each column. The information is useful for many statistical packages but it is not what SQLite expects, so it complains about the header. Despite the complaint, the database will be created correctly.

Briefly, the data are organized as follows.
(a) The .csv files ('comma separated values') do not actually use a comma as the field delimiter. Instead, the vertical bar character '|' (ASCII Octal 174 Decimal 124 Hex 7C) is used. If you read the .csv files using Microsoft Excel, Open Office, or Libre Office, you will need to set the field-separator to be '|'. Check your software documentation to understand how to do this.
(b) Each school-related record is indexed by an identifer called 'ageid'. The ageid uniquely identifies each school and consequently serves as the appropriate variable for JOIN-ing records in different data files. For example, the first school-related record after the header line in file 'students-headed-bar.csv' shows the ageid of the school as 40000. The relevant school name can be found by looking in the file 'ageidtoname-headed-bar.csv' to discover that the the ageid of 40000 corresponds to a school called 'Corpus Christi Catholic School'.
(3) In addition to the variable 'ageid' each record is also identified by one or two 'year' variables. The most important purpose of a year identifier will be to indicate the year that is relevant to the record. For example, if one turn again to file 'students-headed-bar.csv', one sees that the first seven school-related records after the header line all relate to the school Corpus Christi Catholic School with ageid of 40000. The variable that identifies the important differences between these seven records is the variable 'studentyear'. 'studentyear' shows the year to which the student data refer. One can see, for example, that in 2008, there were a total of 410 students enrolled, of whom 185 were girls and 225 were boys (look at the variable names in the header line).
(4) The variables relating to years are given different names in each of the different files ('studentsyear' in the file 'students-headed-bar.csv', 'financesummaryyear' in the file 'financesummary-headed-bar.csv'). Despite the different names, the year variables provide the second-level means for joining information acrosss files. For example, if you wanted to relate the enrolments at a school in each year to its financial state, you might wish to JOIN records using 'ageid' in the two files and, secondarily, matching 'studentsyear' with 'financialsummaryyear'.
(5) The manipulation of the data is most readily done using the SQL language with the SQLite database but it can also be done in a variety of statistical packages.
(6) It is our intention for Edition 2016-2 to create large 'flat' files suitable for use by non-researchers who want to view the data with spreadsheet software. The disadvantage of such 'flat' files is that they contain vast amounts of redundant information and might not display the data in the form that the user most wants it.
(7) Geocoding of the schools is not available in this edition.
(8) Some files, such as 'sector-headed-bar.csv' are not used in the creation of the database but are provided as a convenience for researchers who might wish to recode some of the data to remove redundancy.
(9) A detailed example of a suitable SQLite query can be found in the file 'school-data-sqlite-example.sql'. The same query, used in the context of analyses done with the excellent, freely available R statistical package (http://www.r-project.org) can be seen in the file 'school-data-with-sqlite.R'.

A school zone or catchment area is a defined area from which the school or preschool accepts its core intake of students. The school or preschool gives priority enrolment to children who live inside that zone or catchment area.

