







































AUSTRALIAN POPULATION STUDIES   2021 | Volume 5 | Issue 1 | pages 65-76 

 

© Temple et al. 2021. Published under the Creative Commons Attribution-NonCommercial licence 3.0 Australia (CC BY-NC 
3.0 AU). Journal website: www.australianpopulationstudies.org 

Introductory Guide 

Understanding survey data available for 
researchers working in ageing: the 
CEPAR Metadata Database on Ageing 

Jeromey B. Temple*  The University of Melbourne 

Tanara Vieira Sousa  The University of Melbourne 

Ruth Williams  The University of Melbourne 

Jay Stiles  The University of Melbourne 

Libby Brooke  The University of Melbourne 

Josh Knight  The University of Melbourne 

* Corresponding author. Email: Jeromey.Temple@unimelb.edu.au. Melbourne School of Population 

and Global Health, The University of Melbourne, Melbourne, Vic 3010, Australia. 

Paper received 8 February 2021; accepted 23 April 2021; published 31 May 2021 

Abstract 

Background 

For researchers working in gerontology or the demography of ageing, knowledge of and access to 

population-based data, which includes mature age respondents, is critical. The collection of 

metadata (information describing data) supports researchers in their search for relevant data. 

Aims 

This proof-of-concept project seeks to develop a metadata database including metadata on 

Australian sample surveys relevant to ageing over the period 2010 – 2018. 

Data and methods 

We used a five-stage approach to create the Centre for Excellence in Population Ageing Research 

(CEPAR) metadata database on ageing: 1) identification of in-scope survey datasets; 2) indexing the 

in-scope surveys; 3) scraping metadata from publicly available sources; 4) appending metadata to a 

master database; and 5) creation of a webtool to enable users to search and export metadata and 

obtain contact details for the relevant data custodian. 

Results 

The CEPAR Metadata Database webtool is available from: https://mspgh.unimelb.edu.au/centres-

institutes/centre-for-health-policy/research-group/metadata-database. 

Conclusions 

Metadata database collections can assist researchers to identify what data has been collected (for 

their specific research interest in ageing), how it was collected and how to gain access to the data.  

Key words 

Metadata; ageing; survey data; Australian Bureau of Statistics; data archive  

 

http://www.australianpopulationstudies.org/
mailto:Jeromey.Temple@unimelb.edu.au
https://mspgh.unimelb.edu.au/centres-institutes/centre-for-health-policy/research-group/metadata-database
https://mspgh.unimelb.edu.au/centres-institutes/centre-for-health-policy/research-group/metadata-database


66 Temple et al.  Australian Population Studies 5 (1) 2021 

1. Introduction 

Evidence-driven gerontology and demography research relies on rigorous population-based empirical 

data. However, the Australian Association of Gerontology (2017) states there is no single national 

independent organisation that can collate, translate and identify gaps in evidence related to ageing 

with the aim of informing Australian policy. The collection of metadata (information describing data) 

can support important evidence-driven research. Mapping metadata is a preparatory step towards 

understanding what information is being captured, how it is captured, as well as improving data 

collection. Therefore, metadata can play a role in demonstrating the scope, foundations and gaps in 

current evidence-driven gerontology research which can inform future directions.  

More generally, a key task that all social scientists face when beginning a new project is 

understanding the variety of data that exists in order to explore the underlying research question. 

Although excellent data archives are available in Australia, including the Australian Data Archive 

(ADA) and the Australian Bureau of Statistics (ABS) microdata collections, there is currently no single 

point of metadata information specifically tailored to researchers working in ageing. 

