Compliance with Disciplinary and Legal Regulations

As a researcher, you are expected to abide by the Australian Code for the Responsible Conduct of Research. [1] Among other responsibilities set out in the Code, researchers are expected to “comply with relevant legislation, policies and guidelines”. Alongside the Code, universities have policies to ensure the responsible conduct of research. Further, academic disciplines often have different considerations that must be acknowledged, such as methodological approaches, perspectives, ethical codes [2] and evaluation practices. [3]

Why is it relevant?

Ethical and regulatory compliance is integral to good research and practice. Research integrity enables confidence in data collection, outputs and recommendations, [4] alongside the fair and dignified treatment of research subjects. [5] Complying with discipline-specific and legal requirements also minimises the risks of physical or mental harm to research subjects and the research team, as well as managing potential risks to associated organisations’ reputations. [6]

Practical steps

Key concepts to understand:

  • Research integrity: conduct research honestly, ethically and rigorously. [1]
  • Responsible data management: retain accurate, clear, secure and complete records of all data and materials used in research. [1]
  • Authorship and publication practices: appropriately credit those who have made significant contributions to research and associated outputs. [7]
  • Supervision and mentoring: supervisors have responsibilities towards those under their guidance, particularly to provide support, work cooperatively and ensure that all relevant training is completed. [8]
  • Research misconduct accusations: if you are accused of breaching the Code, the investigating institution must abide by the principles laid out in Section 3 [9], allowing the accused to be heard, decisions to be made based on evidence and without bias.

Guidelines to consult:

  • Research ethics for human and animal research: your research must go through an ethics review process aligned with the subject and methods; different standards are required for ethics exemptions, human and animal research (see the “Research Ethics” factsheet).
  • Laboratory regulations regarding biosafety and chemical / radiation material: check the regulations of the laboratories you will be working in to ensure that you comply and understand what safety precautions must be taken (see the “Health and Safety” factsheet).
  • Indigenous Research Governance: research involving Indigenous participants or knowledge requires consideration and understanding to ensure culturally appropriate methods and actions (see the factsheets on “Co-design with Indigenous Communities” and “Engage with Indigenous Communities”).
  • Funding and Grant Compliance: funding and grants often come with specific guidelines as to how research must be conducted and managed, and how funds can be spent. [10]
  • Disciplinary and Professional standards: You must consider and comply with standards relevant to your discipline and profession such as methodological approaches, laboratory protocols, authorship conventions, and peer-review ethics.

AI and Authorship

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This factsheet is currently being prepared by the Responsible Science team. It will provide an overview of the topic, explain its relevance to responsible research, and include practical guidance and further resources.

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Authorship Order and Recognition

Why is it relevant?

Authorship order is one of the main ways scientific credit is distributed and interpreted. Positions such as first, last, and corresponding author are often used to infer contribution, leadership, responsibility, and seniority. As a result, authorship order can influence hiring, promotion, funding opportunities, professional visibility, and career progression. 

Because particular authorship positions have acquired significant value, discussions about authorship order are often sensitive and contested. At the same time, the meaning attached to these positions is not universal. Different disciplines rely on different conventions, and collaborators may hold different expectations about who should occupy particular positions and what those positions represent. When these assumptions remain implicit, authorship order can become a source of misunderstanding, disagreement, or tension within research teams. 


Hidden Assumptions

Authorship order is often treated as a straightforward representation of contribution. This assumes that different forms of work can be compared, arranged in a single sequence, and interpreted consistently by collaborators, readers, and evaluators. 

In practice, these assumptions do not always hold. Different disciplines use different conventions, contributions may be difficult to compare directly, and the same authorship order may be understood in different ways. 


Practical Steps

Authorship order is often treated as the outcome of a project. However, teams may benefit from discussing how authorship order will be determined. Making expectations, criteria, and decision-making processes explicit can help reduce misunderstandings and support more transparent discussions about recognition and credit. 

Questions that may be useful to discuss include: 

  • What authorship conventions are relevant to this project?
  • Which contributions are most important for this particular output?
  • How will different forms of work be recognised and compared?
  • How will changes in contribution be reflected if the project evolves?
  • Who will participate in authorship order discussions?
  • How will disagreements be addressed?

Because authorship order distributes recognition and opportunity, all contributors share responsibility for ensuring that the process is understood and open to discussion. 


Case Study

The CLEAR Lab, led by Max Liboiron, developed an “Equity in Author Order” protocol to support discussions about authorship order. Rather than beginning with individual authors, the process starts by identifying the forms of labour that made the project possible and discussing which contributions were most central to the work. The team then maps contributors to these forms of labour and collectively discusses how authorship order should be established. The protocol also encourages researchers to reflect on how different forms of work are recognised and how existing inequalities may shape authorship decisions.  

