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Research Methodology: An Introduction

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Egesa Romans

According to Mugenda & Mugenda (2010), research is process of carrying out a diligent inquiry or a critical examination of a given phenomenonexhaustive study that follows some logical sequence. Mouly defines research as a process of arriving at effective solutions to problems through systematic collection, analysis and interpretation of data. Research also involves a critical analysis of existing conclusions or theories with regard to newly discovered facts Research is the continued search for knowledge and understanding of the world around us. Clifford Woody argues that research is the process of designing and redefining problems, formulating hypothesis or suggested solutions, collecting, organizing and evaluating data, making deductions and reaching conclusions and carefully testing the conclusions to determine whether they fit the formulated hypothesis.

Bakhtawer Zain

Research is any original and systematic investigation undertaken to increase knowledge and understanding and to establish facts and principles. It comprises the creation of ideas and generation of new knowledge that lead to new and improved insights and the development of new material, devices, products and processes. The word " research " perhaps originates from the old French word recerchier that meant to 'search again'. It implicitly assumes that the earlier search was not exhaustive and complete and hence a repeated search is called for.

Educational Journal

Ganizani Nkhambule

In the simplest of terms, the research definition is a process of seeking out knowledge. This knowledge can be new, or it can support an already known fact. The purpose of research is to inform and is based on collected and analyzed data. This exploration occurs systematically, where it is either tested or investigated to add to a body of knowledge. Research is a systematic and scientific approach to understanding the world around us. It is a process of inquiry that involves the collection and analysis of data to answer questions or solve problems.

Oscar Masaka

lulu farhan

Akshay Jain

Research may be very broadly defined as systematic gathering of data and information and its analysis for advancement of knowledge in any subject. research attempts to find answer intellectual and practical questions through application of systematic methods. Types of research can be classified in many different ways.

Melkammesrat Netsanet

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Research methodology : a step-by-step guide for beginners

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• What Is a Research Methodology? | Steps & Tips

What Is a Research Methodology? | Steps & Tips

Published on August 25, 2022 by Shona McCombes and Tegan George. Revised on November 20, 2023.

Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation , or research paper , the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research and your dissertation topic .

It should include:

• The type of research you conducted
• How you collected and analyzed your data
• Any tools or materials you used in the research
• How you mitigated or avoided research biases
• Why you chose these methods
• Your methodology section should generally be written in the past tense .
• Academic style guides in your field may provide detailed guidelines on what to include for different types of studies.
• Your citation style might provide guidelines for your methodology section (e.g., an APA Style methods section ).

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Table of contents

How to write a research methodology, why is a methods section important, step 1: explain your methodological approach, step 2: describe your data collection methods, step 3: describe your analysis method, step 4: evaluate and justify the methodological choices you made, tips for writing a strong methodology chapter, other interesting articles, frequently asked questions about methodology.

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Your methods section is your opportunity to share how you conducted your research and why you chose the methods you chose. It’s also the place to show that your research was rigorously conducted and can be replicated .

It gives your research legitimacy and situates it within your field, and also gives your readers a place to refer to if they have any questions or critiques in other sections.

You can start by introducing your overall approach to your research. You have two options here.

Option 1: Start with your “what”

What research problem or question did you investigate?

• Aim to describe the characteristics of something?
• Explore an under-researched topic?
• Establish a causal relationship?

And what type of data did you need to achieve this aim?

• Quantitative data , qualitative data , or a mix of both?
• Primary data collected yourself, or secondary data collected by someone else?
• Experimental data gathered by controlling and manipulating variables, or descriptive data gathered via observations?

Option 2: Start with your “why”

Depending on your discipline, you can also start with a discussion of the rationale and assumptions underpinning your methodology. In other words, why did you choose these methods for your study?

• Why is this the best way to answer your research question?
• Is this a standard methodology in your field, or does it require justification?
• Were there any ethical considerations involved in your choices?
• What are the criteria for validity and reliability in this type of research ? How did you prevent bias from affecting your data?

