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The TK and BC Labels are an initiative for Indigenous communities and local organizations. Developed through sustained partnership and testing within Indigenous communities across multiple countries, the Labels allow communities to express local and specific conditions for sharing and engaging in future research and relationships in ways that are consistent with already existing community rules, governance, and protocols for using, sharing and circulating knowledge and data. ADA has an interest in establishing the means for providing suitable representation of indigenous knowledge within the Dataverse software. This includes functionality in Dataverse to: - link to and incorporate identified sources for indigenous knowledge representation, such as TKLabels and Notices - curation processes for managing the creation, reading, updating, and deleting of metadata - present curated metadata (e.g., TKLabels and TKNotices) in catalogue records - allow external aggregators to harvest this metadata (specifically the IDN Data Catalogue, but a preferably standardized model that allows for multiple external parties to harvest)

Research outputs produce a range of data types with varying levels of purpose and significance. While research projects are separate to the function of a data archive, the underlying data and computing expertise, and persistent infrastructure needs are similar - especially where outputs are of significant research and national value. To achieve persistence, preservation, and accessibility, both archives and project outputs require high levels of specialised data archival, management, and computing expertise. The Australian Data Archive (ADA) has been providing a national service for the deposit, archiving and dissemination of digital research data since 1981; primarily in the area of the social sciences. ADA has progressively become more involved in significant research projects producing or using Indigenous data, with both Indigenous and non-Indigenous research partners. These projects, often based on extension of existing relationships, have involved collaboration and support for Indigenous data collections. Concern for data sensitivity, security, formal archival processes, and longevity are a priority – increasingly in support of Indigenous Data Sovereignty and Governance. This presentation will give an overview of several recent ADA projects in this area: Technical liaison and infrastructure support for the Return, Reconcile, Renew Project, for the return of Ancestral Remains; a data audit project with ANU First Nations Portfolio; data management and archival direction for an Aboriginal land council; a second collaboration with the land council, to pilot an Indigenous CMS for digital cultural preservation. These case studies will consider gaps that exist in research output support for both persistent infrastructure and archival data management, gaps evident from the fact that ADA is being approached to fulfil these roles, often through relationships, rather than formal channels. The presentation will conclude with a discussion of future needs in research output support, particularly for sensitive data projects involving Indigenous data.

The UK data services landscape has evolved substantially over the past decade. New legal frameworks for accessing data have been implemented; new methods for accessing and combining data have emerged and advances in technology, tools and methods have continued to grow. The landscape has become more complex, with a growing number of infrastructures providing services to data owners and data users serving a variety of needs. Many of these changes have occurred in the years since the Economic and Social Research Council (ESRC) established the UK Data Service and other data service investments. The main questions that our data services face are: how can current infrastructures connect more closely to deliver unified services for researchers?; what is the foundation needed to support researchers in an ever changing legal/technology/policy landscape?; how can data services work to support societal changes, e.g. addressing inequality in data, working in an environmentally sustainable way? To respond to the increasing demands on social science data, ESRC has embarked on a strategic review called Future Data Services (FDS), to scope out how data services can deliver services to a broader, more diverse audience with an increasing need for interdisciplinarity. The rest of the world is also experiencing similar challenges; therefore this session should be of interest to a broad international audience. This panel will bring together experts for a discussion on the future of data services. ESRC will present its recommendations that have been developed through its work on FDS. We will invite the panelists and the audience to challenge us on what is required to respond to community needs in an ever-changing landscape. The panel will discuss the components that make up a ‘good’ data service and how data services could be utilised to deliver even greater public good.