A dataset of schools apparent retention rates or ARR, all school sector in Victoria, from census year 2012 to 2023.\r This dataset is prepared and based on data collected from schools as part of the February School Census conducted on the last school day of February each year. It presents information for all government and non-government schools and student enrolments in Victoria, in particular secondary school years. The majority of the statistical data in this publication is drawn from school administration systems. The dataset includes analysis by school sector and sex, Koorie status, as well as on government schools by region.\r Apparent retention rates (ARR) are calculated based on aggregate enrolment data and provide an indicative measurement of student engagement in secondary education. The Department of Education and Training (DET) computes and publishes ARR data at a state-wide and DET region level only.\r \r The term "apparent" retention rate reflects that retention rates are influenced by factors not taken into account by this measure such as: Student repeating year levels, Interstate and overseas migration, Transfer of students between education sectors or schools, Student who have left school previously, returning to continue their school education.\r The ARR for year 7 to 12 (ARR 7-12) refers to the Year 12 enrolment expressed as a proportion of the Year 7 enrolment five years earlier. The ARR for year 10 to 12 (ARR 10-12) refers to the Year 12 enrolment expressed as a proportion of the Year 10 enrolment two years earlier.\r \r Please note that the ABS calculates apparent retention using the number of full-time school students only whereas at the DET we use the number of full-time equivalent school enrolments. Data reported in the ABS Schools, Australia collection is based on enrolment data collected in August by all jurisdictions.\r \r The Department has found that computing ARR at geographical areas smaller than DET regions (e.g. LGA, Postcode) can produce erratic and misleading results that are difficult to interpret or make use of. In small populations, relatively small changes in student numbers can create large movements in apparent retention rates. These populations might include smaller jurisdictions, Aboriginal and Torres Strait Islander students, and subcategories of the non-government affiliation. There are a number of reasons why apparent rates may generate results that differ from actual rates. \r Apparent retention rates provide an indicative measure of the number of full-time school students who have stayed in school, as at a designated year and grade of education. It is expressed as a percentage of the respective cohort group that those students would be expected to have come from, assuming an expected rate of progression of one grade per year.\r \r Provided ARR is a result of calculation of the whole census and is NOT to be re-calculated by average or sum.

AU: Adolescents Out of School: Male: % of Male Lower Secondary School Age data was reported at 1.888 % in 2022. This records an increase from the previous number of 1.405 % for 2021. AU: Adolescents Out of School: Male: % of Male Lower Secondary School Age data is updated yearly, averaging 1.240 % from Dec 1993 (Median) to 2022, with 7 observations. The data reached an all-time high of 1.888 % in 2022 and a record low of 0.650 % in 1996. AU: Adolescents Out of School: Male: % of Male Lower Secondary School Age data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Social: Education Statistics. Adolescents out of school are the percentage of lower secondary school age adolescents who are not enrolled in school.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;Weighted average;

AU: Lower Secondary School Starting Age data was reported at 12.000 Year in 2023. This stayed constant from the previous number of 12.000 Year for 2022. AU: Lower Secondary School Starting Age data is updated yearly, averaging 12.000 Year from Dec 1970 (Median) to 2023, with 54 observations. The data reached an all-time high of 12.000 Year in 2023 and a record low of 12.000 Year in 2023. AU: Lower Secondary School Starting Age data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Social: Education Statistics. Lower secondary school starting age is the age at which students would enter lower secondary education, assuming they had started at the official entrance age for the lowest level of education, had studied full-time throughout and had progressed through the system without repeating or skipping a grade.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;;

This dataset presents data on education and employment available from the ABS Data by Region statistics. This release of Data by Region presents various data for 2011-2019 and Census of Population and Housing data for 2011 and 2016 and is based on the Statistical Area 4 (SA4) 2016 boundaries. The dataset includes information in the following specified areas of education and employment: Early Childhood - Enrolment and Attendance in Preschool Programs, Non-School Qualifications, Higher Education Loan Program (HELP) Repayments, Highest Year of School Completed, Occupation of Employed Persons, Youth Engagement in Work or Study, Jobs in Australia and Labour Force.

Data by Region contains a standard set of data for each region type, depending on the availability of statistics for particular geographies. Data are sourced from a wide variety of collections, both ABS and non-ABS. When analysing these statistics, care needs to be taken as time periods, definitions, methodologies, scope and coverage can differ across collections. Where available, data have been presented as a time series - to enable users to assess changes over time. However, when looked at on a period to period basis, some series may sometimes appear volatile. When analysing the data, users are encouraged to consider the longer term behaviour of the series, where this extra information is available.

For more information please visit the Explanatory Notes.

AURIN has made the following changes to the original data:

• Spatially enabled the original data with the ABS Australian Statistical Geography Standard (ASGS) SA4 2016 dataset.

• Some data values in Data by Region have been randomly adjusted or suppressed to avoid the release of confidential details.