Our solution to this problem was to develop a metadata database as a proof-of-concept project. Our 

metadata database includes metadata on Australian sample surveys relevant to ageing (e.g., 

including data dictionary elements, variable names, data types and formats, and survey and sampling 

design) between the period 2010 – 2018. The scope further considers surveys collected by the ABS or 

deposited with the ADA, where metadata is publicly available. The metadata database webtool we 

developed enables users to search and export metadata and obtain contact details for the relevant 

data custodian. In the remainder of this paper, we outline components of metadata, the 

methodology employed to create the metadata database and provide a worked example of searching 

and extracting data from the provided webtool. We conclude with a summary of future uses and 

research priorities, as well as outlining the limitations to the approach employed. 

2. What is metadata? 

Metadata is data about data; it is information that describes other data. More precisely, it is the 

formalised structured description of content, quality, condition, or other characteristics of data. A 

principal purpose of metadata is to assist users in finding relevant information and discovering 

resources through bringing similar information together and providing the location of information. In 

addition, metadata provides access to information on the extent of completeness of the content; the 

context of the content; and structural relationships within and between information (Gilliland 2016). 

Metadata assists in identifying and organising electronic resources by relevant criteria, provides 

digital identification, and supports the archiving and preservation of resources (United Nations 

Statistical Commission and Economic Commission for Europe 1995).  

Metadata can be stored internally, in the same file as the described data (also known as embedded 

metadata), or externally, in a separate file from the described data. A metadata repository generally 

stores the metadata separate from the data (NISO 2004). Many types of metadata exist including 

descriptive, structural, administrative, reference and statistical metadata.  



Australian Population Studies 5 (1) 2021 Temple et al.  67 

Descriptive metadata describes a resource, typically to search and locate an object, such as title, 

author, subjects, keywords, or publisher (Gilliland 2016). For example, the survey name. 

Statistical metadata describes information about statistical data and the processes of collection. 

Statistical data repositories have their own requirements for metadata to describe not only the 

source and quality of the data but also what statistical processes were used to create the data. This is 

of particular importance to both validate and improve the process of statistical data production 

(Gilliland 2016). 

Reference metadata describes aspects of the contents of statistical data and includes explanatory 

information on the context of the data (Eurostat 2019). For example, a data item list, reporting on 

the representativeness of the dataset. 

Structural metadata describes information about the structure and interrelationships of data, such 

as types, versions, tables, columns, keys and indexes (Gilliland 2016).  

Administrative metadata describes information that assists to manage a resource, such as when and 

how it was created, acquisition and location information, file type, and who has access rights. Two 

subsets of administrative metadata include Rights Management Metadata, which concerns 

intellectual property rights, and Preservation Metadata, which contains information needed to 

archive and preserve a resource (Gilliland 2016). 

The creation and archiving of metadata are important as it ensures that information and resources 

will survive and continue to be accessible in the future. Archiving and preservation allow tracking the 

lineage of an object, detailing physical characteristics and documenting behaviour which can be 

mimicked in future technologies (Gilliland 2016). 

In the CEPAR metadata database, we include components of descriptive, structural, and 

administrative metadata. The database also includes links to details on statistical and reference 

metadata. The metadata itself is external, i.e., detached from the underlying survey data. 

3. Methods 

We used a five-stage approach to collect and display the metadata (Figure 1). Ethics approval for this 

project was granted by the Melbourne School of Population and Global Health Human Ethics 

Advisory Group (HEAG) – Ethics ID: 1852885.2.  

Stage one involved identifying relevant sample surveys to include. Stage two centred on indexing the 

survey data. Stage three comprised scraping the metadata for each survey in the index file. Stage 

four included appending all the metadata for construction into a database. Finally, in stage five, a 

webtool was developed to enable users to search key terms in the metadata database. 

For stage one, public repositories of Australian sample survey data were searched with the following 

inclusion criteria: sample survey data, unit-record files, publicly available, collected in 2010 or later 

and data related to participants aged 45 years and over. Surveys were excluded if they contained 

qualitative or mixed-methods data or had no available details of the data custodian. The stage one 

search was carried out from 17th of July 2018 until 1st of November 2018.   



