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Research Project – Definition, Writing Guide and Ideas

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Research Project

Definition :

Research Project is a planned and systematic investigation into a specific area of interest or problem, with the goal of generating new knowledge, insights, or solutions. It typically involves identifying a research question or hypothesis, designing a study to test it, collecting and analyzing data, and drawing conclusions based on the findings.

Types of Research Project

Types of Research Projects are as follows:

Basic Research

This type of research focuses on advancing knowledge and understanding of a subject area or phenomenon, without any specific application or practical use in mind. The primary goal is to expand scientific or theoretical knowledge in a particular field.

Applied Research

Applied research is aimed at solving practical problems or addressing specific issues. This type of research seeks to develop solutions or improve existing products, services or processes.

Action Research

Action research is conducted by practitioners and aimed at solving specific problems or improving practices in a particular context. It involves collaboration between researchers and practitioners, and often involves iterative cycles of data collection and analysis, with the goal of improving practices.

Quantitative Research

This type of research uses numerical data to investigate relationships between variables or to test hypotheses. It typically involves large-scale data collection through surveys, experiments, or secondary data analysis.

Qualitative Research

Qualitative research focuses on understanding and interpreting phenomena from the perspective of the people involved. It involves collecting and analyzing data in the form of text, images, or other non-numerical forms.

Mixed Methods Research

Mixed methods research combines elements of both quantitative and qualitative research, using multiple data sources and methods to gain a more comprehensive understanding of a phenomenon.

Longitudinal Research

This type of research involves studying a group of individuals or phenomena over an extended period of time, often years or decades. It is useful for understanding changes and developments over time.

Case Study Research

Case study research involves in-depth investigation of a particular case or phenomenon, often within a specific context. It is useful for understanding complex phenomena in their real-life settings.

Participatory Research

Participatory research involves active involvement of the people or communities being studied in the research process. It emphasizes collaboration, empowerment, and the co-production of knowledge.

Research Project Methodology

Research Project Methodology refers to the process of conducting research in an organized and systematic manner to answer a specific research question or to test a hypothesis. A well-designed research project methodology ensures that the research is rigorous, valid, and reliable, and that the findings are meaningful and can be used to inform decision-making.

There are several steps involved in research project methodology, which are described below:

Define the Research Question

The first step in any research project is to clearly define the research question or problem. This involves identifying the purpose of the research, the scope of the research, and the key variables that will be studied.

Develop a Research Plan

Once the research question has been defined, the next step is to develop a research plan. This plan outlines the methodology that will be used to collect and analyze data, including the research design, sampling strategy, data collection methods, and data analysis techniques.

Collect Data

The data collection phase involves gathering information through various methods, such as surveys, interviews, observations, experiments, or secondary data analysis. The data collected should be relevant to the research question and should be of sufficient quantity and quality to enable meaningful analysis.

Analyze Data

Once the data has been collected, it is analyzed using appropriate statistical techniques or other methods. The analysis should be guided by the research question and should aim to identify patterns, trends, relationships, or other insights that can inform the research findings.

Interpret and Report Findings

The final step in the research project methodology is to interpret the findings and report them in a clear and concise manner. This involves summarizing the results, discussing their implications, and drawing conclusions that can be used to inform decision-making.

Research Project Writing Guide

Here are some guidelines to help you in writing a successful research project:

• Choose a topic: Choose a topic that you are interested in and that is relevant to your field of study. It is important to choose a topic that is specific and focused enough to allow for in-depth research and analysis.
• Conduct a literature review : Conduct a thorough review of the existing research on your topic. This will help you to identify gaps in the literature and to develop a research question or hypothesis.
• Develop a research question or hypothesis : Based on your literature review, develop a clear research question or hypothesis that you will investigate in your study.
• Design your study: Choose an appropriate research design and methodology to answer your research question or test your hypothesis. This may include choosing a sample, selecting measures or instruments, and determining data collection methods.
• Collect data: Collect data using your chosen methods and instruments. Be sure to follow ethical guidelines and obtain informed consent from participants if necessary.
• Analyze data: Analyze your data using appropriate statistical or qualitative methods. Be sure to clearly report your findings and provide interpretations based on your research question or hypothesis.
• Discuss your findings : Discuss your findings in the context of the existing literature and your research question or hypothesis. Identify any limitations or implications of your study and suggest directions for future research.
• Write your project: Write your research project in a clear and organized manner, following the appropriate format and style guidelines for your field of study. Be sure to include an introduction, literature review, methodology, results, discussion, and conclusion.
• Revise and edit: Revise and edit your project for clarity, coherence, and accuracy. Be sure to proofread for spelling, grammar, and formatting errors.
• Cite your sources: Cite your sources accurately and appropriately using the appropriate citation style for your field of study.

Examples of Research Projects

Some Examples of Research Projects are as follows:

• Investigating the effects of a new medication on patients with a particular disease or condition.
• Exploring the impact of exercise on mental health and well-being.
• Studying the effectiveness of a new teaching method in improving student learning outcomes.
• Examining the impact of social media on political participation and engagement.
• Investigating the efficacy of a new therapy for a specific mental health disorder.
• Exploring the use of renewable energy sources in reducing carbon emissions and mitigating climate change.
• Studying the effects of a new agricultural technique on crop yields and environmental sustainability.
• Investigating the effectiveness of a new technology in improving business productivity and efficiency.
• Examining the impact of a new public policy on social inequality and access to resources.
• Exploring the factors that influence consumer behavior in a specific market.

Characteristics of Research Project

Here are some of the characteristics that are often associated with research projects:

• Clear objective: A research project is designed to answer a specific question or solve a particular problem. The objective of the research should be clearly defined from the outset.
• Systematic approach: A research project is typically carried out using a structured and systematic approach that involves careful planning, data collection, analysis, and interpretation.
• Rigorous methodology: A research project should employ a rigorous methodology that is appropriate for the research question being investigated. This may involve the use of statistical analysis, surveys, experiments, or other methods.
• Data collection : A research project involves collecting data from a variety of sources, including primary sources (such as surveys or experiments) and secondary sources (such as published literature or databases).
• Analysis and interpretation : Once the data has been collected, it needs to be analyzed and interpreted. This involves using statistical techniques or other methods to identify patterns or relationships in the data.
• Conclusion and implications : A research project should lead to a clear conclusion that answers the research question. It should also identify the implications of the findings for future research or practice.
• Communication: The results of the research project should be communicated clearly and effectively, using appropriate language and visual aids, to a range of audiences, including peers, stakeholders, and the wider public.

Importance of Research Project

Research projects are an essential part of the process of generating new knowledge and advancing our understanding of various fields of study. Here are some of the key reasons why research projects are important:

• Advancing knowledge : Research projects are designed to generate new knowledge and insights into particular topics or questions. This knowledge can be used to inform policies, practices, and decision-making processes across a range of fields.
• Solving problems: Research projects can help to identify solutions to real-world problems by providing a better understanding of the causes and effects of particular issues.
• Developing new technologies: Research projects can lead to the development of new technologies or products that can improve people’s lives or address societal challenges.
• Improving health outcomes: Research projects can contribute to improving health outcomes by identifying new treatments, diagnostic tools, or preventive strategies.
• Enhancing education: Research projects can enhance education by providing new insights into teaching and learning methods, curriculum development, and student learning outcomes.
• Informing public policy : Research projects can inform public policy by providing evidence-based recommendations and guidance on issues related to health, education, environment, social justice, and other areas.
• Enhancing professional development : Research projects can enhance the professional development of researchers by providing opportunities to develop new skills, collaborate with colleagues, and share knowledge with others.

Research Project Ideas

Following are some Research Project Ideas:

Field: Psychology

• Investigating the impact of social support on coping strategies among individuals with chronic illnesses.
• Exploring the relationship between childhood trauma and adult attachment styles.
• Examining the effects of exercise on cognitive function and brain health in older adults.
• Investigating the impact of sleep deprivation on decision making and risk-taking behavior.
• Exploring the relationship between personality traits and leadership styles in the workplace.
• Examining the effectiveness of cognitive-behavioral therapy (CBT) for treating anxiety disorders.
• Investigating the relationship between social comparison and body dissatisfaction in young women.
• Exploring the impact of parenting styles on children’s emotional regulation and behavior.
• Investigating the effectiveness of mindfulness-based interventions for treating depression.
• Examining the relationship between childhood adversity and later-life health outcomes.

Field: Economics

• Analyzing the impact of trade agreements on economic growth in developing countries.
• Examining the effects of tax policy on income distribution and poverty reduction.
• Investigating the relationship between foreign aid and economic development in low-income countries.
• Exploring the impact of globalization on labor markets and job displacement.
• Analyzing the impact of minimum wage laws on employment and income levels.
• Investigating the effectiveness of monetary policy in managing inflation and unemployment.
• Examining the relationship between economic freedom and entrepreneurship.
• Analyzing the impact of income inequality on social mobility and economic opportunity.
• Investigating the role of education in economic development.
• Examining the effectiveness of different healthcare financing systems in promoting health equity.

Field: Sociology

• Investigating the impact of social media on political polarization and civic engagement.
• Examining the effects of neighborhood characteristics on health outcomes.
• Analyzing the impact of immigration policies on social integration and cultural diversity.
• Investigating the relationship between social support and mental health outcomes in older adults.
• Exploring the impact of income inequality on social cohesion and trust.
• Analyzing the effects of gender and race discrimination on career advancement and pay equity.
• Investigating the relationship between social networks and health behaviors.
• Examining the effectiveness of community-based interventions for reducing crime and violence.
• Analyzing the impact of social class on cultural consumption and taste.
• Investigating the relationship between religious affiliation and social attitudes.

Field: Computer Science

• Developing an algorithm for detecting fake news on social media.
• Investigating the effectiveness of different machine learning algorithms for image recognition.
• Developing a natural language processing tool for sentiment analysis of customer reviews.
• Analyzing the security implications of blockchain technology for online transactions.
• Investigating the effectiveness of different recommendation algorithms for personalized advertising.
• Developing an artificial intelligence chatbot for mental health counseling.
• Investigating the effectiveness of different algorithms for optimizing online advertising campaigns.
• Developing a machine learning model for predicting consumer behavior in online marketplaces.
• Analyzing the privacy implications of different data sharing policies for online platforms.
• Investigating the effectiveness of different algorithms for predicting stock market trends.

Field: Education

• Investigating the impact of teacher-student relationships on academic achievement.
• Analyzing the effectiveness of different pedagogical approaches for promoting student engagement and motivation.
• Examining the effects of school choice policies on academic achievement and social mobility.
• Investigating the impact of technology on learning outcomes and academic achievement.
• Analyzing the effects of school funding disparities on educational equity and achievement gaps.
• Investigating the relationship between school climate and student mental health outcomes.
• Examining the effectiveness of different teaching strategies for promoting critical thinking and problem-solving skills.
• Investigating the impact of social-emotional learning programs on student behavior and academic achievement.
• Analyzing the effects of standardized testing on student motivation and academic achievement.

Field: Environmental Science

• Investigating the impact of climate change on species distribution and biodiversity.
• Analyzing the effectiveness of different renewable energy technologies in reducing carbon emissions.
• Examining the impact of air pollution on human health outcomes.
• Investigating the relationship between urbanization and deforestation in developing countries.
• Analyzing the effects of ocean acidification on marine ecosystems and biodiversity.
• Investigating the impact of land use change on soil fertility and ecosystem services.
• Analyzing the effectiveness of different conservation policies and programs for protecting endangered species and habitats.
• Investigating the relationship between climate change and water resources in arid regions.
• Examining the impact of plastic pollution on marine ecosystems and biodiversity.
• Investigating the effects of different agricultural practices on soil health and nutrient cycling.

Field: Linguistics

• Analyzing the impact of language diversity on social integration and cultural identity.
• Investigating the relationship between language and cognition in bilingual individuals.
• Examining the effects of language contact and language change on linguistic diversity.
• Investigating the role of language in shaping cultural norms and values.
• Analyzing the effectiveness of different language teaching methodologies for second language acquisition.
• Investigating the relationship between language proficiency and academic achievement.
• Examining the impact of language policy on language use and language attitudes.
• Investigating the role of language in shaping gender and social identities.
• Analyzing the effects of dialect contact on language variation and change.
• Investigating the relationship between language and emotion expression.

Field: Political Science

• Analyzing the impact of electoral systems on women’s political representation.
• Investigating the relationship between political ideology and attitudes towards immigration.
• Examining the effects of political polarization on democratic institutions and political stability.
• Investigating the impact of social media on political participation and civic engagement.
• Analyzing the effects of authoritarianism on human rights and civil liberties.
• Investigating the relationship between public opinion and foreign policy decisions.
• Examining the impact of international organizations on global governance and cooperation.
• Investigating the effectiveness of different conflict resolution strategies in resolving ethnic and religious conflicts.
• Analyzing the effects of corruption on economic development and political stability.
• Investigating the role of international law in regulating global governance and human rights.

Field: Medicine

• Investigating the impact of lifestyle factors on chronic disease risk and prevention.
• Examining the effectiveness of different treatment approaches for mental health disorders.
• Investigating the relationship between genetics and disease susceptibility.
• Analyzing the effects of social determinants of health on health outcomes and health disparities.
• Investigating the impact of different healthcare delivery models on patient outcomes and cost effectiveness.
• Examining the effectiveness of different prevention and treatment strategies for infectious diseases.
• Investigating the relationship between healthcare provider communication skills and patient satisfaction and outcomes.
• Analyzing the effects of medical error and patient safety on healthcare quality and outcomes.
• Investigating the impact of different pharmaceutical pricing policies on access to essential medicines.
• Examining the effectiveness of different rehabilitation approaches for improving function and quality of life in individuals with disabilities.

Field: Anthropology

• Analyzing the impact of colonialism on indigenous cultures and identities.
• Investigating the relationship between cultural practices and health outcomes in different populations.
• Examining the effects of globalization on cultural diversity and cultural exchange.
• Investigating the role of language in cultural transmission and preservation.
• Analyzing the effects of cultural contact on cultural change and adaptation.
• Investigating the impact of different migration policies on immigrant integration and acculturation.
• Examining the role of gender and sexuality in cultural norms and values.
• Investigating the impact of cultural heritage preservation on tourism and economic development.
• Analyzing the effects of cultural revitalization movements on indigenous communities.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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The What: Defining a research project

During Academic Writing Month 2018, TAA hosted a series of #AcWriChat TweetChat events focused on the five W’s of academic writing. Throughout the series we explored The What: Defining a research project ; The Where: Constructing an effective writing environment ; The When: Setting realistic timeframes for your research ; The Who: Finding key sources in the existing literature ; and The Why: Explaining the significance of your research . This series of posts brings together the discussions and resources from those events. Let’s start with The What: Defining a research project .

Before moving forward on any academic writing effort, it is important to understand what the research project is intended to understand and document. In order to accomplish this, it’s also important to understand what a research project is. This is where we began our discussion of the five W’s of academic writing.

Q1: What constitutes a research project?

According to a Rutgers University resource titled, Definition of a research project and specifications for fulfilling the requirement , “A research project is a scientific endeavor to answer a research question.” Specifically, projects may take the form of “case series, case control study, cohort study, randomized, controlled trial, survey, or secondary data analysis such as decision analysis, cost effectiveness analysis or meta-analysis”.

Hampshire College offers that “Research is a process of systematic inquiry that entails collection of data; documentation of critical information; and analysis and interpretation of that data/information, in accordance with suitable methodologies set by specific professional fields and academic disciplines.” in their online resource titled, What is research? The resource also states that “Research is conducted to evaluate the validity of a hypothesis or an interpretive framework; to assemble a body of substantive knowledge and findings for sharing them in appropriate manners; and to generate questions for further inquiries.”