Once you have introduced your reader to your methodological approach, you should share full details about your data collection methods .

Quantitative methods

In order to be considered generalizable, you should describe quantitative research methods in enough detail for another researcher to replicate your study.

Here, explain how you operationalized your concepts and measured your variables. Discuss your sampling method or inclusion and exclusion criteria , as well as any tools, procedures, and materials you used to gather your data.

Surveys Describe where, when, and how the survey was conducted.

• How did you design the questionnaire?
• What form did your questions take (e.g., multiple choice, Likert scale )?
• Were your surveys conducted in-person or virtually?
• What sampling method did you use to select participants?
• What was your sample size and response rate?

Experiments Share full details of the tools, techniques, and procedures you used to conduct your experiment.

• How did you design the experiment ?
• How did you recruit participants?
• How did you manipulate and measure the variables ?
• What tools did you use?

Existing data Explain how you gathered and selected the material (such as datasets or archival data) that you used in your analysis.

• Where did you source the material?
• How was the data originally produced?
• What criteria did you use to select material (e.g., date range)?

The survey consisted of 5 multiple-choice questions and 10 questions measured on a 7-point Likert scale.

The goal was to collect survey responses from 350 customers visiting the fitness apparel company’s brick-and-mortar location in Boston on July 4–8, 2022, between 11:00 and 15:00.

Here, a customer was defined as a person who had purchased a product from the company on the day they took the survey. Participants were given 5 minutes to fill in the survey anonymously. In total, 408 customers responded, but not all surveys were fully completed. Due to this, 371 survey results were included in the analysis.

• Information bias
• Omitted variable bias
• Regression to the mean
• Survivorship bias
• Undercoverage bias
• Sampling bias

Qualitative methods

In qualitative research , methods are often more flexible and subjective. For this reason, it’s crucial to robustly explain the methodology choices you made.

Be sure to discuss the criteria you used to select your data, the context in which your research was conducted, and the role you played in collecting your data (e.g., were you an active participant, or a passive observer?)

Interviews or focus groups Describe where, when, and how the interviews were conducted.

• How did you find and select participants?
• How many participants took part?
• What form did the interviews take ( structured , semi-structured , or unstructured )?
• How long were the interviews?
• How were they recorded?

Participant observation Describe where, when, and how you conducted the observation or ethnography .

• What group or community did you observe? How long did you spend there?
• How did you gain access to this group? What role did you play in the community?
• How long did you spend conducting the research? Where was it located?
• How did you record your data (e.g., audiovisual recordings, note-taking)?

Existing data Explain how you selected case study materials for your analysis.

• What type of materials did you analyze?
• How did you select them?

In order to gain better insight into possibilities for future improvement of the fitness store’s product range, semi-structured interviews were conducted with 8 returning customers.

Here, a returning customer was defined as someone who usually bought products at least twice a week from the store.

Surveys were used to select participants. Interviews were conducted in a small office next to the cash register and lasted approximately 20 minutes each. Answers were recorded by note-taking, and seven interviews were also filmed with consent. One interviewee preferred not to be filmed.

• The Hawthorne effect
• Observer bias
• The placebo effect
• Response bias and Nonresponse bias
• The Pygmalion effect
• Recall bias
• Social desirability bias
• Self-selection bias

Mixed methods

Mixed methods research combines quantitative and qualitative approaches. If a standalone quantitative or qualitative study is insufficient to answer your research question, mixed methods may be a good fit for you.

Mixed methods are less common than standalone analyses, largely because they require a great deal of effort to pull off successfully. If you choose to pursue mixed methods, it’s especially important to robustly justify your methods.

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Next, you should indicate how you processed and analyzed your data. Avoid going into too much detail: you should not start introducing or discussing any of your results at this stage.