68 Temple et al.  Australian Population Studies 5 (1) 2021 

 

Figure 1: Process to develop the CEPAR Metadata Database 

 

In stage two all files that fulfilled the inclusion criteria were indexed with the following fields: file 

creation date, sample design, survey type, unit of analysis, sample frame, details of fieldwork, 

geographic sampling frame and survey description. 

During stage three, each survey was scraped for the following metadata: variable name, label, 

categories, and survey identifier. Where possible, surveys were scraped using the statistical software 

programme R. Surveys were manually scraped if they could not be scraped by R. Selected .xml files 

were also made available from the ADA. 

In stage four, all the scraped files were then appended using Stata 15.1. This file was then used to 

create an exportable CSV file. Finally, in stage five, the CSV file was uploaded to 

https://mspgh.unimelb.edu.au/centres-institutes/centre-for-health-policy/research-

Stage 1

•Identification of relevant sources of surveys
•Inclusion criteria: sample survey data, unit-record files, publicly available, 2010 or 
later and data related to participants aged 45 years and over.

•Exclusion criteria: administrative, qualitative or mixed-methods data or had no 
details of the data custodian available.

Stage 2

•Index the available surveys
•All surveys were indexed with the following fields: file creation date, sample design, 
survey type, unit of analysis, sample frame, details of fieldwork, geographic 
sampling frame, and survey description.

Stage 3

•Scrape metadata from surveys
•All metadata include: variable name, label, categories and survey identifier. 

•Statistical software programme R and manually.

Stage 4

•Append all metadata in a unique database
•All the scraped files were appended using Stata 15.1. This file was then used to 
create the database.

Stage 5

•Development of the website/repository
•The metadata database was created and linked to a website interface with simple 
search functions.

https://mspgh.unimelb.edu.au/centres-institutes/centre-for-health-policy/research-group/metadata-database


Australian Population Studies 5 (1) 2021 Temple et al.  69 

group/metadata-database. This simple webtool developed by the University of Melbourne allows 

users to search and export the metadata. 

The principal repositories identified during stage one of this study were: 

• Australian Bureau of Statistics (ABS). The ABS holds a very detailed collection of microdata, 

spanning microdata datasets from the 1970s through to the present day. Microdata can be 

accessed as a basic CURF (Confidentialised Unit Record File) through ABS microdata download, or 

more detailed microdata can be obtained through the ABS Datalab. Selected microdata can also 

be analysed through the ABS TableBuilder product. However, for the metadata database, the 

scope includes only CURF and Datalab products (or former RADL – Remote Access Data 

Laboratory products). TableBuilder was excluded, because although the product is built from 

microdata, routine procedures used by researchers (e.g., regression models), are not possible 

with this product. The URL for ABS microdata collections, including details for access is: 

https://www.abs.gov.au/websitedbs/d3310114.nsf/home/microdata+entry+page.  

• Australian Data Archive (ADA). The ADA, situated at the Australian National University, holds over 

6,000 datasets from 1838 onwards. Over ,1500 datasets are available within the ADA Dataverse 

for users to apply for access. The data collections cover a range of topics including health, social 

attitudes, and electoral and opinion surveys among others. The Household, Income and Labour 

Dynamics in Australia (HILDA) and Ten to Men surveys are also available through the ADA. The 

URL for ADA data collections, including details for access, is: https://ada.edu.au/.  

Included surveys from the ABS repository had a minimum of ‘expanded CURF’ or ‘microdata’ 

available and totalled 46 surveys and 14,011 variables. Thirty-five surveys were extracted from the 

ADA repository, totalling 10,457 variables. HILDA (71,014 variables available) and Ten to Men surveys 

were included in the ADA repository. HILDA metadata were sourced from the publicly accessible 

HILDA Data Dictionary (https://www.online.fbe.unimelb.edu.au/HILDAodd/Default.aspx). The 

Australian Longitudinal Study on Women's Health (ALSWH) included 13,174 variables from 8 waves. 