TweetChat participant @TheInfoSherpa , who is currently “investigating whether publishing in a predatory journal constitutes blatant research misconduct, inappropriate conduct, or questionable conduct,” summarized these ideas stating, “At its simplest, a research project is a project which seeks to answer a well-defined question or set of related questions about a specific topic.” TAA staff member, Eric Schmieder, added to the discussion that“a research project is a process by which answers to a significant question are attempted to be answered through exploration or experimentation.”

In a learning module focused on research and the application of the Scientific Method, the Office of Research Integrity within the U.S. Department of Health and Human Services states that “Research is a process to discover new knowledge…. No matter what topic is being studied, the value of the research depends on how well it is designed and done.”

Wenyi Ho of Penn State University states that “Research is a systematic inquiry to describe, explain, predict and control the observed phenomenon.” in an online resource which further shares four types of knowledge that research contributes to education, four types of research based on different purposes, and five stages of conducting a research study. Further understanding of research in definition, purpose, and typical research practices can be found in this Study.com video resource .

Now that we have a foundational understanding of what constitutes a research project, we shift the discussion to several questions about defining specific research topics.

Q2: When considering topics for a new research project, where do you start?

A guide from the University of Michigan-Flint on selecting a topic states, “Be aware that selecting a good topic may not be easy. It must be narrow and focused enough to be interesting, yet broad enough to find adequate information.”

Schmieder responded to the chat question with his approach.“I often start with an idea or question of interest to me and then begin searching for existing research on the topic to determine what has been done already.”

@TheInfoSherpa added, “Start with the research. Ask a librarian for help. The last thing you want to do is design a study thst someone’s already done.”

The Utah State University Libraries shared a video that “helps you find a research topic that is relevant and interesting to you!”

Q2a: What strategies do you use to stay current on research in your discipline?

The California State University Chancellor’s Doctoral Incentive Program Community Commons resource offers four suggestions for staying current in your field:

• Become an effective consumer of research
• Read key publications
• Attend key gatherings
• Develop a network of colleagues

Schmieder and @TheInfoSherpa discussed ways to use databases for this purpose. Schmieder identified using “journal database searches for publications in the past few months on topics of interest” as a way to stay current as a consumer of research.

@TheInfoSherpa added, “It’s so easy to set up an alert in your favorite database. I do this for specific topics, and all the latest research gets delivered right to my inbox. Again, your academic or public #librarian can help you with this.” To which Schmieder replied, “Alerts are such useful advancements in technology for sorting through the myriad of material available online. Great advice!”

In an open access article, Keeping Up to Date: An Academic Researcher’s Information Journey , researchers Pontis, et. al. “examined how researchers stay up to date, using the information journey model as a framework for analysis and investigating which dimensions influence information behaviors.” As a result of their study, “Five key dimensions that influence information behaviors were identified: level of seniority, information sources, state of the project, level of familiarity, and how well defined the relevant community is.”

Q3: When defining a research topic, do you tend to start with a broad idea or a specific research question?

In a collection of notes on where to start by Don Davis at Columbia University, Davis tells us “First, there is no ‘Right Topic.’”, adding that “Much more important is to find something that is important and genuinely interests you.”

Schmieder shared in the chat event, “I tend to get lost in the details while trying to save the world – not sure really where I start though. :O)” @TheInfoSherpa added, “Depends on the project. The important thing is being able to realize when your topic is too broad or too narrow and may need tweaking. I use the five Ws or PICO(T) to adjust my topic if it’s too broad or too narrow.”

In an online resource , The Writing Center at George Mason University identifies the following six steps to developing a research question, noting significance in that “the specificity of a well-developed research question helps writers avoid the ‘all-about’ paper and work toward supporting a specific, arguable thesis.”

• Choose an interesting general topic
• Do some preliminary research on your general topic
• Consider your audience
• Start asking questions
• Evaluate your question
• Begin your research

USC Libraries’ research guides offer eight strategies for narrowing the research topic : Aspect, Components, Methodology, Place, Relationship, Time, Type, or a Combination of the above.

Q4: What factors help to determine the realistic scope a research topic?

The scope of a research topic refers to the actual amount of research conducted as part of the study. Often the search strategies used in understanding previous research and knowledge on a topic will impact the scope of the current study. A resource from Indiana University offers both an activity for narrowing the search strategy when finding too much information on a topic and an activity for broadening the search strategy when too little information is found.

The Mayfield Handbook of Technical & Scientific Writing identifies scope as an element to be included in the problem statement. Further when discussing problem statements, this resource states, “If you are focusing on a problem, be sure to define and state it specifically enough that you can write about it. Avoid trying to investigate or write about multiple problems or about broad or overly ambitious problems. Vague problem definition leads to unsuccessful proposals and vague, unmanageable documents. Naming a topic is not the same as defining a problem.”

Schmieder identified in the chat several considerations when determining the scope of a research topic, namely “Time, money, interest and commitment, impact to self and others.” @TheInfoSherpa reiterated their use of PICO(T) stating, “PICO(T) is used in the health sciences, but it can be used to identify a manageable scope” and sharing a link to a Georgia Gwinnett College Research Guide on PICOT Questions .

By managing the scope of your research topic, you also define the limitations of your study. According to a USC Libraries’ Research Guide, “The limitations of the study are those characteristics of design or methodology that impacted or influenced the interpretation of the findings from your research.” Accepting limitations help maintain a manageable scope moving forward with the project.

Q5/5a: Do you generally conduct research alone or with collaborative authors? What benefits/challenges do collaborators add to the research project?

Despite noting that the majority of his research efforts have been solo, Schmieder did identify benefits to collaboration including “brainstorming, division of labor, speed of execution” and challenges of developing a shared vision, defining roles and responsibilities for the collaborators, and accepting a level of dependence on the others in the group.

In a resource on group writing from The Writing Center at the University of North Carolina at Chapel Hill, both advantages and pitfalls are discussed. Looking to the positive, this resource notes that “Writing in a group can have many benefits: multiple brains are better than one, both for generating ideas and for getting a job done.”

Yale University’s Office of the Provost has established, as part of its Academic Integrity policies, Guidance on Authorship in Scholarly or Scientific Publications to assist researchers in understanding authorship standards as well as attribution expectations.

In times when authorship turns sour , the University of California, San Francisco offers the following advice to reach a resolution among collaborative authors:

• Address emotional issues directly
• Elicit the problem author’s emotions
• Acknowledge the problem author’s emotions
• Express your own emotions as “I feel …”
• Set boundaries
• Try to find common ground
• Get agreement on process
• Involve a neutral third party

Q6: What other advice can you share about defining a research project?

Schmieder answered with question with personal advice to “Choose a topic of interest. If you aren’t interested in the topic, you will either not stay motivated to complete it or you will be miserable in the process and not produce the best results from your efforts.”

For further guidance and advice, the following resources may prove useful:

• 15 Steps to Good Research (Georgetown University Library)
• Advice for Researchers and Students (Tao Xie and University of Illinois)
• Develop a research statement for yourself (University of Pennsylvania)

Whatever your next research project, hopefully these tips and resources help you to define it in a way that leads to greater success and better writing.

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What Is Research Methodology? A Plain-Language Explanation & Definition (With Examples)

By Derek Jansen (MBA)  and Kerryn Warren (PhD) | June 2020 (Last updated April 2023)

If you’re new to formal academic research, it’s quite likely that you’re feeling a little overwhelmed by all the technical lingo that gets thrown around. And who could blame you – “research methodology”, “research methods”, “sampling strategies”… it all seems never-ending!

In this post, we’ll demystify the landscape with plain-language explanations and loads of examples (including easy-to-follow videos), so that you can approach your dissertation, thesis or research project with confidence. Let’s get started.

Research Methodology 101

• What exactly research methodology means
• What qualitative , quantitative and mixed methods are
• What sampling strategy is
• What data collection methods are
• What data analysis methods are
• How to choose your research methodology
• Example of a research methodology

What is research methodology?

Research methodology simply refers to the practical “how” of a research study. More specifically, it’s about how  a researcher  systematically designs a study  to ensure valid and reliable results that address the research aims, objectives and research questions . Specifically, how the researcher went about deciding:

• What type of data to collect (e.g., qualitative or quantitative data )
• Who  to collect it from (i.e., the sampling strategy )
• How to  collect  it (i.e., the data collection method )
• How to  analyse  it (i.e., the data analysis methods )

Within any formal piece of academic research (be it a dissertation, thesis or journal article), you’ll find a research methodology chapter or section which covers the aspects mentioned above. Importantly, a good methodology chapter explains not just   what methodological choices were made, but also explains  why they were made. In other words, the methodology chapter should justify  the design choices, by showing that the chosen methods and techniques are the best fit for the research aims, objectives and research questions. 

So, it’s the same as research design?

Not quite. As we mentioned, research methodology refers to the collection of practical decisions regarding what data you’ll collect, from who, how you’ll collect it and how you’ll analyse it. Research design, on the other hand, is more about the overall strategy you’ll adopt in your study. For example, whether you’ll use an experimental design in which you manipulate one variable while controlling others. You can learn more about research design and the various design types here .

Need a helping hand?

What are qualitative, quantitative and mixed-methods?

Qualitative, quantitative and mixed-methods are different types of methodological approaches, distinguished by their focus on words , numbers or both . This is a bit of an oversimplification, but its a good starting point for understanding.

Let’s take a closer look.

Qualitative research refers to research which focuses on collecting and analysing words (written or spoken) and textual or visual data, whereas quantitative research focuses on measurement and testing using numerical data . Qualitative analysis can also focus on other “softer” data points, such as body language or visual elements.

It’s quite common for a qualitative methodology to be used when the research aims and research questions are exploratory  in nature. For example, a qualitative methodology might be used to understand peoples’ perceptions about an event that took place, or a political candidate running for president. 

Contrasted to this, a quantitative methodology is typically used when the research aims and research questions are confirmatory  in nature. For example, a quantitative methodology might be used to measure the relationship between two variables (e.g. personality type and likelihood to commit a crime) or to test a set of hypotheses .

As you’ve probably guessed, the mixed-method methodology attempts to combine the best of both qualitative and quantitative methodologies to integrate perspectives and create a rich picture. If you’d like to learn more about these three methodological approaches, be sure to watch our explainer video below.

What is sampling strategy?

Simply put, sampling is about deciding who (or where) you’re going to collect your data from . Why does this matter? Well, generally it’s not possible to collect data from every single person in your group of interest (this is called the “population”), so you’ll need to engage a smaller portion of that group that’s accessible and manageable (this is called the “sample”).

How you go about selecting the sample (i.e., your sampling strategy) will have a major impact on your study.  There are many different sampling methods  you can choose from, but the two overarching categories are probability   sampling and  non-probability   sampling .

Probability sampling  involves using a completely random sample from the group of people you’re interested in. This is comparable to throwing the names all potential participants into a hat, shaking it up, and picking out the “winners”. By using a completely random sample, you’ll minimise the risk of selection bias and the results of your study will be more generalisable  to the entire population. 

Non-probability sampling , on the other hand,  doesn’t use a random sample . For example, it might involve using a convenience sample, which means you’d only interview or survey people that you have access to (perhaps your friends, family or work colleagues), rather than a truly random sample. With non-probability sampling, the results are typically not generalisable .

To learn more about sampling methods, be sure to check out the video below.

What are data collection methods?

As the name suggests, data collection methods simply refers to the way in which you go about collecting the data for your study. Some of the most common data collection methods include:

• Interviews (which can be unstructured, semi-structured or structured)
• Focus groups and group interviews
• Surveys (online or physical surveys)
• Observations (watching and recording activities)
• Biophysical measurements (e.g., blood pressure, heart rate, etc.)
• Documents and records (e.g., financial reports, court records, etc.)

The choice of which data collection method to use depends on your overall research aims and research questions , as well as practicalities and resource constraints. For example, if your research is exploratory in nature, qualitative methods such as interviews and focus groups would likely be a good fit. Conversely, if your research aims to measure specific variables or test hypotheses, large-scale surveys that produce large volumes of numerical data would likely be a better fit.

What are data analysis methods?

Data analysis methods refer to the methods and techniques that you’ll use to make sense of your data. These can be grouped according to whether the research is qualitative  (words-based) or quantitative (numbers-based).

Popular data analysis methods in qualitative research include:

• Qualitative content analysis
• Thematic analysis
• Discourse analysis
• Narrative analysis
• Interpretative phenomenological analysis (IPA)
• Visual analysis (of photographs, videos, art, etc.)

Qualitative data analysis all begins with data coding , after which an analysis method is applied. In some cases, more than one analysis method is used, depending on the research aims and research questions . In the video below, we explore some  common qualitative analysis methods, along with practical examples.  

Moving on to the quantitative side of things, popular data analysis methods in this type of research include:

• Descriptive statistics (e.g. means, medians, modes )
• Inferential statistics (e.g. correlation, regression, structural equation modelling)

Again, the choice of which data collection method to use depends on your overall research aims and objectives , as well as practicalities and resource constraints. In the video below, we explain some core concepts central to quantitative analysis.

How do I choose a research methodology?

As you’ve probably picked up by now, your research aims and objectives have a major influence on the research methodology . So, the starting point for developing your research methodology is to take a step back and look at the big picture of your research, before you make methodology decisions. The first question you need to ask yourself is whether your research is exploratory or confirmatory in nature.

If your research aims and objectives are primarily exploratory in nature, your research will likely be qualitative and therefore you might consider qualitative data collection methods (e.g. interviews) and analysis methods (e.g. qualitative content analysis). 

Conversely, if your research aims and objective are looking to measure or test something (i.e. they’re confirmatory), then your research will quite likely be quantitative in nature, and you might consider quantitative data collection methods (e.g. surveys) and analyses (e.g. statistical analysis).

Designing your research and working out your methodology is a large topic, which we cover extensively on the blog . For now, however, the key takeaway is that you should always start with your research aims, objectives and research questions (the golden thread). Every methodological choice you make needs align with those three components. 

Example of a research methodology chapter

In the video below, we provide a detailed walkthrough of a research methodology from an actual dissertation, as well as an overview of our free methodology template .

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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195 Comments

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Thanks for your comment.

We can’t write your methodology for you. If you’re looking for samples, you should be able to find some sample methodologies on Google. Alternatively, you can download some previous dissertations from a dissertation directory and have a look at the methodology chapters therein.

All the best with your research.

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Sure. You’re welcome to book an initial consultation with one of our Research Coaches to discuss how we can assist – https://gradcoach.com/book/new/ .

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MLA Jansen, Derek, and Kerryn Warren. “What (Exactly) Is Research Methodology?” Grad Coach, June 2021, gradcoach.com/what-is-research-methodology/.

APA Jansen, D., & Warren, K. (2021, June). What (Exactly) Is Research Methodology? Grad Coach. https://gradcoach.com/what-is-research-methodology/

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Module 1: Introduction: What is Research?

Learning Objectives

By the end of this module, you will be able to:

• Explain how the scientific method is used to develop new knowledge
• Describe why it is important to follow a research plan

The Scientific Method consists of observing the world around you and creating a  hypothesis  about relationships in the world. A hypothesis is an informed and educated prediction or explanation about something. Part of the research process involves testing the  hypothesis , and then examining the results of these tests as they relate to both the hypothesis and the world around you. When a researcher forms a hypothesis, this acts like a map through the research study. It tells the researcher which factors are important to study and how they might be related to each other or caused by a  manipulation  that the researcher introduces (e.g. a program, treatment or change in the environment). With this map, the researcher can interpret the information he/she collects and can make sound conclusions about the results.

Research can be done with human beings, animals, plants, other organisms and inorganic matter. When research is done with human beings and animals, it must follow specific rules about the treatment of humans and animals that have been created by the U.S. Federal Government. This ensures that humans and animals are treated with dignity and respect, and that the research causes minimal harm.