Cross-national Social Sciences survey FAIR implementation case studies report. Project Deliverable D6.1 for EC WIDERA-funded project "WorldFAIR: Global cooperation on FAIR data policy and practice". This report provides an overview of the data harmonisation practices of comparative (cross-national) social surveys, through case studies of: (1) the European Social Survey (ESS) and (2) a satellite study, the Australian Social Survey International – European Social Survey (AUSSI-ESS). To do this, we compare and contrast the practices between the Australian Data Archive and Sikt.no, the organisations responsible for the data management of ESS and AUSSI-ESS. The case studies consider the current data management and harmonisation practices of study partners in the ESS, including an analysis of the current practices with FAIR data standards, particularly leveraging FAIR Implementation Profiles (FIPs) and FAIR Enabling Resources (FERs). The comparative analysis of the two case studies considers key similarities and differences in the management of the two data collections. Core differences in the use of standards and accessible, persistent registry services are highlighted, as these impact on the potential for shared, integrated reuse of services and content between the two partner organisations. The report concludes with a set of recommended practices for improved management and automation of ESS data going forward—setting the stage for Phase 2 of WorldFAIR Work Package 6—and outlines the proposed means for implementing this management in the two partner organisations. These recommendations focus on three areas of shared interest: Aligning standards Establishing common tools Establishing and using registries in order to advance implementation of the FAIR principles, and to improve interoperability and reusability of digital data in social sciences research. Visit WorldFAIR online at http://worldfair-project.eu. WorldFAIR is funded by the EC HORIZON-WIDERA-2021-ERA-01-41 Coordination and Support Action under Grant Agreement No. 101058393.&nbsp

The CADRE (Coordinated Access for Data, Research and Environments) Platform project has produced a framework to guide the development of an information exchange that will underpin a decision-support system, that draws heavily on the Five Safes framework. As a first step towards operationalising the Five Safes framework to streamline researcher access to sensitive data – this interpretive exercise was broken into three major sections: (1) the context for the framework development is in Australia (2) the conceptualisation of the Five Safes grounded against requirements from social science researchers overseeing access to quantitative and qualitative data as the basis for capturing relevant information (3) the means with which to operationalise the framework including information and data models. In November 2021 the first version of the framework was published and circulated to the CADRE international advisory board and to experts in project partner and affiliate organisations for feedback. This presentation will include: lessons learned in developing the framework; the critical feedback from experts in sensitive research data management incorporated in the latest version; and, reflections on moving from case-by-case basis decision-making to more systematic and enhanced means for enabling access to sensitive data for research. Steven McEachern The Australian National University Australia Heather Leasor The Australian National University Australia Marina McGale The Australian National University Australia Julie McLeod University of Melbourne Australia Kate O'Connor La Trobe University Australia Nicole Davis University of Melbourne Australia Ingrid Mason The Australian National University Australia

The CADRE (Coordinated Access for Data, Research and Environments) Platform project has produced a prototype information graph for the CADRE Information Exchange. Work undertaken to combine multiple information sources reveals the value of collating, synthesising and visualising (1) information about researchers, their projects, and their history of working with sensitive data, (2) technical authorisation and authentication information and (3) sensitive data access request information. This panel session will enable a deep dive into the informatics and analytics associated with sensitive social science data management especially where research workflows work across multiple systems in institutional and national research infrastructure as a technical ecosystem. Ingrid Mason The Australian National University Australia Steven McEachern The Australian National University Australia Amir Aryani Swinburne University Australia Peter Vats Research Graph Foundation Australia John Scullen The Australian Access Federation Australia