Details of the repositories, including the number of sample surveys (upper panel) and variables 

(lower panel) can be found in Table 1. From 86 indexed files, 49.4% were cross-sectional, 29.4% 

repeated cross-sectional and 11.8% longitudinal surveys. Thirty-five (39.3%) surveys have ‘individual’ 

level as the unit of analysis and almost all were ‘national’ level surveys. 

4. Example of the Database 

In this section we detail a simple example from the metadata database. The landing page for the 

metadata database is found here: https://mspgh.unimelb.edu.au/centres-institutes/centre-for-

health-policy/research-group/metadata-database. From the landing page (Figure 2), users can access 

information on: (1) all surveys included in the metadata database (Click on Data Sources); (2) the 

research team (Click on About Us); (3) outputs arising as part of this project (Click on Outputs); (4) 

details on funders of the project (Click on Supporters). By selecting ‘The Database’ button, users can 

choose to download a user guide (this paper) or launch the database. 

  

https://mspgh.unimelb.edu.au/centres-institutes/centre-for-health-policy/research-group/metadata-database
https://www.abs.gov.au/websitedbs/d3310114.nsf/home/microdata+entry+page
https://ada.edu.au/
https://www.online.fbe.unimelb.edu.au/HILDAodd/Default.aspx
https://mspgh.unimelb.edu.au/centres-institutes/centre-for-health-policy/research-group/metadata-database
https://mspgh.unimelb.edu.au/centres-institutes/centre-for-health-policy/research-group/metadata-database


70 Temple et al.  Australian Population Studies 5 (1) 2021 

Table 1: Summary of ageing metadata 

 Australian 
Bureau of 
Statistics 

(ABS) 

Australian 
Data Archive 

(ADA) 

Household, 
Income and 

Labour 
Dynamics in 

Australia 
(HILDA)1 

Australian 
Longitudinal 

Study on 
Women's 

Health 
(ALSWH) 

Ten to Men1 Total 

Number of surveys 

# Surveys/files 46 35 1 1 2 85 

# Waves - - 17 8 2 - 

Survey type 

Census 8  
(17.4%) 

0  
(0.0%) 

0  
(0.0%) 

0  
(0.0%) 

0  
(0.0%) 

8  
(9.4%) 

Cross-sectional 11  
(23.9%) 

31  
(88.6%) 

0  
(0.0%) 

0  
(0.0%) 

0  
(0.0%) 

42  
(49.4%) 

Longitudinal 2  
(4.4%) 

4  
(11.5%)2 

1  
(100.0%) 

1  
(100.0%) 

2  
(100.0%) 

10  
(11.8%) 

Repeated Cross-
sectional 

25 
(54.4%) 

0  
(0.0%) 

0  
(0.0%) 

0  
(0.0%) 

0  
(0.0%) 

25  
(29.4%) 

Survey unit of analysis 

Business 6  
(13.0%) 

0  
(0.0%) 

0  
(0.0%) 

0  
(0.0%) 

0  
(0.0%) 

6  
(7.1%) 

Individual 1  
(2.2%) 

33  
(94.3%) 

0  
(0.0%) 

1  
(100.0%) 

0  
(0.0%) 

35  
(41.2%) 

Individual and 
other levels 

4  
(8.7%) 

2  
(5.7%) 

1  
(100.0%) 

0  
(0.0%) 

0  
(0.0%) 

7  
(8.2%) 

Household 1  
(2.2%) 

0  
(0.0%) 

0  
(0.0%) 

0  
(0.0%) 

2 
(100.0%) 

3  
(3.5%) 

Private dwellings 34  
(73.9%) 

0  
(0.0%) 

0  
(0.0%) 

0  
(0.0%) 

0  
(0.0%) 

34  
(40.0%) 