No matter what topic is being studied, the value of the research depends on how well it is designed and done. Therefore, one of the most important considerations in doing good research is to follow the design or plan that is developed by an experienced researcher who is called the  Principal Investigator  (PI). The PI is in charge of all aspects of the research and creates what is called a  protocol  (the research plan) that all people doing the research must follow. By doing so, the PI and the public can be sure that the results of the research are real and useful to other scientists.

Module 1: Discussion Questions

• How is a hypothesis like a road map?
• Who is ultimately responsible for the design and conduct of a research study?
• How does following the research protocol contribute to informing public health practices?

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• Research Objectives | Definition & Examples

Research Objectives | Definition & Examples

Published on July 12, 2022 by Eoghan Ryan . Revised on November 20, 2023.

Research objectives describe what your research is trying to achieve and explain why you are pursuing it. They summarize the approach and purpose of your project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement . They should:

• Establish the scope and depth of your project
• Contribute to your research design
• Indicate how your project will contribute to existing knowledge

Table of contents

What is a research objective, why are research objectives important, how to write research aims and objectives, smart research objectives, other interesting articles, frequently asked questions about research objectives.

Research objectives describe what your research project intends to accomplish. They should guide every step of the research process , including how you collect data , build your argument , and develop your conclusions .

Your research objectives may evolve slightly as your research progresses, but they should always line up with the research carried out and the actual content of your paper.

Research aims

A distinction is often made between research objectives and research aims.

A research aim typically refers to a broad statement indicating the general purpose of your research project. It should appear at the end of your problem statement, before your research objectives.

Your research objectives are more specific than your research aim and indicate the particular focus and approach of your project. Though you will only have one research aim, you will likely have several research objectives.

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Research objectives are important because they:

• Establish the scope and depth of your project: This helps you avoid unnecessary research. It also means that your research methods and conclusions can easily be evaluated .
• Contribute to your research design: When you know what your objectives are, you have a clearer idea of what methods are most appropriate for your research.
• Indicate how your project will contribute to extant research: They allow you to display your knowledge of up-to-date research, employ or build on current research methods, and attempt to contribute to recent debates.

Once you’ve established a research problem you want to address, you need to decide how you will address it. This is where your research aim and objectives come in.

Step 1: Decide on a general aim

Your research aim should reflect your research problem and should be relatively broad.

Step 2: Decide on specific objectives

Break down your aim into a limited number of steps that will help you resolve your research problem. What specific aspects of the problem do you want to examine or understand?

Step 3: Formulate your aims and objectives

Once you’ve established your research aim and objectives, you need to explain them clearly and concisely to the reader.

You’ll lay out your aims and objectives at the end of your problem statement, which appears in your introduction. Frame them as clear declarative statements, and use appropriate verbs to accurately characterize the work that you will carry out.

The acronym “SMART” is commonly used in relation to research objectives. It states that your objectives should be:

• Specific: Make sure your objectives aren’t overly vague. Your research needs to be clearly defined in order to get useful results.
• Measurable: Know how you’ll measure whether your objectives have been achieved.
• Achievable: Your objectives may be challenging, but they should be feasible. Make sure that relevant groundwork has been done on your topic or that relevant primary or secondary sources exist. Also ensure that you have access to relevant research facilities (labs, library resources , research databases , etc.).
• Relevant: Make sure that they directly address the research problem you want to work on and that they contribute to the current state of research in your field.
• Time-based: Set clear deadlines for objectives to ensure that the project stays on track.

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

Methodology

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

Research objectives describe what you intend your research project to accomplish.

They summarize the approach and purpose of the project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement .

Your research objectives indicate how you’ll try to address your research problem and should be specific:

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement .

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

A research aim is a broad statement indicating the general purpose of your research project. It should appear in your introduction at the end of your problem statement , before your research objectives.

Research objectives are more specific than your research aim. They indicate the specific ways you’ll address the overarching aim.

Scope of research is determined at the beginning of your research process , prior to the data collection stage. Sometimes called “scope of study,” your scope delineates what will and will not be covered in your project. It helps you focus your work and your time, ensuring that you’ll be able to achieve your goals and outcomes.

Defining a scope can be very useful in any research project, from a research proposal to a thesis or dissertation . A scope is needed for all types of research: quantitative , qualitative , and mixed methods .

To define your scope of research, consider the following:

• Budget constraints or any specifics of grant funding
• Your proposed timeline and duration
• Specifics about your population of study, your proposed sample size , and the research methodology you’ll pursue
• Any inclusion and exclusion criteria
• Any anticipated control , extraneous , or confounding variables that could bias your research if not accounted for properly.

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What is Research? – Purpose of Research

• By DiscoverPhDs
• September 10, 2020

The purpose of research is to enhance society by advancing knowledge through the development of scientific theories, concepts and ideas. A research purpose is met through forming hypotheses, collecting data, analysing results, forming conclusions, implementing findings into real-life applications and forming new research questions.

What is Research

Simply put, research is the process of discovering new knowledge. This knowledge can be either the development of new concepts or the advancement of existing knowledge and theories, leading to a new understanding that was not previously known.

As a more formal definition of research, the following has been extracted from the Code of Federal Regulations :

While research can be carried out by anyone and in any field, most research is usually done to broaden knowledge in the physical, biological, and social worlds. This can range from learning why certain materials behave the way they do, to asking why certain people are more resilient than others when faced with the same challenges.

The use of ‘systematic investigation’ in the formal definition represents how research is normally conducted – a hypothesis is formed, appropriate research methods are designed, data is collected and analysed, and research results are summarised into one or more ‘research conclusions’. These research conclusions are then shared with the rest of the scientific community to add to the existing knowledge and serve as evidence to form additional questions that can be investigated. It is this cyclical process that enables scientific research to make continuous progress over the years; the true purpose of research.

What is the Purpose of Research

From weather forecasts to the discovery of antibiotics, researchers are constantly trying to find new ways to understand the world and how things work – with the ultimate goal of improving our lives.

The purpose of research is therefore to find out what is known, what is not and what we can develop further. In this way, scientists can develop new theories, ideas and products that shape our society and our everyday lives.

Although research can take many forms, there are three main purposes of research:

• Exploratory: Exploratory research is the first research to be conducted around a problem that has not yet been clearly defined. Exploration research therefore aims to gain a better understanding of the exact nature of the problem and not to provide a conclusive answer to the problem itself. This enables us to conduct more in-depth research later on.
• Descriptive: Descriptive research expands knowledge of a research problem or phenomenon by describing it according to its characteristics and population. Descriptive research focuses on the ‘how’ and ‘what’, but not on the ‘why’.
• Explanatory: Explanatory research, also referred to as casual research, is conducted to determine how variables interact, i.e. to identify cause-and-effect relationships. Explanatory research deals with the ‘why’ of research questions and is therefore often based on experiments.

Characteristics of Research

There are 8 core characteristics that all research projects should have. These are:

• Empirical  – based on proven scientific methods derived from real-life observations and experiments.
• Logical  – follows sequential procedures based on valid principles.
• Cyclic  – research begins with a question and ends with a question, i.e. research should lead to a new line of questioning.
• Controlled  – vigorous measures put into place to keep all variables constant, except those under investigation.
• Hypothesis-based  – the research design generates data that sufficiently meets the research objectives and can prove or disprove the hypothesis. It makes the research study repeatable and gives credibility to the results.
• Analytical  – data is generated, recorded and analysed using proven techniques to ensure high accuracy and repeatability while minimising potential errors and anomalies.
• Objective  – sound judgement is used by the researcher to ensure that the research findings are valid.
• Statistical treatment  – statistical treatment is used to transform the available data into something more meaningful from which knowledge can be gained.

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Types of Research

Research can be divided into two main types: basic research (also known as pure research) and applied research.

Basic Research

Basic research, also known as pure research, is an original investigation into the reasons behind a process, phenomenon or particular event. It focuses on generating knowledge around existing basic principles.

Basic research is generally considered ‘non-commercial research’ because it does not focus on solving practical problems, and has no immediate benefit or ways it can be applied.

While basic research may not have direct applications, it usually provides new insights that can later be used in applied research.

Applied Research

Applied research investigates well-known theories and principles in order to enhance knowledge around a practical aim. Because of this, applied research focuses on solving real-life problems by deriving knowledge which has an immediate application.

Methods of Research

Research methods for data collection fall into one of two categories: inductive methods or deductive methods.

Inductive research methods focus on the analysis of an observation and are usually associated with qualitative research. Deductive research methods focus on the verification of an observation and are typically associated with quantitative research.

Qualitative Research

Qualitative research is a method that enables non-numerical data collection through open-ended methods such as interviews, case studies and focus groups .

It enables researchers to collect data on personal experiences, feelings or behaviours, as well as the reasons behind them. Because of this, qualitative research is often used in fields such as social science, psychology and philosophy and other areas where it is useful to know the connection between what has occurred and why it has occurred.

Quantitative Research

Quantitative research is a method that collects and analyses numerical data through statistical analysis.

It allows us to quantify variables, uncover relationships, and make generalisations across a larger population. As a result, quantitative research is often used in the natural and physical sciences such as engineering, biology, chemistry, physics, computer science, finance, and medical research, etc.

What does Research Involve?

Research often follows a systematic approach known as a Scientific Method, which is carried out using an hourglass model.

A research project first starts with a problem statement, or rather, the research purpose for engaging in the study. This can take the form of the ‘ scope of the study ’ or ‘ aims and objectives ’ of your research topic.

Subsequently, a literature review is carried out and a hypothesis is formed. The researcher then creates a research methodology and collects the data.

The data is then analysed using various statistical methods and the null hypothesis is either accepted or rejected.

In both cases, the study and its conclusion are officially written up as a report or research paper, and the researcher may also recommend lines of further questioning. The report or research paper is then shared with the wider research community, and the cycle begins all over again.

Although these steps outline the overall research process, keep in mind that research projects are highly dynamic and are therefore considered an iterative process with continued refinements and not a series of fixed stages.

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An In Press article is a paper that has been accepted for publication and is being prepared for print.

Dr Singh earned his PhD in Nanotechnology from Indian Institute of Technology Guwahati (IIT Guwahati), India in 2018. He is now a Senior Research Fellow developing low cost and biocompatible micro/nanomotors for anti-cancer therapy.

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Research Project – Concept, parts and examples

We explain what a research project is and the parts that make it up. Also, the steps to make one and examples.

What is a research project?

It is understood by research project a methodological document, often academic , which explains and describes in detail the set of procedures that will be undertaken, the hypothesis pursued with them and the bibliographic support available, for an exploration to come in a specific area of ​​knowledge: science, science social, humanities, etc. It is a specialized report prior to conducting experiments or documentary reviews.

Research projects are usually used in the academic and scientific-technological fields, since they are areas that basically train researchers and that have funded projects to develop a particular area of ​​human knowledge.

Commonly research projects are evaluated by a specialized and impartial jury , who must decide whether the researcher or group of them proposes a possible, valuable and worthy investigation to confer a university degree or a financing quota (and even both).

Graduate theses, in this sense, are usually preceded by a research project where it is made clear what is intended to be done and how.

Parts of a research project

Typically, a research project contains most of the following items:

• Tentative title. A working name of the research, tentatively summarizing the topic to be addressed and the focus.
• Problem Statement. An introduction to the research topic, emphasizing the most relevant aspects for it and the questions to be solved.
• Background. A review of previous research on the same or similar topics, explaining how it differs and what aspects are inherited from them.
• Justification. Closely linked to the above, it gives a perspective on how much research will contribute to the field of knowledge in which it is inserted and why it should be financed or taken into account.
• Theoretical framework. A relationship between the theoretical content and the steps of the investigation, detailing the axes on which it will be based, the theoretical sources to which it will draw and why.
• Objectives. Here the general objective of the investigation, its primary and central role, and then also the specific objectives, that is, secondary, linked to each stage of the investigation will be explained.
• Methodological framework. A list of the procedures and practical steps to follow during the investigation, provided with explanations regarding the procedures themselves: why choose one type of experiment over another, detail a work schedule, a budget relationship, etc.
• Bibliographic references. It details the bibliographic content consulted, whether it provided citations and key texts, or it only served to create a frame of reference for the research.

Steps to develop a research project

Broadly speaking, the steps to develop a project should be:

• Define the theme. It cannot begin to be investigated without having at least some coordinates regarding what it is that interests us and why. At this point personal passions come into play.
• Make a bibliographic Achaean. Review everything said on the subject, the main authors, compile material, refine the sources to which you will go and give them a first reading.
• Define the objectives. Once you know what has been said about it, you can choose your own path, a series of questions that trigger the investigation.
• Define the method. It refers to choosing which authors to work with, in what way, with what experiments, what type of research to carry out, etc.
• Prepare the report. Write the sections of the project and check that they express the desired points of view.

Research project example

• Tentative title of the investigation

The figure of the beggar in 19th century French literature

• Problem Statement

French literature of the nineteenth century is heir to the Enlightenment and therefore sticks to the realistic school, trying to reflect the problems of the real and everyday world. In this context, the beggar emerges as a figure freed from social pressure and capable of making judgments, in which the author’s own thought could be reflected.

In most approaches to literary realism, attention is paid to the figure of the social outcasts: beggars and prostitutes. This is what the critic Pinkster (1992) does in his book on Baudelaire’s poems dedicated to poverty, among other critics of interest.

• Justification

Understanding the correlation between the beggar and the 19th century French author will give us clues regarding the history of the notion of “author” in the West and its entry into crisis at the beginning of the 20th century, which could explain the emergence of the avant-garde, among them surrealism, born in France itself.

• Theoretical framework

The work of Pinkster (1992) et. al., as well as books The beggar as a universal archetype (Fourier, 2007) and the works of Charles Baudelaire, Jean Barnaby Amé and Alphonse Allais, which will be our corpus of study.

– Course objective: To verify the discursive meaning of the character of the beggar in three French authors of the 19th century.

– Specific objectives:

a.- Demonstrate the recurrence of the figure of the beggar.

b.- Review the speech put into the mouth of the beggar taking into account the political context of the time.

c.- Check what was found with the opinions expressed by the authors.

• Methodological framework

The works will be read and the findings will be critically collated. Then an explanatory monograph will be written.

• Bibliography

– Pinkster, E. (1992). 19th century French literature .

– Fourier, M. (2007). The beggar as a universal archetype .

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Home » Education » What is the Difference Between Research and Project

What is the Difference Between Research and Project

The main difference between research and project is that research is the systematic investigation and study of materials and sources to establish facts and reach new conclusions, while a project is a specific and finite activity that gives a measurable and observable result under preset requirements.

Both research and projects use a systematic approach. We also sometimes use the term research project to refer to research studies.

Key Areas Covered

1.  What is Research       – Definition, Features 2. What is a Project      – Definition, Features 3.  Difference Between Research and Project      – Comparison of Key Differences

Research, Project

What is Research

Research is a careful study a researcher conducts using a systematic approach and scientific methods. A research study typically involves several components: abstract, introduction ,  literature review ,  research design, and method , results and analysis, conclusion, bibliography. Researchers usually begin a formal research study with a hypothesis; then, they test this hypothesis rigorously. They also explore and analyze the literature already available on their research subject. This allows them to study the research subject from multiple perspectives, acknowledging different problems that need to be solved.

There are different types of research, the main two categories being quantitative research and qualitative research. Depending on their research method and design, we can also categorize research as descriptive research, exploratory research, longitudinal research, cross-sectional research, etc.

Furthermore, research should always be objective or unbiased. Moreover, if the research involves participants, for example, in surveys or interviews, the researcher should always make sure to obtain their written consent first.

What is a Project

A project is a collaborative or individual enterprise that is carefully planned to achieve a particular aim. We can also describe it as a specific and finite activity that gives a measurable and observable result under preset requirements. This result can be tangible or intangible; for example, product, service, competitive advantage, etc. A project generally involves a series of connected tasks planned for execution over a fixed period of time and within certain limitations like quality and cost. The Project Management Body of Knowledge (PMBOK) defines a project as a “temporary endeavor with a beginning and an end, and it must be used to create a unique product, service or result.”