• Where data was not available, not available for publication, nil or rounded to zero in the original data, it has been set to null.

• Columns and rows that did not contain any values in the original data have been removed.

Export Data Access API

National education initiatives and a number of online education services rely on a current and accurate list of schools in Australia. In order to operate, schools must be registered with the respective school registration authority in each state or territory. ACARA has obtained the list from all 14 school registration authorities in Australia in order to create the Australian Schools List. This list provides an update of all schools and campuses in Australia. It also includes school location, school type and school sector attributes.

The ASL was last updated on 28 May 2020 to reflect Term 2 - 2020

https://asl.acara.edu.au/

Metadata

AU: School Enrollment: Preprimary: % Gross data was reported at 160.214 % in 2020. This records a decrease from the previous number of 166.161 % for 2019. AU: School Enrollment: Preprimary: % Gross data is updated yearly, averaging 78.549 % from Dec 1971 (Median) to 2020, with 45 observations. The data reached an all-time high of 166.993 % in 2016 and a record low of 64.549 % in 1981. AU: School Enrollment: Preprimary: % Gross data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Social: Education Statistics. Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Preprimary education refers to programs at the initial stage of organized instruction, designed primarily to introduce very young children to a school-type environment and to provide a bridge between home and school.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed October 24, 2022. https://apiportal.uis.unesco.org/bdds.;Weighted average;

AU: School Enrollment: Primary: Male: % Gross data was reported at 99.048 % in 2022. This records a decrease from the previous number of 99.797 % for 2021. AU: School Enrollment: Primary: Male: % Gross data is updated yearly, averaging 105.714 % from Dec 1971 (Median) to 2022, with 52 observations. The data reached an all-time high of 112.456 % in 1971 and a record low of 99.048 % in 2022. AU: School Enrollment: Primary: Male: % Gross data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Social: Education Statistics. Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;Weighted average;

This dataset presents a range of data items sourced from a wide variety of collections, both Australian Bureau of Statistics (ABS) and non-ABS. The data is derived from the November 2024 release of Data by region. Individual data items present the latest reference year data available on Data by region. This layer presents data by Local Government Areas (LGA), 2021.

The Education and employment theme is based on groupings of data within Data by region. Concepts, sources and methods for each dataset can be found on the Data by region methodology page.

The Education and employment theme includes:

Enrolments in preschool or preschool programs Attendance in preschool or preschool programs Highest year of school completed (Census)
Jobs in Australia Labour force status (Census) Non-school qualifications (Census) Occupation of employed persons (Census) Youth engagement in work/study (Census)

When analysing these statistics:

Time periods, definitions, methodologies, scope, and coverage can differ across collections.
Some data values have been randomly adjusted or suppressed to avoid the release of confidential data, this means

    some small cells have been randomly set to zero
    care should be taken when interpreting cells with small numbers or zeros.

Data and geography references

Source data publication: Data by region Geographic boundary information: Australian Statistical Geography Standard (ASGS) Edition 3 Further information: Data by region methodology, reference period 2011-24 Source: Australian Bureau of Statistics (ABS)

Made possible by the Digital Atlas of Australia

The Digital Atlas of Australia is a key Australian Government initiative being led by Geoscience Australia, highlighted in the Data and Digital Government Strategy. It brings together trusted datasets from across government in an interactive, secure, and easy-to-use geospatial platform. The Australian Bureau of Statistics (ABS) is working in partnership with Geoscience Australia to establish a set of web services to make ABS data available in the Digital Atlas of Australia.

Contact the Australian Bureau of Statistics

Email geography@abs.gov.au if you have any questions or feedback about this web service.
Subscribe to get updates on ABS web services and geospatial products.

Privacy at the Australian Bureau of Statistics Read how the ABS manages personal information - ABS privacy policy.