Number of variables 

# Variables 14,011 10,457 71,014 13,174 1,645 110,301 

Survey type 

Census 1,837  
(13.1%) 

0  
(0.0%) 

0  
(0.0%) 

0  
(0.0%) 

0  
(0.0%) 

1,837  
(1.7%) 

Cross-sectional 3,666  
(26.2%) 

7,923 
(75.8%) 

0  
(0.0%) 

0  
(0.0%) 

0  
(0.0%) 

11,591 
(10.5%) 

Longitudinal 128  
(0.9%) 

2,532 
(24.2%)2 

71,014 
(100.0%) 

13,174  
(100.0%) 

1,645  
(100.0%) 

88,493 
(80.2%) 

Repeated Cross-
sectional 

8,380  
(59.8%) 

0  
(0.0%) 

0  
(0.0%) 

0  
(0.0%) 

0  
(0.0%) 

8,380  
(7.6%) 

Survey unit of analysis 

Business 484  
(3.5%) 

0  
(0.0%) 

0  
(0.0%) 

0  
(0.0%) 

0  
(0.0%) 

484  
(0.4%) 

Individual 105  
(0.8%) 

7,704 
(73.7%) 

0  
(0.0%) 

13,174  
(100.0%) 

0  
(0.0%) 

20,983 
(19.0%) 

Individual and 
other levels 

2,584 
(18.4%) 

2,756 
(26.3%) 

71,014 
(100.0%) 

0  
(0.0%) 

0  
(0.0%) 

76,351 
(69.2%) 

Household 780  
(5.6%) 

0  
(0.0%) 

0  
(0.0%) 

0  
(0.0%) 

1,645 
(100.0%) 

2,425  
(2.2%) 

Private dwellings 10,058 
(71.8%) 

0  
(0.0%) 

0  
(0.0%) 

0  
(0.0%) 

0  
(0.0%) 

10,058 
(9.1%) 

Notes: (1) ‘Household, Income and Labour Dynamics in Australia Survey – HILDA’ and ‘Ten to Men survey’ were available in 

ADA’s repository. (2) The 3 waves of the “Building a New Life in Australia (BNLA): The Longitudinal Study of Humanitarian 

Migrants” are available in 4 files at ADA. 



Australian Population Studies 5 (1) 2021 Temple et al.  71 

 

Figure 2: Metadata database on ageing landing page 

 

When the ‘Launch the Database’ option is selected, the following screen of the webtool is loaded as 

a new tab (Figure 3). The key variables included in the database are as follows: 

Variable: The original variable name provided in the source documentation. 

Label: The description of the variable provided in the source documentation. 

Categories: The items available in categorical variables. 

DS_label: Name of the dataset. 

DS_format: Dataset format. 

DS_link: URL to find further information about the dataset, including contact details for the data 

custodian. 

DS_design: The type of survey sampling design, e.g., stratified, random sample. 

DS_type: Whether the survey is cross-sectional, repeated cross-sectional, or longitudinal. 

DS_unit: Unit of analyses of the survey instrument, e.g., individual, households. 

DS_samp: Details of the survey sampling design. 

DS_desc_fw: Simple fieldwork details where available. 

DS_source: Where the survey is sourced from, e.g., ABS, ADA. 

 



72 Temple et al.  Australian Population Studies 5 (1) 2021 

 

Figure 3: Metadata database webtool 

 

A number of placeholder variables (e.g., DS_name, _merge) are included for administrative purposes 

or for potential updates in the future. To simplify the display, click ‘Select Columns’. Ensure Variable, 

Label, Categories, DS_label and DS_link are selected (highlighted in blue). The remaining variables 

should be deselected or highlighted in grey (Figure 4). Click ‘Apply’. 

 

 

Figure 4: Selection of variables in the Metadata database webtool. 