Difference Between Research and Project

Research is a careful study conducted using a systematic approach and scientific methods, whereas a project is a collaborative or individual enterprise that is carefully planned to achieve a particular aim.

Research studies are mainly carried out in academia, while projects can be seen in a variety of contexts, including businesses.

The main aim of the research is to seek or revise facts, theories, or principles, while the main aim of a project is to achieve a tangible or intangible result; for example, product, service, competitive advantage, etc.

The main difference between research and project is that research is the systematic investigation and study of materials and sources to establish facts and reach new conclusions, while the project is a specific and finite activity that gives a measurable and observable result under preset requirements.

1. “ What Is a Project? – Definition, Lifecycle and Key Characteristics .” Your Guide to Project Management Best Practices .

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What does research project mean?

Definitions for research project re·search project, this dictionary definitions page includes all the possible meanings, example usage and translations of the word research project ., princeton's wordnet rate this definition: 0.0 / 0 votes.

scientific research, research project noun

research into questions posed by scientific theories and hypotheses

Wikipedia Rate this definition: 0.0 / 0 votes

Research project.

Research is "creative and systematic work undertaken to increase the stock of knowledge". It involves the collection, organization and analysis of evidence to increase understanding of a topic, characterized by a particular attentiveness to controlling sources of bias and error. These activities are characterized by accounting and controlling for biases. A research project may be an expansion on past work in the field. To test the validity of instruments, procedures, or experiments, research may replicate elements of prior projects or the project as a whole. The primary purposes of basic research (as opposed to applied research) are documentation, discovery, interpretation, and the research and development (R&D) of methods and systems for the advancement of human knowledge. Approaches to research depend on epistemologies, which vary considerably both within and between humanities and sciences. There are several forms of research: scientific, humanities, artistic, economic, social, business, marketing, practitioner research, life, technological, etc. The scientific study of research practices is known as meta-research.

ChatGPT Rate this definition: 0.0 / 0 votes

A research project is a systematic, in-depth study or investigation undertaken to discover new facts, interpret existing information, or develop a new concept, theory or understanding about a particular subject or topic. This project often involves collecting data, conducting experiments, or analyzing information, and is structured around a hypothesis or a research question. It can be carried out by an individual, a team, an institution, or a combination of these, and could be for academic, scientific, corporate, or personal purposes.

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Chaldean Numerology

The numerical value of research project in Chaldean Numerology is: 2

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The numerical value of research project in Pythagorean Numerology is: 2

Examples of research project in a Sentence

I look at the Tree of 40 Fruit as an artwork, a research project and a form of conservation.

Brian Behlendorf :

If this were the web, what year would we be in? i've felt that we were in 1995, but with this release I am ready to say we are in 1996, when you started to see enterprises saying 'Now it is not just a research project .'.

Joseph Kornegay :

There’s not another way, i’m a dog lover, I’m a veterinarian, I’ve had dogs all my life. So every time we have a research project we want to be as sure as we can that the research will be valuable because the dogs are valuable. Not financially valuable, but valuable as individuals.

Clint Watts :

The single most important source of political information was links to unverified WikiLeaks stories. We believe there was automation pushing those links around but that is our next research project . part of the reason active measures worked in the U.S. election is because the commander in chief used Russian active measures at times against his opponents.

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What is Research Project definition/concept

In any scientific research process, the final result is reflected in a research project , which presents a new idea based on a methodology . Research Project

The three fundamental questions

There are three questions that every research project must answer: what is investigated, why is research needed? and how, when and with what resources is the study carried out?

General aspects of a research project

The first step is to define the issue to be addressed. Normally, every research project brings the solution to a problem and, therefore, is about defining, delimiting, approaching or formulating an initial problem.

A work or research project is approached as an original study on a subject that presents some difficulty.

In any research project it is necessary to establish what kind of study should be carried out (there are descriptive, experimental, case studies, etc.).

It is necessary to clearly state what is the main objective of the project and what are the secondary objective s.

For a survey to be valid it is necessary to determine a study sample (for example, the specific population of a territory ). On the other hand, a presentation technique (for example, of a probabilistic type) must be introduced .

Regarding technical aspects, it is necessary to use variable measurement scales, which can be qualitative or quantitative. On the other hand, a plan for data analysis , evaluation reports, bibliographical references in accordance with established guidelines, material resources , etc., must be established.

Of course, it is essential that the research is based on the scientific method accepted by the scientific community and, at the same time, that there is a defined theoretical framework .

Stages of a research project

In general, the following steps can be established:

1) definition of the theme and justification of the project;

2) research planning ;

3) documentation and field analysis ;

4) analysis and interpretation of data  ;

5) writing the writing ;

6) final presentation of the project.

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• Introduction
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a Uninfected included acute cohort (n = 1092) and postacute cohort (n = 999) participants. Uninfected participants had no known history of SARS-CoV-2 infection. Acute uninfected participants were enrolled within 30 days of a SARS-CoV-2 negative test result, while postacute uninfected participants were enrolled more than 30 days after a SARS-CoV-2 negative test result.

b Participants who completed visit without reaching end of visit window were included in this count.

A, Optimal score cutoff for classifying a participant as PASC positive using cross-validation (eMethods in Supplement 3 ). The decision rule based on symptoms is intended to identify participants with PASC. PASC status for participants not meeting the score threshold requires consideration of additional data inputs.

B, Symptom frequencies among PASC-positive participants for symptoms that contribute to the PASC score. Many other symptoms have high frequency in PASC-positive participants (eTable 8 in Supplement 3 ).

C, Distribution of Patient-Reported Outcomes Measurement Information System (PROMIS) Global 10 responses among participants with a zero PASC score and among participants within nonzero PASC score quintiles. The PROMIS Global 10 provides an assessment of quality of life along 10 dimensions, each rated on a 5-point scale. The shading corresponds to frequency within each column on a scale from 0% to 100%.

a Additional severity criteria required (eTables 1 and 2 in Supplement 3 ).

A, Dendrogram illustrating how PASC-positive participants with similar symptom profiles cluster. Each branch in the dendrogram represents a participant, and each cluster represents a subgroup of participants.

B. Heatmap of symptom frequencies within PASC unspecified and within each PASC-positive subgroup. The shading corresponds to frequency within each column on a scale from 0% to 100%.

a Although unsupervised learning uses 12 symptoms selected by least absolute shrinkage and selection operator (LASSO) (Figure 2), many other symptoms occur in combination with these 12.

Trial Protocol

Statistical Analysis Plan

eReferences

eTable 1. Symptoms Considered in Analysis

eTable 2. Additional Severity Criteria Applied to Symptoms for Analysis

eTable 3. PROMIS Global-10 Questions Used

eTable 4. Vaccination Category Definitions

eTable 5. RECOVER-Adult Additional Demographic and Clinical Characteristics by Infection Status

eTable 6. RECOVER-Adult Demographic Characteristics by Sub-cohort

eTable 7. Symptoms That Correlate With Symptoms Contributing to PASC Score

eTable 8. Symptom Frequencies Among PASC-Positive Participants

eTable 9. PASC Frequencies by Time Since Index Date, Infected Participants

eTable 10. PASC Onset and Resolution Over Time

eTable 11. PASC Subgroup Distributions

eFigure 1. Symptom Frequency, Full Analysis Cohort

eFigure 2. New Onset Symptom Frequency, Full Analysis Cohort

eFigure 3. Symptom Frequency, Full Cohort, Without Severity Scores

eFigure 4. Distribution of Time From Index to Analysis Visit Date

eFigure 5. Symptom Frequency, Acute, Omicron

eFigure 6. Symptom Frequency, Post-Acute, Pre-Omicron

eFigure 7. Symptom Frequency, Post-Acute, Omicron

eFigure 8. Symptom Frequency, Acute, Fully Vaccinated, Omicron

Nonauthor Collaborators. RECOVER Consortium

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Thaweethai T , Jolley SE , Karlson EW, et al. Development of a Definition of Postacute Sequelae of SARS-CoV-2 Infection. JAMA. 2023;329(22):1934–1946. doi:10.1001/jama.2023.8823

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Development of a Definition of Postacute Sequelae of SARS-CoV-2 Infection

• 1 Massachusetts General Hospital, Boston
• 2 Harvard Medical School, Boston, Massachusetts
• 3 University of Colorado Anschutz Medical Campus, Aurora
• 4 Brigham and Women’s Hospital, Boston, Massachusetts
• 5 University of Alabama at Birmingham
• 6 Case Western Reserve University, Cleveland, Ohio
• 7 Patient-Led Research Collaborative, Calabasas, California
• 8 Icahn School of Medicine at Mount Sinai, New York, New York
• 9 The University of Arizona College of Medicine, Tucson
• 10 Stanford University School of Medicine, Stanford, California
• 11 Emory University School of Medicine, Atlanta, Georgia
• 12 Mass General Brigham, Boston, Massachusetts
• 13 New York University Grossman School of Medicine, New York
• Editorial Disentangling the Postacute Sequelae of SARS-CoV-2 Robert Gross, MD, MSCE; Vincent Lo Re III, MD, MSCE JAMA
• Medical News & Perspectives How Primary Care Physicians Can Recognize and Treat Long COVID Esther Wei-Yun Landhuis, PhD JAMA
• Comment & Response Postacute Sequelae of SARS-CoV-2 Infection—Reply Tanayott Thaweethai, PhD; Andrea S. Foulkes, ScD JAMA
• Comment & Response Postacute Sequelae of SARS-CoV-2 Infection Ayush Batra, MD; Avindra Nath, MD; Igor J. Koralnik, MD JAMA
• Original Investigation Documentation of Diagnostic Codes for Long COVID in the National Veterans Affairs Health Care System George N. Ioannou, BMBCh, MS; Aaron Baraff, MS; Alexandra Fox, MSIS; Troy Shahoumian, PhD; Alex Hickok, MS; Ann M. O’Hare, MD; Amy S. B. Bohnert, PhD; Edward J. Boyko, MD, MPH; Matthew L. Maciejewski, PhD; C. Barrett Bowling, MD, MSPH; Elizabeth Viglianti, MD; Theodore J. Iwashyna, MD, PhD; Denise M. Hynes, MPH, PhD, RN JAMA Network Open
• Original Investigation Prevalence and Correlates of Long COVID Symptoms Among US Adults Roy H. Perlis, MD, MSc; Mauricio Santillana, PhD; Katherine Ognyanova, PhD; Alauna Safarpour, PhD; Kristin Lunz Trujillo, PhD; Matthew D. Simonson, PhD; Jon Green, PhD; Alexi Quintana, BA; James Druckman, PhD; Matthew A. Baum, PhD; David Lazer, PhD JAMA Network Open
• Original Investigation Complexity and Challenges of the Clinical Diagnosis and Management of Long COVID Ann M. O’Hare, MA, MD; Elizabeth K. Vig, MD, MPH; Theodore J. Iwashyna, MD, PhD; Alexandra Fox, PhD; Janelle S. Taylor, PhD; Elizabeth M. Viglianti, MD; Catherine R. Butler, MD, MA; Kelly C. Vranas, MD, MCR; Mark Helfand, MD, MPH; Anaïs Tuepker, PhD, MPH; Shannon M. Nugent, PhD; Kara A. Winchell, MA; Ryan J. Laundry, BS; C. Barrett Bowling, MD, MSPH; Denise M. Hynes, RN, PhD; Matthew L. Maciejewski, PhD; Amy S. B. Bohnert, PhD; Emily R. Locke, MPH; Edward J. Boyko, MD, MPH; George N. Ioannou, BMBCh, MS; VA COVID Observational Research Collaboratory (CORC) JAMA Network Open

Question   What symptoms are differentially present in SARS-CoV-2–infected individuals 6 months or more after infection compared with uninfected individuals, and what symptom-based criteria can be used to identify postacute sequelae of SARS-CoV-2 infection (PASC) cases?

Findings   In this analysis of data from 9764 participants in the RECOVER adult cohort, a prospective longitudinal cohort study, 37 symptoms across multiple pathophysiological domains were identified as present more often in SARS-CoV-2–infected participants at 6 months or more after infection compared with uninfected participants. A preliminary rule for identifying PASC was derived based on a composite symptom score.

Meaning   A framework for identifying PASC cases based on symptoms is a first step to defining PASC as a new condition. These findings require iterative refinement that further incorporates clinical features to arrive at actionable definitions of PASC.

Importance   SARS-CoV-2 infection is associated with persistent, relapsing, or new symptoms or other health effects occurring after acute infection, termed postacute sequelae of SARS-CoV-2 infection (PASC), also known as long COVID . Characterizing PASC requires analysis of prospectively and uniformly collected data from diverse uninfected and infected individuals.

Objective   To develop a definition of PASC using self-reported symptoms and describe PASC frequencies across cohorts, vaccination status, and number of infections.

Design, Setting, and Participants   Prospective observational cohort study of adults with and without SARS-CoV-2 infection at 85 enrolling sites (hospitals, health centers, community organizations) located in 33 states plus Washington, DC, and Puerto Rico. Participants who were enrolled in the RECOVER adult cohort before April 10, 2023, completed a symptom survey 6 months or more after acute symptom onset or test date. Selection included population-based, volunteer, and convenience sampling.

Exposure   SARS-CoV-2 infection.

Main Outcomes and Measures   PASC and 44 participant-reported symptoms (with severity thresholds).

Results   A total of 9764 participants (89% SARS-CoV-2 infected; 71% female; 16% Hispanic/Latino; 15% non-Hispanic Black; median age, 47 years [IQR, 35-60]) met selection criteria. Adjusted odds ratios were 1.5 or greater (infected vs uninfected participants) for 37 symptoms. Symptoms contributing to PASC score included postexertional malaise, fatigue, brain fog, dizziness, gastrointestinal symptoms, palpitations, changes in sexual desire or capacity, loss of or change in smell or taste, thirst, chronic cough, chest pain, and abnormal movements. Among 2231 participants first infected on or after December 1, 2021, and enrolled within 30 days of infection, 224 (10% [95% CI, 8.8%-11%]) were PASC positive at 6 months.

Conclusions and Relevance   A definition of PASC was developed based on symptoms in a prospective cohort study. As a first step to providing a framework for other investigations, iterative refinement that further incorporates other clinical features is needed to support actionable definitions of PASC.

More than 658 million people worldwide have been infected with SARS-CoV-2. 1 Postacute sequelae of SARS-CoV-2 infection (PASC), also known as long COVID and defined as ongoing, relapsing, or new symptoms or conditions present 30 or more days after infection, is a major clinical and public health concern. 2 - 6 Short- and long-term effects of PASC have substantial impacts on health-related quality of life, earnings, and health care costs. 7 , 8 Most existing PASC studies have focused on individual symptom frequency and have generated widely divergent estimates of prevalence due to their retrospective design and lack of an uninfected comparison group. Moreover, defining PASC precisely is difficult because it is heterogeneous, composed of conditions with variable and potentially overlapping etiologies (eg, organ injury, viral persistence, immune dysregulation, autoimmunity, and gut dysbiosis). 9 , 10

It is of significant public health and scientific importance to better research the underlying mechanisms of PASC and potential preventive and therapeutic interventions. This effort requires data collection on SARS-CoV-2–infected and –uninfected individuals in a large prospective cohort study designed specifically to characterize PASC. Additionally, simultaneous consideration of multiple symptoms that persist over time and application of appropriate analytical techniques are essential. Further consideration of changes in PASC frequency and its manifestations over the course of the COVID-19 pandemic, due to variable SARS-CoV-2 strains, new treatment and prevention strategies, and repeat infections, is important.