The CADRE (Coordinated Access for Data, Research and Environments) Platform project is a co-investment of nine organisations ranging from research intensive universities, federal government research organisations, and national research infrastructure providers. The central aim in developing the CADRE Platform is to expedite researcher access to sensitive data, by augmenting existing authentication and authorisation infrastructure services and establishing shared request management and decision-support services.The CADRE project has used the Five Safes framework as a lens to understand and translate researcher, data owner and custodian, and service provider infrastructure requirements. The framework has been used conceptually to enable dialogue and establish a common information model that is designed to work across social boundaries and anticipates different types of protocols (technical, informational, social and cultural); and sensitive data sharing arrangements and research workflows that run across multiple systems through interoperation and complex collaborations.The presentation will provide an overview of the Five Safes implementation framework developed by the CADRE team, along with a demonstration of the application of the framework to data managed by the Australian Data Archive (ADA). Several examples of cross-jurisdictional, cross-sectoral and cross-community data sharing arrangements that have served as use cases shaping the CADRE information model and driving the development of the CADRE Information Exchange will be referenced. These inter-institutional data sharing arrangements are the foundation for making sensitive data accessible for research through the Australian Data Archive:Ten to Men ��� Longitudinal Study on Male Health (Australian Institute of Family Studies) DOI: 10.26193/JDE1TDNational Drug Strategy Household Survey (Australian Institute of Health and Welfare) DOI: 10.26193/WRHDULSurvey of the Bangladeshi People (Asia Foundation) DOI: 10.26193/D1SRMZ Footprints in Time ��� Longitudinal Study of Indigenous Children (National Centre for Longitudinal Data, Department of Social Services) DOI: 10.26193/ICEBFPCADRE PartnersThe Australian National UniversityThe Australian Access FederationThe Research Graph FoundationAustralian Institute of Health and WelfareAustralian Institute of Family StudiesAARNetAustralian Research Data CommonsUniversity of Melbourne (SOCEY)UNSW (ERICA, Centre for Big Data Research in Health)Swinburne University (Social Data Analytics Lab)Partner co-investment in the CADRE platform project is $3.9M. The CADRE Platform project received $1.9M investment (https://doi.org/10.47486/PL106) from the Australian Research Data Commons (ARDC). The ARDC is funded by the National Collaborative Research Infrastructure Strategy (NCRIS). ReferencesAustralian Data Archive, https://ada.edu.au/, accessed 20/10/2021CADRE Platform Project, https://cadre5safes.org.au/, accessed 20/10/2021Office of the National Data Commissioner, Australian Government (2019), Best Practice Guide to Applying Data Sharing Principles, 15 March 2019, https://www.datacommissioner.gov.au/sites/default/files/2019-08/data-sharing-principles-best-practice-guide-15-mar-2019.pdf, accessed 20/10/2021ABOUT THE AUTHORSDr Steven McEachern is the Director of the Australian Data Archive and CADRE project lead (ANU). https://orcid.org/0000-0001-7848-4912Ingrid Mason is the CADRE project and technical manager (ANU). https://orcid.org/0000-0002-0658-6095

The CADRE Five Safes framework is an output of the CADRE (Coordinated Access for Researchers, Data and Environments) Platform project. The CADRE framework includes the conceptual underpinnings and the guardrails for sensitive data access management and the range of information associated with the Five Safes principles that can be operationalised in a decision-support system. In the Introduction and Context sections the CADRE Platform project and the social science research data management practices associated with sensitive quantitative and qualitative data are outlined. In the Conceptualisation section a full exploration is undertaken of: the uptake of Five Safes framework in Australia; the concepts from the Five Safes framework i.e., projects, people, data, settings and outputs; additional concepts i.e., organisations and groups; and key intersections and alignments of these concepts. In the Operationalisation section the information requirements associated with managing sensitive data access requests and provisioning research environment services for sensitive data analysis are evaluated and initial information and data models are proposed for the CADRE information exchange. In the Appendices the user requirements from project partners helping to develop the CADRE Platform are summarised and sensitive data categorisations are collated.

These slides were used for Dr Steven McEachern's presentation to the Social Dynamics of Data Interoperability IG session at RDA18 (https://www.rd-alliance.org/groups/social-dynamics-data-interoperability-ig). The RDA 18 Plenary was held from 3 - 11 November 2021 (https://www.rd-alliance.org/rdas-18th-plenary-programme).

Project proposal for the Intergrated Research Infrastructure for Social Sciences (IRISS).