AU: School Enrollment: Tertiary: Female: % Gross data was reported at 128.283 % in 2022. This records a decrease from the previous number of 135.733 % for 2021. AU: School Enrollment: Tertiary: Female: % Gross data is updated yearly, averaging 31.968 % from Dec 1970 (Median) to 2022, with 36 observations. The data reached an all-time high of 141.460 % in 2015 and a record low of 11.092 % in 1970. AU: School Enrollment: Tertiary: Female: % Gross data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Social: Education Statistics. Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Tertiary education, whether or not to an advanced research qualification, normally requires, as a minimum condition of admission, the successful completion of education at the secondary level.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;Weighted average;

This dataset presents the On Track Survey for Victorian schools. The data spans the year of 2012 and is aggregated by Local Government Areas (LGA) from the 2011 Australian Statistical Geography …Show full descriptionThis dataset presents the On Track Survey for Victorian schools. The data spans the year of 2012 and is aggregated by Local Government Areas (LGA) from the 2011 Australian Statistical Geography Standard (ASGS). The On Track Survey is conducted in April-May and involves a short telephone survey of school leavers who attended school in the previous year and who agreed to participate in the survey. Results for individual schools are usually published in June of the survey year. The On Track survey seeks to: Offer a consistent and comprehensive approach to monitoring the transitions of school leavers. Report the information to schools, TAFE institutions and other education providers, organisations concerned with assisting young people, policy makers, parents and students. Provide detailed analyses of the transitions experienced by different groups of leavers. Enable education providers to use the findings to monitor and improve their programs. Provide a referral service for school leavers who appear to be experiencing difficulties in the transitions process. Copyright attribution: Government of Victoria - Department of Education and Training, (2013): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 3.0 Australia (CC BY 3.0 AU)

This database is comprised of 951 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 509 males (54%) and 442 females (46%). Their ages ranged from 12 to 16 years (M = 13.69, SD = 0.72). Seven participants did not report their age. The majority were born in Australia (N = 849, 89%). The next most common countries of birth were China (N = 24, 2.5%), the UK (N = 23, 2.4%), and the USA (N = 9, 0.9%). Data were drawn from students at five Australian independent secondary schools.

The data contains item responses for the Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. The Social media question asked about frequency of use with the question “How often do you use social media?”. The response options ranged from constantly to once a week or less. Items measuring Fear of Missing Out were included and incorporated the following five questions based on the APS Stress and Wellbeing in Australia Survey (APS, 2015). These were “When I have a good time it is important for me to share the details online; I am afraid that I will miss out on something if I don’t stay connected to my online social networks; I feel worried and uncomfortable when I can’t access my social media accounts; I find it difficult to relax or sleep after spending time on social networking sites; I feel my brain burnout with the constant connectivity of social media. Internal consistency for this measure was α = .81. Self compassion was measured using the 12-item short-form of the Self-Compassion Scale (SCS-SF; Raes et al., 2011).

The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels.

References:

Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. _{https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4}

Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. _{https://doi.org/10.1002/cpp.702}

Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. _{https://doi.org/10.1016/S0005-7967(98)00034-5}

Ants are deeply embedded in the Australian psyche. Whether you are aware of it or not, you encounter ants pretty much every day of your life in Australia. School of Ants Australia aims to document the diversity, distribution and diet preferences of Australia?s dominant ground foraging ants; those ubiquitous little black ants that infiltrate homes, backyards, parks and schools.

Uncover a world of ants at your own feet, in your backyard, school or park. By becoming a citizen scientist you can help us locate damaging invasive species, compare and contrast species of common little black ants across the country, and add important records to our understanding of ant biodiversity. Records like this are crucial in our understanding of how the ranges of organisms change with our changing climate and landscapes.

Ants are ubiquitous in Australia. They occupy every habitat and landscape across all States and Territories (excluding Antarctica). Their sensitivity to disturbances of many sorts means they can be used as bioindicators of landscape health, reforestation and mine site recovery. They are important predators, pest controllers and soil engineers, but can also become pests themselves.

Ants also move around with humans all the time, so finding out what ants are where can help us pinpoint problem ants before they cause problems for humans, our environment or agriculture in Australia. The Red Imported Fire Ant, the Yellow Crazy Ant, Electric Ant and the Argentine Ant are examples of introduced ants that have become problematic.