This study is part of the National Institutes of Health’s Researching COVID to Enhance Recovery (RECOVER) Initiative, which seeks to understand, treat, and prevent PASC ( https://recovercovid.org/ ). In this first analysis of data from the RECOVER adult cohort, criteria for identifying PASC based on self-reported symptoms are delineated and several distinctive PASC subphenotypes with varying impacts on well-being and physical health are described. This study was enriched with self-referred participants to promote inclusive participation. Estimates were expected to be more accurate in the subcohort of participants enrolled within 30 days of acute infection, for whom selection bias based on PASC would be minimal.

Unlike electronic health records and most existing cohort studies, data from this study captured PASC-specific self-reported symptoms based on standardized questionnaires developed with input from patient representatives. This report is an adequately powered, prospective study of PASC based on participant-reported symptoms that included both infected and uninfected individuals over the course of the pandemic. Notably, unlike prior reports, the paradigm presented here does not rely on predefined clinical symptoms; instead, a definition of PASC as a new condition specific to SARS-CoV-2 infection is proposed.

Institutional review boards at NYU Grossman School of Medicine, serving as a single institutional review board, and other participating institutions reviewed and approved the protocol. All participants provided written informed consent to participate in research.

The RECOVER adult cohort study included SARS-CoV-2–infected and –uninfected participants (the trial protocol is in Supplement 1 and the statistical analysis plan in Supplement 2 ). All infected participants met World Health Organization suspected, probable, or confirmed criteria. 11 Index for infected participants was defined as date of first positive SARS-CoV-2 test result or COVID-19 symptom onset. Uninfected participants had no known history of SARS-CoV-2 infection and index was defined as a past negative SARS-CoV-2 test result date. Participants belonged to either the acute cohort (enrolled ≤30 days since index) or the postacute cohort (enrolled >30 days to 3 years after index). Participants were recruited from 85 sites across the United States and completed office visits and remote surveys developed with early engagement of patients, support group stakeholders, and multidisciplinary clinical experts. 12

Adult participants enrolled prior to April 10, 2023 (N = 13 754) were considered ( Figure 1 ). Enrollment is ongoing, and not all enrolled participants have reached eligibility for inclusion. The analysis cohort included participants with a study visit completed 6 months or more after the index date ( Table 1 ). Uninfected participants with a reported on-study infection and participants who had no symptom survey data were excluded. A subgroup of participants also belonged to the RECOVER pregnancy cohort. Race and ethnicity were captured via participant self-report using fixed categories to better understand racial and ethnic differences in sequelae due to SARS-CoV-2 infection (eMethods in Supplement 3 ).

The analysis used the first study visit at 6 months or more after the index date. The exposure was SARS-CoV-2 infection prior to study enrollment. Uninfected participants with antibody results at enrollment indicating prior infection were reclassified as infected and assigned an index date 90 days prior. The primary outcome was the presence of each of 44 symptoms (eTable 1 in Supplement 3 ). Using these symptoms, a PASC definition was developed. The primary analysis used symptom presence for inclusivity; sensitivity analysis considered new-onset symptoms. Results were reported for 3 additional age- and sex-dependent symptoms (eTable 1 in Supplement 3 ).

Results were reported overall and within 3 subcohorts: acute Omicron (n = 2231 infected, n = 388 uninfected; index date on or after December 1, 2021); postacute pre-Omicron (n = 3732 infected, n = 290 uninfected; index date before December 1, 2021); and postacute Omicron (n = 2666 infected, n = 438 uninfected; index date on or after December 1, 2021). Acute cohort participants with a pre-Omicron index date (17 infected, 2 uninfected) were included in overall analyses.

Balancing weights were used to account for differences in the age, sex, and race and ethnicity distributions between infected and uninfected participants (eMethods in Supplement 3 ). Symptom frequency was defined as the proportion reporting a symptom and exceeding corresponding moderate to severe symptom severity threshold (eTables 1 and 2 in Supplement 3 ). Symptoms with frequency of 2.5% or greater were considered. Symptom frequencies by infection status were reported and adjusted odds ratios (aORs) were calculated using weighted logistic regression. In sensitivity analysis, new-onset symptom frequency was defined as the proportion of participants with the symptom at study visit among those without the symptom in the year prior to the index date. Symptom frequencies characterized the study cohort and were not unbiased estimators of population-level prevalence due to the cohort sampling strategies. Symptom frequency estimates within the acute Omicron subcohort were expected to be more aligned with the corresponding population frequencies.

A rule for identifying PASC was derived. Symptoms differentiating infected and uninfected participants were identified using least absolute shrinkage and selection operator (LASSO) with balancing weights. 13 Each symptom was assigned a score based on the estimated coefficients and participants were assigned a total score by summing the symptom scores for each reported symptom. Using 10-fold cross-validation, an optimal score threshold for PASC was selected (eMethods in Supplement 3 ). Participants meeting the PASC score threshold were classified as PASC positive; others were classified as PASC unspecified. The proportions were reported.

Participants classified as PASC positive were clustered into subgroups using unsupervised learning (K-means consensus clustering 14 followed by hierarchical clustering 15 ) including symptoms identified with LASSO. Symptoms highly correlated with those identified by LASSO were reported. The distribution of PASC score and Patient-Reported Outcomes Measurement Information System (PROMIS) Global Health 10 general quality of life (Q2), general physical health (Q3), and ability to carry out everyday physical activities (Q6) (eTable 3 in Supplement 3 ) were reported.

Rates of PASC were assessed by infection status, sex, age, and vaccination status at the index date (eTable 4 in Supplement 3 ), reinfection (between index and analysis visit), and visit month. The proportion of participants meeting criteria for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS, defined based on RECOVER survey; eMethods in Supplement 3 ) who were PASC positive at the same visit was reported. Sensitivity analyses removed the symptom severity thresholds and separately added well-being and physical health requirements for PASC (Q2 or Q3: fair or poor; Q6: moderate or worse). Inverse probability weighting was applied to account for loss to follow-up in the acute Omicron subcohort (eMethods in Supplement 3 ).

A total of 9764 participants (8646 infected; 1118 uninfected) met study criteria ( Figure 1 , 71% female [6932/9712]; 16% Hispanic/Latino [1592/9664]; 15% non-Hispanic Black [1417/9664]; 58% fully vaccinated at the index date [5585/9633]; median age, 47 years [IQR, 35-60]). After application of balancing weights, the distributions of age, sex, and race and ethnicity were the same in infected and uninfected participants ( Table 1 ). In the weighted cohort, uninfected participants were more likely to be fully vaccinated (77% vs 55%). Comorbidity frequencies were similar between infected and uninfected participants (eTable 5 in Supplement 3 ). Uninfected participants were more likely to be self-referrals or recruited via community outreach (eTable 5 in Supplement 3 ). A total of 1260 of 6932 female participants (18%) were in the pregnancy cohort.

In the full cohort, 37 symptoms had frequency of 2.5% or greater and aORs were 1.5 or greater (infected vs uninfected participants) for all 37 (eFigure 1 in Supplement 3 ). Symptoms (using severity thresholds) with more than 15% absolute difference in frequencies (infected vs uninfected) included postexertional malaise (PEM) (28% vs 7%; aOR, 5.2 [95% CI, 3.9-6.8]), fatigue (38% vs 17%; aOR, 2.9 [95% CI, 2.4-3.4]), dizziness (23% vs 7%; aOR, 3.4 [95% CI, 2.6-4.4]), brain fog (20% vs 4%; aOR, 4.5 [95% CI, 3.2-6.2]), and gastrointestinal (GI) symptoms (25% vs 10%; aOR, 2.7 [95% CI, 2.2-3.4]).

In infected participants, the frequencies of new-onset symptoms (with severity thresholds) were similar, including PEM (28%), fatigue (37%), dizziness (21%), brain fog (20%), and GI symptoms (20%) (eFigure 2 in Supplement 3 ). The corresponding observed symptom frequencies without severity thresholds were higher (eg, fatigue, 47%; brain fog, 40%) (eFigure 3 in Supplement 3 ).

The distributions of demographics and comorbidities were comparable across the acute Omicron, postacute pre-Omicron, and postacute Omicron subcohorts, though there was a higher proportion unvaccinated in the postacute pre-Omicron subcohort (eTable 6 in Supplement 3 ). Time from the index date to analysis visit ranged from 6 to 15 months in the acute Omicron and postacute Omicron subcohorts and 6 to 39 months in the postacute pre-Omicron subcohort (eFigure 4 in Supplement 3 ). Generally, symptom frequencies and the differences between infected and uninfected participants were lower in the acute Omicron subcohort, higher in the postacute Omicron subcohort, and highest in the postacute pre-Omicron subcohort (eFigures 5-7 in Supplement 3 ). Symptom frequencies in acute Omicron participants who were also fully vaccinated were the lowest (eFigure 8 in Supplement 3 ).

Using the full cohort, LASSO identified 12 symptoms with corresponding scores ranging from 1 to 8 ( Table 2 ). The optimal PASC score threshold used was 12 or greater ( Figure 2 A). The symptoms (ordered by decreasing frequencies among participants with a qualifying PASC score) were PEM, fatigue, brain fog, dizziness, GI symptoms, palpitations, changes in sexual desire or capacity, loss of or change in smell or taste, thirst, chronic cough, chest pain, and abnormal movements. Symptoms correlated with the selected symptoms included dry mouth, weakness, headaches, tremor, muscle and abdominal pain, fever/sweats/chills, and sleep disturbance (eTable 7 in Supplement 3 ).

The proportion with a qualifying PASC score in the full cohort (subject to selection bias) was 1990 of 8646 infected participants (23%) and 41 of 1118 of uninfected participants (3.7%) (overall: 2031/9764 [21%]). Among participants with PASC, the most common symptoms were PEM (87%), fatigue (85%), brain fog (64%), dizziness (62%), GI (59%), and palpitations (57%) ( Figure 2 B; eTable 8 in Supplement 3 ). Higher PASC scores were associated with worse PROMIS Global 10 scores ( Figure 2 C).

The proportion of infected participants with PASC in the acute Omicron subcohort was 10% (95% CI, 8.8%-11%; 224/2231). After adjustment for missing data, the estimated rate was 9.8% (95% CI, 8.6%-11%). It was greater in the postacute pre-Omicron (1320/3732 [35%; 95% CI, 34%-37%]) and postacute Omicron (442/2666 [17%; 95% CI, 15%-18%]) subcohorts ( Table 3 ). Symptom frequencies among PASC-infected participants were similar across subcohorts, with a few notable exceptions, including brain fog, GI symptoms, and palpitations (eTable 8 in Supplement 3 ). The proportion of PASC positivity was lower among fully vaccinated than unvaccinated participants (acute Omicron: 9.7% vs 17%; postacute pre-Omicron: 31% vs 37%; postacute Omicron: 16% vs 22%) ( Table 3 ). In the Omicron cohorts, the estimated proportion of PASC positivity was greater among reinfected participants compared with participants with 1 reported infection (acute Omicron: 20% vs 9.7%; postacute Omicron: 21% vs 16%) ( Table 3 ).

Among infected participants in the full cohort, the proportions of PASC positivity were 39% (299/757) among hospitalized participants and 22% (1636/7387) among not hospitalized participants during acute infection; 19% (442/2377) among males and 25% (1540/6221) among females; and 20% (885/4389) among those aged 18 to 45 years and 28% (904/3175) (28%) among those aged 46 to 65 years. In cross-sectional analysis, the proportion of PASC-positive participants was consistent over the visit month used in analysis (eTable 9 in Supplement 3 ). In subgroups with repeated visits at 6 and 9 months after the index date, PASC positivity varied over study visit, though 68% of PASC-positive participants remained positive at the subsequent visit (eTable 10 in Supplement 3 ). Among infected participants meeting criteria for ME/CFS, 98% met the criteria for PASC.

The proportions of PASC increased to 27% (2300/8646) among infected participants and 4.7% (52/1118) among uninfected participants when severity scores were not included in the PASC score determination. After applying additional PROMIS Global 10 criteria to qualify as PASC positive, 17% (1434/8646) were PASC positive among infected participants and 3.0% (34/1118) among uninfected participants.

Four PASC subgroups were identified ( Figure 3 A). Features of PASC subgroups included loss of or change in smell or taste (100%) in cluster 1 (n = 477); PEM (99%) and fatigue (84%) in cluster 2 (n = 405); brain fog (100%), PEM (99%), and fatigue (94%) in cluster 3 (n = 587); and fatigue (94%), PEM (94%), dizziness (94%), brain fog (94%), GI (88%), and palpitations (86%) in cluster 4 (n = 562) ( Figure 3 B). Twenty-six percent of the PASC-unspecified group also met PROMIS Global 10 criteria compared with 53% of participants in cluster 1, 69% in cluster 2, 77% in cluster 3, and 86% in cluster 4. Among infected participants, 456 of 1540 females (30%) and 102 of 442 males (23%) with PASC were in cluster 4. A total of 277 of 885 participants aged 18 to 45 years (31%), 254 of 904 participants aged 46 to 65 years (28%), and 29 of 198 participants aged older than 65 years (15%) with PASC were in cluster 4.

The proportion of PASC-positive infected participants in cluster 4 was higher within the postacute pre-Omicron (31%) than postacute Omicron (23%) and acute Omicron (23%) subcohorts (eTable 11A in Supplement 3 ). Overall, among PASC-positive infected participants, the proportion in cluster 4 among fully vaccinated compared with unvaccinated participants was 23% vs 32% (eTable 11B in Supplement 3 ). The distribution of clusters was similar for participants with a single reported infection compared with those with more than 1 infection, though the results varied by prevalent SARS-CoV-2 strain (eTable 11C in Supplement 3 ).

This study reported early results from a prospective, survey-based cohort of adult SARS-CoV-2–infected and –uninfected individuals with ascertainment of patient-reported symptoms. A data-driven scoring framework was developed to classify PASC as a condition specific to SARS-CoV-2 infection. Based on this PASC score, 10% of participants first infected on or after December 1, 2021, and enrolled within 30 days of infection were classified as PASC positive at 6 months after infection. Increasing levels of the PASC score were associated with progressively worse measures of well-being and functioning. Although only 12 symptoms contributed to the PASC score, other symptoms correlated with this subgroup are individually important, considering their potential adverse impact on health-related quality of life.

PASC positivity was more common and associated with more severe manifestation in participants infected in the pre-Omicron era. Though participants with earlier infection may have been more likely to enroll in the RECOVER adult cohort because of known PASC, multiple studies reported an association between PASC and early pandemic variants. 16 Among participants with a first infection during the Omicron era, PASC frequency was higher among those with recurrent infections, corroborating electronic health record–based studies. 4 , 17 - 23 Though studies on the effect of vaccination are conflicting, these findings of modest reduction in PASC frequency among fully vaccinated participants align with recent systematic reviews. 24 , 25

This study found that long-term symptoms associated with SARS-CoV-2 infection spanned multiple organ systems. The diversity of symptoms may be related to persistent viral reservoirs, autoimmunity, or direct differential organ injury. The symptoms identified are consistent with those reported in studies that assessed PASC manifestations (eMethods in Supplement 3 ). However, by simultaneously considering the contributions of multiple self-reported symptoms, a PASC-scoring algorithm that provides a framework for diagnosing PASC was developed.

Given the heterogeneity of PASC symptoms, determining whether PASC represents one unified condition or reflects a group of unique phenotypes is important. Recent evidence supports the presence of PASC phenotypes, although characterization of these phenotypes is inconsistent and largely dependent on available data. 2 , 6 , 22 , 23 Accurate phenotypic stratification has important implications for investigations into the pathophysiological processes underlying PASC and clinical trial design. PASC subgroups that demonstrate overlap with conditions previously described in clinical practice are detailed here, including olfactory dysfunction, cardiopulmonary sequelae, neurocognitive impairment, ME/CFS, and dysautonomia 26 - 30 and overlap with those reported by the National COVID Cohort Collaborative. 6 Biological samples from these participants may enable the development of biomarkers of PASC and reveal insights into the mechanistic underpinnings of PASC that inform choice of therapeutic interventions and case selection in upcoming clinical trials for PASC.