This document provides a high level summary of theRDA 17 Plenary Session titled "Establishing a Sensitive Data Interest Group" (https://www.rd-alliance.org/plenaries/rda-17th-plenary-meeting-edinburgh-virtual/establishing-sensitive-data-interest-group). This session was run by the newly formed (yet to be endorsed) RDA Sensitive Data Interest Group (https://www.rd-alliance.org/groups/sensitive-data-interest-group).The RDA 17 Plenary was held from April 19 - 23, 2021 (https://www.rd-alliance.org/rdas-17th-plenary-meeting-programme). This documentis designed to complement the session recording (https://www.youtube.com/watch?v=AoydTRrYSEE) by highlighting the next steps and future directions from the session. Authors are listed in alphabetical order using the CRediT Contributor framework (https://casrai.org/credit/). Dharma Akmon: Writing – review & editing Nichola Burton: Writing – review & editing Romain David: Conceptualization, Writing – original draft, Writing – review & editing Sarah Davidson: Writing – review & editing Steven McEachern: Writing – review & editing Aleksandra Michalewicz: Writing – original draft, Writing – review & editing Priyanka Pillai: Writing – original draft Rita Silva: Writing – original draft, Writing – review & editing Kristal Spreadborough: Writing – original draft, Writing – review & editing, Project administration

As sensitive data are increasingly used for research purposes, reducing the risk of data misuse has become particularly crucial. At the same time, as demonstrated during the COVID19 crisis, sharing high-quality data is a sine qua non to assess and compare research results and to leverage data to their fullest capacity. The Sensitive Data Interest Group aims to promote the FAIR principles and reproducible research, while drawing attention to the unique risks associated with sensitive data and exploring mitigation strategies for these risks. Synthesis research that aggregates data at large scales often uses several kinds of sensitive data, but the ethical and legal issues are often not fully addressed, especially when harmonising differing ethical and legal considerations across regions. Further complicating matters, “sensitive data” are often not even defined in the same way. As a result, reproducing research in different regions or contexts is often difficult, and sensitive data sharing processes are not well sustained. In this poster, our group proposes the following working definition of sensitive data, adapted from David et al., 2020, “Templates for FAIRness evaluation criteria - RDA-SHARC IG” https://zenodo.org/record/3922069#.YCJU7ehKg2w : Information that is regulated by law due to possible risk for plants, animals, individuals and/or communities and for public and private organisations. Sensitive personal data include information related to racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership and data concerning the health or sex life of an individual. These data that could be identifiable and potentially cause harm through their disclosure. For local and government authorities, sensitive data is related to security (political, diplomatic, military data, biohazard concerns, etc.), environmental risks (nuclear or other sensitive installations, for example) or environmental preservation (habitats, protected fauna or flora, in particular). The sensitive data of a private body concerns in particular strategic elements or elements likely to jeopardise its competitiveness. Our Sensitive Data IG will workshop this definition and present a summary of the aims of the group and our charter under the RDA validation process. Through the Sensitive Data IG, we aim to provide a forum for a range of communities to share their requirements and jointly develop strategies, support, recommendations and guidelines relevant to sensitive data. We propose defining common goals around how to address the risk associated with different types of sensitive data (e.g. ecological data, indigenous data, human health data, etc.), as well as to responsibly disseminate, aggregate, and use preexisting heterogeneous sensitive data at a global scale. This group will partner with other IGs and WGs to produce recommendations and guidelines around sensitive data (for example, “Sensitive Data Toolkit for Researchers Part 2: Human Participant Research Data Risk Matrix”, https://zenodo.org/record/4088954#.YFZCUq_7Q2w) . We welcome participation and contributions from the entire RDA community and more broadly., POSTER LEAD: Romain David (European Research Infrastructure on Highly Pathogenic Agents [ERINHA], 0000-0003-4073-7456) CO-AUTHORS (in alphabetical order): Dharma Akmon (University of Michigan, 0000-0002-1359-0586); Nichola Burton (Australian Research Data Commons, 0000-0003-4470-4846); Steven McEachern (Australian Data Archive, 0000-0001-7848-4912); Aleksandra Michalewicz (University of Melbourne, 0000-0002-7328-2470); Priyanka Pillai (University of Melbourne, 0000-0002-3768-8895); Hervé Raoul (ERINHA), Audrey Richard (ERINHA); Rita Rb-Silva (Instituto Português Oncologia do Porto Francisco Gentil, EPE, University of Porto, 0000-0002-1422-0974); Kristal Spreadborough (University of Melbourne, 0000-0002-7022-3213); Diana Stepanyan (ERINHA); Frankie Stevens (Australia's Academic and Research Network, 0000-0002-2556-473X).