Avoid Duplicate Publications

Duplicate publication ‘refers to the practice of submitting a paper with identical or near-identical content to more than one journal, without alerting the editors or readers to the existence of its earlier published version.’ [1] Duplicate publication, also termed ‘self-plagiarism’, is largely driven by the prevailing academic ‘publish-or-perish’ culture. [2]

Salami publication (also called ‘segmented publication’) is another form of redundant publication where multiple articles are derived from the same study. Although there is no overlap in the text, it is characterised by a similarity in hypothesis, methodology, or results. [3]


Why is it relevant?

The practice of duplicate and salami publications has several negative effects. It may breach international copyright laws and waste publication resources through inefficient use. The redundant publication of original data is also problematic as it may lead to the overweighting of the results of a single study from the double-counting of data. [4] Finally, salami publications are unprofessional and artificially enlarge an author’s scientific work, providing undeserved benefit. [5]


Practical Steps

  1. All results derived from a single study should be reported within one article.  
  2. Manuscripts containing identical or substantially similar content should be submitted to only one journal at a time and published once. However, this requirement may vary across disciplines and countries. Authors are therefore, advised to carefully check the target journal’s guidelines prior to submission.  
  3. The rule against duplicate publication does not apply to the publication of complete reports that follow a preliminary report, such as a letter to the editor or an abstract presented at a scientific meeting. [6]
  4. The submission of the same paper to multiple journals for secondary publication may be beneficial for the purposes of reaching a larger audience (e.g., in a different language or an abridged version). If secondary publication is to take place, then there must be agreement between the editors of both journals (e.g., with respect to copyright) and the duplicate nature of the publication must be clearly indicated in the title and with a reference to the primary publication. [7]
  5. Follow any additional requirements contained within the journal’s policy on redundant/duplication publication. [8]

Case Study

A scientist from Swinburne University had dozens of his papers retracted over concerns about duplication of data. The alleged misconduct was reported by a whistle blower in the same research area who was concerned about the level of duplication, as well as falsification, plagiarism and ghost authorship. The University conducted an internal investigation into this alleged research misconduct, and the scientist lost his job. [9]

Avoid p-Hacking and Bias

P-hacking is generally defined as ‘choosing to report a subset of multiple dependent variables or adding observations until the effect of interest is significant’. [1] There are many ways one can engage in p-hacking. [2] Typically, we think of p-hacking as the practice of using trial and error to fish for statistically significant relationships (a low p-value) with no regard to the theoretical underpinnings of the research. Selective reporting is a broader concept and often involves failing to publish studies that do not produce statistically significant results. The following example illustrates the practice of p-hacking:

A researcher studies whether a new diet improves weight loss compared to a control group.

  1. They collect data and initially find no statistically significant effect (p = 0.12).
  2. Instead of reporting this null result, they:
    • Remove a few ‘outliers’ (participants who gained weight).
    • Re-run the analysis -> p = 0.07 (still not significant).
    • Then:
      • Try different statistical models
      • Test only a subgroup (e.g., participants under age 40)
      • Adjust which variables are controlled for
  3. Eventually, one analysis shows p = 0.04.
  4. The researcher reports only this significant result and omits all other analyses.

 


Why is it relevant?

P-hacking often arises from the academic ‘publish or perish’ culture. The problem is exacerbated by scientific journals’ tendency to favour statistically significant or novel results. In this environment, researchers may, often unintentionally, make biased analytical choices. P‑hacking is also sustained by a lack of clear education and sometimes even institutional mandates. [3]

However, it is a detrimental practice that contributes to the proliferation of false-positive results and the inability to replicate prior studies. This has undermined confidence in some scientific results. [4] It can also lead to devastating consequences when data that has been manipulated is relied upon, especially in sensitive fields such as medicine. [5]


Practical Steps

You may inadvertently engage in outcome fishing as there are many ways in which one can analyse a dataset, and each of these decisions may contain implicit bias. [3] There are some practical steps to avoid this:

  1. Acknowledge degrees of freedom. Be aware, and explicitly report on, the degrees of freedom within the design, analysis, and reporting of statistical analysis which can be used to generate statistically significant results. [3]
  2. Justify all choices in data preparation and analysis. Ensure that all choices to exclude outliers, transform variables, analyse a certain range or with certain dependent variables are well-justified.
  3. Consider implementing preregistration. Preregistration requires you to provide a specific, precise and exhaustive plan of the study. Each step within the plan needs to be detailed, providing only one way in which it can be interpreted or implemented. [6] This avoids outcome fishing.
  4. Minimise bias in analysis. Whenever possible, perform analyses without knowing which data points belong to which groups, making it difficult to intentionally manipulate results toward a specific outcome.
  5. Report results transparently. Fully report all measures, conditions, and analyses including insignificant results.