First, the proposed paradigm and accompanying decision rule require iterative refinement as additional data become available. The PASC score provides an operational definition of PASC and requires further refinement and validation. RECOVER recruitment is ongoing, and not all participants have reached the analysis stage. Evolution and refinement of the phenotypes are anticipated as additional data become available.

Second, selection bias was likely among postacute cohort participants that may have affected frequency estimates including distribution of subphenotypes because PASC severity may impact study participation. Differential attrition of symptomatic and asymptomatic participants at follow-up visits could also have biased frequency estimates though use of inverse probability weighting in the acute cohorts mitigated this bias.

Third, uninfected participants may have had prior asymptomatic SARS-CoV-2 infections not detected due to variations in antibody production and persistence, weakening the discriminant characteristics of this PASC score threshold.

Fourth, symptoms were self-reported and only some symptoms integrate severity scales. Participants could report other symptoms as free text; these were not included in this analysis.

Fifth, confounding may have impacted effect sizes, eg, vaccination status may have been higher in participants at higher risk of PASC, attenuating a vaccination effect. Additionally, PASC status can change over time, perhaps due to underlying mechanistic changes.

Sixth, more than 200 symptoms of PASC have been reported, each with the potential of being life-altering and debilitating, and the symptoms highlighted herein may not reflect the severity or impact of other symptoms.

This symptom-based PASC definition represents a first step for identifying PASC cases and serves as a launching point for further investigations. Definition of a classification rule for PASC requires an updated algorithm that incorporates symptoms as well as biological features. Future analyses must consider the relationships among age, sex, race and ethnicity, social determinants of health, vaccination status after index date, comorbidities, and pregnancy status during infection on the risk of PASC and the distribution of PASC subgroups.

Accepted for Publication: May 1, 2023.

Published Online: May 25, 2023. doi:10.1001/jama.2023.8823

Corresponding Author: Andrea S. Foulkes, ScD, Massachusetts General Hospital Biostatistics, 50 Staniford St, Ste 560, Boston, MA 02114 ( [email protected] ).

RECOVER Consortium Authors: George A. Alba, MD; Radica Alicic, MD; Natasha Altman, MD; Khamal Anglin, MD, MPH; Urania Argueta, BS; Hassan Ashktorab, PhD; Gaston Baslet, MD; Ingrid V. Bassett, MD, MPH; Lucinda Bateman, MD; Brahmchetna Bedi, PhD; Shamik Bhattacharyya, MD, MS; Marie-Abele Bind, PhD; Andra L. Blomkalns, MD, MBA; Hector Bonilla, MD; Patricia A. Bush, MS, EdD; Mario Castro, MD, MPH; James Chan, MA; Alexander W. Charney, MD, PhD; Peter Chen, MD; Lori B. Chibnik, PhD, MPH; Helen Y. Chu, MD, MPH; Rebecca G. Clifton, PhD; Maged M. Costantine, MD; Sushma K. Cribbs, MD, MSc; Sylvia I. Davila Nieves, MS; Steven G. Deeks, MD; Alexandria Duven, RN; Ivette F. Emery, PhD; Nathan Erdmann, MD, PhD; Kristine M. Erlandson, MD, MS; Kacey C. Ernst, PhD, MPH; Rachael Farah-Abraham, PhD; Cheryl E. Farner, MSN; Elen M. Feuerriegel, PhD; Judes Fleurimont, MPH; Vivian Fonseca, MD; Nicholas Franko, BS; Vivian Gainer, MS; Jennifer C. Gander, PhD; Edward M. Gardner, MD; Linda N. Geng, MD, PhD; Kelly S. Gibson, MD; Minjoung Go, MD, MPH; Jason D. Goldman, MD, MPH; Halle Grebe, BS; Frank L. Greenway, MD; Mounira Habli, MD; John Hafner, MD, MPH; Jenny E. Han, MD, MS; Keith A. Hanson, MD, PhD; James Heath, PhD; Carla Hernandez, RN; Rachel Hess, MD, MS; Sally L. Hodder, MD; Matthew K. Hoffman, MD, MPH; Susan E. Hoover, MD, PhD; Beatrice Huang, BA; Brenna L. Hughes, MD; Prasanna Jagannathan, MD; Janice John, MS, MHCDS; Michael R. Jordan, MD; Stuart D. Katz, MD, MS; Elizabeth S. Kaufman, MD; John D. Kelly, MD; Sara W. Kelly, PhD, MPH; Megan M. Kemp, BA; John P. Kirwan, PhD; Jonathan D. Klein, MD, MPH; Kenneth S. Knox, MD; Jerry A. Krishnan, MD, PhD; Andre Kumar, MD; Adeyinka O. Laiyemo, MD; Allison A. Lambert, MD; Margaret Lanca, PhD; Joyce K. Lee-Iannotti, MD; Brian P. Logarbo, MD, MS; Michele T. Longo, MD; Carlos A. Luciano, MD; Karen Lutrick, PhD; Jason H. Maley, MD, MS; Jai G. Marathe, MD, MBBS; Vincent Marconi, MD; Gailen D. Marshall, MD, PhD, MS; Christopher F. Martin, MBA; Yuri Matusov, MD; Alem Mehari, MD; Hector Mendez-Figueroa, MD; Robin Mermelstein, PhD; Torri D. Metz, MD, MS; Richard Morse, BA; Jarrod Mosier, MD; Christian Mouchati, MD; Janet Mullington, PhD; Shawn N. Murphy, MD, PhD; Robert B. Neuman, MD; Janko Z. Nikolich, MD, PhD; Ighovwerha Ofotokun, MD; Elizabeth Ojemakinde, MD, MPH; Anna Palatnik, MD; Kristy Palomares, MD, PhD; Tanyalak Parimon, MD; Samuel Parry, MD; Jan E. Patterson, MD; Thomas F. Patterson, MD; Rachel E. Patzer, PhD, MPH; Michael J. Peluso, MD; Priscilla Pemu, MD, MS; Christian M. Pettker, MD; Beth A. Plunkett, MD, MPH; Kristen Pogreba-Brown, PhD; Athena Poppas, MD; John G. Quigley, MD; Uma Reddy, MD; Rebecca Reece, MD; Harrison Reeder, PhD; W. B. Reeves, MD; Eric M. Reiman, MD; Franz Rischard, DO, MSc; Jonathan Rosand, MD, MS; Dwight J. Rouse, MD; Adam Ruff, BS; George Saade, MD; Grecio J. Sandoval, PhD; Shannon M. Schlater, MS; Fitzgerald Shepherd, MD; Zaki A. Sherif, PhD; Hyagriv Simhan, MD; Nora G. Singer, MD; Daniel W. Skupski, MD; Amber Sowles, RN, BSN; Jeffrey A. Sparks, MD, MMSc; Fatima I. Sukhera, MD; Barbara S. Taylor, MD; Larissa Teunis, MPA; Robert J. Thomas, MD; John M. Thorp, MD, MS; Paul Thuluvath, MD; Amberly Ticotsky, MPH, RN; Alan T. Tita, MD, PhD; Katherine R. Tuttle, MD; Alfredo E. Urdaneta, MD; Daisy Valdivieso, BS; Timothy M. VanWagoner, PhD; Andrew Vasey, MD; Monica Verduzco-Gutierrez, MD; Zachary S. Wallace, MD; Honorine D. Ward, MD; David E. Warren, PhD; Steven J. Weiner, MS; Shelley Welch, MS; Sidney W. Whiteheart, PhD; Zanthia Wiley, MD; Juan P. Wisnivesky, MD, DrPH; Lynn M. Yee, MD; Sokratis Zisis, MD.

Affiliations of RECOVER Consortium Authors: Massachusetts General Hospital, Boston (Alba, Bassett, Bind, Chan, Chibnik, Morse, Murphy, Reeder, Rosand, Wallace); Harvard Medical School, Boston, Massachusetts (Lanca, Mullington, Plunkett); University of Colorado Anschutz Medical Campus, Aurora (Altman, Erlandson, Feuerriegel); Brigham and Women’s Hospital, Boston, Massachusetts (Baslet, Bhattacharyya, Sparks); University of Alabama at Birmingham (Erdmann); Case Western Reserve University, Cleveland, Ohio (Zisis); Icahn School of Medicine at Mount Sinai, New York, New York (Charney, Wisnivesky); Stanford University School of Medicine, Stanford, California (Go, Kumar, Urdaneta); Emory University School of Medicine, Atlanta, Georgia (Bedi, Cribbs, Han, Ofotokun); New York University Grossman School of Medicine, New York (Katz); University of Washington, Seattle (Alicic, Franko, Kemp, Lambert, Tuttle); University of California, San Francisco (Anglin, Argueta, Deeks, Grebe, Huang, Peluso, Valdivieso); Howard University, Washington, DC (Ashktorab); Bateman Horne Center, Salt Lake City, Utah (Bateman); Stanford University, Stanford, California (Blomkalns, Bonilla, Geng, Jagannathan); Kaiser Foundation Health Plan of Georgia Inc, Atlanta (Bush, Neuman); University of Kansas Medical Center, Kansas City (Castro); Cedars-Sinai Medical Center, Los Angeles, California (Chen, Matusov); University of Washington School of Medicine, Seattle (Chu); George Washington University, Washington, DC (Clifton); The Ohio State University, Columbus (Costantine); Universidad de Puerto Rico Recinto de Ciencias Medicas, San Juan, Puerto Rico (Davila Nieves); Swedish Medical Center, Seattle, Washington (Duven, Goldman); MaineHealth, Portland (Emery); The University of Arizona, Tucson (Ernst, Lutrick, Nikolich, Pogreba-Brown); Emory University, Atlanta, Georgia (Farah-Abraham, Marconi, Martin, Patzer, Teunis, Wiley); The University of Texas Health Science Center at San Antonio (Farner, J. E. Patterson, T. F. Patterson, Taylor, Verduzco-Gutierrez); University of Illinois Chicago (Fleurimont, Mermelstein); Tulane University Health Sciences Center, New Orleans, Louisiana (Fonseca); Partners HealthCare Systems, Boston, Massachusetts (Gainer); Kaiser Permanente Georgia, Atlanta (Gander); Denver Health, Denver, Colorado (Gardner); MetroHealth Medical Center, Cleveland, Ohio (Gibson, Singer); Pennington Biomedical Research Center, Baton Rouge, Louisiana (Greenway, Kirwan); TriHealth, Cincinnati, Ohio (Habli); University of Illinois Chicago College of Medicine (Hafner); University of Illinois College of Medicine at Peoria (Hanson, S. W. Kelly); Institute for Systems Biology, Seattle, Washington (Heath); UH Cleveland Medical Center, Cleveland, Ohio (Hernandez); University of Utah Schools of the Health Sciences, Salt Lake City (Hess); West Virginia Clinical and Translational Science Institute, Morgantown (Hodder); Christiana Care Health Services Inc, Newark, Delaware (Hoffman); Sanford Health, Sioux Falls, South Dakota (Hoover); Duke University, Durham, North Carolina (Hughes); Cambridge Health Alliance, Cambridge, Massachusetts (John, Ticotsky); Tufts Medical Center, Boston, Massachusetts (Jordan, Ward); MetroHealth Campus of Case Western Reserve University, Cleveland, Ohio (Kaufman); University of California, San Francisco (J. D. Kelly); University of Illinois Chicago (Klein, Quigley); The University of Arizona College of Medicine, Phoenix (Knox, Lee-Iannotti); University of Illinois Hospital and Health Sciences System, Chicago (Krishnan); Howard University College of Medicine, Washington, DC (Laiyemo, Mehari, Sherif); Tulane University, New Orleans, Louisiana (Logarbo); Tulane School of Medicine, New Orleans, Louisiana (Longo); Universidad de Puerto Rico, San Juan, Puerto Rico (Luciano); Beth Israel Deaconess Medical Center, Boston, Massachusetts (Maley, Thomas); Boston University, Boston, Massachusetts (Marathe); University of Mississippi, Oxford (Marshall); McGovern Medical School at The University of Texas Health Science Center at Houston (Mendez-Figueroa); University of Utah Health, Salt Lake City (Metz, Schlater, Sowles); University of Arizona, Tucson (Mosier); Case Western Reserve University School of Medicine, Cleveland, Ohio (Mouchati); Morehouse School of Medicine, Atlanta, Georgia (Ojemakinde, Pemu); Medical College of Wisconsin, Milwaukee (Palatnik); Saint Peter’s University Hospital, Brunswick, New Jersey (Palomares); Cedars-Sinai Health System, Los Angeles, California (Parimon); University of Pennsylvania, Philadelphia (Parry); Yale School of Medicine, New Haven, Connecticut (Pettker); NorthShore University HealthSystem, Evanston, Illinois (Plunkett); Warren Alpert Medical School, Brown University, Providence, Rhode Island (Poppas); Columbia University Irving Medical Center, New York, New York (Reddy); West Virginia University School of Medicine, Morgantown (Reece); Department of Medicine, The University of Texas Health Science Center at San Antonio (Reeves); Banner Alzheimer’s Institute, Phoenix, Arizona (Reiman); Banner University Medical Center Tucson, Arizona (Rischard); Brown University, Providence, Rhode Island (Rouse); The University of Kansas Medical Center, Kansas City (Ruff); Eastern Virginia Medical School, Norfolk (Saade); Milken Institute of Public Health, The George Washington University, Washington, DC (Sandoval); Boston University School of Medicine, Boston, Massachusetts (Shepherd); University of Pittsburgh, Pittsburgh, Pennsylvania (Simhan); Weill Cornell Medicine, New York, New York (Skupski); The University of Oklahoma, Norman (Sukhera); The University of North Carolina at Chapel Hill (Thorp); Mercy Medical Center, Baltimore, Maryland (Thuluvath); University of Alabama, Birmingham (Tita); University of Oklahoma Health Sciences Center, Oklahoma City (VanWagoner); University of Nebraska Medical Center, Omaha (Vasey, Warren); The George Washington University Biostatistics Center, Rockville, Maryland (Weiner); West Virginia University, Morgantown (Welch); University of Kentucky, Lexington (Whiteheart); Feinberg School of Medicine, Northwestern University, Chicago, Illinois (Yee).

Author Contributions: Drs Thaweethai and Foulkes had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Thaweethai, Jolley, Karlson, Levy, McComsey, Parthasarathy, Singh, Shinnick, Altman, Bassett, Chan, Charney, Chibnik, Clifton, Deeks, Erdmann, Ernst, Fonseca, Gander, Gibson, Heath, Hodder, Hughes, Jagannathan, Jordan, Katz, J. Kelly, Kirwan, Knox, Krishnan, Kumar, Laiyemo, Lee-Iannotti, Maley, Marshall, Mehari, Mendez-Figueroa, Mermelstein, Metz, Mosier, Mullington, Murphy, Nikolich, Ofotokun, J. Patterson, Patzer, Peluso, Pemu, Pettker, Poppas, Reeves, Reiman, Sherif, Teunis, Thomas, Tita, Verduzco-Gutierrez, Wisnivesky, Yee, Zisis, Horwitz, Foulkes.

