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How to Translate Research: The Wisdom of the Crowd

• | November 28, 2020

50 researchers share their experiences translating research for practitioners. Use this crowd-sourced wisdom to bring your research to people who can use it.

Translating research for practice — bringing academic insights to managers and other users — can seem simple. But, like any skill, it must be learned. Fortunately, others are glad to share their experiences.

This article is the third in a series of articles on research translation. Previously, we wrote about translating research without removing abstraction , and presented NBS’s step-by-step Guide to Translating Research . 

This article draws on the wisdom of the (researcher) crowd. In collaboration with the Impact Scholar Community , NBS surveyed researchers on their experiences with research translation. More than 50 people provided resources, reflection, and advice.

Some researchers are lucky enough to have a university media team that can help with the translation process. But understanding the basics can facilitate your work with media staff and give you independence and flexibility.

We present survey insights following the structure of the NBS Guide to Translating Research , integrating some content from that document as well.

Design by Abby Litchfield

Step 1: Identify Your Audience

Who would benefit from your research discoveries? Ideally, you identify relevant audiences early in your project, as knowing potential users can help you refine your research question.

In identifying an audience, be as specific as possible. Our survey found that “business leaders” were the most popular audience (42/50 responses) followed by “government” and “general public.” But these are large categories. What kind of business leader or manager will find your research insights useful? Supply chain managers, human resources professional, and bank CEOs may have different needs.

Step 2: Identify Your Core Message

With your audience in mind, identify your most relevant insights. What might your audience want to know, based on your understanding of their resources and challenges?

Note that while researchers are trained to describe phenomena, managers often seek recommendations for action. Emphasize practical take homes and guidance without losing the complexity of your insights or straying too far from your data.

Respondents to our survey shared these ideas about crafting a core message:

“Practitioners take for granted that we did the ‘due diligence’ of rigor, and they just want the results. And those results put in their context, with practical, concrete implications.” – Adrián Zicari, ESSEC

“Exploratory, qualitative, or descriptive research contributes to theory but doesn’t always yield immediate and clear insights for practice. This makes it difficult to translate this type of work to a business audience.” – Merriam Haffar, University of Michigan alumna

“Translate abstract concepts into the ‘language’ of the people you want to benefit with your work. They need to make sense of what you do to help you develop the ‘solution’ and apply it to their context….Abstract concepts are always just a guiding lens, not a final solution.” – Esther Hennchen, University College Dublin

Step 3: Identify Outlets to Reach the Audience

What outlets and formats will reach your desired audience? For business audiences, useful outlets may include industry and professional associations and practitioner-oriented journals and conference. The Conversation can be a great way to reach the general public. The Web has many targeted newsletters and platforms. And social media is also a powerful tool.

Respondents to our survey shared these ideas about effective outlets and formats:

“Ask to moderate sessions in practitioner conferences on your themes. Ask to be part of expert networks. Send your reports to governmental officials. I always think: 1 paper, 1 case study (for pedagogy), 1 report (or 1 article for media). It does not always happen but I try.” – Delphine Gibassier, Audencia Business School

“Always prepare PPTs in English and the language of the population who participated in the research. Plan for technical reports, manuals, catalogue-like “light” publications, flyers, YouTube videos etc.” – Ieva Zebryte, Universidad de La Frontera/ ISM University of Management and Economics

“In my research team, we usually prepare reports with a summary of main findings of the research, and then we send this report to practitioners that have participated in the study. We research mainly in the hotel industry and we also send the reports to professional associations in this industry.” – Jose Molina, University of Alicante

“Reflect on your strengths, the skills you wish to develop, the networks you wish to contribute to, and the impact you wish to make in order to focus the means by which you translate your research. It may not make sense to invest time and effort into something that you do not feel good at or do not enjoy. I have found success in podcasting, and using a social media scheduler to develop a communication calendar to translate my research outputs.” — Steven Curtis, Lund University

On the survey, we asked individuals to rank different research translation channels. Here’s the order, from those viewed as most impactful to those viewed as least impactful: (1) media outlets (e.g. Harvard Business Review , general media), (2) in-person events (e.g. corporate conferences) (3) university communications (e.g. social media and web), (4) personal outreach (e.g. social media).

Step 4: Access Resources for Research Translation (If Possible)

You are not alone! Even if your university doesn’t have its own media staff, you can likely find others to help you on the translation journey.

In particular, draw on friends and family outside academia for feedback on your communications. Linguist Jeffrey Punske calls this approach the “Grandma Test”: describing your work to someone completely outside your field as a way to identify jargon.

Survey respondents shared these ideas on possible resources:

“You can start small to test your translation (family, practitioners who partner up with your university…) both orally (presentation) and written (e.g. blog post).” – Frederic Dufays, KU Leuven

“Former students or colleagues who are currently working outside academia but are interested in staying connected with your university/department are a great resource.” – Heather Schoonover, Lund University

“I engage undergraduate and graduate students in all of my research so that they can follow up by doing the translation of research into practice.” – Ieva Zebryte, Universidad de La Frontera/ ISM University of Management and Economics

“Encourage faculty to include knowledge management strategies in grant proposals and budgets” – Anonymous

Access additional guidance (multiple suggestions): Responsible Research in Business and Management , Integration and Implementation Sciences Network , On Writing Well , LSE Impact Blog  

Step 5: Craft a “Pitch”

Often, translation will involve an initial contact with gatekeepers or intermediaries (journalists, conference organizers), before you engage your desired audience. For them, you’ll develop a “pitch”: a short statement of what you have to offer that will encourage those intermediaries to ask you for more.

For the pitch, write a headline and a few supporting paragraphs. The headline should be less than 10 words and emphasize relevant keywords: e.g., what someone might type into a Google search. An example might be: “How to Motivate People Toward Sustainability.”

In supporting paragraphs, briefly identify your topic, why it matters, and your evidence base. (More guidance on pitch development is available in an NBS conversation with editors from Harvard Business Review and The Conversation.)

Keep reflecting: why would a busy manager, oriented toward action, pause and read what you’ve written? What is the practical puzzle that your research is solving?

Step 6: Develop Full Content

Communicating with practitioners requires that we keep our insights short, clear, and engaging. We have to unlearn or at least momentarily suspend the language of academia and use language that is accessible to all. For engagement, starting with a question or a story can be a useful hook .

Survey respondents shared these ideas on communicating with practitioners:

“Don’t use jargon or long, complex sentences or paragraphs. Practitioners like anecdotes and stories that breath[e] life into research findings….You have to speak differently (have a different writing voice) when writing for popular or business audiences. Academic voice will ensure no readers. Read a bit on copywriting—i.e. marketing writing. It is needed to ‘hook’ casual readers.” – Kevin Taylor, Stetson University

“[Move] beyond statistical analysis results, to focus on the real meaning of results.” – Rosa Maria Dangelico, Sapienza University of Rome

“Use stats on pervasiveness of idea(s) for their business success (or agency success, or organization’s success)” – Hildy Teegen University of S. Carolina

“I’ve found that when it comes to writing practitioner-facing articles, business audiences respond best to articles that have a clear and strong perspective.” – Merriam Haffar, University of Michigan alumna

“I use Canva for creating visually appealing graphics and presentations; Adobe Audition for recording and editing sound clips or podcasts; CoSchedule to schedule posts across social media and blogs; Pixabay, Pexels, The Noun Project for free imagery and icons” — Steven Curtis, Lund University

Always: Recognize Translation Is Only Part of Research Impact

Survey respondents emphasized that greater research impact comes from close collaboration with practitioners. Research translation is about sharing your research with practitioners, while research co-creation involves developing your research in interaction with practitioners. At NBS, we have often engaged academics and managers in such co-creation. These efforts are more resource-intensive than translation but can result in powerful insights and impact.

Additionally, focusing on how to craft insights for translation can minimize the importance of mobilization. Translation can be passive: sharing insights and hoping that someone uses them. Truly getting your ideas into use is an active exercise that requires partnering. For example, managers may be willing to use the research insights if they hear about them from their peers at an industry conference.

Mobilizing requires participating in public discourse — engaging in open conversation about ideas. Note that often you’ll be drawing on insights not only from a specific project but from your research agenda and beyond.

Survey respondents shared these ideas on learning from, and working with, practitioners:

“Qualitative and longitudinal research projects are a perfect way to build relationships that last and are helpful both for research and for impact.” –  Giuseppe Delmestri, WU Vienna

“Co-design, co-production and co-interpretation/implementation are key to impact, so interactive engagement throughout is needed, e.g. workshops are an important vehicle.” – Stefan Kaufman, Founder, Waste and Circular Economy Collaboration at BehaviourWorks Australia, Monash University

“Keep in mind that as a social scientist you are not so much “creating knowledge” as asking participants to share their knowledge, and then reassembling it to create something new.  Much of the knowledge we are ‘translating’ was their knowledge in the first place” – Alice Palmer, consultant

“[When participating in a podcast], the thing that helped me was to frame my role not as the definitive expert or solution-giver but as someone who can help dispel certain stereotypes, question assumptions, bring nuance to a politicized issue.” – Nevena Radoynovska, Emlyon Business School

More about the Survey Respondents

Here’s a bit more about the individuals who responded to the survey. We received 47 responses, from researchers in 19 countries.

Career stage: 40% are PhD students or postdocs, 21% are assistant professors or lecturers, 17% are associate professors or senior lecturers, 17% are professors, and 4% are consultants.

Experience with research translation: 47% had less than 2 years of experience, 28% had 2-5 years of experience, and 26% had more than 5 years.

Success in research translation: 9% saw themselves as successful or very successful, 66% saw themselves as somewhat successful, and 26% saw themselves as not successful at all.

Time spent on research translation: 32% of respondents spent less than 10% of their time on research translation, 45% of respondents spent 10-25% of their time, and 23% of respondents spent more than 25% of time.

Sequence for considering translation: Individuals thought about translation before starting a research project (57%), while conducting the research project (66%), and after completing the research project (60%). (Multiple answers possible.)

Share Your Ideas on Research Translation

What do research translation and impact mean to you? Share your ideas in the comment section. And, please consider working with both organizations behind this survey:

NBS always welcomes your content ideas ( submit them online ) and reflections on co-creation (write to Garima Sharma, [email protected] ).

The Impact Scholar Community is a community for early-career research scholars who want to connect research to impact. Find out more and join .

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• Categories: Articles , Sustainable Business Education

Garima Sharma is an Assistant Professor at Kogod School of Business, American University. Her research focuses on sustainability, social entrepreneurship and related tensions of purpose and profits. She is also interested in understanding how research impacts practice, and has created many resources on co-creation for NBS, available here: https://nbs.net/cocreation/. Garima has published in many journals and is on the editorial review boards of Academy of Management Journal, and Organization & Environment. Garima received her PhD from Case Western Reserve University, after which she was a postdoctoral fellow at NBS and Ivey Business School, Western University.

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Maya Fischhoff is the Knowledge Manager for the Network for Business Sustainability. She has worked at NBS since 2012. She has a PhD in environmental psychology from the University of Michigan and has worked for government, business, and non-profits. She also covered the celebrity beat on her college newspaper. Working for NBS allows her to combine her passions for sustainability, research, and journalism.

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Find a research supervisor in translation studies

Staff in the Department of Modern Languages who supervise PhD/MA by Research in the area of translation studies.

Dr Michela Baldo

Lecturer in Translation Studies Programme Lead for the campus MA in Translation Studies

I am a Lecturer in Translation Studies at the University of Birmingham and I teach across the provision of core and optional modules for the MA programme in Translation Studies. My research interests revolve around Italian-Canadian writing and its translation into Italian and around the role of translation in Italian queer feminist activism. In addition to teaching, I have been working as a ...

Dr Monica Borg

Lecturer in Italian Studies Educational Enhancement Fellow (CEEF) College of Arts and Law (CAL) Associate Lecturer, The Open University

• Modernism and the discourses of subversion (comparative approaches)
• Translation studies (especially history of translation)
• Technology-assisted teaching and learning
• Language pedagogy

Dr Hilary Brown

Senior Lecturer in Translation Studies

• Cultural history of C17 and C18 Europe
• Anglo-German cultural relations
• History of translation
• Literary translation
• Gender and translation
• Women intellectuals pre-1900, particularly women translators

Dr Anissa Daoudi

Senior Lecturer in Arabic and Translation Studies Language Co-ordinator for Arabic

• Translation and new media, with particular reference to the Arabic-speaking region
• Translation and popular culture
• Gender and language use in the context of translation

Professor Louise Hardwick

Professor of Francophone Studies and World Literature AHRC Early Career Leadership Fellow Associate Fellow, Homerton College, University of Cambridge

• French and Francophone Literature
• World Literature
• Postcolonial Studies
• Translation in Academia
• Women's Writing
• Environmental Humanities

Dr Sofia Malamatidou

• corpus-based translation studies in general
• translation and language change (esp. use of diachronic corpora in translation studies)
• translation of tourism texts
• translation of visual material

Dr Elisenda Marcer Cortés

Associate Professor in Catalan Studies

• Translating National Identity
• The Reception of Catalan Literature in English

Dr Anna Milsom

Lecturer in Modern Languages

I teach Spanish language and translation, drawing on my practice as a literary translator and my background in visual arts. I am interested in creativity in translation, co-translation and the ways that art, translation and text-making can interact.