This dataset presents information from G16 – Highest year of school completed by age by sex in Australia based on the general community profile from the 2021 Census. It contains characteristics of persons, families, and dwellings by Statistical Areas Level 2 (SA2), 2021, from the Australian Statistical Geography Standard (ASGS) Edition 3.

This dataset is part of a set of web services based on the 2021 Census. It can be used as a tool for researching, planning, and analysis. The data is based on place of usual residence (that is, where people usually live, rather than where they were counted on Census night), unless otherwise stated.

Small random adjustments have been made to all cell values to protect the confidentiality of respondents. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For further information see the 2021 Census Privacy Statement, Confidentiality, and Introduced random error/perturbation.

Made possible by the Digital Atlas of Australia The Digital Atlas of Australia is an Australian Government initiative being led by Geoscience Australia. It will bring together trusted datasets from across government in an interactive, secure, and easy-to-use geospatial platform. The Australian Bureau of Statistics (ABS) is working in partnership with Geoscience Australia to establish a set of web services to make ABS data available in the Digital Atlas.

Contact the Australian Bureau of Statistics (ABS) If you have questions, feedback or would like to receive updates about this web service, please email geography@abs.gov.au. For information about how the ABS manages any personal information you provide view the ABS privacy policy.

Data and geography references Source data publication: G16 – Highest year of school completed by age by sex Geographic boundary information: Australian Statistical Geography Standard (ASGS) Edition 3 Further information: About the Census, 2021 Census product release guide – Community Profiles, Understanding Census geography Source: Australian Bureau of Statistics (ABS)

We conducted a field study at a K-12 private school in the suburbs of Melbourne, Australia. The data capture contained two elements: First, a 5-month longitudinal field study In-Gauge using two outdoor weather stations, as well as indoor weather stations in 17 classrooms and temperature sensors on the vents of occupant-controlled room air-conditioners; these were collated into individual datasets for each classroom at a 5-minute logging frequency, including additional data on occupant presence. The dataset was used to derive predictive models of how occupants operate room air-conditioning units. Second, we tracked 23 students and 6 teachers in a 4-week cross-sectional study En-Gage, using wearable sensors to log physiological data, as well as daily surveys to query the occupants' thermal comfort, learning engagement, emotions and seating behaviours. This is the first publicly available dataset studying the daily behaviours and engagement of high school students using heterogeneous methods. The combined data could be used to analyse the relationships between indoor climates and mental states of school students.

The detailed data descriptor has been published in Nature Scientific Data. For more details on the dataset, please check the paper. https://doi.org/10.1038/s41597-022-01347-w.

Please cite the following papers if the dataset is used in a publication:

[1] Gao, N., Marschall, M., Burry, J. , Watkins, S., Salim, F. Understanding occupants’ behaviour, engagement, emotion, and comfort indoors with heterogeneous sensors and wearables. Sci Data 9, 261 (2022).

[2] Gao, N., Shao, W., Rahaman, M. S., & Salim, F. D. (2020). n-Gage: Predicting in-class Emotional, Behavioural and Cognitive Engagement in the Wild. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4(3), 1-26.

Data Notes:

• The data only includes students learning a language on average for more than 1 hour per week for 35 or more weeks a year.

• Includes students studying a language through the Secondary College of Languages (formerly Saturday School of Community Languages).

• In 2021, the Language Participation Collection for Years 7-9 students was moved from August to May.

• Programs in Languages other than English for Years K-6 and the Language Participation for Years 7-9 data collections were not conducted in 2022, in line with the department’s commitment to “clear the decks” for schools in Term 2 2022.

Data Source:

• Schools and Students: Statistical Bulletin. Centre for Education Statistics and Evaluation.

State school utilities expenditure such as electricity, sewerage, waste, garbage and water - excluding telecommunications.

*This information is no longer updated. Please contact the Open Data Team for further information.