Acquisition, analysis, or interpretation of data: Thaweethai, Jolley, Karlson, Levitan, Levy, McComsey, McCorkell, Nadkarni, Parthasarathy, Singh, Walker, Selvaggi, Shinnick, Schulte, Atchley-Challenner, Alba, Alicic, Anglin, Ashktorab, Argueta, Baslet, Bassett, Bateman, Bedi, Bhattacharyya, Bind, Blomkalns, Bonilla, Bush, Castro, Chan, Charney, Chen, Chibnik, Chu, Clifton, Costantine, Cribbs, Davila Nieves, Deeks, Duven, Emery, Erdmann, Erlandson, Farah-Abraham, Farner, Feuerriegel, Fleurimont, Fonseca, Franko, Gainer, Gander, Gardner, Geng, Go, Goldman, Grebe, Greenway, Habli, Hafner, Han, Hanson, Heath, Hernandez, Hess, Hodder, Hoffman, Hoover, Huang, John, Katz, Kaufman, J. Kelly, S. Kelly, Kemp, Kirwan, Klein, Laiyemo, Lambert, Lanca, Lee-Iannotti, Logarbo, Longo, Luciano, Lutrick, Maley, Marathe, Marconi, Martin, Matusov, Metz, Morse, Mosier, Mouchati, Mullington, Murphy, Neuman, Ofotokun, Ojemakinde, Palatnik, Palomares, Parimon, Parry, T. Patterson, Patzer, Peluso, Pemu, Plunkett, Pogreba-Brown, Quigley, Reddy, Reece, Reeder, Rischard, Rosand, Rouse, Ruff, Saade, Sandoval, Schlater, Shepherd, Sherif, Simhan, Singer, Skupski, Sowles, Sparks, Sukhera, Taylor, Teunis, Thomas, Thorp, Thuluvath, Ticotsky, Tita, Tuttle, Urdaneta, Valdivieso, VanWagoner, Vasey, Verduzco-Gutierrez, Wallace, Ward, Warren, Weiner, Welch, Whiteheart, Wiley, Wisnivesky, Yee, Zisis, Horwitz, Foulkes.

Drafting of the manuscript: Thaweethai, Jolley, Karlson, McComsey, McCorkell, Parthasarathy, Singh, Walker, Selvaggi, Bedi, Farner, Fleurimont, Gander, Jordan, J. Kelly, Kirwan, Kumar, Taylor, Teunis, Zisis, Foulkes.

Critical revision of the manuscript for important intellectual content: Thaweethai, Jolley, Karlson, Levitan, Levy, McComsey, McCorkell, Nadkarni, Parthasarathy, Walker, Selvaggi, Shinnick, Schulte, Atchley-Challenner, Alba, Alicic, Altman, Anglin, Ashktorab, Argueta, Baslet, Bassett, Bateman, Bhattacharyya, Bind, Blomkalns, Bonilla, Bush, Castro, Chan, Charney, Chen, Chibnik, Chu, Clifton, Costantine, Cribbs, Davila Nieves, Deeks, Duven, Emery, Erdmann, Erlandson, Ernst, Farah-Abraham, Feuerriegel, Fonseca, Franko, Gainer, Gander, Gardner, Geng, Gibson, Go, Goldman, Grebe, Greenway, Habli, Hafner, Han, Hanson, Heath, Hernandez, Hess, Hodder, Hoffman, Hoover, Huang, Hughes, Jagannathan, John, Jordan, Katz, Kaufman, J. Kelly, S. Kelly, Kemp, Kirwan, Klein, Knox, Krishnan, Laiyemo, Lambert, Lanca, Lee-Iannotti, Logarbo, Longo, Luciano, Lutrick, Maley, Marathe, Marconi, Marshall, Martin, Matusov, Mehari, Mendez-Figueroa, Mermelstein, Metz, Morse, Mosier, Mouchati, Mullington, Murphy, Neuman, Nikolich, Ofotokun, Ojemakinde, Palatnik, Palomares, Parimon, Parry, J. Patterson, T. Patterson, Patzer, Peluso, Pemu, Pettker, Plunkett, Pogreba-Brown, Poppas, Quigley, Reddy, Reece, Reeder, Reeves, Reiman, Rischard, Rosand, Rouse, Ruff, Saade, Sandoval, Schlater, Shepherd, Sherif, Simhan, Singer, Skupski, Sowles, Sparks, Sukhera, Thomas, Thorp, Thuluvath, Ticotsky, Tita, Tuttle, Urdaneta, Valdivieso, VanWagoner, Vasey, Verduzco-Gutierrez, Wallace, Ward, Warren, Weiner, Welch, Whiteheart, Wiley, Wisnivesky, Yee, Zisis, Horwitz, Foulkes.

Statistical analysis: Thaweethai, Selvaggi, Shinnick, Schulte, Bind, Chan, Chibnik, Reeder, Taylor, Foulkes.

Obtained funding: Thaweethai, Karlson, Levy, McComsey, Parthasarathy, Bassett, Charney, Chu, Cribbs, Deeks, Erlandson, Ernst, Heath, Hess, Katz, J. Kelly, Kirwan, Knox, Krishnan, Maley, Martin, Metz, Murphy, Nikolich, Parry, T. Patterson, Patzer, Peluso, Pemu, Quigley, Reeves, Reiman, Saade, Sherif, Simhan, Taylor, Teunis, Tita, Tuttle, Warren, Wisnivesky, Horwitz, Foulkes.

Administrative, technical, or material support: Levy, McComsey, Nadkarni, Singh, Anglin, Ashktorab, Argueta, Bassett, Bedi, Bhattacharyya, Bush, Chan, Charney, Clifton, Costantine, Cribbs, Davila Nieves, Emery, Farah-Abraham, Farner, Feuerriegel, Fleurimont, Franko, Gainer, Gander, Gardner, Geng, Go, Grebe, Habli, Hafner, Han, Hanson, Heath, Hernandez, Hess, Hodder, Hoffman, Huang, Jagannathan, John, Katz, J. Kelly, S. Kelly, Kirwan, Klein, Krishnan, Kumar, Laiyemo, Lambert, Lanca, Lee-Iannotti, Luciano, Lutrick, Maley, Marshall, Martin, Mehari, Mendez-Figueroa, Metz, Morse, Mosier, Mouchati, Mullington, Murphy, Neuman, Ojemakinde, Palomares, Parimon, J. Patterson, Patzer, Peluso, Pettker, Pogreba-Brown, Poppas, Reiman, Rouse, Saade, Schlater, Shepherd, Simhan, Sowles, Sparks, Sukhera, Taylor, Teunis, Thuluvath, Tita, Urdaneta, Valdivieso, Wallace, Ward, Weiner, Yee, Horwitz, Foulkes.

Supervision: Thaweethai, Jolley, Karlson, Levy, McComsey, Parthasarathy, Singh, Walker, Alba, Bassett, Bateman, Bedi, Blomkalns, Bonilla, Castro, Chan, Charney, Chen, Chu, Costantine, Emery, Erlandson, Farah-Abraham, Gander, Goldman, Greenway, Hafner, Han, Heath, Hernandez, Hodder, Hoffman, Huang, Jagannathan, Jordan, Katz, J. Kelly, Kirwan, Knox, Krishnan, Lee-Iannotti, Longo, Martin, Mendez-Figueroa, Metz, Mullington, Murphy, Neuman, Nikolich, Ofotokun, Patzer, Peluso, Plunkett, Poppas, Reiman, Rosand, Saade, Shepherd, Simhan, Skupski, Sukhera, Thorp, Thuluvath, Tita, Ward, Warren, Whiteheart, Yee, Horwitz, Foulkes.

Other - gave feedback based on study design: Singer.

Other - cohort recruitment and study supervision: Alba.

Other - was one of the original Boston Hub investigators involved in application for funding: Mullington.

Other - operationalization: Farah-Abraham.

Other - literature review: Atchley-Challenner.

Other - site PI team management: Marconi.

Other - data procurement: Kumar.

Conflict of Interest Disclosures: Dr Thaweethai reported receiving grants from the National Institutes of Health (NIH)/National Heart, Lung, and Blood Institute (NHLBI) and serving as co-investigator of the RECOVER Data Resource Core. Dr Jolley reported receiving grants from the NIH/NHLBI. Dr Karlson reported receiving grants from the NIH. Dr Levitan reported receiving grants from the NIH and Amgen. Dr Levy reported receiving grants from the NHLBI and personal fees or research support from Entrinsic Bioscience, Nocion Therapeutics, Gossamer Bio, AstraZeneca, Novartis, Sanofi, Amgen, Genentech, Pieris Pharmaceuticals, and SRA. Dr McComsey reported serving as a consultant for Gilead, Merck, Janssen, and GlaxoSmithKline and receiving research support from Pfizer, Genentech, and Roche. Ms McCorkell reported receiving personal fees from NIH RECOVER and research funding as a subcontractor from the NIH and the Patient-Centered Outcomes Research Institute (PCORI). Dr Parthasarathy reported receiving grants from the NIH, NHLBI, Sergey-Brin Foundation, and Regeneron and personal fees from Jazz Pharmaceuticals. Dr Singh reported receiving grants from Pfizer and serving as an advisor to Regeneron and Gilead. Dr Walker reported receiving grants from the NHLBI. Mr Shinnick reported receiving grants from the NIH/NHLBI. Dr Horwitz reported receiving grants from the NIH and serving on an ad hoc committee for the National Academy of Medicine. Dr Foulkes reported receiving grants from the NIH/NHLBI and personal fees from the Round Table Group and serving as principal investigator of the RECOVER Data Resource Core. Dr Alba reported receiving personal fees from Minerva Biotechnologies. Dr Alicic reported receiving grants from the NIH, the Centers for Disease Control and Prevention, and Travere Therapeutics and personal fees from Boehringer Ingelheim. Dr Ashktorab reported receiving grants from the NIH. Dr Bhattacharyya reported receiving grants from NIH RECOVER, Alexion Pharmaceuticals, UCB, and Massachusetts Consortium on Pathogen Readiness and personal fees from American Academy of Neurology and UpToDate. Dr Castro reported receiving grants from the NIH, American Lung Association, PCORI, AstraZeneca, Gala Therapeutics, Genentech, GlaxoSmithKline, Novartis, Pulmatrix, Sanofi, Shionogi, and Theravance Biopharma; personal fees from Allakos, Amgen, OM Pharma, Pfizer, Pioneering Medicines, AstraZeneca, GlaxoSmithKline, Genentech, Teva Pharmaceutical Industries, Sanofi, Merck, Novartis, Arrowhead Pharmaceuticals, and Regeneron; stock options from Aer Therapeutics; and royalties from Elsevier. Dr Chu reported receiving grants from the NIH and personal fees from Merck, Vir Biotechnology, AbbVie, Ellume, Pfizer, and the Bill and Melinda Gates Foundation; serving on advisory boards for Merck and AbbVie; conducting CME teaching with Medscape, Vindico, and Clinical Care Options; and receiving research funding from Gates Ventures and support and reagents from Ellume and Cepheid. Dr Clifton reported receiving grants from the NIH. Dr Costantine reported receiving grants from the NIH, Baxter International, and Siemens Healthcare and personal fees from Quidel, Progenity, and Siemens Healthcare. Dr Davila Nieves reported receiving grants from the NIH. Dr Deeks reported receiving grants from the NIH. Dr Emery reported receiving grants from the NIH. Dr Erdmann reported receiving grants from the NIH and personal fees from Perspectum and having a patent for Plantform. Dr Erlandson reported receiving grant funding and advisory fee payments from Gilead Sciences (paid to the University of Colorado) and advisory fee payments from Merck and ViiV (paid to the University of Colorado) in the last 3 years. Ms Gainer reported receiving grants from NIH RECOVER. Dr Geng reported receiving grants from Pfizer and personal fees from UnitedHealthcare. Dr Gibson reported receiving grants from the NIH. Dr Go reported receiving grants from the NIH. Dr Goldman reported receiving grants from Gilead Sciences and Merck; personal fees from Gilead Sciences and Eli Lilly; contracted research from Gilead Sciences, Eli Lilly, and Regeneron Pharmaceuticals; and nonfinancial support from Adaptive Biotechnologies, Labcorp, and Monogram Biosciences. Dr Greenway reported receiving grants from the NIH. Dr Heath reported receiving grants from Merck and Gilead Sciences, personal fees from Regeneron Pharmaceuticals, and being a board member for IsoPlexis. Dr Hess reported receiving grants from the NIH and being a member of a data and safety monitoring board for Astellas Pharmaceuticals. Dr Hodder reported receiving grants from the NHLBI. Dr Hoffman reported receiving grants from the NHLBI. Dr Hughes reported receiving honorarium from UpToDate and personal fees from AMAG Pharmaceuticals. Ms John reported receiving grants from the NIH. Dr Katz reported receiving grants from the NIH. Dr Klein reported receiving grants from NIH RECOVER and consulting fees from Gilead Sciences. Dr Knox reported receiving grants from the NIH. Dr Krishnan reported receiving grants from the NIH/NHLBI; personal fees from GlaxoSmithKline, AstraZeneca, CereVu Medical, BData, Propeller, ResMed, American Board of Internal Medicine, American Academy of Allergy, Asthma, and Immunology; and research funding from the NIH, PCORI, American Lung Association, COPD Foundation, and the Sergey Brin Family Foundation. Dr Laiyemo reported receiving grants from the NIH. Dr Lambert reported receiving grants from the NIH and the PCORI and serving as a site principal investigator for trials funded by Vertex and Aceragan. Dr Luciano reported receiving grants from the NIH and speaker fees from Sanofi-Genzyme. Dr Marathe reported receiving grants from NIH RECOVER and funding from Boston Medical Center. Dr Marconi reported receiving grants from the Centers for Disease Control and Prevention, Veteran Affairs, and the NIH; grants, personal fees, nonfinancial support, and other from Eli Lilly and Gilead; grants and personal fees from ViiV; and nonfinancial support from Bayer. Dr Marshall reported receiving grants from the NIH. Mr Martin reported receiving grants from the NIH. Dr Metz reported receiving grants from the NIH and Pfizer and personal fees from Pfizer. Mr Morse reported receiving grants from the NIH/NHLBI. Dr Mullington reported receiving grants from the NIH and Open Medicine Foundation and speaker and book chapter contribution for Idorsia Pharmaceuticals. Dr Murphy reported receiving grants from the NIH/NHLBI. Dr Nikolich reported receiving grants from the NIH/NHLBI. Dr Ojemakinde reported receiving grants from the NIH. Dr Parry reported receiving grants from NIH RECOVER paid to Penn and funding from the Sergey Brin Family Foundation. Dr Peluso reported receiving personal fees from Gilead Sciences and AstraZeneca. Dr Pogreba-Brown reported receiving grants from NIH RECOVER. Dr Poppas reported owning stock in GE and serving as an UpToDate contributor and co-editor of Hurst and Fuster’s The Heart. Dr Quigley reported receiving grants from the NIH, Pfizer, AbbVie, and Teva Pharmaceutical Industries and personal fees from Alexion Pharmaceuticals, Servier Laboratories, Agios Pharmaceuticals, and Rigel Pharmaceuticals. Dr Reiman reported receiving grants from the NIH. Dr Rischard reported receiving grants from the NIH/NHLBI, Bayer, Janssen, Merck, Aerovate Therapeutics, and Respira Therapeutics and consulting relationships with Acceleron Pharma and United Therapeutics. Dr Rosand reported receiving grants from the NIH and the American Heart Association and personal fees from the National Football League and Takeda Pharmaceutical Co. Dr Shepherd reported receiving grants from NIH RECOVER. Dr Simhan reported receiving grants from the NIH. Dr Singer reported receiving grants from Case Western Reserve University and MetroHealth. Dr Sparks reported receiving grants from the National Institute of Arthritis and Musculoskeletal and Skin Diseases, the R. Bruce and Joan M. Mickey Research Scholar Fund, the Llura Gund Award for Rheumatoid Arthritis Research and Care, Brigham and Women’s Hospital, and Bristol Myers Squibb and personal fees from Bristol Myers Squibb, AbbVie, Amgen, Boehringer Ingelheim, Gilead Sciences, Inova Diagnostics, Janssen, Optum, and Pfizer. Dr Thomas reported receiving personal fees from Guidepoint Global and GLG Councils and having a patent for ECG-spectrogram with royalties paid from MyCardio LLC through Beth Israel Deaconess Medical Center. Dr Thuluvath reported receiving grants from Mercy Medical Center. Dr Tita reported receiving grants from Pfizer. Dr VanWagoner reported receiving grants from the NIH. Dr Vasey reported receiving grants from NIH RECOVER. Dr Wallace reported receiving grants from Bristol Myers Squibb, Sanofi, and Horizon Therapeutics and personal fees from Sanofi, Novartis, Shionogi, Visterra, Horizon Therapeutics, PPD Inc, Zenas BioPharma, and Medpace. Dr Warren reported receiving grants from the NIH. Mr Weiner reported receiving grants from the NIH. Ms Welch reported receiving grants from the NIH. Dr Wisnivesky reported receiving personal fees from PPD Inc, Banook, Sanofi, Atea Pharmaceuticals, and Prospero and grants from Regeneron Pharmaceuticals, Axella, Sanofi, and Arnold. Dr Yee reported receiving grants from the NIH. No other disclosures were reported.