In quantitative research , your analysis will be based on numbers. In your methods section, you can include:

• How you prepared the data before analyzing it (e.g., checking for missing data , removing outliers , transforming variables)
• Which software you used (e.g., SPSS, Stata or R)
• Which statistical tests you used (e.g., two-tailed t test , simple linear regression )

In qualitative research, your analysis will be based on language, images, and observations (often involving some form of textual analysis ).

Specific methods might include:

• Content analysis : Categorizing and discussing the meaning of words, phrases and sentences
• Thematic analysis : Coding and closely examining the data to identify broad themes and patterns
• Discourse analysis : Studying communication and meaning in relation to their social context

Mixed methods combine the above two research methods, integrating both qualitative and quantitative approaches into one coherent analytical process.

Above all, your methodology section should clearly make the case for why you chose the methods you did. This is especially true if you did not take the most standard approach to your topic. In this case, discuss why other methods were not suitable for your objectives, and show how this approach contributes new knowledge or understanding.

In any case, it should be overwhelmingly clear to your reader that you set yourself up for success in terms of your methodology’s design. Show how your methods should lead to results that are valid and reliable, while leaving the analysis of the meaning, importance, and relevance of your results for your discussion section .

• Quantitative: Lab-based experiments cannot always accurately simulate real-life situations and behaviors, but they are effective for testing causal relationships between variables .
• Qualitative: Unstructured interviews usually produce results that cannot be generalized beyond the sample group , but they provide a more in-depth understanding of participants’ perceptions, motivations, and emotions.
• Mixed methods: Despite issues systematically comparing differing types of data, a solely quantitative study would not sufficiently incorporate the lived experience of each participant, while a solely qualitative study would be insufficiently generalizable.

Remember that your aim is not just to describe your methods, but to show how and why you applied them. Again, it’s critical to demonstrate that your research was rigorously conducted and can be replicated.

1. Focus on your objectives and research questions

The methodology section should clearly show why your methods suit your objectives and convince the reader that you chose the best possible approach to answering your problem statement and research questions .

2. Cite relevant sources

Your methodology can be strengthened by referencing existing research in your field. This can help you to:

• Show that you followed established practice for your type of research
• Discuss how you decided on your approach by evaluating existing research
• Present a novel methodological approach to address a gap in the literature

3. Write for your audience

Consider how much information you need to give, and avoid getting too lengthy. If you are using methods that are standard for your discipline, you probably don’t need to give a lot of background or justification.

Regardless, your methodology should be a clear, well-structured text that makes an argument for your approach, not just a list of technical details and procedures.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Measures of central tendency
• Chi square tests
• Confidence interval
• Quartiles & Quantiles

Methodology

• Cluster sampling
• Stratified sampling
• Thematic analysis
• Cohort study
• Peer review
• Ethnography

Research bias

• Implicit bias
• Cognitive bias
• Conformity bias
• Hawthorne effect
• Availability heuristic
• Attrition bias

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

In a scientific paper, the methodology always comes after the introduction and before the results , discussion and conclusion . The same basic structure also applies to a thesis, dissertation , or research proposal .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

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Title: deepfake generation and detection: a benchmark and survey.

Abstract: In addition to the advancements in deepfake generation, corresponding detection technologies need to continuously evolve to regulate the potential misuse of deepfakes, such as for privacy invasion and phishing attacks. This survey comprehensively reviews the latest developments in deepfake generation and detection, summarizing and analyzing the current state of the art in this rapidly evolving field. We first unify task definitions, comprehensively introduce datasets and metrics, and discuss the development of generation and detection technology frameworks. Then, we discuss the development of several related sub-fields and focus on researching four mainstream deepfake fields: popular face swap, face reenactment, talking face generation, and facial attribute editing, as well as foreign detection. Subsequently, we comprehensively benchmark representative methods on popular datasets for each field, fully evaluating the latest and influential works published in top conferences/journals. Finally, we analyze the challenges and future research directions of the discussed fields. We closely follow the latest developments in this https URL .