Case Study

The head of Cornell University’s Food and Brand Lab, Brian Wansink, has become notorious for instructing his research students to dig through data sets to discover relationships that were statistically significant. In an email to a student, he described the processes of data analysis as “squeez[ing] some blood out of this rock.” Following these revelations, Cornell opened an investigation into research misconduct and many of these studies were retracted. [7]

Copyright

Copyright is a set of rights granted by the government to protect the particular form, way, or manner in which information or concepts are expressed. Copyright does not protect ideas, concepts, styles, techniques, or information, but rather the form in which these things are expressed. Other subject matter that is not able to be protected with copyright includes names, titles, slogans, people, and people’s images.  

Copyright is administered and enforced on a country-by-country basis. In Australia, the relevant law is the Copyright Act 1968. Owners of copyright have several exclusive rights to control the use of their material and different rights apply to different types of material. Anyone who wants to use the copyrighted material needs to obtain permission from the copyright owner. 

Although copyright laws are enforced nationally, international copyright treaties have resulted in broadly consistent protection across signatory countries, including mutual recognition of copyright and a copyright right term that generally lasts for the life of the creator plus 70 years.  

Copyright protection generally extends to two categories of material: ‘works’ and ‘other subject matter’. 

Examples of works include: 

  • Literary works – i.e. the written word, including books, journal articles, instruction manuals, reports, computer programs and databases. 
  • Artistic works – includes paintings, drawings, cartoons, sculptures, diagrams, buildings, photographs and maps. 
  • Dramatic works – includes choreography (dance), screenplays, plays and mime pieces. 
  • Musical works – includes music itself, separate from any lyrics or sound recordings, 

The ‘other subject matter’ category covers sound recordings, films, and TV and radio broadcasts. 


Criteria for Copyright Protection

Copyright protection is free and automatic. There is no need to apply in Australia as there is no system of registration.  

To qualify for copyright protection, a work must be ‘original’. For the purposes of copyright, original simply means that the work has not been copied from another source. 


Why is it relevant?

Copyright is relevant to researchers because it governs how research outputs can be used, shared, and reused. Many common research materials, such as journal articles, images, datasets, software code, and teaching resources, are protected by copyright automatically when they are created. Understanding copyright helps researchers use third-party materials appropriately, share their own work under suitable terms, and navigate publishing agreements with journals or other publishing outlets. For example, researchers may choose to share their outputs under open licences such as those developed by Creative Commons, which allow others to reuse material under specified conditions. 

It is also important in collaborative and institutional research settings, where questions may arise about authorship, ownership, and how and when research outputs can be disseminated or reused.  


Practical Steps

  1. Understand who owns copyright. Copyright automatically protects many research outputs. Works created by employees in the course of their employment may be owned by their employer, subject to institutional policies. Check your institution’s policies to understand ownership arrangements. 
  2. Clarify rights in collaborations. When working with collaborators from other institutions or with external partners, discuss authorship, copyright ownership, and how research outputs may be shared or reused. 
  3. Check permissions when using third-party material. If you want to reuse figures, images, tables, or other works created by others, check whether permission is required. Some works may be available under open licences such as those developed by Creative Commons, which allow reuse under specific conditions. 
  4. Check publishing agreements carefully.  Academic publishers sometimes require authors to transfer copyright as part of the publication process. Before signing, check whether you can retain certain rights, such as sharing your work in an institutional repository or reusing it in future research and teaching. 

As with all intellectual property issues, your institution’s intellectual property office should be able to clarify and assist with any copyright related matters that come up in the course of your work.  


Case Study

In Australia, the use of copyrighted material to train AI models is a subject of ongoing debateThe Australian Government has definitively ruled out introducing a text and data mining exemption, and so there will be no carve out for AI developers to train their models on Australian creative worksNow, various other reforms are being debated such as the implementation of a collective or voluntary licensing framework. This case highlights the challenge of updating copyright rules to keep pace with emerging technologies such as generative AI. [1] 

Dual-Use, Misuse & Security Risks

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Equity, Inclusion & Benefit Sharing

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Inclusivity, Diversity and Equity

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Reporting & Whistleblowing

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