Funding/Support: This research was funded by the NIH (OTA OT2HL161841, OT2HL161847, and OT2HL156812) as part of the Researching COVID to Enhance Recovery (RECOVER) research program. Additional support for Drs Foulkes, Thaweethai, and Schulte was provided by R01 HL162373.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Group Information: The RECOVER Consortium nonauthor collaborators are listed in Supplement 4 .

Disclaimer: This content is solely the responsibility of the authors and does not necessarily represent the official views of the RECOVER Initiative, the NIH, or other funders.

Data Sharing Statement: See Supplement 5 .

Additional Information: This study is part of the RECOVER initiative, which seeks to understand, treat, and prevent the postacute sequelae of SARS-CoV-2 infection. For more information on RECOVER, visit https://recovercovid.org/ . We thank all the participants enrolled in the RECOVER Initiative, the National Community Engagement Group (NCEG), and all patient, caregiver and community representatives.

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What the Next Generation of Project Management Will Look Like

• Rachel Longhurst
• Woojin Choi

Research identifies 10 skills that will have a disproportionate impact on performance.

Traditional project management skills, such as project governance or project management methodology, aren’t sufficient to meet changing organizational needs.  Gartner recently surveyed 373 project management leaders to identify the “next generation” skills — from organizational awareness to financial acumen — that have a disproportionate impact on performance. They also identified three future-focused project manager roles: the teacher, the fixer, and the orchestrator — all of which highlight the uniquely human aspects of project management that go beyond performing discrete, repetitive tasks.

The future of the project manager role has been hotly debated as a number of trends shift organizational dynamics:

• RL Rachel Longhurst is a director within the Gartner IT Leaders and Tech Professionals research practice advising clients on strategic portfolio management, including project and portfolio management and application portfolio management.
• WC Woojin Choi is a senior principal within the Gartner IT Leaders and Tech Professionals research practice advising clients on strategic portfolio management.

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Microsoft Ignite 2023: AI transformation and the technology driving change

Nov 15, 2023 | Frank X. Shaw - Chief Communications Officer, Microsoft

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As we reach the end of 2023, nearly every industry is undergoing a collective transformation – discovering entirely new ways of working due to AI advancements.

Microsoft Ignite is a showcase of the advances being developed to help customers, partners and developers achieve the total value of Microsoft’s technology and reshape the way work is done.

As we round out the year, there are strong signals of AI’s potential to transform work. Take our latest Work Trend Index . Eight months ago, we introduced Copilot for Microsoft 365 to reduce digital debt and increase productivity so people can focus on the work that is uniquely human. What everyone wants to know now is: Will Copilot really change work, and how? Our research, using a combination of surveys and experiments, shows the productivity gains are real:

• 70% of Copilot users said they were more productive and 68% said it improved the quality of their work; 68% say it helped jumpstart the creative process.
• Overall, users were 29% faster at specific tasks (searching, writing and summarizing).
• Users caught up on a missed meeting nearly 4x faster.  
• 64% of users said Copilot helps them spend less time processing email.
• 87% of users said Copilot makes it easier to get started on a first draft.
• 75% of users said Copilot “saves me time by finding whatever I need in my files.”
• 77% of users said once they use Copilot, they don’t want to give it up.

Today, we will make about 100 news announcements that touch on multiple layers of an AI-forward strategy, from adoption to productivity to security. We’ll zoom in on a few key areas of impact below.

Rethinking cloud infrastructure Microsoft has led with groundbreaking advances like partnerships with OpenAI and the integration of ChatGPT capabilities into tools used to search, collaborate, work and learn. As we accelerate further into AI, Microsoft is rethinking cloud infrastructure to ensure optimization across every layer of the hardware and software stack.

At Ignite we are announcing new innovations across our datacenter fleet, including the latest AI optimized silicon from our industry partners and two new Microsoft-designed chips.

• Microsoft Azure Maia, an AI Accelerator chip designed to run cloud-based training and inferencing for AI workloads such as OpenAI models, Bing, GitHub Copilot and ChatGPT.
• Microsoft Azure Cobalt, a cloud-native chip based on Arm architecture optimized for performance, power efficiency and cost-effectiveness for general purpose workloads.
• Additionally, we are announcing the general availability of Azure Boost , a system that makes storage and networking faster by moving those processes off the host servers onto purpose-built hardware and software.

Complementing our custom silicon, we are expanding partnerships with our silicon providers to provide infrastructure options for customers.

• We’ll be adding AMD MI300X accelerated virtual machines (VMs) to Azure. The ND MI300 VMs are designed to accelerate the processing of AI workloads for high range AI model training and generative inferencing, and will feature AMD’s latest GPU, the AMD Instinct MI300X.
• The preview of the new NC H100 v5 Virtual Machine Series built for NVIDIA H100 Tensor Core GPUs, offering greater performance, reliability and efficiency for mid-range AI training and generative AI inferencing. We’re also announcing plans for the ND H200 v5 Virtual Machine Series, an AI-optimized VM featuring the upcoming NVIDIA H200 Tensor Core GPU.

Extending the Microsoft Copilot experience Over the past year we have continued to refine our vision for Microsoft Copilot, a set of tools that help people achieve more using AI. To go beyond individual productivity, we are extending Microsoft Copilot offerings across solutions to transform productivity and business processes for every role and function – from office workers and front-line workers to developers and IT professionals.

Microsoft is the Copilot company, and we believe in the future there will be a Copilot for everyone and for everything you do. Some of our Copilot-related announcements and updates include:

• Microsoft Copilot for Microsoft 365: This month, Copilot for Microsoft 365 became generally available for enterprises. Already customers like Visa, BP, Honda and Pfizer and partners like Accenture, EY, KPMG, Kyndryl and PwC are using Copilot. We continue to bring new value, based on learnings from our Early Access Program and other research channels. The new Microsoft Copilot Dashboard shows customers how Copilot is impacting their organization – with insights like those found in our Work Trend Index. We’re introducing new personalization capabilities that help Copilot offer responses that are tailored to your unique preferences and role. To empower teamwork, new features for Copilot in Outlook help you prep for meetings, and during meetings, new whiteboarding and note-taking experiences for Copilot in Microsoft Teams keep everyone on the same page. And customers who need it can now use Copilot during a meeting without transcription retention. When you give Copilot a seat at the table, it goes beyond being your personal assistant to helping the entire team – check out the Microsoft 365 blog for updates across the suite including PowerPoint, Excel, Microsoft Viva and more.
• Microsoft Copilot Studio: AI transformation begins by tapping into an organization’s unique data and workflows. Microsoft Copilot Studio is a low-code tool designed to customize Microsoft Copilot for Microsoft 365 by integrating business-critical data and build custom copilots for internal or external use. Copilot Studio works with connectors, plugins and GPTs, allowing IT teams to steer Copilot to the best data sources for specific queries.
• Microsoft Copilot for Service: The newest copilot to provide role-based support helps businesses accelerate their AI transformation of customer service. Copilot for Service includes Microsoft Copilot for Microsoft 365 and helps extend existing contact centers with generative AI. In customer interactions, agents can ask Copilot for Service questions in natural language and receive relevant insights based on data sources from knowledge repositories, leading to faster and smarter resolutions.
• Copilot in Microsoft Dynamics 365 Guides: Combining the power of generative AI and mixed reality, this copilot helps frontline workers complete complex tasks and resolve issues faster without disrupting workflow. Available first on HoloLens 2, the hands-free copilot will help service industry professionals use natural language and human gestures to offer interactive guidance through content and holograms overlaid on the equipment.
• Microsoft Copilot for Azure: This is an AI companion for IT that simplifies day-to-day IT administration. More than just a tool, it is a unified chat experience that understands the user’s role and goals, and enhances the ability to design, operate and troubleshoot apps and infrastructure. Copilot for Azure helps IT teams gain new insights into their workloads, unlock untapped Azure functionality and orchestrate tasks across both cloud and edge.
• Bringing Copilot to everyone : Our efforts to simplify the user experience and make Copilot more accessible to everyone starts with Bing, our leading experience for the web. Bing Chat and Bing Chat Enterprise will now simply become Copilot. With these changes, when signed in with a Microsoft Entra ID, customers using Copilot in Bing, Edge and Windows will receive the benefit of commercial data protection. Over time, Microsoft will also expand the eligibility of Copilot with commercial data protection to even more Entra ID (formerly Azure Active Directory) users at no additional cost. Copilot (formerly Bing Chat and Bing Chat Enterprise) will be out of preview and become generally available starting Dec. 1. Learn more here .

Reinforcing the data and AI connection AI is only as good as the data that fuels it. That’s why Microsoft is committed to creating an integrated, simplified experience to connect your data to our AI tools .

Microsoft Fabric is part of that solution. Available now, Microsoft Fabric reshapes how teams work with data by bringing everyone together on a single, AI-powered platform that unifies all those data estates on an enterprise-grade data foundation.

Copilot in Microsoft Fabric also integrates with Microsoft Office and Teams to foster a data culture to scale the power of data value creation throughout the organization. We’ve made more than 100 feature updates since Build and expanded our ecosystem with industry leading partners , and have over 25,000 customers including Milliman, Zeiss, London Stock Exchange and EY using it today.

Unlocking more value for developers with Azure AI We continue to expand choice and flexibility in generative AI models to offer developers the most comprehensive selection. With Model-as-a-Service , a new feature in the model catalog we announced at Microsoft Build, pro developers will be able to easily integrate the latest AI models, such as Llama 2 from Meta and upcoming premium models from Mistral, and Jais from G42, as API endpoints to their applications. They can also customize these models with their own data without needing to worry about setting up and managing the GPU infrastructure, helping eliminate complexity.

With the preview of Azure AI Studio , there is now a unified and trusted platform to help organizations more easily explore, build, test and deploy AI apps – all in one place. With Azure AI Studio, you can build your own copilots, train your own, or ground other foundational and open models with data that you bring.

And Vector Search , a feature of Azure AI Search, is now generally available, so organizations can generate highly accurate experiences for every user in their generative AI applications.

The new GPT-3.5 Turbo model with a 16K token prompt length will be generally available and GPT-4 Turbo will be in public preview in Azure OpenAI Service at the end of November 2023. GPT-4 Turbo will enable customers to extend prompt length and bring even more control and efficiency to their generative AI applications.

GPT-4 Turbo with Vision is coming soon to preview and DALL · E 3 is now available in public preview in Azure OpenAI Service , helping fuel the next generation of enterprise solutions along with GPT-4, so organizations can pursue advanced functionalities with images. And when used with our Azure AI Vision service, GPT-4 Turbo with Vision even understands video for generating text outputs, furthering human creativity.

Enabling the responsible deployment of AI Microsoft leads the industry in the safe and responsible use of AI. The company has set the standard with an industry-leading commitment to defend and indemnify commercial customers from lawsuits for copyright infringement – the Copilot Copyright Commitment (CCC).

Today, Microsoft takes its commitment one step further by announcing the expansion of the CCC to customers using Azure OpenAI Service. The new benefit will be called the Customer Copyright Commitment. As part of this expansion, Microsoft has published new documentation to help Azure OpenAI Service customers implement technical measures to mitigate the risk of infringing content. Customers will need to comply with the documentation to take advantage of the benefit.

And Azure AI Content Safety is now generally available, helping organizations detect and mitigate harmful content and create better online experiences. Customers can use Azure AI Content Safety as a built-in-safety system within Azure OpenAI Service, for open-source models as part of their prompt engineering in Azure Machine Learning, or as a standalone API service.

Introducing new experiences in Windows to empower employees, IT and developers We continue to invest in and build Windows to empower people to navigate the platform shift to AI. We are thrilled to introduce new experiences in Windows 11 and Windows 365 for IT and employees that unlock new ways of working and make more AI accessible across any device. To further our mission of making Windows the home for developers and the best place for AI development, we announced a host of new AI and productivity tools for developers , including Windows AI Studio.

Announcing NVIDIA AI foundry service Aimed at helping enterprises and startups supercharge the development, tuning and deployment of their own custom AI models on Microsoft Azure, NVIDIA will announce their AI foundry service running on Azure. The NVIDIA AI foundry service pulls together three elements – a collection of NVIDIA AI Foundation models, NVIDIA NeMo framework and tools, and NVIDIA DGX Cloud AI supercomputing and services – that give enterprises an end-to-end solution for creating custom generative AI models. Businesses can then deploy their models with NVIDIA AI Enterprise software on Azure to power generative AI applications, including intelligent search, summarization and content generation.

Strengthening defenses in the era of AI The threat landscape has evolved dramatically in recent years, and at Microsoft Ignite we are introducing new technologies across Microsoft’s suite of security solutions to help defenders make the world a safer place.

Microsoft Sentinel and Microsoft Defender XDR (previously Microsoft 365 Defender) will be combined to create the industry’s first Unified Security Operations Platform, with embedded Security Copilot experiences. With built-in generative AI, it’s a single, powerful experience focused on protecting threats at machine speed and aiding defenders by simplifying the complexity of their environment.

Additionally, the expansion of Security Copilot embedded within Intune, Purview and Entra will help IT administrators, compliance units and identity teams simplify complex scenarios. In Entra, identity administrators can quickly troubleshoot identity access. In Purview, data security alerts deliver rich context to help resolve problems faster. In Intune, IT administrators can use “what if” analysis to keep business running while improving governance and compliance.

And that’s just a snapshot of what we’ll be announcing at Ignite. As a reminder, you can view keynote sessions from Satya Nadella, Rajesh Jha and Jared Spataro, Charlie Bell and Vasu Jakkal, and Scott Guthrie live or on-demand.

Plus, you can get more on all these announcements by exploring the Book of News , the official compendium of all today’s news, and the product blogs below.

Watch the keynotes and get all the latest photos, videos and more from Microsoft Ignite

The online event for Microsoft Ignite

With a systems approach to chips, Microsoft aims to tailor everything ‘from silicon to service’ to meet AI demand

Introducing new Copilot experiences to boost productivity and elevate customer experiences across the organization

Simplify IT management with Microsoft Copilot for Azure – save time and get answers fast

Introducing Microsoft Copilot Studio and new features in Copilot for Microsoft 365

Announcing general availability of vector search and semantic ranker in Azure AI Search

GPT-4 Turbo with Vision on Azure OpenAI Service 

How Azure AI Content Safety helps protect users from the classroom to the chatroom

Elevating the developer experience on Windows with new AI tools and productivity tools

Microsoft unveils expansion of AI for security and security for AI at Microsoft Ignite

Tags: AI , Azure AI Content Safety , Azure AI Studio , Microsoft 365 , Microsoft Copilot , Microsoft Fabric , Microsoft Ignite 2023 , Microsoft Security Copilot , Model-as-a-Service

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