 

For the purposes of this example, let us imagine a researcher is interested in investigating measures 

related to disability. The user can either select ‘Global Search’ or ‘Field Search’. A global search will 

search for the term across all variables in the database. A field search enables the user to specify the 

search term for single or multiple variables in the database. To conduct a global search, simply enter 



Australian Population Studies 5 (1) 2021 Temple et al.  73 

‘disability’ in the text box and click the magnifying glass icon. This produces 3,496 records containing 

disability (Figure 5). As an example, the first record is from the Disability, Ageing and Carers survey, 

with variable name DISBSTAT, variable label ‘Disability status’ with the link to dataset information 

provided in the DS_link column. As this variable is a categorical variable, the column ‘Categories’ 

shows each category of disability considered. If the user wishes to download the results of the 

search, simply click ‘Download all matched results in a CSV file’. 

 

 

Figure 5: Example global search for the term ‘disability’ 

 

To clear the search, click ‘Clear Search’, delete the ‘disability’ text in the Global Search text box and 

click the magnifying glass icon. This will reload the full database. The user may only be interested in 

measures which include disability in the ‘Label’ and ‘Categories’ column. To complete this search, 

type ‘disability’ in the Field Search text box. Select ‘Label’ from the adjacent combo-box. Next, click 

‘Add Field’. Again, enter ‘disability’ in the new Field Search text box. Select ‘Categories’ from the 

adjacent combo-box. Clicking ‘search’ narrows the number of records from 3,496 to 146 (Figure 6). 

 

 

Figure 6: Example field search for the term ‘disability’ in ‘Label’ and ‘Categories’ columns 

 

Clicking ‘Reset Field’ followed by ‘Search’ will once again reload the full database. With the full 

database reloaded, the user can click ‘Download all matched results in a CSV file’ to download the 

full database. The ‘Select Columns’ option may need to be reset if the user wishes to download all 

variables in the database, by clicking ‘Select All’ followed by the ‘Apply’ button. To exit the metadata 

database, simply close the tab. 



74 Temple et al.  Australian Population Studies 5 (1) 2021 

5. Conclusions 

In this paper, we have reported on our proof-of-concept project to develop a metadata database for 

researchers working in the field of ageing. Our tool, alongside other metadata collections as they 

become more comprehensive, will assist with: 

• making unknown datasets ‘discoverable’ by researchers; 

• linking researchers to data custodians to facilitate data access; 

• increasing researcher productivity by providing a one-stop-shop to understand measures 

collected of interest to researchers in ageing; 

• assisting with the preparation of grants by pointing to all existing data on a specific topic in a very 

efficient manner; 

• assisting with understanding the potential for data linkage (when legal and/or feasible); and 

• facilitating new research conversations across disciplines. 

In short, metadata transparency can help empower researchers to identify what data exists around a 

given topic (e.g., a specific research interest in ageing), how the data was collected, and how to gain 

access to the data. Understanding the structure, limitations, definition, and description of data also 

serves to protect against misinterpretation or misuse, thereby ensuring the integrity of the research 

output. Metadata itself may also be of interest to academics, as a means to understand the historical 

evolution of these measures and point to gaps in our knowledge. Understanding data collections 

requires analysis of taxonomies of variables and their inter-relationships, theoretical frameworks, 

and research methodologies employed in ageing research. Thus, collecting metadata can help inform 

an epistemological map of the ‘state of play’ in Australian gerontology. In doing so, the extraction 

and analysis of metadata can play a critical role in the evolution of measures relevant to ageing 

research. With an increasing ageing population and reliance on data, metadata on ageing has 

growing relevance now and into the future.  

5.1. Limitations 

Our project considered a very specific scope of data for inclusion: sample survey data available at 

two major data repositories between the period 2010 to 2018. Many other datasets are likely to be 

of interest to researchers in the field of gerontology and the demography of ageing. For example, 

large administrative datasets, surveys held by research institutions or researchers, qualitative or 

mixed-methods data, spatial databases as well as vital statistics. These important data sources are 

omitted from our project. Moreover, our search was carried out from 17th July 2018 until 1st 

November 2018. Other datasets housed at both the ABS and ADA are likely to be in-scope but 

published online after November 2018 and therefore not included in the metadata database for this 

study. 