Dr Isobel Palmer

Lecturer in Russian

• Russian poetry (19th-21st Century)
• Russian Formalism and literary theory
• Comparative modernisms
• (Modernist) performance culture  
• Thaw culture
• Urban humanities
• Intersections between literature and new media

Dr Natalia (Natasha) Rulyova

Associate Professor in Russian

•  20th Century Russian poetry (Joseph Brodsky's poetry and auto-translations)
• Genre and translation
• Translation, self-translation and collaborative translation with a specific focus on English to Russian, and Russian to English
• Translation of hybrid texts to majority languages with a focus on the translation of Russophone literature into English

Professor Emma Tyler

Professor of Translator Education Head of School of Language, Culture, Art History and Music Senior Fellow of the Higher Education Academy

I specialise in training students to become translators. Until very recently, I was the convenor of the MA in Translation Studies (campus and distance) and led to project to update and relaunch the programme. I am also the Director of Taught Admissions for the College of Arts and Law.

Dr Emma Wagstaff

Senior Lecturer in French Studies

I came to Birmingham in 2006, and am based in the Department of Modern Languages. Among a range of teaching and research interests, I am particularly keen to promote the study of French literature and the visual arts, and the interactions between them.

Dr Jules Whicker

Senior Teaching Fellow in Hispanic Studies

I am an enthusiastic teacher and student of Hispanic Literature and Culture and especially that of the Spanish Golden Age, an era that originated many of the most compelling, sophisticated and influential works in Hispanic literature and art. I also have a long-standing interest in translation, having translated (independently and in collaboration) several golden-age comedias , a quantity of ...

Dr Jenny Wong

Assistant Professor Programme Director for MA in Interpreting with Translation

• Chinese Shakespeare
• Translation of religious texts
• Translation of tourism texts
• Gender and translation 
• Literary translation 
• Translation education
• Interpreting training

Dr Xiaohui Yuan

Associate Professor in Interpreting and Translation Studies

•  Intercultural pragmatics in translation and interpreting
• Interpreter’s roles and neutrality
• User response to translated texts and audio-visual translation
• Using translation and interpreting in mediation, and cultural influence on mediation.

Modern languages staff research supervision areas

• Arabic studies
• Colonial and postcolonial studies
• Digital humanities in modern languages
• Exile and migration
• French studies
• German studies
• Hispanic studies
• Italian studies
• Linguistics
• Medieval studies
• Memory studies
• Nineteenth century studies
• Modern languages (audio-visual)
• Russian studies
• Sexuality and gender studies
• Translation studies

Translation Studies MPhil/PhD

London, Bloomsbury

At the UCL Centre for Translation Studies (CenTraS), we enjoy an international reputation for the quality of our research and teaching in a wide range of translation and interpreting-related subjects, as well as translation technology.

UK tuition fees (2024/25)

Overseas tuition fees (2024/25), programme starts, applications accepted.

• Entry requirements

A Master’s degree with Merit (ideally Distinction) in translation studies, in a language and culture subject or other relevant field from a UK university, or an overseas qualification of an equivalent standard. Admission is dependent on the submission of a detailed research project proposal and applicants must have the agreement of their potential supervisor before submitting a formal application.

The English language level for this programme is: Level 4

UCL Pre-Master's and Pre-sessional English courses are for international students who are aiming to study for a postgraduate degree at UCL. The courses will develop your academic English and academic skills required to succeed at postgraduate level.

Further information can be found on our English language requirements page.

Equivalent qualifications

Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from the International Students website .

International applicants can find out the equivalent qualification for their country by selecting from the list below. Please note that the equivalency will correspond to the broad UK degree classification stated on this page (e.g. upper second-class). Where a specific overall percentage is required in the UK qualification, the international equivalency will be higher than that stated below. Please contact Graduate Admissions should you require further advice.

About this degree

Research proposals which engage with theoretical, linguistic and technical aspects of translation and interpreting are welcomed. Examples of current research projects undertaken by PhD students in Translation Studies include the translation of humour in video games, the subtitling of gender stereotypes, translating British and American science fiction, exploring the notion of reflexivity in translation, and translating political speeches.

How to apply:

As a first step, please complete the Online Enquiry Form, which will be considered at our next regular PhD supervisors meeting. PhD places are tightly limited and we are only able to encourage those with outstanding research proposals to move ahead to a formal application to UCL. Please therefore take care to present a fully developed 500-word summary of your project as part of this enquiry. Further information on writing research proposals can be found in the ' Need to Know ' box on our Postgraduate Research page. Please do not apply formally to UCL until you have received a response regarding your initial enquiry.

Who this course is for

This MPhil/PhD is for applicants with a strong interest in conducting multi-disciplinary research, who may have completed post-graduate training or study and want to develop an advanced critical analysis in a specific translation research area. The programme is for applicants with a background or interest in translation theory and history; audio visual translation; literary translation and performance; translation technology; languages and interpreting. It is suitable for both recent Masters graduates as well as early or mid-career professionals.

What this course will give you

Located in the heart of multicultural London, UCL provides a uniquely rich environment for researching translation and interpreting in all its facets. Doctoral students can draw on a broad and diverse range of expertise from the Centre for Translation Studies (CenTraS), the Centre for Multidisciplinary and Intercultural Inquiry (CMII) and the School of European Languages, Culture and Society (SELCS).

Students are supported by a dynamic research culture, a stimulating environment and excellent opportunities for research training. UCL runs numerous seminar series and guest lectures, and researchers have access to state-of-the-art translation technology as well as world-class libraries, including those at UCL itself, the British Library, the School of Advanced Study, and the School of Oriental and African Studies.

The foundation of your career

The programme provides students with a range of professional and academic skills that will enable them to pursue careers in translation, higher education, government agencies, non-governmental organisations, international bodies, and other institutions around the world.

Recent PhD graduates have gone on to pursue postdoctoral study, have obtained lectureships in translation studies at reputable universities in the UK and abroad (Australia, Italy, Singapore, Spain, Taiwan), and have joined companies such as British Telecom, Expedia and Paramount.

Employability

With the research training and experience gained during the PhD, students are excellently placed to pursue a career in the fields of academia and professional translating and interpreting.

Translation PhD students will acquire extensive transferable skills, including the ability to analyse and process vast amounts of data, to teach courses in their field of expertise, to present research to small and large audiences, to network with diverse groups. This ample and highly adaptable skill base gives students an unparalleled edge and employment opportunities.  

UCL is extremely well positioned to offer students opportunities for networking and to establish academic and professional contacts. Supervision and mentorship is available from world-leading researchers, with 83% of SELCS-CMII research activity being graded 4* ‘world leading’ and 3* ‘internationally excellent’ in the REF 2021.

PhD students are actively involved in attending and organising seminar series and guest lectures, and have the opportunity to liaise with world-renowned scholars and experts in the field of translation and interpreting. Students have opportunities to engage in numerous projects involving research such as Global Health and Crisis Translation, Audio-visual Translation, as well as translation technology and theory.

Teaching and learning

Research students undertake relevant induction sessions and can take advantage of the Doctoral Skills Development Programme. PhD students meet regularly in term time with their supervisors and may be offered opportunities to gain valuable teaching experience and participate in reading groups and conferences.

To successfully upgrade to a PhD you are required to submit a piece of writing (this is usually based on one chapter from your thesis and a chapter plan for the remainder). You are also required to present and answer questions about this work to a panel consisting of your subsidiary supervisor and another member of the Faculty who acts as an independent assessor.

PhD students should treat their research programme as a full-time job, which equates roughly to 35 hours per week, or 15 hours for Part-time students. Students agree to a timetable of regular meetings with the Principal Supervisor to effectively manage the progression of project aims. This is flexible, at some points it may be necessary to meet more or less often.

Full-time students can expect to meet supervisors every two weeks during the academic year, and part-time students every four weeks. If a student has external funding, they should also ensure they meet the Terms & Conditions of the funder.

Research areas and structure

UCL offers expertise in translation technology, audiovisual translation, localisation, literary and theatre translation, history of translation, translator and interpreting training, technical and scientific translation, translation and accessibility to the media, translation theory.

Research environment

Research students are encouraged to participate in research seminars and networks across and outside SELCS-CMII. Students contribute significantly to the research environment through the organisation of annual conferences, and participation in seminars and online journals. 

Our Transcluster, a suite of 60 IT stations, is equipped with cutting-edge eye-tracking equipment and programmes, developed specifically for CenTraS staff and research students. Students can access special collections at UCL and other world-class libraries (Senate House and British Library) within walking distance of campus. As well as access to research support in the form of academic skills courses, student-led workshops and reading groups.

The length of registration for the research degree programmes is usually three years for full-time and five years for part-time. You are required to register initially for the MPhil degree with the expectation of transfer to PhD after successful completion of an upgrade viva 9-18 months after initial registration.

Upon successful completion of your approved period of registration you may register as a completing research student (CRS) while you write up your thesis

In the first year, you will be required to take part in a mandatory Skills Seminar Programme. You are expected to agree with your supervisor the basic structure of your research project, an appropriate research method and a realistic plan of work. You will produce and submit a detailed outline of your proposed research to your supervisor for their comments and feedback and be given the opportunity to present your research to UCL academic staff and fellow PhD students

In the second year, you will be expected to upgrade from MPhil to a PhD. To successfully upgrade to a PhD you are required to submit a piece of writing (this is usually based on one chapter from your thesis and a chapter plan for the remainder). You are also required to present and answer questions about this work to a panel consisting of your subsidiary supervisor and another member of the Faculty who acts as an independent assessor.

Accessibility

Details of the accessibility of UCL buildings can be obtained from AccessAble accessable.co.uk . Further information can also be obtained from the UCL Student Support and Wellbeing team .

Fees and funding

Fees for this course.

The tuition fees shown are for the year indicated above. Fees for subsequent years may increase or otherwise vary. Where the programme is offered on a flexible/modular basis, fees are charged pro-rata to the appropriate full-time Master's fee taken in an academic session. Further information on fee status, fee increases and the fee schedule can be viewed on the UCL Students website: ucl.ac.uk/students/fees .

Additional costs

Additional costs may include expenses such as books, stationery, printing or photocopying, and conference registration fees.

The department strives to keep additional costs low. Books and journal articles are usually available via the UCL library (hard copies or via e-journal subscriptions).

The wealth of departmental seminars / colloquiums / symposiums and student organised work in progress sessions give ample opportunities to present research, receive feedback and participate in discussion.

For more information on additional costs for prospective students please go to our estimated cost of essential expenditure at Accommodation and living costs .

Funding your studies

For more details about departmental funding available to postgraduate research students in the department, please refer to our Funding, Scholarships and Prizes (Research) webpage .

For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website .

Quirk PhD Scholarship

Deadline: 26 January 2024 Value: Fees and maintenance (3yrs) Criteria Based on both academic merit and financial need Eligibility: UK

All applicants must identify and contact potential supervisors before making their application. For more information see our ' Need to Know ' page.

Please note that you may submit applications for a maximum of two graduate programmes (or one application for the Law LLM) in any application cycle.

Choose your programme

Please read the Application Guidance before proceeding with your application.

Year of entry: 2024-2025

Got questions get in touch.

Centre for Multidisciplinary and Intercultural Inquiry

[email protected]

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What Makes a Good Research Supervisor?

Understanding Expectations

For research supervisors, the role is assigned as part of a broader and more complex faculty role that may include teaching responsibilities, administrative committee assignments, research development, and the fundraising and writing tasks that can accompany that research. In that context, being a supervisor may not be perceived as such a central role as it may for the supervisee.

For research supervisees, the expectations they may have of their respective supervisors can often be guided by good or bad experiences with prior supervisors, but they will typically include such variables as commitment, accessibility, regularity of communication, and supportiveness.