The "Introduction to the first round of Platforms projects" showcased the 10 projects from the ARDC Platforms program that commenced in 2020. The Platforms program aims to increase the number of researchers with access to platforms, both in terms of absolute number and in terms of diversity of disciplines. Platforms are “a set of online services, often with associated integration and/or orchestration functions and connections to specific data resources, that is intended to enable researchers to carry out some of their research activities”. Platforms are also called virtual research environments or virtual laboratories. This presentation includes: FAIMS 3.0 Electronic Field Notebooks - Shawn Ross (Macquarie University) The Australian Characterisation Commons at Scale - Wojtek James Goscinski (Monash University) E-Research Institutional Cloud Architecture (ERICA): secure cloud computing for sensitive microdata - Louisa Jorm (UNSW) Australian Imaging Service - Ryan Sullivan (University of Sydney) Australian Transport Research Cloud - Stuar Barr (AURIN) The BioCommons BYOD Expansion Project - Nigel Ward (Australian BioCommons) EcoCommons Australia - Elisa Bayraktarov (Griffith University) Environments to accelerate Machine Learning-Based Discovery - Komathy Padmanabhan (Monash University) Establishing Australia’s Scalable Drone Cloud - Steve Quenette (Monash University) CADRE: Coordinated Access for Data, Researchers and Environments - Steven McEachern (ANU) The video recording of this session can be viewed on ARDC's YouTube channel:https://youtu.be/Ig2yIld4ZYM

Anonymization of survey data collected from the same respondents at different points in time, such as online panel data, is a special case in statistical disclosure control (SDC) space. While online panels play an important role today in survey research, data archives disseminating such data have to face particular privacy restrictions. Traditional SDC methods assume the released data to be static, but they are not in the case of online panels with several waves of data collection. Disseminating panel data could increase disclosure risk as the abundance of demographic information included in the original data permits linking data of different waves. This can lead to the accumulation of identifying information, identification, and the loss of trust in the data producer/depositor. To solve this problem, we developed a procedure using traditional SDC methods for decreasing disclosure risk while selecting a limited number of demographic variables in the released data. We used the following selection criteria: importance for analysis, contribution to global identification risk, availability of background information, and the effect on data utility. We will present the results of the anonymization of several waves of Life in Australia panel data from both SDC perspectives - disclosure risk and data utility.

The Australian population census is now over 100 years old. The capacity to access, analyze and visualize census data is however highly problematic for Australian researchers. Spatial analysis in particular is limited, with digital boundaries for the Australian census dating back only to 1981, despite the existence of census boundary maps dating back to the 1830s. The Australian Data Archive (ADA) and the Australian Urban Research Institute (AURIN) have been working to improve this access, as part of the Tinker project. With support from the Australian Research Data Commons and the 16 HASS-DEVL project partners, ADA and AURIN have been working to curate and link the tabular census data held at ADA with the spatial analysis environment operated by AURIN, enable access to census data prior to 1991 through the AURIN environment. This paper describes the project framework, the technical and administrative workflows established as part of the project to integrate ADA and AURIN systems, the census data resources now available, and next steps in the census support. The paper then concludes by outlining the potential for future project - a 250 year profile of the Australian population from colonization in 1770 to the present day.