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• Published: 01 April 2024

Complexity of avian evolution revealed by family-level genomes

• Josefin Stiller   ORCID: orcid.org/0000-0001-6009-9581 1 ,
• Shaohong Feng   ORCID: orcid.org/0000-0002-2462-7348 2 , 3 , 4 , 5 ,
• Al-Aabid Chowdhury 6 ,
• Iker Rivas-González   ORCID: orcid.org/0000-0002-0515-0628 7 ,
• David A. Duchêne   ORCID: orcid.org/0000-0002-5479-1974 8 ,
• Qi Fang   ORCID: orcid.org/0000-0002-9181-8689 9 ,
• Yuan Deng 9 ,
• Alexey Kozlov   ORCID: orcid.org/0000-0001-7394-2718 10 ,
• Alexandros Stamatakis   ORCID: orcid.org/0000-0003-0353-0691 10 , 11 , 12 ,
• Santiago Claramunt   ORCID: orcid.org/0000-0002-8926-5974 13 , 14 ,
• Jacqueline M. T. Nguyen   ORCID: orcid.org/0000-0002-3076-0006 15 , 16 ,
• Simon Y. W. Ho   ORCID: orcid.org/0000-0002-0361-2307 6 ,
• Brant C. Faircloth   ORCID: orcid.org/0000-0002-1943-0217 17 ,
• Julia Haag   ORCID: orcid.org/0000-0002-7493-3917 10 ,
• Peter Houde   ORCID: orcid.org/0000-0003-4541-5974 18 ,
• Joel Cracraft   ORCID: orcid.org/0000-0001-7587-8342 19 ,
• Metin Balaban 20 ,
• Uyen Mai 21 ,
• Guangji Chen   ORCID: orcid.org/0000-0002-9441-1155 9 , 22 ,
• Rongsheng Gao 9 , 22 ,
• Chengran Zhou   ORCID: orcid.org/0000-0002-9468-5973 9 ,
• Yulong Xie 2 ,
• Zijian Huang 2 ,
• Zhen Cao 23 ,
• Zhi Yan   ORCID: orcid.org/0000-0003-2433-5553 23 ,
• Huw A. Ogilvie   ORCID: orcid.org/0000-0003-1589-6885 23 ,
• Luay Nakhleh   ORCID: orcid.org/0000-0003-3288-6769 23 ,
• Bent Lindow   ORCID: orcid.org/0000-0002-1864-4221 24 ,
• Benoit Morel 10 , 11 ,
• Jon Fjeldså   ORCID: orcid.org/0000-0003-0790-3600 24 ,
• Peter A. Hosner   ORCID: orcid.org/0000-0001-7499-6224 24 , 25 ,
• Rute R. da Fonseca   ORCID: orcid.org/0000-0002-2805-4698 25 ,
• Bent Petersen   ORCID: orcid.org/0000-0002-2472-8317 8 , 26 ,
• Joseph A. Tobias   ORCID: orcid.org/0000-0003-2429-6179 27 ,
• Tamás Székely   ORCID: orcid.org/0000-0003-2093-0056 28 , 29 ,
• Jonathan David Kennedy 30 ,
• Andrew Hart Reeve   ORCID: orcid.org/0000-0001-5233-6030 24 ,
• Andras Liker 31 , 32 ,
• Martin Stervander   ORCID: orcid.org/0000-0002-6139-7828 33 ,
• Agostinho Antunes   ORCID: orcid.org/0000-0002-1328-1732 34 , 35 ,
• Dieter Thomas Tietze   ORCID: orcid.org/0000-0001-6868-227X 36 ,
• Mads Bertelsen 37 ,
• Fumin Lei   ORCID: orcid.org/0000-0001-9920-8167 38 , 39 ,
• Carsten Rahbek   ORCID: orcid.org/0000-0003-4585-0300 25 , 30 , 40 , 41 ,
• Gary R. Graves   ORCID: orcid.org/0000-0003-1406-5246 30 , 42 ,
• Mikkel H. Schierup   ORCID: orcid.org/0000-0002-5028-1790 7 ,
• Tandy Warnow 43 ,
• Edward L. Braun   ORCID: orcid.org/0000-0003-1643-5212 44 ,
• M. Thomas P. Gilbert   ORCID: orcid.org/0000-0002-5805-7195 8 , 45 ,
• Erich D. Jarvis 46 , 47 ,
• Siavash Mirarab   ORCID: orcid.org/0000-0001-5410-1518 48 &
• Guojie Zhang   ORCID: orcid.org/0000-0001-6860-1521 2 , 3 , 5 , 49  