A Tailored Solution

With those common variables identified, it would seem that an individual supervisory relationship would be fairly straightforward—just make the necessary fine adjustments to each of those variables based on the displayed needs of the individual supervisee—some will need more support, more frequent communication, etc. However, if it really is that easy, why do so many postgraduate research students complain about supervisors who don’t “get” them, or who appear to be simply “going through the motions,” or who don’t seem to care if they graduate or not?

Supervisor relationship contracts may be built on clear outcomes to be achieved (graduation, research publication, etc.), but for those students being supervised, success comes in the form of “walking the talk.” In practice, this means making an investment of time to learn about the individual needs of the student and then adjusting your behavior accordingly.

The Functions of Supervision

Alfred Kadushin’s work on theories of supervision in social work (which is built on the earlier work of John Dawson) grouped the functions of supervision into three distinct areas:

• Educational—helping the student achieve the necessary competence in research methodology to conduct independent research upon graduation
• Administrative—guiding the student through the necessary internal and external protocols of a postdoctoral research project
• Supportive—providing an appropriate level of emotional support for the student to feel capable in tackling a complex research project. This can vary from reassurance to inspirational and even “tough love” reminders of expectations if needed.

Soft Skills

For experienced faculty who prefer not to be categorized by academic theory, good supervisory skills can best be found in the realm of “soft” managerial skills. Remaining empathetic, flexible, and sensitive to the needs of the aspiring research professionals under your care will put you on the path to success. There are no guarantees that there won’t be some rough spots, especially when a passionate researcher resists a suggested reorientation of a topic to which he or she has been committed since high school, but if a culture of mutual respect and professionalism has been established from the outset, the experience should be a positive one for all involved.

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• Communication
• Paediatrics: Healthy Start to Life
• Musculoskeletal: Movement in Health
• NeuroRehabilitation and Ageing
• Professional Education
• Digital Health

Knowledge Translation and Impact

• Research & engagement themes

The ‘Knowledge Translation and Impact Theme’ (KIT) aims to capture the outstanding work done in SHRS to translate research evidence into practice, resulting in meaningful impacts on knowledge, health and on society. It also strives to promote excellence in the future practice and science of knowledge translation.

For clarity, this theme uses the following definition of knowledge translation:

“Knowledge translation is a dynamic and iterative process that includes synthesis, dissemination, exchange and ethically-sound application (implementation) of knowledge” (Adapted from Canadian Institutes of Health Research, 2015). Additionally, this research theme acknowledges the importance of, and overlap if this definition with, the T1-T5 translational framework.

This theme is relevant to all SHRS disciplines (audiology, occupational therapy, physiotherapy and speech pathology) and works together with all other SHRS research themes. It recognises knowledge translation as fundamental to the school’s teaching, research and clinic work and as a core focus of all our staff and students.

Knowledge Translation and Impact Planner (KTIPs)

KTIPs Instructional Guide

The KTIPs Planning Template is designed to accompany the KTIPs Instructional guide.  It can be completed electronically and is a place to document your plans as you progress through the accompanying KTIPS Instructional guide.

KTIPs Planning Template

The KTIPs Introductory Video is a brief video which provides an introduction and overview of the KTIPs designed to orientate users about how to use the resource.

SHRS is nationally and internationally recognised for translating research into a wide range of clinical and social arenas with direct health and social impacts, influence on policy, excellence in preparing the future health workforce, practical and economic outcomes and contribution to knowledge. This theme will further enhance our staff and students’ capacity and excellence in knowledge translation.

Examples where knowledge translation has led to tangible impacts on health include the areas of:

• Telerehabilitation
• Musculoskeletal conditions e.g. MyBackPain
• Stroke rehabilitation e.g. Queensland Aphasia Research Centre
• Hearing loss e.g.  Active Communication Education (ACE)
• Traumatic brain injury
• Cerebral Palsy
• Mental health

Importantly, impacts can also be seen from translating research about teaching and learning , which has resulted in improved student experience and outcomes in SHRS and influenced teaching and learning practices and outcomes internationally. Two great examples are the use of evidence-based simulation  and embedding of work integrated learning  in all our programs. 

Finally, research about knowledge translation itself, (e.g. knowledge translation science) in SHRS has resulted in improved capacity of clinicians to use knowledge translation methods (with resulting clinical benefits), and improved capabilities of SHRS staff, HDRs and students (our future clinicians) to harness knowledge translation methodologies.

Staff overview

Chair:   Kirstine Shrubsole  Conjoint Research Fellow in Speech Pathology

Co-chair: Emmah Doig , Conjoint Senior Research Fellow

Academic Staff

Sally Bennett , Professor in in Occupational Therapy

David Copland ,  Professor in Speech Pathology

Louise Hickson , Associate Dean External Engagement, Faculty of Health and Behavioural Sciences

Leanne Johnston , Associate Professor 

Trevor Russell , Professor, Director RECOVER Injury Research Centre

Katrina Williams , Senior Lecturer in Physiotherapy

Lucy Thomas , Senior Lecturer in Physiotherapy

Caitlin Hamilton , Lecturer in Occupational Therapy

Tomomi McAuliffe , Lecturer, Occupational Therapy

Georgina Clutterbuck , Lecturer in Physiotherapy

Freyr Patterson , Lecturer in Occupational Therapy

Wei Qi Koh , Lecturer in Occupational Therapy

Chloe Bryant , Lecturer in Occupational Therapy

Alice Jones , Honorary Professor and casual academic

Ray Lang , Casual Academic

Research Staff

Sarah Wallace , NHMRC Senior Research Fellow

Michelle King , Research Fellow

Megan Auld , Honorary Research Fellow

Yanfei Xie , Postdoctoral Research Fellow

Marie-Pierre Cyr , Postdoctoral Research Fellow

Megan Ross , Postdoctoral Research Fellow

Catherine Travers , Postdoctoral Research Fellow

Conjoint staff

Jacki Liddle , Conjoint Associate Professor  in Occupational Therapy

Kirstine Shrubsole , Conjoint Research Fellow in Speech Pathology

Elise Gane , Conjoint Research Fellow in Physiotherapy

Clinic Staff

Maree Maloney

Denis Guguere

Anna Pearson

Research projects

Hdr students, featured publications.

UQ Health and Behavioural Science Faculty- Research Translation Awardees:

Trevor Russell (2014)

Louise Hickson (2016)

Sally Bennett  (2017)

Annie Hill (2018)

Michelle Sterling (RECOVER) (2020)

Jenny Setchell (2019)

UQ Health and Behavioural Science Faculty – Consumer and Community Involvement Research Award

Queensland Aphasia Research Centre (QARC) Team (2022)

UQ School of Health and Rehabilitation Sciences- Research Translation Awardees:

Emmah Doig and Megan Auld (2020)

For all general current HDR student queries and enquiries related to applications contact the HDR Liaison Officer: 

[email protected]  or +61 7 3365 7123

Find more HDR student information

For all enquiries related to research in the School, contact the Senior Administration Officer (Research): 

[email protected]  or +61 7 336 52074

Dr Emmah Doig

Dr Kirstine Shrubsole

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Translation Studies and Chinese discourse studies

Dr Wei Wang is an Associate Professor and the current Chair of Chinese Studies at the University of Sydney. His research interests include discourse studies, sociolinguistics, translation studies, and language education. His recent research focuses on sociolinguistics and (critical) discourse analysis, especially contemporary Chinese discourse, and is characterised by a highly interdisciplinary approach. His recent book publications include Ethnic identities of Kam People in Contemporary China: Government versus Local Perspectives (Routledge, 2021) and Analysing Chinese Language and Discourse across Layers and Genres (Benjamins, 2020). His journal articles appear in Discourse Studies, Applied Linguistics Review, Journal of Multicultural Discourses, Journal of Chinese Language and Discourse, Australian Review of Applied Linguistics, Perspectives , and many other international academic journals.

Associate Professor Wei Wang .

Research location

Chinese Studies, School of Languages and Cultures (SLC)

Research interests

• Chinese/English translation studies
• Contemporary Chinese discourse studies
• Sociolinguistics
• Chinese language education
• Intercultural communication

Additional information

1. If you are interested in this research opportunity, you are encouraged to email the potential supervisor directly.  To find their email address, follow the link provided to their profile page. 

When contacting them, you should describe your academic educational background and research experience, and include an academic transcript and CV (resume). You should also include a research proposal (1500-2000 words); refer to How to write a research proposal for guidance . You should explain why you want to undertake a PhD and how you believe your research topic aligns with the supervisor’s own research. You may be asked to supply a sample of written work.

2. Your potential supervisor may offer you advice on developing your research proposal before you submit your application. You will need to provide a written statement from your potential supervisor that they have agreed to supervise your project.

3. If you would like general advice in your subject area before submitting an application, contact an academic advisor listed here: https://www.sydney.edu.au/arts/study/postgraduate-research/postgraduate-research-contact.html

Want to find out more?

• Interested in this opportunity? Want to know what to do next? Find out all you need to know about the application process including how to approach a potential supervisor via email and how to develop a research proposal.
• Browse for other opportunities within the Chinese Studies, School of Languages and Cultures (SLC) .
• Contact us to find out what's involved in applying for a PhD. Domestic students and international students.

Opportunity ID

The opportunity ID for this research opportunity is 3270

A behind-the-scenes blog about research methods at Pew Research Center

For our latest findings, visit pewresearch.org .

How we translate survey questions to be fielded around the world

Pew Research Center conducts surveys all around the world on a variety of topics, including politics, science and gender. The source questionnaires for these surveys are developed in English and then translated into target languages. A 2014 survey about religion in  Latin America , for instance, was translated into Spanish, Portuguese and Guarani.

Other projects require more translations. For example, between 2019 and 2020, we fielded a large national survey in India on  religion and national identity , as well as  gender roles , with interviews conducted in 17 languages. (See below for a  short description  of how we identify our target languages for translation.)

In this post, we’ll explain current best practices in survey questionnaire translation, based on academic literature, and discuss how the Center has applied these approaches to our international work.

Best practices in questionnaire translation

Best practices  for translating multilingual surveys have evolved considerably over time. An approach called “back translation” was once the standard quality control procedure in survey research, but the “team approach,” also known as the “ committee approach ,” is now more widely accepted.

Back translation involves the following process:

• A translator translates the source language questionnaire into the target language.
• A different translator translates the target language questionnaire back into the source language (hence the term “back translation”).
• Researchers check how well the back translation aligns with the source language questionnaire.
• Researchers use the comparison between the two documents to draw conclusions about potential errors in the target language translation that need to be remedied.

The back translation technique is meant to assure researchers that translations into a target language are asking the same questions as the original questionnaire. Back translations can identify mistranslations and be time — and resource — efficient. Researchers often use back translations to make a list of potential issues for translators to investigate.

Yet research has shown that this technique only detects  some  translation flaws. For example,  a poor initial translation  — one that uses awkward sentence structure and literal language — could be accompanied by a good back translation into the source language. The back translation might smooth out the poor translation choices made by the initial translator, making it unlikely that the first bad translation  would be detected .

On the flip side, a poor back translation may produce false alarms about the initial translation, adding time and cost to the process. Back translation is further faulted for not identifying how translators should  fix problems .

Today, many researchers prefer the team approach to survey questionnaire translation. The team approach generally requires  translators, reviewers and adjudicators  who bring different kinds of expertise to the translation process, like best practices in translation techniques, native mastery over the target language, research methods expertise and insight on the specific study design and topic. This approach has the advantages of being more reliable in diagnosing multiple kinds of translation issues and identifying how to fix them. One application of this approach is called the  TRAPD method :

•   T ranslation: Two or more native speakers of the target language each produce unique translation drafts.
•   R eview: The original translators and other bilingual experts in survey research critique and compare the translations, and together they agree on a final version.
•   A djudication: A fluent adjudicator, who understands the research design and the subject matter, signs off on the final translation.
•   P retest: The questionnaire is tested in the target language in a small-scale study to verify or refine the translation.
•   D ocumentation: All translations, edits and commentary are documented to support  decision making . Documentation also helps streamline future translation work in the same language by reusing text that has already been translated, reviewed and tested.

Pew Research Center’s team approach for international surveys

Pew Research Center does not have a team of trained linguists on staff, so in each project we collaborate with two external agencies to translate our international questionnaires. The initial translation is done by a local team contracted to conduct the survey in a given country. That translation is later reviewed for accuracy and consistency by a separate verification firm. (The verification firm also reviews issues that arise across languages within the same survey project to make sure we make translation decisions consistently.)