A second major limitation of this project relates to data quality. Collating even this range of metadata 

was a considerable task, with data dictionaries available in different formats (Excel, word processing 

documents and PDF files). Although our procedures automated through R and Stata helped to 

streamline the process, many errors had to be manually corrected. Given the size of the database, 

errors are likely to remain and users of the metadata database should note this limitation. A further 

limitation is that the webtool is a relatively simple search tool. Nonetheless, users are given the 

option to download the full database as a *.csv file to use with other software. 



Australian Population Studies 5 (1) 2021 Temple et al.  75 

Finally, a limited number of variables have been added to the database. Curating the metadata to 

include conceptual, theoretical or analytical methodologies would greatly improve the coverage of 

the database. For example, tagging variables from each survey which are of use to researchers 

working on particular ageing theories, or tagging variables with key areas of thematic interest from 

domestic and international conferences. 

5.2. Summary and future research 

In this paper we have described a proof-of-concept project which seeks to develop a metadata 

database including metadata on Australian sample surveys relevant to ageing. We have outlined our 

five-stage approach to create the CEPAR metadata database on ageing and provided a webtool for 

users to interrogate the data, or to export the data for further use. Nonetheless, improvements can 

be made with regard to coverage of data sources (to include for example administrative data), 

temporal coverage (increasing the time period and spatial heterogeneity of data collections), and 

procedures to improve the underlying data quality of the database – when for example, only PDF or 

word processor documents are the only public source of microdata data dictionaries. 

Beyond these improvements, research using the metadata database itself has strong academic merit. 

Using metadata, such queries may include:  

• The availability of measures of interest to gerontologists in Australian datasets. 

• The complementarity of evolving theories and conceptual frameworks with data availability. 

• The historical evolution of measurement. 

• The strengths of current data collections. 

• Identification of weaknesses and how they can be improved in future data collections.  

Improving our knowledge through metadata can limit misinformation and misuse of data, avoid 

duplication of research effort while simultaneously enhancing reliability and integrity of research 

outputs. Such important and wide-reaching outputs include the creation of informed social policy to 

improve the lives of older Australians, both current and future. 

Key messages 

• This introductory guide explains the concept of metadata and its utility to researchers specifically 

in the field of ageing. 

• The methodology for creating the metadata database is described and explained. 

• An example of searching and extracting data using the CEPAR metadata database webtool is 

described. 

• Researchers are provided with full access to the underlying database and a webtool to interrogate 

the database. 

Acknowledgements 

Funding for this project was provided by the ARC Centre for Excellence in Population Ageing 

Research (CEPAR) and the Melbourne School of Population and Global Health, University of 

Melbourne. The authors acknowledge the assistance of Sebastian Kocar, data archivist from the 



76 Temple et al.  Australian Population Studies 5 (1) 2021 

Australian Data Archive. We also acknowledge the development work of Prof. Richard Sinnott, Glenn 

Jayaputera, Zelong Cong, and Alwyn Pan on the webtool. 

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https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Metadata#:~:text=Reference%20metadata%20(sometimes%20called%20explanatory,a%20semantic%20point%20of%20view
https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Metadata#:~:text=Reference%20metadata%20(sometimes%20called%20explanatory,a%20semantic%20point%20of%20view
https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Metadata#:~:text=Reference%20metadata%20(sometimes%20called%20explanatory,a%20semantic%20point%20of%20view
https://www.getty.edu/publications/intrometadata/setting-the-stage/
https://www.getty.edu/publications/intrometadata/setting-the-stage/
https://www.niso.org/publications/understanding-metadata
https://unece.org/fileadmin/DAM/stats/publications/metadatamodeling.pdf