The DDI Alliance released a prototype of DDI 4. DDI 4 is a large UML model library for describing research and other data. Subsets of the library--called views--are provided for specific user perspectives. Encodings in other technologies, XML Schema and OWL/RDF, are available as well. DDI 4 has potential as a metadata standard for cross-domain research data. Large parts of DDI 4 are independent from the social science domain. Especially in the areas of data description, conceptual, and workflows (for process description), DDI 4 will be useful for a cross-domain approach. The DDI 4 data description is based on two powerful techniques: first, the datum-based approach which enables the description of data in many forms (also data lakes and legacy forms); second, the variable cascade which links conceptual definitions down to the instance variable. The presentation will focus on the above mentioned features in the context of cross-domain use cases, and will describe the current status of DDI 4 development after a public review of the DDI 4 prototype release.

This session presents an integrated picture of the current activities and future plans of the Data Documentation Initiative (DDI) Alliance. Presentation 1: DDI's current product line: DDI Codebook, DDI Lifecycle and related products. The Alliance continues to support, develop, and encourage the adoption of these specifications. Presentation 2: Preliminary release of DDI 4, the newest DDI version based on an information model. Slated for a mid-2018 release, this "prototype" is not intended for production but provides an opportunity to test and provide feedback on how DDI 4 describes data capture, data stores, transformation processes, studies, and classification management. Functionality will be presented through use cases. The presentation will highlight the model-driven production framework which generates representations like XML and RDF, and the documentation. Presentation 3: The new strategic plan of the DDI Alliance and the vision of a DDI-based infrastructure for the empirical social sciences. The strategic plan covers the standards (i.e. maintaining multiple lines of specifications), community goals (i.e. engagement with the global digital research infrastructure, and solving common problems with current DDI users), and organizational goals of the DDI Alliance. The infrastructure vision proposes a combination of a curated common data element registry with a portal of existing DDI metadata repositories.

Recent developments in government open data and open access policies have increased the emphasis on data management policies and procedures internationally. Shifts in practices have come at both ends of the research lifecycle. The expectations of funders have placed increasing demands on institutions to understand, support and manage the data outputs of projects within their institutions more effectively. Research funders have also increased emphasis on data management planning at the start of research projects, often as a condition of the provision of grant funding. This symposium, convened by the International Federation of Data Organisations, will explore the impact of these shifts in national policies on the policies and practices of two major groups in this space: data archives and institutional repositories. The panel will begin with a detailed analysis of the range of policies in existence internationally, drawing on the recent IFDO data policy survey. This is followed by presentations from four international speakers – two from each of the data archive and institutional repository communities. The panel will then conclude with an open discussion of the likely directions for both national data management policies and directions for supporting these policies in the archive and repository communities.

This paper will showcase the results of a recent project run by the Australian Data Archive that aims to better understand the data sharing and data citation behaviours and attitudes of Australian social science researchers both in Australia and internationally. We define these two behaviours as follows: a) data sharing: "the voluntary provision of information from one individual or institution to another for purposes of legitimate scientific research" (Boruch, 1985) b) data citation: "the practice of providing a reference to data in the same way as researchers routinely provide a bibliographic reference to outputs such as journal articles, reports and conference papers" (ANDS, 2011) Drawing on the theory of planned behaviour (Azjen, 1991), the study explores researchers' current and intended data sharing and data citation practices, attitudes towards each behaviour, and perceived social and institutional barriers and supports for data sharing and data citation within their organisation and discipline. The paper will present the results of the ADA survey, and compare with two recent US studies to explore cross-national similarities and differences in data sharing and citation behavior.