Our team approach, which includes extensive documentation about why individual decisions are made throughout the process, allows the survey and subject matter experts at the Center to serve as adjudicators when disagreements arise between translators and verifiers.

Here’s a closer look at our translation process:

Step 1: Translation assessment

International survey research at Pew Research Center tends to be interviewer-administered — that is, questionnaires are read aloud to respondents either in person or over the phone — as opposed to the  self-administered web questionnaires  we typically field in the United States. We design our interviewer-administered English questionnaires to be conversational, and we want that to come through in other languages and cultures, too.

A conversational tone, though, can sometimes introduce phrasing that is difficult to translate. In drafting questions, the team pays special attention to American English idioms and colloquialisms that need to be clarified at the outset. In such instances, we provide instructions to translators on how best to convey our meaning. For example, we asked people in India which of two statements was “closer to” their opinion. Since we did not mean physical proximity, we provided alternative phrases as examples: “most similar to” and “most agrees with.” In this way, we hopefully preempted some translation issues.

Members of the translation teams at both external agencies also do initial reviews of the questionnaire to see if other idioms, complex sentence structures or ambiguous phrasings need to be adjusted in the source questionnaire, or whether translation notes should be provided to the translation team.

Step 2: Translation

The local field agency carefully reviews and translates the full English questionnaire into the local language(s).

Step 3: Verification and discussion

The verifying agency evaluates the questionnaire translation line-by-line, noting translations with which they agree or disagree. Verifiers leave comments explaining any disagreements and offer alternate translations. The annotated translation is then sent back to the field agency’s translators, who comment on each item and sometimes propose alternate translations.

Many translation issues are easily resolved. For example, a verifier may catch spelling, typographic or grammatical errors in the translation. But in other cases, the verifier may disagree with the translator on how a particular word or phrase should be translated at the level of meaning, and there could be more than one valid way to translate the text, each with its own strengths and weaknesses. In such cases, the translator and verifier typically correspond until consensus is reached. They can also ask Center researchers to clarify the intent of a question or word. This process typically involves at least two rounds of back-and-forth discussion to reach agreement on all final translations.

In our survey of India, for example, we sought to ask the following question: “Do your children ever read scripture?” The question was initially translated into Hindi in a way that specifically referred to  Hindu  scriptures, even though the question was asked of all respondents, including Muslims and Christians. The verifier suggested a more general term, which improved the accuracy of the translation.

Step 4: Testing translations

The Center tests survey question translations before they are fielded in order to refine our questionnaires. In India, we conducted 100 pretest interviews across six states and union territories — including participants speaking 16 different local languages — to assess and improve how respondents comprehended the words and concepts we used in our questions. This process involved feedback from our interviewer field staff, who assessed how respondents understood the questions and how easy or awkward it was for the interviewers to read the questions aloud. (For more information about our testing procedures for the project in India, see “ Developing survey questions on sensitive topics in India .”)

Choosing into which language(s) to translate

The Center generally translates international questionnaires into languages that enhance the national representativeness of our survey sample. We always include the national or dominant language(s) in a country. To determine which, if any, additional languages to use, we look at the share of the population who speak other languages and their geographic distribution. We also consult our local partners about the languages that may be a primary language of an important subgroup of interest, such as an ethnic or religious minority group.

Our 2019-2020 survey of India, for instance, included an oversample in the country’s least-populated Northeast region to ensure we could robustly analyze the attitudes and behaviors of Hindus, Muslims and Christians living there. This oversampling led us to translate our questionnaire into languages only spoken by small segments of the national population. One such language was Mizo, an official language of the state of Mizoram — even though Mizoram accounts for less than 0.1% of the Indian population.

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Copyright 2024 Pew Research Center

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• v.5(1); 2021

A framework for clinical and translational research in the era of rigor and reproducibility

Chris wichman.

Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE

Lynette M. Smith

Introduction:.

Rigor and reproducibility are two important cornerstones of medical and scientific advancement. Clinical and translational research (CTR) contains four phases (T1–T4), involving the translation of basic research to humans, then to clinical settings, practice, and the population, with the ultimate goal of improving public health. Here we provide a framework for rigorous and reproducible CTR.

In this paper we define CTR, provide general and phase-specific recommendations for improving quality and reproducibility of CTR with emphases on study design, data collection and management, analyses and reporting. We present and discuss aspects of rigor and reproducibility following published examples of CTR from the literature, including one example that shows the development path of different treatments that address anaplastic lymphoma kinase-positive (ALK+) non-small cell lung cancer (NSCLC).

It is particularly important to consider robust and unbiased experimental design and methodology for analysis and interpretation for clinical translation studies to ensure reproducibility before taking the next translational step. There are both commonality and differences along the clinical translation research phases in terms of research focuses and considerations regarding study design, implementation, and data analysis approaches.

Conclusions:

Sound scientific practices, starting with rigorous study design, transparency, and team efforts can greatly enhance CTR. Investigators from multidisciplinary teams should work along the spectrum of CTR phases, and identify optimal practices for study design, data collection, data analysis, and results reporting to allow timely advances in the relevant field of research.

Introduction

Clinical and translational research (CTR) has been experiencing a resurgence since the mid-2000s along with an embracing of team science within the CTR community [ 1 – 4 ]. Around this same time, articles questioning or discussing the validity of published research results began to emerge in academic as well as nonacademic publications [ 5 – 7 ]. This perceived lack of trust in research has had an impact on investment and scalability [ 8 ] and has led to the formation of guidelines for how research studies should be reported, and a focus on scientific rigor and reproducibility by funding agencies, internal review boards, and editors alike [ 9 , 10 ].

Experience at our institution indicates that many investigators show interest in conducting CTR in their early research career. Our institution is currently funded with an Institutional Developmental Award Program Infrastructure for Clinical and Translational Research (IDeA-CTR). In the past 4 years, 68.4% (130 out of 190) of applicants for scholar program and pilot project awards were at assistant professor or lower rank. This high percentage of junior investigators applying for CTR funding indicates the need for education on rigor and reproducibility in CTR. Early career investigators and investigators new to the field of CTR alike, may have questions regarding the definition of the phases of CTR, how their research fits into the CTR spectrum, how to move their research from one phase to the next, and how to ensure rigor and reproducibility of their research.

While the definition of rigor is largely agreed upon, the definition of reproducibility is not [ 5 , 6 , 11 ]. Rigor means the study design, materials, conditions, data cleaning, analyses, interpretations, and reporting of results that are developed and documented in such a way as to produce unbiased results [ 12 ]. In contrast to rigor, reproducibility tends to have discipline-specific definitions ranging from an independent analyst getting the exact same result using the original data and code, to quantifying reproducibility with a measure such as the standard deviation of results [ 13 – 15 ]. As biostatisticians, we view reproducibility as the ability to obtain a consistent result when independent researchers utilize the same inclusion/exclusion criteria, study protocol, data cleaning rules, and analysis plan. Here consistency refers to parameter estimates being in the same direction and of similar magnitude with overlapping confidence intervals (CI). For example, if an original study estimated the effect of a 1 year increase in age on systolic blood pressure to be 2.3 (95% CI = (1.3, 3.3)) mmHg and the study repeated by an outside group under the same conditions obtained an estimated effect of a 1 year increase in age of 1.6 (95% CI = (0.4, 2.8)) mmHg on systolic blood pressure, the results of the two studies would be judged as consistent.

In this paper, we define CTR, and provide general and phase-specific recommendations for improving rigor and reproducibility of CTR with emphases on study design, data collection and management, analysis, and reporting. To guide the discussion and demonstrate the flow between translational research phases, we follow the development path of different treatments that address anaplastic lymphoma kinase-positive (ALK+) non-small cell lung cancer (NSCLC), as well as studies that demonstrate specific CTR challenges.

Defining Clinical and Translational Research

One definition of CTR is moving research from bench to bedside to communities and back again. This definition seems clear enough, but categorizing any particular study into the CTR spectrum is challenging for new and established investigators alike. Broadly, T0 is defined as basic research, T1 as translating basic research to humans, T2 as translating findings to patients, T3 as translating research to general practice care, and T4 as translating research to populations or communities (Table  1 ). There is some disagreement where various study types should fall along the CTR spectrum. Fort et al describe the evolution of CTR definitions in the literature based on a clustering algorithm and gives a summary of the emerging consensus [ 16 ]. Surkis et al used a machine learning approach to classify studies based on a series of questions [ 17 ]. Main sources of disagreement for definition of studies along the CTR spectrum in the literature are whether Phase I clinical trials should be considered T1 or T2, if Phase IV clinical trials should be T2 or T3, if comparative effectiveness research should be T2, T3, or T4, and if health services research should be classified as T2 or T3. We created a compromise definition based on the goal of the research, defining all clinical trials as T2, and comparative effectiveness and health services research as T3, noting that there is disagreement about their classification (Table  1 ). Our suggestion is to classify a particular study into one of the CTR phases based on the goals of that study.

Clinical and translational research classification definitions

* Studies with disagreement in the literature as to their classification

Figure  1 is an example of how small-molecule targeted cancer therapies are developed using ALK+ NSCLC as the target. Each circle represents a different phase of CTR and the black interconnecting lines indicate that the research path may be sequential, in parallel, or a hybrid of the two. The parallel aspect is demonstrated in Shaw et al which spans T0 and T1 [ 18 ]. The sequential aspect is demonstrated in the T2 phase with {"type":"clinical-trial","attrs":{"text":"NCT01449461","term_id":"NCT01449461"}} NCT01449461 [ 19 ], ASCEND-5 [ 20 ], {"type":"clinical-trial","attrs":{"text":"NCT00932893","term_id":"NCT00932893"}} NCT00932893 [ 21 ], ALEX [ 22 ], and ALTA-1L [ 23 ]. Finally, the T3 and T4 phases are represented by a comparative effectiveness [ 24 ] and a cost effectiveness study [ 25 ], respectively.

Phases of clinical translational research.

Study Design

The first step in designing a study requires defining study objectives and hypotheses. Usually when moving research along the CTR spectrum there is an overarching objective, such as improving progression-free survival (PFS) in metastatic lung cancer patients. From this overarching objective, each phase of CTR will have “subobjectives” and testable hypotheses. As research moves along the CTR spectrum, each new study’s rationale is supported by results from the earlier phase studies or pilot studies. Hypotheses formed for CTR are built on the knowledge obtained from earlier phases (Table  2 ).

Study design considerations

The objectives and hypotheses should be matched with primary and secondary outcomes that are selected in advance. The study is designed around the primary question, and a clear question promotes good study design. To move to the next phase of the CTR spectrum, feasibility data or pilot information should be collected as secondary outcomes for planning purposes. In a T0/T1 research study, the primary objective is to build the knowledge base around the disease of interest, including basic science studies with animal models of human disease, or proof of concept studies. One example, from the T0/T1 phase, is a study by Soda et al which identifies novel transforming genes in NSCLC that can be used as therapeutic targets [ 18 ]. To meet their objective, researchers formulated a series of testable hypotheses using cell lines and mouse models to meet their goals. They identified a subset of NSCLC patients that express a transforming fusion kinase who have the EML4–ALK gene as a potential therapeutic target or a diagnostic molecular marker. In the continuation of these findings, a T2 research study was conducted to determine if crizotinib is superior to standard therapy in ALK-positive lung cancer (those that have the EML4–ALK gene) in an open label study, with the primary outcome of PFS [ 20 ].

Defining the study population is an important component of study design. The generalizability of the results relies on the eligibility criteria, which defines the population of interest. Early phase CTR (T1/T2) tend to have narrow eligibly criteria in order to reduce variability in the outcomes measured. This reduced variability is translated into differences that are more easily detected when testing hypotheses; however, these results are not widely generalizable. When moving further along the CTR spectrum (T3/T4), eligibility criteria are relaxed, thus allowing for more heterogeneity in the population. Subsequent results are more generalizable; however, with the increased variability, larger sample sizes are needed to detect the same or similar differences. In the T2 study comparing crizotinib to standard therapy, subjects were eligible if they had locally advanced or metastatic NSCLC that was positive for ALK rearrangements, with additional criteria regarding age and performance status [ 21 ]. Eligibility criteria should be clearly defined regardless as to whether they are strict or not. In order for a study to be reproducible (external validity), the population in which the original study was conducted must be known.

Regardless of where the research is on the CTR spectrum, good study design requires consideration of sample size and power of the study. Sample size justification in CTR serves a number of purposes. The primary purpose is to ensure there is adequate power to detect a clinically important difference specified by the scientific hypotheses. An underpowered study is nonreproducible. In an underpowered study, a statistically significant difference will appear by chance alone, which is not reproducible, or nonsignificant results with large p -values in an underpowered study may have been significant in a fully powered study. An important secondary purpose is in prespecifying a primary outcome variable. By prespecifying the primary outcome, this determines the analytical plan, and thus works to avoid reporting bias later in the study (the temptation of changing the primary outcome variable after the study has ended).