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• Evolutionary biology
• Genome evolution
• Molecular evolution
• Phylogenetics

Despite tremendous efforts in the past decades, relationships among main avian lineages remain heavily debated without a clear resolution. Discrepancies have been attributed to diversity of species sampled, phylogenetic method, and the choice of genomic regions 1–3 . Here, we address these issues by analyzing genomes of 363 bird species 4 (218 taxonomic families, 92% of total). Using intergenic regions and coalescent methods, we present a well-supported tree but also a remarkable degree of discordance. The tree confirms that Neoaves experienced rapid radiation at or near the Cretaceous–Paleogene (K–Pg) boundary. Sufficient loci rather than extensive taxon sampling were more effective in resolving difficult nodes. Remaining recalcitrant nodes involve species that challenge modeling due to extreme GC content, variable substitution rates, incomplete lineage sorting, or complex evolutionary events such as ancient hybridization. Assessment of the impacts of different genomic partitions showed high heterogeneity across the genome. We discovered sharp increases in effective population size, substitution rates, and relative brain size following the K–Pg extinction event, supporting the hypothesis that emerging ecological opportunities catalyzed the diversification of modern birds. The resulting phylogenetic estimate offers novel insights into the rapid radiation of modern birds and provides a taxon-rich backbone tree for future comparative studies.

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Authors and affiliations.

Section for Ecology and Evolution, Department of Biology, University of Copenhagen, Copenhagen, Denmark

Josefin Stiller

Center for Evolutionary & Organismal Biology, & Women’s Hospital, Zhejiang University School of Medicine, Hangzhou, China

Shaohong Feng, Yulong Xie, Zijian Huang & Guojie Zhang

Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China

Shaohong Feng & Guojie Zhang

Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China

Shaohong Feng

Innovation Center of Yangtze River Delta, Zhejiang University, Jiashan, China

School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia

Al-Aabid Chowdhury & Simon Y. W. Ho

Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark

Iker Rivas-González & Mikkel H. Schierup

Center for Evolutionary Hologenomics, The Globe Institute, University of Copenhagen, Copenhagen, Denmark

David A. Duchêne, Bent Petersen & M. Thomas P. Gilbert

BGI-Shenzhen, Beishan Industrial Zone, Shenzhen, China

Qi Fang, Yuan Deng, Guangji Chen, Rongsheng Gao & Chengran Zhou

Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany

Alexey Kozlov, Alexandros Stamatakis, Julia Haag & Benoit Morel

Institute of Computer Science, Foundation for Research and Technology Hellas, Heraklion, Greece

Alexandros Stamatakis & Benoit Morel

Institute for Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany

Alexandros Stamatakis

Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada

Santiago Claramunt

Department of Natural History, Royal Ontario Museum, Toronto, Ontario, Canada

College of Science and Engineering, Flinders University, Bedford Park, South Australia, Australia

Jacqueline M. T. Nguyen

Research Institute, Australian Museum, Sydney, New South Wales, Australia

Department of Biological Sciences and Museum of Natural Science, Louisiana State University, Baton Rouge, LA, USA