The management, publication, and preservation of datasets have become issues of increasing importance for universities, research institutions, and government agencies. While the reasons and mandates for these activities, and the kinds of datasets collected, differ among these types of institutions, other aspects of data management throughout the research lifecycle concern all of them, including (but not limited to): the discoverability of their data; the choice of metadata standard(s) and the creation of metadata; providing visualization and interaction with data; selection and migration of data formats for long-term preservation; policy development; and storage requirements. Yet, these types of institutions tend to follow different paths in data management and curation, choose different infrastructures, metadata standards and platforms. Are these different approaches inevitably rooted in the differences between these types of organizations and their missions and culture? Or are there lessons they could learn from each other to improve their own practice? The purpose of this symposium is to explore that question. We will have presentations first, then form breakout groups along the lines of different aspects such as platform choice, policy developments, metadata creation. At the end, all will come back together to share the results of their discussion.

The importance of data citation for understanding the impact of social surveys has becoming increasingly recognized as a priority concern among research infrastructure providers and funders (ANDS, 2012, NSF, 2012; Ball and Duke, 2012). For data archives, data citation provides a mechanism to understand the dissemination activities of the archive, particularly in enabling access to data for secondary use. While social science data archives have long recommended or required the use of citations as a condition of access to datasets, the compliance with this condition is minimal (Piwowar, 2011). For this reason, many data archives and repositories have implemented or are currently exploring new mechanisms for enabling data citation, such as DOIs. Such a pilot study being conducted by the Australian Data Archive (ADA). This project involves three elements: - a review of the current literature on data citation practices in Australian and international social science - a survey of current practice among users of 5 major Australian social science data sets - a pilot study of the use of DOIs with ADA datasets. The paper will present the current results of this project, recommendations for the ADA regarding data citation, and implications for data archives and repositories, more generally.

During the first year of the ODIN project the needs of Humanities and Social Science researchers were investigated through examining the use and re-use of British Birth Cohort Studies. This report contains the discipline context and characteristics associated with this research area, it outlines the current state of unique and persistent identifier adoption and use and outlines preliminary workflows to build on the opportunities that exist in this area. Year two of ODIN will compare disciplines build on these preliminary workflows to create concrete processes.

This paper presents an overview of ADA-Lab, a virtual research laboratory for Australian social scientists. The recently completed ASeSS project established the Australian Data Archive (ADA), launched in August 2011 (www.ada.edu.au). ADA is now working to extend these services, to develop the ADA-Lab which will advance and develop our current services developed by: [a] continuing the extension and diversification of ADA's holdings into new thematic sub-archives and data formats; [b] further development of the ADA visualisation tools [c] creating the underlying architecture and capacity of the existing cloud service to support a viable virtual laboratory for ADA. The new ADA-lab environment will extend the existing ADA storage and online analysis facilities to provide an integrated environment for data access and computation using ADA and external data sources in a high-performance environment. The ADA-Lab environment consists of four elements: 1. Data storage foundation 2. Web analysis environment 3. Remote access environment 4. Secure environment The ADA-Lab development program will also provide a new set of researcher and archivist tools for enabling existing and new ADA data to be used in the ADA-Lab. The paper will provide an overview of the core ADA-Lab infrastructure, implementation plans, and an overview of progress to date.

The expansion of data holdings to incorporate qualitative content has been a major emphasis of the Australian Data Archive since 2007, focussed on the establishment of ADA Qualitative (formerly AQuA). While there have been significant challenges in efforts during this time to encourage qualitative researchers to deposit content with the archive, the deposit of these new data forms have also created new challenges for the archive in ingesting, processing and dissemination. These challenges have been threefold: - methodological: what changes do researchers need to make in their methods to support archival practice - technical: how does ADA adapt its existing metadata schema and data management software (DDI2 and Nesstar) to support qualitative content - practical: how are processing procedures for archivists changed when documenting qualitative content This paper explores each of these challenges in turn, focussing particularly on the adoption of the QuDEx schema developed by the UK Data Archive to support qualitative data archiving. The paper will discuss ADA's experience with the use of the QuDEx schema to address these three challenges, and provide suggestions for future developments of the schema and qualitative archiving more generally.

A discussion paper inviting input into the Academy's Decadal Plan for Social Science Research Infrastructure 2023-32