After the primary outcome is selected, the most important consideration for sample size is not statistical but scientific in nature, that of a clinically important difference (or effect) in the primary outcome. This difference would be meaningful for the scientific community and would be considered an important result. An estimate of a clinically important difference can come from expert opinion, scientific literature, and/or pilot data. An example of a clinically important difference can be found in the study comparing crizotinib vs. chemotherapy in ALK+ NSCLC [ 21 ]. Researchers determined that a 56% improvement in PFS, corresponding to a 2.5 month difference, with crizotinib (median PFS of 7.0 months) is a clinically important difference when compared to chemotherapy (median PFS of 4.5 months), requiring a sample size of 347. This is opposed to a statistically significant difference which is focused on obtaining a p -value less than 0.05. Any difference can be made to be statistically significant with a large enough sample size. If the crizotinib study had 10,000 subjects per group, they could detect a difference of 0.23 months between treatment arms, which would be considered a small nonimportant difference between groups.

Once the clinically important difference is defined, then type I error (alpha), power, and the study design (number of groups, type of study such as noninferiority or longitudinal) can be used to calculate a sample size for the study. Consideration should be given for variability, multiple comparison correction, and within subject correlation, as required.

It is also important to consider the use of technical replicates vs. biological replicates, especially for T0 and T1 studies. Technical replicates are repeated measurements of the same sample, at roughly the same time, that measure variability of the process or experiment [ 26 ]. Biological replicates are measurements on independent biological samples that measure biologic variability [ 26 ]. Figure  2 shows an example of three technical vs. three biological replicates, notice how the technical replicates are all taken from the same mouse, whereas the biological replicates all come from separate mice (this is also applicable to human studies). Note that technical replicates cannot replace biological replicates in a study. Hypotheses are generally related to biological processes and variability at the biological level is needed for statistical comparisons. The use of technical replicates, in addition to biological replicates, will allow estimates of how reproducible the measurement equipment and protocols are for the experiment. Large technical variability can be attributed to numerous sources, including different lots of reagents, different equipment, or the samples were run on different days, or measurements were taken by different individuals. The reasons can be numerous and show the importance of good documentation of procedures. Once you have established that the technical variability is small, the analyses stage can be simplified by averaging over the technical replicates, giving one observation for each biological replicate for use in statistical models. The advantage of averaging over technical replicates is the analysis is greatly simplified, however a more complex analysis which includes the technical replicates in a mixed model allows us to better account for the nested structure of the data and multiple levels of variability.

Comparison of Technical vs. Biological Replicates.

During the T2 phase, particularly for clinical trials, sample size calculations and study designs often allow early stopping for efficacy, futility, or safety and should be approached in a rigorous manner. Early stopping at an interim analysis for safety or efficacy is necessary for ethical reasons, if one of the treatments is unsafe or superior to the other, it would not be ethical to continue to enroll subjects on either the unsafe or clearly inferior treatment. Additionally, stopping for futility is an important way to save patient and other resources for other more promising studies. In a randomized synbiotic trial to prevent sepsis in infants located in rural India, the study was stopped early for efficacy [ 27 ]. This study followed best practices for rigorous interim analyses, avoiding bias and maintaining operating characteristics of the study design by utilizing a Data Safety Monitoring Board so investigators remained blinded and multiple comparison adjustment determined by an a priori O’Brien–Fleming rule for stopping early.

Later phase studies in the CTR spectrum, T3 and T4, may utilize special designs, such as cluster randomization. As the name suggests, these studies randomize clusters, such as communities, schools, or hospitals. Some of the benefits of cluster randomized designs is that they can be logistically more feasible, they avoid within cluster contamination of the intervention groups, and they allow more people to be randomized in large public health trials. However, they require expertise in cluster sampling methods and analysis. These studies require that the correlation of individuals within a cluster be taken into account, inflating the sample size needs. Depending on the within cluster correlation, measured as the intraclass correlation, sample sizes may need to be inflated by a factor of 1.2 to 6.3 [ 28 ]. Regardless of the study design selected, care needs to be taken to design the study in a rigorous manner, utilizing best practices for the design chosen.

Randomization and blinding can apply to studies along the entire CTR spectrum in order to eliminate bias. At the T0/T1 phase, randomization and blinding can be applied to animal studies or doing assessments for biomarker studies. Animals should be randomly assigned to treatment groups, with sex as a stratification factor, meaning that female and male animals should be randomized separately. Randomization should be performed using a computer program or random digit table. Ideally, animals should have their own cages; this is because animals housed in the same cage are correlated with one another producing a “cage effect.” The cage effect is due to the animals interacting with each other and sharing food, water, and other resources. Researchers doing the assessments should be blinded, if possible, to the treatment assignment in order to produce an unbiased result.

Stratified randomization and block randomization can be used to minimize unbalance and to ensure that treatment groups are equally represented across strata and are balanced over time (blocking). Stratification is often used for smaller clinical trials in order to prevent imbalance in important prognostic factors at baseline. Study site is often used as a stratification factor in multicenter studies, along with gender, age, or disease stage as applicable. One limitation in using stratified randomization is that as the number of variables or factors increases the number of strata becomes large. In Shaw et al randomization was stratified by Eastern Cooperative Oncology Group performance status (0–1 vs. 2), presence of brain metastases (yes vs. no), and prior therapy with epidermal growth factor receptor kinase inhibitors (yes vs. no) [ 21 ], giving 6 strata. If researchers also wanted to stratify by age group (<65 vs. ≥65) and gender (male vs. female), then the number of strata goes up to 24. Depending on the total sample size, some of the strata could include a very small number of subject if any. Therefore, the number of stratification variables should be limited to those that are most important to keep the number of strata to a minimum.

Biologic variables, such as age and sex, should be considered both at the study design phase and at the analysis phase, as well as other important prognostic variables to prevent bias and obtain valid results. In T0/T1 studies, sex is an important biologic variable to consider for animal studies. Important differences by sex can be missed if only males or females are studied. Biologic variables should be planned for when calculating sample sizes and during the randomization process as stratification variables. If researchers want to be sure to capture treatment differences in each sex, the sample sizes are effectively doubled. If differences in treatment effects between sexes are not anticipated, then males and females can be combined for sample size calculation, but plans should include testing for sex differences in outcome. In later phase studies (T2–T4), biologic variables such as age, body mass index (BMI), race, gender, socioeconomic status, or underlying health conditions should also be considered at the design phase (eligibility criteria and stratification) and at the analysis phase in terms of reporting and model adjustment. One benefit of including stratification during randomization and analysis is that it can increase the power of the study by reducing variability in group comparisons [ 29 ].

Considerations made during the design phase of the study, including objectives, hypotheses, sample size considerations, randomization, stratification and biologic variables, as described above, should be laid out in a detailed protocol or manual of procedures to ensure scientific rigor and replicability/reproducibility. In early phase studies, this could be a document describing all the laboratory procedures that need to be followed, how records should be kept, and a log where protocol deviations can be listed. In clinical trials, a protocol is necessary. This document will describe the study in detail, giving the background, design, study schema, eligibility criteria, definitions of outcomes and adverse events, hypotheses, statistical considerations, and stopping criteria, among others. This document should be kept up to date, with any changes as amendments. Protocol deviations should be documented and reported, and the protocol should be available for review. For example, the protocol for the Shaw et al study of crizotinib in ALK-positive patients is available at NEJM.org [ 21 ]. The protocol corresponding to this clinical trial gives the background of the study science; gives the primary objective to demonstrate PF-02341066 (crizotinib) that is superior to standard of care in advanced NSCLC with an event involving the ALK gene locus; and multiple secondary objectives. It also provides the sample size estimation, statistical methods for addressing both primary and secondary objectives, procedures and adverse event reporting. This 96 page protocol document describes the study in much greater detail than the primary outcome paper could, allowing for other researchers to replicate this study in a separate patient population. It also allows for a critical assessment of their study methods and reporting, reviewers can determine whether the planned methods, analysis, and reporting match what is described in the primary outcome paper.

Data Collection and Management

The research question, hypotheses, study design, and analysis plan will dictate the data to be collected for each subject. Typically, the amount and complexity of data collected increases as the translational research phase increases. Regardless of the amount or complexity of the data and/or translational phase, there are some tenets for good data collection:

• each row represents a single observation;
• each column represents a variable of interest;
• each biological replicate should have a unique identifier;
• each technical replicate should be tied to its parent biological replicate;
• collect data to the highest degree of fidelity possible; categories can be created during analysis if needed;
• each piece of information should be stored separately (e.g., follow-up date and status should be in separate columns);
• maintain a data dictionary that spells out all definitions and abbreviations.

Common pitfalls in data collection are the mixing of data scales (e.g., recording temperature in degrees, Fahrenheit for some observation, and Celsius in others); inconsistent documentation for missing values; and analytical results being recorded using different criteria (e.g., pathology: one analyst records actual observed count; another analyst records categories of observed counts, such as <10,000). These pitfalls can be avoided or minimized by ensuring that all research staff who will be recording data are trained on the specific requirements as outlined in the research protocol and/or programming the database interface or spread sheet to only allow certain entries to be made. For example, in Excel®, one could use drop down menus to restrict entries, or in a database form, the interface can be programmed to accept only certain entries (e.g., using drop down menus or forcing the date to be recorded using a particular format). Or, if using a data base manager such as REDCap, data entry can be limited via data type restrictions embedded in the frontend worksheets when users populate in REDCap. One advantage of using a program such as REDCap is the audit trail created each time when data is entered or exported.

Note, if more than one person or site will be collecting data, the use of a spreadsheet is dangerous, since it is difficult to maintain an audit trail, data quality checks are not readily performed and the most recent version from each site/data entry person is extremely difficult to track.

In the basic research phase and small scale, single-center studies (T0, T1, and early T2), a simple spreadsheet is often sufficient to store the data. This is especially true if the data are going to be manually transcribed from a primary source (e.g., lab notebook or direct reading from an instrument) to the data collection instrument by a single person. For example, in Soda et al the nude mouse portion of the study would only require a simple spreadsheet with columns for mouse identifier (ID), group, and presence of tumor if the only goal were to determine which of the expression plasmids resulted in tumor formation [ 18 ]. However, because the study also incorporated an immunoblot analysis, some mechanism of tying the immunoblot with the appropriate mouse was needed. This could be as simple as using the mouse ID as part of the image file name. However, a more sophisticated approach would be to store the full file pathway for each image and subject in separate columns in a spreadsheet or in an image table within a database.

Some T0, T1, and T2 studies may benefit from more complex data storage strategies. Specifically, in genetic and -omics studies. These studies typically generate vast amounts of raw, transformed, and processed data. In addition, the meta-data encompassing demographic data, outcome data, processing dates, and processing software should be captured and stored. The data management issues that characterize these types of studies are beyond the scope of this article and interested readers are encouraged to review outside references [ 30 ].

In cases where multiple types or sources of data are required (e.g., demographic, clinical assessment, labs, etc. – typically mid-to-late T2, T3, and T4) a database approach is the best option to reduce errors and to store data efficiently. Databases store data in individual tables or forms based on the nature of the data. Each table or form must share, at a minimum, a unique subject ID so that data from different tables or forms can be “pulled” together for analysis. In addition, the use of electronic data capture (EDC) software may be useful. An EDC allows users to set up electronic forms, similar to hard copy worksheets. Whether directly inputting data into a database or utilizing front-end forms from an EDC, it is recommended to have a project data coordinator (PDC) on the research team. The PDC is responsible for constructing and maintaining the hard copy or EDC forms and the user interface for the database where research personnel enter the data as necessary.

For multicenter studies, the center (or study site) from which the observation originated must also be collected. In a spreadsheet, this is accomplished by adding a column for center and making the appropriate annotation for each observation. In a database, the center must be recorded on each table or form. With the observation or subject ID (and center ID – if required) in each table or form, a single data set representing the entire study can be constructed. For example, in the ASCEND-5 [ 21 ], ALEX [ 22 ], and ALTA-1L [ 23 ] studies, subjects were recruited from multiple centers and countries. Because the center is nested within its country, a separate data column for country is not necessarily needed.