Brant C. Faircloth

Department of Biology, New Mexico State University, Las Cruces, NM, USA

Peter Houde

Department of Ornithology, American Museum of Natural History, New York, NY, USA

Joel Cracraft

Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA, USA

Metin Balaban

Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA

College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China

Guangji Chen & Rongsheng Gao

Department of Computer Science, Rice University, Houston, TX, USA

Zhen Cao, Zhi Yan, Huw A. Ogilvie & Luay Nakhleh

Natural History Museum Denmark, University of Copenhagen, Copenhagen, Denmark

Bent Lindow, Jon Fjeldså, Peter A. Hosner & Andrew Hart Reeve

Center for Global Mountain Biodiversity, Globe Institute, University of Copenhagen, Copenhagen, Denmark

Peter A. Hosner, Rute R. da Fonseca & Carsten Rahbek

Centre of Excellence for Omics-Driven Computational Biodiscovery (COMBio), Faculty of Applied Sciences, AIMST University, Bedong, Kedah, Malaysia

Bent Petersen

Department of Life Sciences, Imperial College London, Silwood Park, Ascot, UK

Joseph A. Tobias

Milner Centre for Evolution, University of Bath, Bath, UK

Tamás Székely

ELKH-DE Reproductive Strategies Research Group, University of Debrecen, Debrecen, Hungary

Center for Macroecology, Evolution, and Climate, The Globe Institute, University of Copenhagen, Copenhagen, Denmark

Jonathan David Kennedy, Carsten Rahbek & Gary R. Graves

HUN-REN-PE Evolutionary Ecology Research Group, University of Pannonia, Veszprém, Hungary

Andras Liker

Behavioural Ecology Research Group, Center for Natural Sciences, University of Pannonia, Veszprém, Hungary

Bird Group, Natural History Museum, Akeman St, Tring, Hertfordshire, United Kingdom

Martin Stervander

CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Porto, Portugal

Agostinho Antunes

Department of Biology, Faculty of Sciences, University of Porto, Porto, Portugal

NABU, Berlin, Germany

Dieter Thomas Tietze

Centre for Zoo and Wild Animal Health, Copenhagen Zoo, Frederiksberg, Denmark

Mads Bertelsen

Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China

College of Life Science, University of Chinese Academy of Sciences, Beijing, China

Institute of Ecology, Peking University, Beijing, China

Carsten Rahbek

Danish Institute for Advanced Study, University of Southern Denmark, Odense, Denmark

Department of Vertebrate Zoology, National Museum of Natural History, Smithsonian Institution, Washington, DC, USA

Gary R. Graves

University of Illinois Urbana-Champaign, Champaign, IL, USA

Tandy Warnow

Department of Biology, University of Florida, Gainesville, FL, USA

Edward L. Braun

University Museum, NTNU, Trondheim, Norway

M. Thomas P. Gilbert

Vertebrate Genome Lab, The Rockefeller University, New York, NY, USA

Erich D. Jarvis

Howard Hughes Medical Institute, Durham, NC, USA

University of California, San Diego, San Diego, CA, USA

Siavash Mirarab

Villum Center for Biodiversity Genomics, Department of Biology, University of Copenhagen, Copenhagen, Denmark

Guojie Zhang

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Corresponding authors

Correspondence to Josefin Stiller , Siavash Mirarab or Guojie Zhang .

Supplementary information

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Peer review file, supplementary data.

Table of all sequenced species with taxonomic grouping according to Howard & Moore. 4th Edition and accession numbers of the used genome assemblies. Given as a separate tab-delimited text file.

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Stiller, J., Feng, S., Chowdhury, AA. et al. Complexity of avian evolution revealed by family-level genomes. Nature (2024). https://doi.org/10.1038/s41586-024-07323-1

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Received : 25 April 2023

Accepted : 15 March 2024

Published : 01 April 2024

DOI : https://doi.org/10.1038/s41586-024-07323-1

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