In T4 studies, researchers are often utilizing national level or large aggregated databases. These datasets can be plagued with their own set of problems, such as: missing data; the collected data not being suitable for answering the research question posed; data coordinators and managers changing overtime and thus the organization of the data and the data collected over time may change; clinical or diagnostic definitions change over time; etc. From a data management perspective, both researchers and programmers must be aware of these limitations. One example of a database that changes over time is the United States Renal Data System which collects data on chronic kidney disease and end stage renal disease [ 31 ]. When purchasing access to this database, the most recent Researchers Guide along with the “What’s New” files (WNF) for each year from 2000 to the last completed year will be provided. The WNF are text files that delineate the changes made to the database structure, such as location of variables, new variables added, variables deleted for the year, and renaming of variables. Building a crosswalk between years and a table of data in common across years prior to querying the data is necessary to ensure as accurate a picture of the data as possible.

Researchers designing studies that utilize multiple forms and tables or receive data from multiple locations should consider employing a data team to include a project manager, data entry personnel, a data coordinator, a data monitor, and maybe an information technology (IT) specialist. The project manager is responsible for understanding the requirements of the protocol and ensuring all sites/researchers are adhering to the protocol and the appropriate data is being collected at the appropriate times. The data coordinator designs and maintains the forms and tables and ensures the appropriate versions are being utilized. Data entry personnel are trained on the protocol requirements and how to transcribe data that is not automatically populated into the database. The data monitor conducts data audits to ensure data quality (correctness and completeness). The data monitor also looks for potential data collection bottlenecks or issues with data collection and relays this to the project manager so that corrective action can be taken. Depending on the research teams’ hardware and software privileges, an IT specialist may also be necessary to navigate the intricacies of storing data electronically.

Data Analysis

The rigor and reproducibility of CTR requires appropriateness of statistical methods for data analysis. The selection of the analytical methods for CTR of all phases should match the study intent, research design, and the type of data being collected for analyses. In this section, we will highlight several important aspects (Table  3 ) that should be considered when identifying statistical analysis methods.

Data collection and analysis considerations.

It is important to understand whether the study intent is exploratory or confirmatory. Exploratory research, often pilot studies, will be conducted when there is little theory or knowledge about the research questions. The goal of the exploratory research will be to generate hypotheses or refine existing hypotheses. The exploratory research may involve multiple outcomes and small sample sizes. In a Phase I/II trial, 137 ALK-rearranged NSCLC patients were recruited to assess the toxicity and efficacy of brigatinib [ 19 ]. Hence, the analyses are mostly descriptive and do not involve hypothesis testing. Contrarily, confirmatory research focuses on identifying reasons that explain the observed phenotypes or phenomenon and involve single or multiple hypothesis tests. When hypotheses involve comparisons among groups, different analytical methods will be used depending on whether the research focus is on equivalence, inferiority or superiority, or differences between groups.

The selection of analysis method also depends on whether the study is experimental or observational. Early phase CTR research, including T0/T1, T2, and some T3 research, may more easily apply experimental design to the study, given they have narrower eligibility criteria and may be conducted in a lab or other more controlled setting. Observational studies, on the other hand, are more likely to be used for later phase CTR, including comparative effectiveness studies in the T3 phase and policy impact assessment studies in the T4 phase. Observational studies tend to involve populations that are more general and assess the research questions in an empirical setting, in which it is not feasible to conduct a controlled experiment. In comparison to the controlled experiment, the observational study may not be able to collect some relevant information due to limited resources or lack of knowledge of when data was collected. Therefore, the covariates from groups under comparisons will not be balanced. Multiple regression or propensity score methodology will be useful to account for the imbalances among these covariates. For treatment studies when treatment assignment cannot be randomized due to ethnic reasons, causal inference techniques or propensity score matching or adjustment are particularly useful for analyses, as seen in the comparative effectiveness study to assess treatment effects of brigatinib vs. ceritinib and alectinib in crizotinib-refractory ALK+ NSCLC patients [ 20 ].

For all phases of CTR, the choice of the analytical method for studies will depend on the data under study, including the type and distribution of outcome variables, and inclusion of hierarchy or repeated measures. All analytical methods have their own assumptions and model diagnostics can be used to assess whether the chosen model is appropriate of an alternative that should be selected. Sensitivity analyses can be conducted to assess the change in the analytical results and inference when different models are applied.

Preparation is key, and investigators should be alert to issues related to study conduct and data collection when planning data analyses, including protocol violation, failure to recruit participants, and missing data. Protocol violations occur when some participants do not conform to the study protocol often in the context of in clinical trials from T2 and T3 CTR. Some examples of protocol violations are: failure to receive the assigned intervention, inappropriately receive another intervention under assessment, receive a prohibited concomitant intervention, or lack assessment of outcome due to loss of follow-up or other reasons [ 32 ]. Intent to treat analyses and per-protocol analyses have been developed to account for protocol violation issues. An intent to treat analysis should be considered the primary analysis and includes all participants randomized, according to randomized group, whereas a per-protocol analyses will include only those participants who complied with the study protocol. Intent to treat analyses will ensure unbiased estimation of intervention effect. Per-protocol analyses are most often conducted as part of a sensitivity analysis and can help assess the effects of intervention without influence of protocol violation or nonadherence. For example, in pragmatic clinical trials from T3 CTR, the participants are heterogeneous and may not adhere to the treatment protocol. The intent to treat analysis is recommended to maintain randomization and minimize the possible confounding when evaluating the intervention effects [ 33 ]. In addition, it is also important to assess how many participants are compliant to the study protocol and the exposure level of intervention for the participants in the intervention group. When participants of the trial received different levels of intervention, an evaluation of the treatment effects based on the actual exposure to intervention like dose-response or treatment on the treated will increase the reproducibility of the study, and provide better advice about the benefit of the evaluated intervention [ 34 ].

The data analyses also need to account for the possibility of unexpectedly high failure in participant recruitment or excessive withdrawals during study conduct. Failure in participant recruitment or excessive withdrawals results in smaller sample size, which could lead to underpowered study. To overcome this underpower challenge, several steps in the analysis can be made to address this. One possibility is that the analyses can be adjusted to use continuous outcomes vs. categorical outcomes, when appropriate. Another is when a comparative study involving multiple groups is under investigation, groups with similar influence in magnitude or directions can be combined to maximize the group size, decrease the number of tests, and optimize the study power. Additionally, exact tests may be preferred over asymptotic tests in small sample situations.

Early drop-out leads to an important missing data issue that adds complexity to the data analyses, especially for later phase CTR. In studies with a large number of subjects, missing data will frequently occur due to various reasons. For example, participants may have nonresponse for questions related to income, administration of medication, or other sensitive questions. In another scenario, the participants may become too fatigued to complete the assessment, or have severe side effects that prevent them to continuing in the study. Analytical methods for handling missing data depend on the type of missing data mechanism that governs the missingness: 1) missing at random when the propensity of missing is not related to observed data or missing data, 2) missing completely at random when the propensity of missing is related to observed data, but not related to missing data, and 3) missing not at random. A popularly used method for handling missing data is complete case analysis, which excludes subjects with missing data. The complete case analysis is especially useful when the study involves a small proportion of missing data. Other commonly considered methods include maximum likelihood, multiple imputation, and full Bayesian methods [ 35 , 36 ].

When the goal of the study is prediction, statistical model validation is crucial for assessing the accuracy of the identified statistical models for predicting outcomes [ 37 ]. Model validation can be conducted internally or externally depending on the availability of external data. In internal validation, the data can be split into a training dataset for developing the prediction model and a validation dataset to validate the prediction performance of the preidentified model. External validation is also very important, though it is not always possible if external data is unavailable. The use of external data that is similar to the testing data will help assess the reproducibility of the prediction model; however, if the external data is quite different to the testing data, the external validation will be useful for assessing the model generalizability.

Development of a statistical analysis plan prior to initiation of the CTR study will help investigators avoid HARKing (hypothesizing after the results are known), and will improve reproducibility and transparency of the study [ 38 ]. HARKing can occur under different scenarios. For example, the investigator may change their a priori hypotheses to different hypotheses with significant results in order to improve their chance of publication. The original study design may fail to collect and/or adjust for important biologic variables, or could conduct many subgroup analyses, or try different choices of cut-points based on data in hand to categorize continuous data, and only report analyses associated with significant results. These practices may lead to irreproducibility issues due to their vulnerability to the small sample sizes or high dependence on individual data. The statistical analysis plan includes key components, such as study objectives, hypotheses to be tested, outcomes and variables that will be collected during the study, and the statistical methods, which contains enough detail to allow other researchers to independently replicate the results.

Developing an analysis plan before data collection will facilitate peer review and maintain continuity of the research team to ensure appropriateness of the analytical method in addressing the research question. The predeveloped analysis plan also helps to prevent confirmation bias (deciding how to handle outliers or missing data, or in meta-analyses which study to include or exclude based on whether the results were in the direction expected or desired by researcher). The predeveloped analysis plan will also allow the researchers to differentiate theory-driven hypotheses instead of data-driven hypotheses. Any adjustment to the statistical analysis plan after data has been collected should be justified and the results from those new analyses should be used cautiously as they were developed post hoc, and may be driven by the collected data.

Results Reporting

A rigorous report of the study can facilitate the study reproducibility and increase study impact. To ensure the rigor of the study report, sufficient detail about the study objectives, design, methods, and materials should be included. When discrepancies occurred between the original study design and the study conduct, it is important to include justification and detailed discussion regarding those discrepancies. Examples include reporting protocol violations, changes in procedures over time, and changes in planned sample size vs. actual sample size. The study report should also include an accurate report of study results and make appropriate study inferences and conclusions without extrapolating the study findings.

A number of guidelines have been developed for different types of CTR studies to improve reporting completeness, transparency, and scientific rigor. The clinical and translational researchers can follow those guidelines based on the CTR study type, or adapt these protocols when reporting their studies. Specifically, the investigators can refer to the Consolidated Standards of Reporting Trials (CONSORT) [ 10 ] for clinical trials, the Strengthen the Reporting of Observational Study in Epidemiology (STROBE) [ 39 ], the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [ 40 ], the Standards for Reporting of Diagnostic Accuracy Studies (STARD) [ 41 ], and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis of Diagnosis (TRIPOD) [ 42 ]. In addition, Prager et al (2019) defined general reporting criteria for the use of academic publishing [ 43 ].

Many CTR studies start as pilot studies with institution support to assess the study feasibility and obtain preliminary data in support of grant preparation and developing future large-scale studies [ 44 ]. Regardless of whether the studies yield positive or negative results, the results from such pilot studies can provide valuable information for refining study processes and hypotheses and guiding future research with respect to design, instruments, and methods. Therefore, the investigators should be encouraged to be transparent regarding their study purpose and publish their study findings even when the study results are negative.

It is important to utilize a rigorous and reproducible approach to advance CTR along the spectrum. In this paper, we focused on how to conduct high quality CTR, and provided general and phase-specific guidelines regarding study design, data collection and management, data analyses, and result reporting. There are many challenges in progressing research along CTR spectrum; and by utilizing best practices in scientific methods, these challenges can be minimized.

Utilizing a team science approach, clinical and translational researchers should involve investigators and translational collaborators from different disciplines from the study’s initiation, and integrate the interdisciplinary expertise and knowledge for forming study concepts, design, and methodology. For example, a team with basic scientists, clinicians, and public health experts will aid in moving the research along CTR spectrum, leading from T0/T1 through T4. It is also important to involve community partners to incorporate their experience in practice, especially for patient-centered outcome research, and facilitate the dissemination of the findings to communities.

There is existing work conducted by Lapchak et al on rigor and reproducibility of stroke translational research. Lapchak focused more on T1 laboratory animal studies to T2 human trials for drug development. They discussed rigor applied to study design. In this paper, we emphasized rigor and reproducibility of clinical translation research under different areas. We considered CTR of all phases, and provided recommendations related to not only study design but also data management and analyses.

To meet the goal of rigorous, reproducible/replicable science, there is a need for transparency [ 45 ]. Transparency allows for clear understanding of design, methods, and analysis. Without transparency, science may be rigorous, but will not be reproducible or replicable. One way to encourage transparency is through the open science initiative [ 46 ]. Through this initiative, researchers are encouraged to preregister studies and to share data and analysis code. With this information available, the scientific community can compare the planned study to the final product. The researcher will be extra careful in all steps of the study because they know the scientific community will have access to the prestudy plan, the actual data, and the analysis code. By preregistering studies or protocols, researchers will be less inclined to “fish” for significant results (also known as p-hacking), hoping for some positive result to publish. Often researchers do not want to make data publicly available with hopes of publishing more themselves. Besides transparency, another argument for publishing deidentified data is that researchers attempting to replicate your study, either through a formal replication process [ 47 ] or as part of a meta-analysis, will lead to many more citations of the original work, increasing its visibility.

To avoid publication bias, all study results, whether positive or negative, should be published. The scientific community is recognizing the importance of publishing studies with negative results, if only to avoid duplication of research efforts and possibly show more promising directions of research. There are now journals devoted to publishing negative results, such as a PLoS ONE collection, called Missing Pieces [ 48 ], which presents inconclusive, negative findings, or failed replications. The Journal of Negative Results in BioMedicine [ 49 ] ceased publication in 2017 because many journals followed their lead in publishing negative studies that they no longer saw a need for this specialized journal.

The majority of research efforts have been made on the early phase CTR. Surkis et al [ 17 ] assessed PubMed ID (PMIDS) of all publications indexed in PubMed to past or present Clinical Translational Science Award (CTSA) grant number for five participating CTSA institutions, and randomly selected 40 papers per institution. Two institutions were invited to manually classify these 200 studies into phases along the CTR spectrum using agreed criteria. Out of 185 papers with clear classification, 106(57.3%) papers belonged to T0 basic science category, while 18(9.7%) papers were classified as T1/2 CTR, and 44(23.8%) were classified as T3/T4 CTR. This evidence implied that there may be a good proportion of T0 research that fail to advance to later phase CTR, or more resources should be allocated to promote CTR research advancement.

The advancement of CTR is not necessarily sequential across spectrums. As shown in Figure  1 , the scientific learning from different parts of the spectrum of CTR can feed into each other at any level and promote the CTR research of lower and higher stages. For example, the recent breakout of COVID-19 infection has motivated CTR of different phases to be undertaken in parallel to understand the mechanism of the virus, identify strategies for infection prevention, and treatment of COVID-19. A T0 CTR by Lu et al (2020) studied the phylogenetic sequencing of the coronavirus to understand the similarity and difference between COVID-19 and other coronaviruses, like MERS and SARS, as well as the outbreaks’ origins [ 50 ]. Simultaneously, a multi-institutional phase 2 trial is underway [ 51 ] to assess the efficacy of Remdsivir in treating COVID-19 infections. Remdsivir, developed by Gilead Sciences, was selected due to its effects in treating other coronaviruses in animal models. Wu et al (2020) did a T4 epidemiologic study to estimate the domestic and global public health risks of coronavirus infection epidemics. Their study indicated the importance of developing a large scale public health COVID-19 intervention to avoid independent self-sustaining outbreaks in major cities globally [ 52 ].

In summary, sound scientific practices, starting with rigorous study design, transparency, and team efforts can greatly enhance CTR. Investigators new to CTR should familiarize themselves with best practices for study design, data collection, data analysis, and results reporting to allow timely advances in their field of research. Research teams that incorporate investigators along the spectrum of CTR phases, biostatisticians, and, depending on phase, community partners can lead to successful CTR research.

Acknowledgements

This work was supported by the National Institute of General Medical Sciences, Grant No. 5U54GM115458. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Health.

Disclosures

The authors declare no conflict of interest.

Understanding Supervisors’ Commentary Practices in Doctoral Research Proposal Writing: A Hong Kong Study

• Published: 20 December 2012
• Volume 22 , pages 473–483, ( 2013 )

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• Shulin Yu 1 , 2 &
• Icy Lee 1  

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While much feedback research in L1 and L2 writing has been conducted in pre-university and university contexts, little attention has been paid to supervisors’ comments at the graduate level. Specifically, the nature and role of supervisors’ commentary on the writing of graduate-level academic genre is under-explored. Designed to fill such an important void in the existing research literature, the present study, framed by the socio-cultural theory, aims to explore the nature and role of supervisors’ written comments on doctoral research proposals. A primarily qualitative analysis of the drafts of three doctoral research proposals, the written comments in the drafts of these proposals, and interview data with three Phd applicants and two prospective PhD supervisors revealed that supervisors’ comments on doctoral research proposals were primarily feedback-oriented rather than assessment-focused. Such a commentary practice played an important role in facilitating the initiation of the applicants into the academic community through scaffolding the academic writing process, building a quasi-supervisory relationship, as well as enhancing motivation and confidence. The paper concludes with the implications of the study, as well as suggestions for future research.

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Appendix A: Supervisor Interview Guide

Have you been approached by potential PhD applicants to comment on their proposals? Do you give feedback to all the proposals you receive from the applicants? If not, in what situations do you decide to give or not to give comments? Why?

If you give comments on the first draft of a research proposal and the applicant makes revisions and sends the new version to you again, what will you do? Continue to give comments to these applicants (e.g. Wang/Li/Sun)? Why or why not? How many drafts will you comment on?

What is your purpose of providing comments on PhD application research proposals like those from Wang/Li/Sun? Do you think potential supervisors should give comments on doctoral proposals at the application stage? Why or why not?

What do you think of the role of doctoral research proposals in the application process? What could you know about the applicants (e.g. Wang/Li/Sun) from their research proposals?

How do you give comments on the doctoral research proposals? What types of comments do you usually give? Do you usually provide judgments on these proposals or offer suggestions to improve them? Or both? Why? What about proposals from Wang/Li/Sun? Which types of comments are more important? Why?

What concerns or problems, if any, do you have in providing comments on doctoral research proposals?

Appendix B: PhD Applicant Interview Guide

In your opinion, what is the purpose of submitting a research proposal when you make a PhD application? What do you want to show by your proposal?

Do you hope to get comments from the supervisors? Why or why not?

What types of comments do you hope to get from the potential supervisors? Judgments on your proposal or suggestions to improve your proposal? Praise or negative comments? Or any other types? Why?

What aspects/areas of PhD research proposals do you hope the supervisors comment on? Why?

What do you think of the comments on your PhD application research proposal given by the potential supervisors? To what extent and how do the supervisors’ comments help you improve your proposal?

What types of comments do you find most useful? Judgments on your proposal or suggestions to improve your proposal? Praise or negative comments? Or any other types?

Do you agree to all the comments from the supervisors? If not, when do you disagree? How do you deal with these comments?

What do you think of the following comments (a few assessment-oriented comments extracted from the proposals are shown to the interviewees)? Are they useful? Why? What do you think when you receive such types of comments? How did this type of comments influence your revisions?

What do you think of the following comments (a few feedback-oriented comments extracted from the proposals are shown to the interviewees)? What do you think when you receive such types of comments? How this type of comments influenced your revisions?

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Yu, S., Lee, I. Understanding Supervisors’ Commentary Practices in Doctoral Research Proposal Writing: A Hong Kong Study. Asia-Pacific Edu Res 22 , 473–483 (2013). https://doi.org/10.1007/s40299-012-0046-9

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Human tumor suppressor PDCD4 directly interacts with ribosomes to repress translation

• Xianwen Ye 1   na1 ,
• Zixuan Huang 1   na1 ,
• Mengjiao Wang 1 ,
• Wanyu Meng 1 ,
• Maojian Miao 1 &
• Jingdong Cheng   ORCID: orcid.org/0000-0003-4442-377X 1  

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Protein translation regulation is a crucial and tightly controlled regulatory process that contributes to phenotypic diversity among cells with identical or similar genotypes. Across all the steps of translation, initiation is the most energy- and time-intensive stage. In eukaryotes, this process starts with the assembly of the 43S preinitiation complex (PIC), comprising 40S ribosome, eIF1, eIF1A, the eIF3 complex (eIF3A-M), eIF5, and the ternary complex (TC, consisting of eIF2α/β/γ, tRNA iMet , and GTP). After 43S PIC assembly, the eIF4F complex (consisting of the DEAD box helicase eIF4A, eIF4B, eIF4E, and eIF4G) is recruited, along with the mRNA, to form the 48S initiation complex (IC). Following the recognition of the first cognate AUG start codon by the 48S IC, the 60S ribosome joins to initiate translation elongation. This process requires a coordination of multiple complexes and factors for rigorous regulation of protein translation. 1 Importantly, cells employ various mechanisms to inhibit translation initiation in response to environmental stress conditions, yet the detailed molecular mechanisms have not been fully elucidated.

PDCD4 functions as a translational repressor by interacting with the initiation factor eIF4A through the MA3 domains (Fig.  1a ), thereby preventing its incorporation into the eIF4F complex. 2 , 3 Besides this crucial role, its mechanism of action is currently poorly understood. To investigate the role of PDCD4 in translation regulation, wild-type PDCD4 with a C-terminal GFP tag was expressed in PDCD4 knockout human DLD-1 cells. Consistent with previous reports, 4 PDCD4 predominantly resides in the nucleoplasm under normal growth conditions. However, stress exposure such as DNA damage or nutrient starvation induced the translocation of PDCD4 into the cytoplasm (Supplementary information, Fig. S 1 ). Subsequently, we fractionated cell lysates from wild-type human HEK293T cells under glucose starvation conditions (treated for 24 h) using a 10%–40% sucrose gradient, enabling the analysis of endogenous PDCD4 distribution among different ribosomal populations. Compared to PDCD4 in the nucleus of normally growing cells, PDCD4 mainly (> 10-fold enriched) associated with the 40S ribosome peak in the cytosol under the starvation condition (Fig.  1b ). We did notice a small amount of PDCD4 in the cytoplasm of the control cells. While we cannot entirely dismiss the possibility that a small amount of PDCD4 might be associated with the 40S ribosome in the cytosol, it is plausible that this association results from the rapid export of PDCD4 triggered by the stress conditions during cell preparation for lysis. Supporting this hypothesis, we observed a rapid export of PDCD4 during the incubation with buffer solutions (Supplementary information, Fig. S 1b ).

a Schematic showing the domain architecture of the human PDCD4 protein. b Human HEK293T cells were either subjected to 24 h of glucose starvation or left untreated. Subsequently, the cytoplasmic lysates were subjected to fractionation on a sucrose gradient ranging from 10% to 40%. PDCD4 antibody was used to detect the distribution of the endogenous PDCD4 over different ribosome populations. Intensity, normalized to a maximum of 1.00, was calculated using ImageJ software. c Cryo-EM maps of PDCD4–40S (left), PDCD4–eIF3G–40S (middle) and PDCD4–43S (right). The composite maps shown are derived from multi-body refinement and after local resolution filtering using either Relion (PDCD4–43S) or DeepEMhancer (PDCD4–40S and PDCD4–eIF3G–40S). The density of the PDCD4–eIF4A complex in the PDCD4–eIF3G–40S map is shown at the lower contour level. The label of the PDCD4–eIF4A complex is boxed out, indicating that it represents only a putative model. d Molecular model of the PDCD4–43S complex highlighting the positions of the PDCD4–eIF4A complex and eIF3G (blue). Helix 18 and helix 34 of the 18S rRNA are colored in yellow, while uS3 and uS5 are represented in blue and green, respectively. e Cryo-EM map of the PDCD4–43S state filtered according to its local resolution. A zoomed insert highlights the closed “latch” region (red circle) within the mRNA channel, in contrast to the open latch in the 43S PIC “State III”. f Overview of the interactions of PDCD4 RBR with the mRNA channel. The 40S is shown as a colored density map derived from the PDCD4–43S state, while the RBR model is shown as sticks fitted into density (transparent) and colored in rainbow. The complete sequence of the RBR is also shown on the right. g – i  Detailed interactions between RBR and the 40S subunit: R102 stacks with base C1701, and R103 interacts with the 18S rRNA backbone ( g ); R110 stacks with base C1698 ( h ); W124 inserts into a hydrophobic pocket in uS3 ( i ). j PDCD4 spatially clashes with the mRNA in the 48S IC (PDB: 7QP7). The mRNA entry channel is indicated by red dashed lines. k A region (amino acids 148–159) of PDCD4 forms an antiparallel β-sheet with the RRM domain of eIF3G (blue). l The overall conformation of the PDCD4–eIF4A complex in the PDCD4–43S state. m Zoomed view highlighting the direct contact between MA3c of PDCD4 and eIF3I (purple). The fitted model for eIF3I is shown to better illustrate its position. n , o Co-IP experiments were performed in human HEK293T cells transiently transfected with PDCD4-Flag and its mutants using anti-Flag beads. Interactions between PDCD4-Flag and the 40S ribosome ( n ) or eIF4A ( o ) were detected by immunoblotting for uS5 and eIF4A, respectively. CTRL: Control. The PDCD4 protein was detected by immunoblotting for the Flag tag. p Proposed model for PDCD4-mediated inhibition of 43S PIC assembly.

We next pursued the structural investigation by using a tetracycline-inducible PDCD4-Flag protein as bait to purify its associated native complexes from densely cultured human HEK 293/Flp-In/T-Rex cells. Our ensemble single-particle cryo-EM analysis revealed three distinct structures, termed PDCD4–40S, PDCD4–eIF3G–40S, and PDCD4–43S (Fig.  1c ). We were able to resolve the structures of PDCD4–40S and PDCD4–eIF3G–40S at resolutions of 2.9 Å and 3.2 Å, respectively, while the reconstruction of the PDCD4–43S remained a lower resolution (Fig.  1c ; Supplementary information, Figs. S 2 –S 4 , Tables S 1 , S 2 and Data S 1 ). However, serendipitously, we obtained an identical PDCD4-containing 43S state at 3.6 Å resolution from the sample that was derived from cycloheximide (CHX)-treated (CHX was added to prevent ribosome runoff) HEK 293/Flp-In/T-Rex cells using the tetracycline-inducible PYM1-Flag as bait (Fig.  1c ; Supplementary information, Figs. S 3 –S 6 , Tables S 1 , S 2 and Data S 1 ). PYM1 was believed to remove all associated exon junction complexes during the pioneer round of translation. However, for unknown reasons, PDCD4 was enriched in the PYM1 pull-out sample, as confirmed by the MS analysis (Supplementary information, Data S 1 ).

In all three identified states, an N-terminal segment of PDCD4 (amino acids 100–145) is positioned within the mRNA entry channel (Fig.  1c ). We thus refer to this segment as the ribosome-binding region (RBR, Fig.  1a ). The PDCD4–40S state represents an idle 40S ribosomal subunit bound to PDCD4 via the RBR, while the rest of PDCD4 is invisible due to its flexibility (Fig.  1c ; Supplementary information, Fig. S 7 ). The structure of the PDCD4–eIF3G–40S state closely resembles that of PDCD4–40S state but with additional density for the initiation factor eIF3G adjacent to the mRNA entry site; this is the same position as previously observed in the 43S PIC (Fig.  1c ; Supplementary information, Fig. S 7 ). 5 , 6 , 7 , 8 In the PDCD4–43S state, an extra structured and well-resolved density was observed, allowing the assignment of the C-terminal MA3 domains of PDCD4 and one copy of eIF4A (Fig.  1c, d ; Supplementary information, Fig. S 7 ). However, the corresponding density in the PDCD4–eIF3G–40S structure was less highly resolved, suggesting the notable flexibility in this region at this state (Fig.  1c ; Supplementary information, Fig. S 7 ).

In the PDCD4–43S state, the 43S PIC closely resembles the previously described intermediate “State I” of the 43S PIC assembly, characterized by the presence of the eIF1 and eIF3 complex but the absence of the TC and eIF1A (Fig.  1e ; Supplementary information, Fig. S 8a ). 7 Transitioning from “State I” to the fully assembled 43S PIC “State III” necessitates the recruitment of initiation factors eIF1A and TC, leading to the opening of the mRNA entry channel at the latch region (latch open) (Supplementary information, Fig. S 8b ). 7 , 9 However, in PDCD4–43S, the latch remains closed (latch closed), as indicated by the proximity of uS3 and h18 (Fig.  1e ). This closed arrangement is stabilized by the RBR of PDCD4 occupying the mRNA entry channel (Fig.  1e ). Moreover, as revealed by 3D classification, PDCD4 was exclusively found in the early “State I” but not in the latter “State II” or “State III” states of the 43S PIC assembly (Supplementary information, Fig. S 3 ). This finding suggests that PDCD4 plays a role in inhibiting the early phase of 43S PIC assembly.

Specifically, we observed that amino acids 100–145 of PDCD4 RBR are positioned within the 40S mRNA entry channel, extending from the decoding center (DC) on the intersubunit side through the channel toward the mRNA entry side (Fig.  1f ). The RBR region can be divided into two segments: the upstream segment (amino acids 100–112), featuring a basic residue-rich “Motif 1”, and the second segment (amino acids 113–143), which includes “Motif 2”, characterized by a conserved “WG” dipeptide (Supplementary information, Fig. S 9 ). Both segments exhibit intensive interactions with the mRNA channel wall formed by uS3, uS5, and the 18S rRNA (Fig.  1g–i ; Supplementary information, Fig. S 10a–f ). Remarkably, PDCD4 RBR not only sterically blocks mRNA and initiator tRNA iMet binding to prevent the formation of the 48S IC, 6 , 8 but also coincides with the position of eIF1A in 43S/48S complexes (Fig.  1j ; Supplementary information, Fig. S 10g, h ), 6 , 7 , 8 explaining the absence of eIF1A in all our structures. Additionally, PDCD4 adopts a very similar conformation to the previously published ribosome hibernation factors SERBP1 and HABP4 (Supplementary information, Fig. S 10i ). 10 These proteins not only bind to the same surface on the 40S ribosome but also share very high sequence similarity with PDCD4, especially at Motifs 1 and 2 (Supplementary information, Fig. S 9 ). Moreover, PDCD4 shares binding sites with the general translation inhibitor NSP1 from SARS-CoV-2 (Supplementary information, Fig. S 10j ). 10 These findings suggest that PDCD4 RBR occupies the mRNA entry channel and prevents further assembly of 43S PIC by likely hindering eIF1A and TC binding.

In addition to PDCD4 RBR, the PDCD4–43S revealed the positioning of the C-terminal MA3 domains of PDCD4 and one copy of eIF4A above the mRNA entry site. The main bridge between the 40S ribosome and the C-terminal MA3 domains is eIF3G. Stable contact between eIF3G and PDCD4 is established via a short stretch of PDCD4 (amino acids 148–159) adjacent to the RBR, which forms an antiparallel β-sheet with the RNA recognition motif (RRM) of eIF3G (Fig.  1k ; Supplementary information, Fig. S 5c–e ). This positions the MA3 domains and eIF4A between the 40S head and the β-propeller domain of the initiation factor eIF3I. The confirmation of the PDCD4–eIF4A complex in the PDCD4–43S state is very similar to the crystal structure of the eIF4A–PDCD4 complex (Fig.  1l ; Supplementary information, Fig. S 11a ). However, we observed that only one copy of eIF4A bound to both MA3 domains of PDCD4 (two-MA3 binding mode) (Supplementary information, Fig. S 11a ). 11 , 12 The other copy that only binds to MA3c (MA3c binding mode) in the crystal structures is missing in the PDCD4–43S state (Fig.  1m ). Instead, the MA3c domain directly contacts eIF3I, which is incompatible with eIF4A positioning in the crystal structures (Supplementary information, Fig. S 11b ). 11 , 12 These results confirm that the “two-MA3 binding mode” in our PDCD4–43S structure is reflective of the physiological interaction mode of the PDCD4–eIF4A complex.

A study by Querido et al. 5 observed two eIF4A molecules in a fully assembled 48S IC, one at the mRNA exit site 8 and the other, similar to our findings, at the mRNA entry site. Although the second eIF4A is likely to be active for mRNA unwinding in this position, the eIF4A observed in our PDCD4–43S state is in an inhibited state (PDCD4 blocks the mRNA-binding interface of eIF4A) (Supplementary information, Fig. S 11c ), complexed with PDCD4 at the mRNA entry site (Fig.  1d ). We thus suggest that this position at the mRNA entry site serves both as a recruitment hub and an inhibition platform for eIF4A.

Based on our structural analysis, we performed mutagenesis studies to validate our structural findings. This included the following mutations: “2A”, “3A” and “5A”, targeting “Motif 1/2”; Δ150–160, disrupting the eIF3G interaction; Δ100–160, removing the RBR and eIF3G interacting region; “M2A” and “4A”, impairing the “two-MA3 binding mode” interface; 12 and “C2A”, impairing the “MA3c binding mode” interface 12 (Supplementary information, Fig. S 12a ). Compared with the wild-type PDCD4, co-immunoprecipitation (Co-IP) experiments showed that the “2A”, “3A” and “5A” mutants completely lost their ability to bind to the ribosome, as indicated by immunoblotting of the ribosomal protein uS5 (Fig.  1n ). The mutants “M2A” and “4A” completely lost eIF4A binding but retained the interaction with the initiation factor eIF3B and the ribosome, while “C2A” exhibited only reduced binding (Fig.  1o ), confirming the “two-MA3 binding mode” hypothesis. 12 Consistent with the Co-IP results, the “2A”, “3A”, “5A” and Δ100–160 mutations abolished their comigration with the 40S peak in the sucrose gradient assay (Supplementary information, Fig. S 12b ). Notably, the Δ150–160 mutation only showed a decreased association, suggesting that eIF3G plays a nonessential role (Supplementary information, Fig. S 12b ). However, none of the mutations in the MA3 domains (“M2A”, “C2A” and “4A”) affected its association with the 40S ribosome (Supplementary information, Fig. S 12b ). Collectively, these data underscore that both “Motif 1” and “Motif 2” in the RBR of PDCD4 are crucial for the association with the ribosome, irrespective of eIF4A interaction.

Based on our studies, we propose a model for the function of PDCD4 in inhibiting translation initiation (Fig.  1p ): during initiation, the free idle 40S subunit (e.g., after successful recycling, phase 1) associates with eIF3G to form an intermediate (phase 2). Subsequently, eIF1 and the remaining components of the eIF3 complex are recruited to form the 43S “State I” (phase 3), and the final recruitment of eIF1A and TC leads to the canonical 43S “State III” (phase 4). Under stress, PDCD4 relocates to the cytoplasm and disrupts phases 1–3 by occupying the mRNA entry channel with its RBR and positioning eIF4A in its inhibited form via its MA3 domains (Fig.  1p ). Thus, PDCD4 not only hampers the activity of eIF4A and the function of the eIF4F complex, but also directly inhibits the ribosome itself, independent of the PDCD4–eIF4A interaction. Our research establishes a link between tumorigenesis and the suppression of translation initiation, providing valuable insights into the underlying mechanisms of translation regulation.

Data availability

All cryo-EM maps and molecular models have been deposited in the Electron Microscopy Data Bank (EMDB) with accession IDs EMD-38752 (state PDCD4–40S), EMD-38753 (state PDCD4–eIF3G–40S), EMD-38754 (state PDCD4–43S), and in the Protein Data Bank (PDB) with accession codes 8XXL (state PDCD4–40S), 8XXM (state PDCD4–eIF3G–40S), 8XXN (state PDCD4–43S).

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Acknowledgements

We thank Dr. Thomas Becker, Prof. Roland Beckmann, and Prof. Fei Xavier Chen for their constructive discussion and invaluable contributions to editing this manuscript. We thank the Center of Cryo-EM at Fudan University for technical support. This research was supported by grants from the National key R&D Program of China (2023YFC2413204), the National Natural Science Foundation of China (32371350), and Shanghai Municipal Science and Technology Commission grants (22410712400, 22ZR1413600) to J.C.

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Minhang Hospital & Institutes of Biomedical Sciences, Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Fudan University, Shanghai, China

Xianwen Ye, Zixuan Huang, Yi Li, Mengjiao Wang, Wanyu Meng, Maojian Miao & Jingdong Cheng

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Contributions

X.Y., Z.H. and J.C. conceived the study. X.Y., Z.H. and M.W. prepared the samples for cryo-EM analysis. Z.H. and Y.L. collected cryo-EM data. J.C. and Y.L. processed the data, and built and refined the models. X.Y., Z.H., W.M. and M.M. checked the distribution of PDCD4 under stress conditions. X.Y., Z.H. and J.C. analyzed and interpreted the structures. X.Y., Z.H. and J.C. wrote the manuscript. All authors commented on the manuscript.

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Correspondence to Jingdong Cheng .

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Supplementary information

Supplementary information, materials and methods, supplementary information, fig. s1, supplementary information, fig. s2, supplementary information, fig. s3, supplementary information, fig. s4, supplementary information, fig. s5, supplementary information, fig. s6, supplementary information, fig. s7, supplementary information, fig. s8, supplementary information, fig. s9, supplementary information, fig. s10, supplementary information, fig. s11, supplementary information, fig. s12, supplementary information, table s1, supplementary information, table s2, supplementary information, data s1, rights and permissions.

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Ye, X., Huang, Z., Li, Y. et al. Human tumor suppressor PDCD4 directly interacts with ribosomes to repress translation. Cell Res (2024). https://doi.org/10.1038/s41422-024-00962-z

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Received : 06 December 2023

Accepted : 08 April 2024

Published : 19 April 2024

DOI : https://doi.org/10.1038/s41422-024-00962-z

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